BEGIN:VCALENDAR
VERSION:2.0
X-WR-CALNAME:10thworlds4
X-WR-CALDESC:Event Calendar
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//Sched.com 10th WorldS4 2026//EN
X-WR-TIMEZONE:UTC
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T073000Z
DTEND:20260728T080000Z
SUMMARY:Registration with Networking Tea / Coffee
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:75e1bdaae04e5d0e2458c55746d0c03e
URL:http://10thworlds4.sched.com/event/75e1bdaae04e5d0e2458c55746d0c03e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T080000Z
DTEND:20260728T081000Z
SUMMARY:Welcome Address By
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:6f9cf107c5877030870fd10816bc7bb8
URL:http://10thworlds4.sched.com/event/6f9cf107c5877030870fd10816bc7bb8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T081000Z
DTEND:20260728T082000Z
SUMMARY:Address By Special Guest & Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:fae0267473e98c3e99665798f979257f
URL:http://10thworlds4.sched.com/event/fae0267473e98c3e99665798f979257f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T082000Z
DTEND:20260728T083000Z
SUMMARY:Address By Special Guest & Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:18a910e0cdd1c9d1fab61bdd9a79fc42
URL:http://10thworlds4.sched.com/event/18a910e0cdd1c9d1fab61bdd9a79fc42
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T083000Z
DTEND:20260728T084000Z
SUMMARY:Address By Special Guest & Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:909689ebb0bd0bce1187d80a48ecbdfc
URL:http://10thworlds4.sched.com/event/909689ebb0bd0bce1187d80a48ecbdfc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T084000Z
DTEND:20260728T085000Z
SUMMARY:Address By Keynote Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:f01f654a284d1638a5f3c2c256ae9a2e
URL:http://10thworlds4.sched.com/event/f01f654a284d1638a5f3c2c256ae9a2e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T085000Z
DTEND:20260728T090000Z
SUMMARY:Address By Special Guest & Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:b68156562972ddc432bdb3106fe9e37c
URL:http://10thworlds4.sched.com/event/b68156562972ddc432bdb3106fe9e37c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T090000Z
DTEND:20260728T091000Z
SUMMARY:Address By Special Guest & Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:197f5541c50e32dc90fd64707a3b89a1
URL:http://10thworlds4.sched.com/event/197f5541c50e32dc90fd64707a3b89a1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T091000Z
DTEND:20260728T092000Z
SUMMARY:Address By Special Guest & Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:09337f6640c6546806eb9c10d26b4a00
URL:http://10thworlds4.sched.com/event/09337f6640c6546806eb9c10d26b4a00
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T092000Z
DTEND:20260728T095000Z
SUMMARY:Panel Discussion: AI for a Better World: Cross‑Sector Innovations Advancing the SDGs
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:e6dc78b20c0bb715f6d18258b29a4749
URL:http://10thworlds4.sched.com/event/e6dc78b20c0bb715f6d18258b29a4749
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T095000Z
DTEND:20260728T095500Z
SUMMARY:Technical Session: Opening Remarks By
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:9c9c20658de34329537e749385d60a94
URL:http://10thworlds4.sched.com/event/9c9c20658de34329537e749385d60a94
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T095500Z
DTEND:20260728T100000Z
SUMMARY:Felicitation & Group Photograph
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Aldgate & Bishopsgate Suit\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:e04e4e0b21cbc791144e3fd91191a380
URL:http://10thworlds4.sched.com/event/e04e4e0b21cbc791144e3fd91191a380
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T103000Z
DTEND:20260728T104500Z
SUMMARY:Tier 0 SOC Agent: An AI-Driven Approach to Automated Alert Triage in Cyber Security Operations Centers
DESCRIPTION:Authors - Majed Rahman\, Mashrur Wasek\, Faisal Quader Abstract - A typical Security Operations Center (SOC) receives 500– 10\,000 alerts a day\, 80–95% of them false alarms\, but a Tier 1 analyst can only meaningfully look at 20–30 in a shift. We present Tier 0 SOC Agent\, an AI layer that triages every alert before a human sees it. For each alert\, the agent runs a four-phase loop (plan\, investigate\, decide\, explain) over a Model Context Protocol (MCP) ecosystem of 45 tools across ten data sources\, and returns a structured verdict with a confidence score\, MITRE ATT&CK techniques\, and the evidence behind the call. A multi-factor scorer combining source reliability\, evidence diversity\, and severity routes each alert to one of four tiers: auto-close\, T1 with a pre-written summary\, T2\, or T3. In a four-day production deployment across two customer tenants and 27 Wazuh agents\, the agent triaged 93 alerts in a mean of 118 s each\, roughly an order of magnitude faster than a human\, with zero runtime failures and a 64% drop in tool calls per alert versus the previous generation. A safety scaffold of five downgrade-only policy rules\, a circuit breaker\, a file-based killswitch\, and a read-only tool filter guarantees the agent cannot auto-close any alert beyond what its verdict and the policy jointly authorize.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:28cab49c0b9896b88b14ecbf8aabc2f2
URL:http://10thworlds4.sched.com/event/28cab49c0b9896b88b14ecbf8aabc2f2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T103000Z
DTEND:20260728T104500Z
SUMMARY:How do robot gestures affect learning outcomes and emotional responses in children?
DESCRIPTION:Authors - HSIU-FENG WANG\, PEI-LUN LEE\, MAO-JEN CHEN Abstract - With the advent of new developments\, social robots are increasingly adopted in educational settings. The impact of social robot gestures on children’s learning outcomes\, emotional responses\, and affect was explored. One hundred and five elementary students participated in the experiment. Findings revealed a significant effect of robot gestures on children’s learning emotions. The impact of social robot gestures on learning outcomes and affect in children was explored. Results indicated that robot gestures significantly influenced learning emotions and knowledge retention. Children preferred interacting with the gesturing social robot\, suggesting enhanced emotional engagement during learning. However\, the effect on knowledge transfer was not significant. These findings underscore the potential of social robot gestures in enhancing children’s engagement and learning effectiveness.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:6be42d20230c5e28b2aac9780f35f892
URL:http://10thworlds4.sched.com/event/6be42d20230c5e28b2aac9780f35f892
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T104500Z
DTEND:20260728T110000Z
SUMMARY:Unmasking the Black Box: A Multi-Parameter Diagnostic Architecture for Deconstructing U.S. University Ranking Dynamics (1996–2026)
DESCRIPTION:Authors - Mashrur Wasek\, Prishita Taksalee\, Faisal Quader Abstract - University rankings influence student enrollment decisions\, institutional funding\, and strategic planning\, yet diagnostic analyses often conflate correlation with actionable drivers\; for example\, by including prior-year rank as a predictor\, which exploits persistence rather than identifying genuine drivers of rank change. This study presents a reproducible\, leakage-safe framework for diagnosing rank determinants using a 31-year panel (1996–2026) of 3\,785 university-year observations across 191 U.S. institutions\, constructed from College Scorecard administrative data and OpenAlex bibliometrics. The approach explicitly distinguishes diagnostic modeling—estimating expected rank from contemporaneous institutional attributes—from predictive modeling\, which exploits temporal persistence. The framework integrates dual methodological lenses: a U.S. News–style observable composite and a Times Higher Education (THE)–style research/teaching proxy framework. Driver importance stability is quantified through year-by-year coefficient analysis\, revealing that retention\, graduation rate\, and selectivity exhibit the highest magnitudes while Pell share\, admission rate\, and several secondary resource related variables show substantial temporal drift. Case studies of MIT\, University of Maryland–College Park\, and George Washington University demonstrate how residual analysis and feature decomposition translate into institution-specific improvement priorities. The framework provides a transparent\, reproducible blueprint for understanding rank movements and identifying high-impact interventions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:dcf5e2b089276ec86bc006297ee6750a
URL:http://10thworlds4.sched.com/event/dcf5e2b089276ec86bc006297ee6750a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T104500Z
DTEND:20260728T110000Z
SUMMARY:Optimising Marketing Decision-Making through Advanced Data Management Techniques in Digital Ecosystems: A Systematic Literature Review
DESCRIPTION:Authors - Jude Osakwe\, Josephina Muntuumo\, Daphine Gondo Abstract - Digital ecosystems have altered how organisations approach marketing decision-making\, creating demand for advanced data management methods. This paper analyses 144 peer-reviewed articles published between 2013 and 2025\, examining customer segmentation algorithms\, real-time data processing systems\, and integrated marketing analytics platforms. Organisations implementing these techniques report marketing return on investment improvements of 15 to 25 percent and conversion rate gains of 10 to 30 percent. A structured comparison of four technique categories\, namely big data analytics\, machine learning\, AI and natural language processing\, and predictive analytics\, reveals distinct performance profiles and deployment trade-offs. Big data analytics delivers the broadest gains but demands the highest infrastructure investment\, while predictive analytics offers a lower-cost entry point with shorter payback periods. Persistent challenges include data quality deficiencies\, skills shortages\, integration difficulties\, privacy compliance obligations\, and organisational resistance to change. The paper contributes a systematic framework and benchmarking evidence\, and maps implementation constraints that explain the gap between reported performance benchmarks and operational outcomes
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:15c1a3d5379afd9624252a952841acde
URL:http://10thworlds4.sched.com/event/15c1a3d5379afd9624252a952841acde
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T110000Z
DTEND:20260728T111500Z
SUMMARY:Digital Transformation of ESG Reporting in the Chemical Industry: A Data-Driven Framework for Sustainability Transparency
DESCRIPTION:Authors - Firangiza Komilovna Fozilova\, Bakhtiyor Vasievich Nasimov\, Feruza Shodibek qizi Hamrayeva\, Shoh-Jakhon Khamdаmov\, Fazilat Akhmedova\, Saida Khabibullaeva\, Doniyor Niyozmetov Abstract - Environmental\, Social\, and Governance (ESG) reporting has become a strategic priority for chemical enterprises due to increasing regulatory pressure\, stakeholder expectations\, and sustainability risks. However\, traditional ESG reporting practices in the chemical industry are often fragmented\, manually intensive\, and limited in real-time transparency. The rapid advancement of digital technologies provides new opportunities to transform ESG disclosure into a datadriven and integrated reporting system. This study proposes a digital transformation framework for ESG reporting in the chemical industry\, emphasizing the integration of enterprise resource planning systems\, blockchain-based traceability\, big data analytics\, and automated sustainability dashboards. The research develops a conceptual model linking digital infrastructure capability\, ESG data quality\, reporting transparency\, and stakeholder trust. Using a quantitative approach\, the study employs Structural Equation Modeling (SEM) to test the relationships among digitalization level\, ESG reporting efficiency\, and organizational performance indicators. Empirical findings indicate that digital integration significantly improves ESG data accuracy\, reduces reporting costs\, and enhances transparency. Moreover\, improved digital ESG reporting positively influences stakeholder confidence and corporate sustainability performance. The proposed framework contributes to the literature by bridging digital transformation theory and ESG reporting research within high-impact industrial sectors. The study provides practical recommendations for chemical enterprises seeking to enhance sustainability governance through digital technologies and offers policy implications for promoting standardized digital ESG ecosystems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:c2baf6b72d28125c27b2346da6316e98
URL:http://10thworlds4.sched.com/event/c2baf6b72d28125c27b2346da6316e98
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T110000Z
DTEND:20260728T111500Z
SUMMARY:A Data Analysis-Assisted Intelligence for the Housing Market in three Myanmar cities: Yangon\, Mandalay\, and Pyin Oo Lwin
DESCRIPTION:Authors - Chann Nyein Soe\, SOKOUT Hamidullah Abstract - Developing countries determine the housing market based on how far from the city center. However\, in Myanmar\, submarkets are stronger than the Central Business District (CBD). The differences in the data analysis become results of the collected housing data for 45 days in Yangon\, Mandalay\, and Pyin Oo Lwin. Price per square foot is the basic unit to compare different properties. The manually collected dataset consists of 710 total listings\, in which apartment in Yangon\, landed houses\, and vacant land in all three cities taken into consideration. Descriptive diagnostic methods\, hedonic regressions\, and segmentation were used. The result indicates that Myanmar housing prices are not determined by a smooth general rule\, but rather distinct submarkets and different pricing behaviors exist. Townships are strongly substantial\, whereas apartments show the strongest evidence of segmentation. Although CBD is statistically meaningful\, the effect differs across property types. Meanwhile\, housing markets exhibit significant differences for each property type. On the other hand\, apartments are naturally based on the utility area and are more stable. Housing markets\, price estimation models\, and property valuation in Myanmar need to change from a city-level single average model to individual property and each township model\, relying on median instead of means\, because of the outliers in elite properties. Despite the scarcity of estate data in Myanmar\, the research supports the Myanmar housing market insights for developing data analysis.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:476cc8bc0f32be9f2c7cd36efe0c8b33
URL:http://10thworlds4.sched.com/event/476cc8bc0f32be9f2c7cd36efe0c8b33
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T111500Z
DTEND:20260728T113000Z
SUMMARY:Digital Prescription Systems and Pharmaceutical Governance: Evidence from Regulatory Reform in Uzbekistan
DESCRIPTION:Authors - Farangiz Furkatzoda Majidova\, Guljahon Madrahimova\, Fotimabonu Doniyorova\, Dinora Alisherovna Baratova\, Shoh-Jakhon Khamdаmov\, Samandar Pulatovich Kurbonov\, Dekhkanova Nargiza Sharifovna Abstract - The digitаlizаtion of heаlthcаre governаnce hаs become а centrаl instrument for improving trаnspаrency\, regulаtory compliаnce\, аnd public heаlth outcomes. In 2024\, Uzbekistаn introduced а mаndаtory electronic prescription (e-prescription) system thаt prohibits phаrmаcies from dispensing prescription drugs without ver-ified digitаl аuthorizаtion. Phаrmаcies were grаnted reаl-time online аccess to physiciаns’ prescriptions through аn integrаted nаtionаl plаtform\, mаrking а sig-nificаnt regulаtory shift in phаrmаceuticаl mаrket control. This study exаmines the impаct of digitаl prescription systems on phаrmаceuticаl governаnce аnd dis-pensing prаctices in Uzbekistаn. The reseаrch develops а conceptuаl frаmework linking digitаl heаlth infrаstructure\, regulаtory compliаnce\, dispensing trаnspаrency\, аnd mаrket аccountаbility. Using а quаntitаtive аpproаch\, the study аnаlyzes survey dаtа from phаrmаcy mаnаgers аnd heаlthcаre professionаls\, complemented by secondаry regulаtory dаtа. Structurаl Equаtion Modeling (SEM) is employed to аssess the relаtionships аmong digitаl system integrаtion\, compliаnce behаvior\, аnd governаnce outcomes. The findings indicаte thаt the introduction of the e-prescription plаtform significаntly reduced unаuthorized drug sаles\, improved trаceаbility of phаrmаceuticаl trаnsаctions\, аnd strength-ened regulаtory oversight. Digitаl verificаtion mechаnisms enhаnced trаnspаrency аnd reduced opportunities for informаl dispensing prаctices. Fur-thermore\, the reform contributed to improved stаkeholder trust in the phаrmаceu-ticаl distribution system. The study contributes to digitаl governаnce аnd heаlth policy literаture by providing empiricаl evidence from аn emerging economy un-dergoing rаpid heаlthcаre digitаlizаtion. The results offer prаcticаl implicаtions for policymаkers seeking to strengthen phаrmаceuticаl regulаtion through in-tegrаted digitаl heаlth infrаstructures.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:c605756d8c70f9cf1390da05c60b4771
URL:http://10thworlds4.sched.com/event/c605756d8c70f9cf1390da05c60b4771
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T111500Z
DTEND:20260728T113000Z
SUMMARY:Human–AI Interaction as a Source of Hedonic and Eudaimonic Well‑Being in Knowledge Work
DESCRIPTION:Authors - Merja Drake\, Mariitta Rauhala Abstract - Artificial intelligence (AI) is increasingly embedded in knowledge work\, reshaping how tasks are performed and how work is experienced. While prior research has emphasized efficiency and productivity gains\, less is known about how everyday human–AI interaction influences employees’ well-being and sense of meaningful work. This study examines AI-enabled work through the dual lenses of hedonic enjoyment and eudaimonic well-being\, focusing on how pleasure-oriented and growth-oriented experiences emerge in organizational contexts. Drawing on a mixed-methods design\, the study combines a survey of 474 Finnish knowledge workers with 44 in-depth interviews conducted in spring 2024. The analysis explores how generative artificial intelligence use and development re-late to psychological resources such as self-efficacy\, proactivity\, autonomy\, work engagement\, and social participation. The findings show that generative artificial intelligence often functions as a hedonic facilitator by streamlining tasks and reducing effort. However\, more sustained well-being is associated with eudaimonic experiences\, particularly opportunities for learning\, participation\, and meaningful contribution to AI development. Importantly\, the results highlight that access to generative AI tools alone is in-sufficient. Organizational practices such as transparent communication\, inclusive development processes\, and shared learning environments play a central role in shaping positive human–AI interaction. The study contributes to Human–Computer Interaction research by offering a human-centered perspective on generative AI as a sociotechnical system that can support both enjoyment and flourishing at work.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:a706f5bc40abbdc1ed75c0a73b72286e
URL:http://10thworlds4.sched.com/event/a706f5bc40abbdc1ed75c0a73b72286e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T113000Z
DTEND:20260728T114500Z
SUMMARY:The Impact of Green Bond Issuance on Agricultural Productivity: A Case Study of Agrobank
DESCRIPTION:Authors - Dilobаr Isomjonovnа Ruzieva\, Gulasal Madrakhimova\, Shirin Akmalovna Zakirxodjayeva\, Gavkhar Jumanova\, Dilshoda Akramova\, Akromjon Rustamovich Gulamov\, Ogabek Sadullayev Abstract - This research evaluates the impact of the 2024 green bond issuance by Agrobank ($455 million total) on agricultural productivity and climate resilience in Uzbekistan. Utilizing a quantitative approach\, the study analyzes the 2025 Green Bonds Allocation and Impact Report and performance metrics from over 19\,000 sub-loans distributed across the Republic's agricultural regions. Findings indicate that the $357.8 million allocated to climate-smart technologies as of late 2025 resulted in annual water savings of 1.003 billion m^3 and an energy reduction of 349 GWh\, directly correlating with a 20–30% increase in net farmer income. The data demonstrates that green-financed drip irrigation systems significantly mitigate the risks of water scarcity while lowering operational costs through reduced fertilizer and electricity usage. The study concludes that green bonds are an effective mechanism for modernizing Uzbekistan's agricultural sector and recommends further expansion of green credit lines to achieve the "Green Economy 2030" targets.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:e7e5afc5e43885689f5229f6b0ba11e7
URL:http://10thworlds4.sched.com/event/e7e5afc5e43885689f5229f6b0ba11e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T113000Z
DTEND:20260728T114500Z
SUMMARY:Digital Currencies as Programmable Financial Infrastructure: A Layered Architecture Framework for Smart\, Secure\, and Sustainable Digital Finance
DESCRIPTION:Authors - Vijak Sethaput\, Supachate Innet Abstract - The rise of digital currencies—encompassing cryptocurrencies\, stablecoins\, tokenized deposits\, and central bank digital currencies (CBDCs)—has outpaced frameworks for understanding their coexistence as financial infrastructure. This paper proposes the Digital Finance Infrastructure Framework (DFIF): a four-layer architecture—Settlement\, Commercial\, Retail\, and Open— that maps five digital currency categories to complementary infrastructure tiers and explicitly links each tier to smart-systems enablers\, sustainability dimensions\, and security considerations. This paper makes four contributions: (1) a seven-dimensional comparative taxonomy of five digital currency categories\; (2) the DFIF framework\; (3) a four-dimensional interoperability model\; and (4) a six-category systemic risk taxonomy with evidence-based smart-system mitigations. We situate the DFIF against four established frameworks (IMF Money Flower\, BIS Singleness of Money\, Carstens Digital Money Galaxy\, Brunnermeier Digital Currency Areas) and validate it against deployment evidence\, including BIS mBridge (reaches MVP stage and BIS exit)\, Project Agorá (seven central banks)\, MiCA full applicability\, Nigeria's eNaira (
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:0f032ebc4a9b8358db6fdda8e6e8486a
URL:http://10thworlds4.sched.com/event/0f032ebc4a9b8358db6fdda8e6e8486a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T114500Z
DTEND:20260728T120000Z
SUMMARY:Measuring the Impact of Emotion-Annotated Phraseology in Low-Resource Language
DESCRIPTION:Authors - Banu Yergesh\, Tilekbergen Mukhamet\, Manas Yergesh\, Aisha Zhumagulova Abstract - Emotion recognition for Kazakh is constrained by limited labeled data and by indirect affective expression through idioms and culturally grounded lexical cues. This paper quantifies the contribution of emotion-annotated phraseology and a semantic knowledge base to seven-way (single-label) emotion classification (Ekman’s six basic emotions plus the Kazakh-specific shamerelated class\, uiat). The aim of this study is to assess the contribution of emotionally annotated idioms and semantically labeled lexical units to emotion recognition in Kazakh texts. We hypothesize that both types of resources improve classification performance\, while their combined use yields the strongest effect\, especially for emotions with culturally and pragmatically marked meanings. We curate KazEmoPhras with 3379 emotion-bearing idioms in Kazakh\, and two semantically tagged lexical resources (1\,200 media units\; 1\,800 classical literature units). Fine-tuning XLM-RoBERTa on the augmented data improves accuracy from 68.3% to 74.9% and weighted F1 from 0.26 to 0.37. We additionally provide an impact-focused ablation protocol to isolate the effects of idiom and semantic features and discuss practical requirements for morphologyaware preprocessing in Kazakh.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:5c1428a248386238cf80fb4f2a33946b
URL:http://10thworlds4.sched.com/event/5c1428a248386238cf80fb4f2a33946b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T114500Z
DTEND:20260728T120000Z
SUMMARY:Building Public Trust Through Digital Investment Governance: Evidence from Nigeria’s IT Project Clearance Process
DESCRIPTION:Authors - Usman G. Abdullahi\, Kashifu I. Abdullahi Abstract - Weak governance of public-sector digital investments continues to undermine the effectiveness of digital transformation initiatives in many developing countries\, resulting in inefficiencies\, project failures\, and erosion of public trust. While existing literature has largely focused on digital service delivery and user adoption\, less attention has been given to upstream governance mechanisms that shape how digital investments are assessed and approved. This paper examines stakeholders' perceptions of the effectiveness of the IT project clearance process\, implemented by Nigeria's National Information Technology Development Agency (NITDA) as a risk-based\, stage-gated centralised digital investment governance mechanism. The study adopted a descriptive survey research design approach. The design was suitable for this study because the research focused on evaluating the perceived impact of the clearance framework on compliance across Ministries\, Departments\, and Agencies (MDAs)\, local content promotion\, reduction in duplication and waste\, standardisation and interoperability\, transparency in IT projects\, and\, more importantly\, trust in public investments. The study employed a quantitative research approach\, which facilitated the objective measurement of respondents' views using numerical data and statistical analysis. The findings from this study indicate a very strong positive perception of the effectiveness of NITDA's IT Projects Clearance process among stakeholders. Using a 5-point Likert scale\, all evaluated attributes recorded mean scores above 4.40\, suggesting widespread agreement that the clearance framework contributes significantly to accountability\, coordination\, transparency\, trust and value optimisation in government IT investments. More importantly\, the study contributes to digital governance literature by reframing IT project assurance as a foundational element of ethical and trust-based governance systems\, offering insights relevant to governments seeking to strengthen oversight of digital investments. The findings are also expected to provide useful insights for policymakers\, government institutions\, ICT regulators\, researchers\, and development practitioners seeking to improve digital governance frameworks and optimise public sector technology investments.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:9a6fefa578947a5ff2d2406908c48d90
URL:http://10thworlds4.sched.com/event/9a6fefa578947a5ff2d2406908c48d90
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T124500Z
DTEND:20260728T130000Z
SUMMARY:Theoretical Vulnerabilities in Quantum Integrity Verification under Bell-Hidden Variable Convergence
DESCRIPTION:Authors - Jose R. Rosas-Bustos\, Jesse Van Griensven The\, Roydon Andrew Fraser\, Sebastian Ratto Valderrama\, Nadeem Said\, Andy Thanos Abstract - CHSH tests are increasingly used as integrity probes for quantum devices and services: when a verifier observes a value S > 2\, it is tempting to treat this as a compact certificate of nonclassical correlations. In operational deployments\, however\, the meaning of a small violation depends on how accurately measurement settings are implemented and how uncertainty is budgeted. We develop a resolution matched comparison between (i) a finite-precision quantum description based on smeared POVMs and (ii) a coarse-grained Bell-local baseline built from deterministic response functions. Using total-variation distance and witness-level tolerances\, we define convergence vicinities where the two descriptions become indistinguishable for a realistic verifier. We then interpret weak violations through relaxed Bell bounds under measurement dependence\, highlighting that controller leakage can compromise CHSH-only acceptance rules even when the device itself is locally causal. Angle-scan simulations map where these vicinities arise\, and IBM Quantum demonstrations illustrate that hardware noise further narrows the practical margin above S = 2. We conclude with controller-aware guidance for integrity pipelines and DIQKD-oriented deployments\, emphasizing diversified tests and explicit uncertainty budgets.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:ebe3869e4e006c587c604e2334542774
URL:http://10thworlds4.sched.com/event/ebe3869e4e006c587c604e2334542774
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T124500Z
DTEND:20260728T130000Z
SUMMARY:Vision Transformer Robustness to Occlusion in Traffic Sign Recognition Using Region Aware Learning
DESCRIPTION:Authors - Waqas Ahmed\, Muhammad Khalid\, Adil Khan\, Gulraiz Khan Abstract - Robust traffic sign recognition being important component in integrated Autonomous Driving Assistance Systems (ADAS)\, driving safety systems\; suffers from many challenges including Occlusions and obstructions in road signs. Occlusions may occur due to natural or man made objects\; weather conditions\, lightening effects\, vegetation\, paint sprays\, parked vehicles may result in traffic sign misclassifications adding more to endangerment of human life and infrastructure. This research contributed by addressing the occlusion problem by devising an occlusion-aware masked autoencoder Vit-OCC for reconstruction of obstructed traffic sign while utilizing German Traffic Sign Recognition Benchmark (GTSRB). Study encompassed four Vit models\; a standard classifier (ViT-CLS)\, a region-aware Cutout-augmented model (ViT-Cutout)\, a model initialized via random-mask Masked Auto encoding (ViT-MAE)\, and a proposed occlusion-aware Masked Autoencoder (ViT-OCC). All the models are evaluated using variable and increasing occlusion levels\, comprising of 9 different levels from 0% being lowest occlusion level to highly obstructed 80% threshold. Results indicate different patterns of degradation among the four models\, although ViT-MAE achieved highest baseline accuracy of 87.23%\, ViT-Cutout exhibited stable performance with the introduction of high occlusion levels. ViT-Cutout ViT-OCC exhibited sharp drop in performance under high occlusion thresholds. This study contributed in a systematic exploration of Vision Transformer robustness across variants under controlled and increasing occlusion thresholds while concluding unique patterns in degradation and stability.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:b73c32811891cc391fc2fea2701494c8
URL:http://10thworlds4.sched.com/event/b73c32811891cc391fc2fea2701494c8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T130000Z
DTEND:20260728T131500Z
SUMMARY:INVESTIGATION OF THE EFFECT OF BEND ANGLE IN AN L-SHAPED CHANNEL ON INCOMPRESSIBLE VISCOUS FLOW
DESCRIPTION:Authors - Almas Temirbekov\, Bekdaulet Khudaibergen\, Nurlan Temirbekov\, Aigul Sagatbekkyzy\n Abstract - The problem to be solved is to determine how the bend angle of an Lshaped channel affects steady two-dimensional incompressible viscous flow and the associated hydraulic losses. Two channel geometries are considered and compared: a channel with a 45° bend and a channel with a 90° bend. Numerical computations are performed for Reynolds numbers Re = 500\, 1000\, and 2000. The flow is described by the stationary Navier–Stokes equations and solved using the finite element method. Newton’s method is applied to solve the resulting nonlinear algebraic system. The numerical results are analyzed using velocity and pressure fields\, stream-function distributions\, pressure drop\, loss coefficient\, and Euler number. The simulations show that the 90° bend produces a stronger rearrangement of the flow downstream of the corner than the 45° bend. Due to the sharper change in flow direction\, the velocity field becomes more distorted\, pressure gradients increase\, and recirculation zones become more pronounced as the Reynolds number grows. In contrast\, the 45° bend provides a smoother flow transition and more regular velocity and pressure distributions. The integral characteristics confirm these observations. For all considered Reynolds numbers\, the 90° bend leads to a higher pressure drop than the 45° bend. Although the loss coefficient and Euler number vary with Re\, the 90° geometry remains less favorable. These results demonstrate that the bend angle significantly affects both local flow structure and pressure-loss characteristics.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:e522ad4e6f978fcc45a884388da64982
URL:http://10thworlds4.sched.com/event/e522ad4e6f978fcc45a884388da64982
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T130000Z
DTEND:20260728T131500Z
SUMMARY:A Constrained Optimization Framework for Calibration-Aware Adaptive Inference in Edge AI Systems
DESCRIPTION:Authors - Moncef Zarrouk\, Abdelmajid Bessate\, Faissal El Bouanani Abstract - Edge artificial intelligence systems require inference mechanisms that jointly account for predictive accuracy and resource consumption. Compact edge models provide low-cost and low-latency inference\, but they may be less reliable on difficult inputs\, whereas larger cloud models are more accurate but expensive to invoke for every sample. In this paper\, we propose a constrained optimization framework for calibration-aware adaptive edge–cloud inference. The edge model first produces a local prediction and a confidence score. This confidence is post-calibrated by scalar temperature scaling\, and a threshold gate then either accepts the local prediction or defers the input to a cloud model. The threshold is selected by solving an empirical constrained optimization problem that maximizes accuracy under an operator-specified of- load budget. We show that temperature scaling preserves the edge-model decision\, that the empirical offload rate is monotone in the threshold\, and that the finite empirical threshold-selection problem can be solved exactly by enumerating the gate partitions induced by the observed confidence scores\, correctly handling the strict deferral boundary. Experiments on Fashion-MNIST show that constrained deferral recovers most of the edge–cloud accuracy gap while offloading only a controlled fraction of inputs. Moreover\, an additive-noise stress test shows that calibration substantially reduces confidence miscalibration when the edge model be- comes unreliable\, confirming that calibration mainly contributes reliabil- ity\, while selective deferral drives the accuracy gain.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:a2c9e0fbf44ef6a10ddcf3508635d98c
URL:http://10thworlds4.sched.com/event/a2c9e0fbf44ef6a10ddcf3508635d98c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T131500Z
DTEND:20260728T133000Z
SUMMARY:Comparative Evaluation of TF-IDF and Multilingual Transformer Models for Fake News Detection in Kazakh and Russian Media
DESCRIPTION:Authors - Ussen Marassulov\, Orken Mamyrbaev\, Gulnur Kazbekova\, Aigerim Yerimbetova\, Madina Sambetbayeva\, Duman Telman Abstract - This paper evaluates fake news detection in Kazakh and Russian media by comparing sparse TF-IDF baselines with multilingual transformer models. The task is formulated as binary text classification with Real (0) and Fake (1) labels. The protocol combines duplicate-aware cleaning\, split-leakage verification\, label-consistency checks\, non-neural baselines\, transformer finetuning\, and bidirectional cross-lingual testing. After exact title + text deduplication\, the corpus contained 38\,013 Kazakh records\, 37\,181 Russian records\, and 75\,194 bilingual records. Five regimes were evaluated: KZ_only\, RU_only\, MIX_only\, KZ->RU\, and RU->KZ. TF-IDF remained highly competitive in in-domain testing\, reaching Macro-F1 = 0.9979 on Kazakh\, 0.9984 on Russian\, and 0.9989 on mixed data. In cross-lingual testing\, multilingual transformers showed clearer advantages: mBERT reached Macro-F1 = 0.9833 in KZ->RU\, while XLM-R achieved 0.9947 in RU->KZ. The results indicate that lexical baselines are strong when train and test data share the same language setting\, whereas multilingual transformers are more reliable when the target language changes.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:104df29f6cb9be340884fce0f102e78e
URL:http://10thworlds4.sched.com/event/104df29f6cb9be340884fce0f102e78e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T131500Z
DTEND:20260728T133000Z
SUMMARY:Deep Learning for Global Navigation Satellite Systems (GNSS) Security
DESCRIPTION:Authors - Guillermo Francia III\, Eman El-Sheikh\, Md Abdur Rahman Abstract - Global Navigation Satellite Systems (GNSS) are essential components of modern unmanned aerial vehicle (UAV) operations\, providing positioning\, navigation\, and timing (PNT) services that enable autonomous flight\, waypoint navigation\, and coordinated mission execution. However\, the increasing reliance of UAVs on radio frequency (RF) communications and GNSS signals has exposed these systems to a growing range of cybersecurity threats\, including spoofing\, jamming\, malware infections\, distributed denial-of-service (DDoS) attacks\, and anomalous network behaviors. This paper investigates the application of deep learning techniques for enhancing RF-based security in GNSS-enabled drone communication networks. A comprehensive drone communication dataset containing 52\,585 records and four traffic classes—normal traffic\, malware infections\, DDoS attacks\, and anomalous behavior—was utilized to develop and evaluate a Deep Learning Radio Frequency Security (DL-RFS) model. To address severe class imbalance\, a balanced TensorFlow data pipeline incorporating stratified sampling\, class-wise dataset generation\, and equal-probability sampling was designed. The proposed neural network architecture employs fully connected layers with Rectified Linear Unit (ReLU) activation functions and a softmax output layer optimized using the Adam optimizer. Experimental evaluation conducted on an NVIDIA A100 GPU demonstrated exceptional classification performance\, achieving an AUC of 0.999\, accuracy of 99.3%\, precision of 99.9%\, recall of 99.9%\, and an F1-score of 1.000. Comparative analysis shows that the proposed DL-RFS model outperforms several state-of-the-art machine learning and deep learning approaches for drone network intrusion detection. The results demonstrate the effectiveness of balanced deep learning pipelines for RF signal security analysis and establish a foundation for future research in GNSS security\, RF fingerprinting\, and AI-driven cyber defense mechanisms for autonomous systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:8c415953ad593bcd511e27d419c1248f
URL:http://10thworlds4.sched.com/event/8c415953ad593bcd511e27d419c1248f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T133000Z
DTEND:20260728T134500Z
SUMMARY:Operationalizing Responsible Development of AI in Education: Estonia’s Five-Year Experience with Hybrid Neural-Symbolic Methods
DESCRIPTION:Authors - Danial Hooshyar\, Yeongwook Yang\, Raija Hamalainen\, Tommi Karkkainen\n Abstract - Recent advances in general intelligence paradigms\, particularly large language models (LLMs)\, have accelerated the adoption of artificial intelligence (AI) in education. However\, recent studies show that LLMs often exhibit shallow adaptivity and struggle to reliably model learners’ evolving knowledge over time. Similar to other deep neural networks\, their opaque nature and susceptibility to bias and spurious correlations can limit alignment with pedagogical principles\, raising concerns regarding transparency\, fairness\, and trustworthiness\, particularly in educational settings classified as high-risk under the EU AI Act. While responsible AI use has received increasing attention\, responsible AI development—an essential prerequisite for responsible use—remains comparatively overlooked. This paper presents Estonia’s five-year experience applying hybrid neural-symbolic AI (NSAI) methods in educational contexts to operationalize responsible AI development. By integrating symbolic knowledge directly into neural learning processes\, the presented approaches aim to support human-centered\, interpretable\, and pedagogically grounded AI systems. In contrast to recent trends emphasizing loosely coupled symbolic components around closed large-scale models\, the Estonian approach focuses on tighter integration between neural and symbolic representations to improve transparency\, learner modeling\, and educational alignment. Through five illustrative case studies developed across educational applications ranging from student performance prediction to deep knowledge tracing\, the paper demonstrates how hybrid neural-symbolic methods can support explainability\, pedagogical robustness\, and responsible AI objectives in education. Finally\, the paper discusses key lessons learned\, remaining challenges\, and future opportunities for applying neural-symbolic AI in education.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:64af94ab28539224a99f620b1a5e2e21
URL:http://10thworlds4.sched.com/event/64af94ab28539224a99f620b1a5e2e21
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T133000Z
DTEND:20260728T134500Z
SUMMARY:Detecting AI-Mutated Malicious API Payloads via Fine-Tuned CodeBERT with Attention-Based Explainability
DESCRIPTION:Authors - Saltanat Adilzhanova\, Gulshat Amirkanova\, Bauyrzhan Amirkhanov\, Dana Sybanova\, Anas Salem Abstract - The increasing adoption of large language models (LLMs) by adversarial actors has introduced a critical threat to web application security: AI-mutated malicious payloads - injection attacks automatically rewritten by LLMs to preserve malicious functionality while evading signaturebased detection. Existing intrusion detection approaches\, including classical machine learning classifiers and deep learning architectures trained on static historical corpora\, do not address this threat class and degrade severely when confronted with LLM-obfuscated variants of known attacks. This paper presents a three-component framework for detecting and explaining AImutated malicious API payloads. First\, a novel three-class labelled dataset is constructed by applying a controlled GPT-4o-mini mutation pipeline\, spanning five structurally distinct obfuscation strategies\, to payloads drawn from three established attack corpora\, with validation against a sandboxed DVWA instance. Second\, a CodeBERT encoder is fine-tuned for three-class classification distinguishing benign traffic\, classically malicious payloads\, and AI-mutated payloads\, achieving a macro-F1 of 0.9472 and an AUC-ROC of 0.9990 on the held-out test set\, with a class-specific F1 of 0.8627 on AI-mutated samples - an improvement of 27.18 points over the strongest baseline. Third\, a dual-layer explainability module combining attention visualisation and SHAP-based token attribution is evaluated through a faithfulness deletion test\, confirming that both methods identify decision-relevant tokens and revealing distinct detection strategies for classical versus AI-mutated payloads. A controlled ablation study demonstrates that AI-mutated training data is a necessary condition for detecting this class\, with class-specific F1 collapsing to zero when such data is withheld. The dataset and model are released publicly to support reproducible research.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:ac3dbb8800ea4af1ac6c48d10c97fe4f
URL:http://10thworlds4.sched.com/event/ac3dbb8800ea4af1ac6c48d10c97fe4f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T134500Z
DTEND:20260728T140000Z
SUMMARY:Towards Sustainable Campus Mobility: A Safety-Aware Low-Voltage Electrical Architecture and Energy-Modelling Framework for Micro-Electric Vehicles
DESCRIPTION:Authors - Okose Divine Ofure\, Azunka N. Ukala Abstract - Low-speed campus mobility presents a practical use case for compact EVs where high-voltage platforms are costly to procure\, operate and maintain. Across campuses\, short trips rely on walking\, motorcycles\, petrol vehicles or informal transport\, despite predictable low-speed use. This creates demand for EVs that can be locally assembled and maintained with available parts. Yet much EV literature focuses on high-voltage platforms\, leaving limited guidance for micro-EVs. This paper presents a safety-aware 48 V architecture for a single-seater campus micro-EV\, integrating lithium-ion storage\, BLDC traction\, fused distribution\, pre-charge sequencing\, contactor switching and 12 V auxiliary supply. An analytical model estimates tractive force\, power demand\, battery current\, energy use and state-of-charge under stop-start use. Results show 40 Wh/km\, with ranges of 18 km\, 21 km and 24 km for 15 Ah\, 17.5 Ah and 20 Ah packs. The 20 Ah option lowers peak C-rate to 1.34 C\, although 40 km needs 2.0–2.5 kWh or mass and loss reduction.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:b0f944e1de8240f083b89c2aab862fe8
URL:http://10thworlds4.sched.com/event/b0f944e1de8240f083b89c2aab862fe8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T134500Z
DTEND:20260728T140000Z
SUMMARY:FIDES: A Trust Assessment Framework for IT Systems
DESCRIPTION:Authors - Noah Holmdin\, Martin Gilje Jaatun Abstract - The increased reliance on IT has resulted in a need for decision support for IT system usage scenarios. This is often done through risk assessment\, where events are evaluated based on their likelihood of consequences and the severity of consequences. However\, this may be difficult in scenarios when there is limited\, uncertain\, conflicting\, or changing information about the IT system’s behaviour. A complementary approach for this is evaluating the system based on a Decision Maker’s (DM) trust in it. This paper introduces a subjective trust assessment framework for IT usage decisions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:5d89ca2c7a948a166a515f4c6d6f5b38
URL:http://10thworlds4.sched.com/event/5d89ca2c7a948a166a515f4c6d6f5b38
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T140000Z
DTEND:20260728T141500Z
SUMMARY:Beyond Technological Modernisation: ICT Development\, EGovernment\, and Governance Transformation in South Korea
DESCRIPTION:Authors - Kyounghee Cho Abstract - South Korea is widely recognised as one of the world’s leading digital societies and a global model of e-government development. While previous studies have primarily examined Korea’s ICT development and e-government achievements from technological and administrative perspectives\, relatively limited attention has been paid to how these developments collectively contributed to broader governance transformation. This paper examines how South Korea’s ICT and e-government development contributed to governance transformation beyond technological modernisation from the 1990s onward. Adopting a qualitative case study approach\, the study analyses governmental policy documents\, e-government development plans\, institutional reports\, and relevant academic literature. Particular attention is given to the Korean Information Infrastructure (KII)\, major e-government initiatives\, and the role of state leadership and institutional coordination in shaping digital transformation. The findings suggest that South Korea’s digital transformation was not merely a technological process but a broader governance transformation. ICT development and e-government expansion facilitated administrative integration\, strengthened institutional coordination\, enhanced governance capacity\, and reshaped interactions between government institutions and citizens. The study argues that these outcomes were driven not only by technological innovation but also by long-term strategic planning\, state leadership\, and institutional capacity. By analysing ICT development and e-government expansion as interconnected dimensions of governance transformation\, the paper contributes to a deeper understanding of digital governance and provides an analytical foundation for future comparative research on digital governance and AI-enabled public administration\, particularly across different institutional contexts such as South Korea and the United Kingdom.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:e67ccff311ade729f363f69ee844b032
URL:http://10thworlds4.sched.com/event/e67ccff311ade729f363f69ee844b032
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T140000Z
DTEND:20260728T141500Z
SUMMARY:Energy-Driven Process Reconstruction (EDPR) for a Closed-Loop Digital Twin of Bakery Production with OpenEgiz
DESCRIPTION:Authors - Gulshat Amirkhanova\, Alikhan Amirkhanov\, Gulnur Tyulepberdinova\, Bauyrzhan Amirkhanov\, Yenlik Faruzkyzy Abstract - Digital twins for manufacturing usually rely on a Manufacturing Execution System (MES) that records what each operation does and when. Many small and medium-sized enterprises (SMEs) lack such systems yet increasingly meter the electricity of individual machines. This paper asks whether a plant's process can be reconstructed\, simulated and optimised from electrical metering alone\, and formalises the answer as Energy-Driven Process Reconstruction (EDPR). The subject is a commercial bakery in Kazakhstan whose fifteen machines are metered and integrated through OpenEgiz\, a digital-twin platform built on the open-source OpenTwins framework\; about 65 million readings span 198 days. EDPR detects machine states from active power\, abstracts them into events\, and links events into per-batch chains by a batch-anchored lead-lag operator with one-to-one assignment in O(N log N) time. With no MES ground truth available\, EDPR is scored on a labelled synthetic benchmark\, where it reaches an eventdetection F1 of 0.97 and\, against three baselines\, is the only method that combines competitive chain accuracy with a valid one-to-one batch partition. On the real plant\, conformance checking raises model precision from 0.22 for a naive day-case model to 1.00 for the EDPR reconstruction\, and a discrete-event twin parameterised only by the energy-derived lead times reproduces the observed throughput of about 21 batches per day. Using the twin\, three demand-side measures are estimated to give a potential electricity-cost reduction of 20.5 per cent at constant output. The pipeline uses only open-source software and permachine power.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:cb6051e9321fc63e395ffa96fd555436
URL:http://10thworlds4.sched.com/event/cb6051e9321fc63e395ffa96fd555436
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T141500Z
DTEND:20260728T143000Z
SUMMARY:A Data Fusion and Machine Learning Platform for Scalable Smart City Last-Mile Route Optimization
DESCRIPTION:Authors - Ekene Michael Ogbejesi\, Ming Jiang Abstract - The rapid growth of e-commerce and smart city infrastructures has intensified the operational challenges associated with last-mile delivery systems. Last-mile logistics remains the most expensive and operationally complex component of modern supply chains due to traffic congestion\, inefficient routing\, fluctuating environmental conditions\, and increasing customer delivery expectations. This study presents a scalable data fusion and machine learning platform designed to optimize smart city last-mile delivery operations through predictive analytics and route optimization techniques. The proposed framework integrates heterogeneous datasets including geospatial coordinates\, delivery agent profiles\, weather conditions\, traffic density\, and vehicle characteristics to improve delivery efficiency and operational decision-making. A comprehensive preprocessing and feature engineering pipeline was developed\, incorporating Haversine distance computation and categorical feature transformation. Multiple machine learning models including XGBoost\, LightGBM\, Random Forest Regression\, and Neural Networks were evaluated for delivery time prediction using historical logistics data. The dataset comprised 1\,000 delivery records with features including distance\, traffic conditions\, vehicle type\, and time-based variables. Data preprocessing involved handling missing values\, outlier treatment\, and feature scaling. Experimental evaluation demonstrated that the Random Forest model achieved the strongest predictive performance with a Root Mean Square Error (RMSE) of 25.15 minutes\, Mean Absolute Error (MAE) of 19.28 minutes\, and an R² score of 0.76. The predictive model was integrated with a Traveling Salesman Problem (TSP)-based optimization module using a nearest-neighbor heuristic algorithm to generate efficient multi-stop delivery routes. The framework was deployed as an interactive Streamlit application supported by Folium geospatial visualization. Results demonstrate that the proposed framework improves delivery prediction accuracy\, route planning efficiency\, and operational scalability within intelligent transportation systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:20789509d749dbb062baff1585d8069d
URL:http://10thworlds4.sched.com/event/20789509d749dbb062baff1585d8069d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T141500Z
DTEND:20260728T143000Z
SUMMARY:Digital Governance in The Generative AI Era: A Qualitative Analysis of Privacy and Trust in South African Organizations
DESCRIPTION:Authors - Tlangelani Promise Mlambo\, Tranos Zuva\, Andrew Brown\, Ramadile Moletsane Abstract - South African organizations are experiencing rapid digital transformation as digital technologies increasingly shape service delivery\, data management\, and interactions between the state and citizens. In this time where machines such as Generative Artificial Intelligence (GenAI) can generate answers from the user prompt\, there’s a need to control their use in order not to harm others. This study examines digital governance in the generative AI era\, focusing on how privacy and trust are managed within organizations in South Africa. The study adapts a qualitative research approach utilizing the traditional literature review and secondary data analysis. There’s a disconnect between the initiated policies and the implementation or regulation of these policies\, therefore\, to develop a comprehensive digital governance framework for South African organizations to help minimize any harm that can be caused by use of Generative Artificial Intelligence. The research shows that in the generative AI era\, is necessary for digital governance to move from only technology-centric strategies to a comprehensive framework that will combine institutional\, ethical and operational aspects. The study contributes theoretically and practically to a deeper understanding of governance challenges and opportunities in South Africa. The study is limited to South African organizations and on secondary data from existing literature. Future work can extend the scope of the study beyond South Africa and conduct empirical and longitudinal studies to gain more insights on digital governance in the generative AI era.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:82f4a03075c55cdd59ece48e32a59360
URL:http://10thworlds4.sched.com/event/82f4a03075c55cdd59ece48e32a59360
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T143000Z
DTEND:20260728T144500Z
SUMMARY:Jacobian-Based Bounds on Mutual Information for MIMO CSI Channels
DESCRIPTION:Authors - Andrea Piroddi\, Maurizio Torregiani Abstract - This paper introduces an information-theoretic framework for bounding the mutual information (MI) between the two-dimensional physical position of a user and the observed multiple-input multiple-output (MIMO) channel state information (CSI). Unlike classical Cram´er–Rao bounds or I-MMSE relations\, the proposed approach explicitly incorporates the spatial variability of the channel via its Jacobian with respect to position. Two complementary results are derived: a local Jacobian-based characterization\, valid under small prior spatial uncertainty with explicit validity conditions\, and a global nonlinear upper bound via path-integral formulations that extends the analysis to arbitrary spatial distributions. Both are derived from first principles under clearly stated assumptions\, and their relationship to classical Cram´er–Rao bounds\, Fisher information\, and Ziv–Zakai bounds is discussed. A quantitative tightness analysis across array sizes M ∈ {8\, 16\, 32\, 64} shows that the local bound is nearly exact for small arrays and low SNR\, while becoming progressively more conservative for larger configurations. A sensitivity analysis of the global bound to the reference point choice is provided\, together with a principled method to reduce this sensitivity via the channel manifold centroid. Validation using Sionna RT ray tracing on the Munich urban scene reveals that realistic channels exhibit lower spatial variability than synthetic line-of-sight models\, with direct implications for CSI-based localization design.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:9fb2ee84817ba71df992bd3584e46d2f
URL:http://10thworlds4.sched.com/event/9fb2ee84817ba71df992bd3584e46d2f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T143000Z
DTEND:20260728T144500Z
SUMMARY:Survival Forests against Deep Survival Models for Cardiovascular Event Prediction in an IoMT Monitoring Pipeline: A Reproducible Benchmark with Explainability
DESCRIPTION:Authors - Gulshat Amirkhanova\, Alikhan Amirkhanov Abstract - Wearable and Internet-of-Medical-Things (IoMT) devices now stream cardiac signals continuously\, yet turning that flow into an early warning still needs a model that says when an adverse event is likely\, not just whether one will occur. We treat that question as a time-toevent problem and benchmark five survival models on the UCI Heart Failure Clinical Records cohort (299 patients\, 96 deaths\, follow-up 4– 285 days): Cox proportional hazards\, penalised Cox\, random survival forest (RSF)\, gradient-boosted survival analysis\, and the deep survival network DeepSurv. Models are compared under 5×5 repeated stratified cross-validation with Harrell’s and IPCW concordance\, the integrated Brier score\, and time-dependent AUC. The two tree-ensemble survival models lead: gradient boosting reaches a C-index of 0.731 and RSF 0.725\, both ahead of Cox (0.709) and well ahead of DeepSurv (0.645)\, which also shows the worst calibration. On a cohort of this size the deep model does not pay its way. Permutation and SHAP analysis of the RSF point to serum creatinine\, ejection fraction and age as the dominant risk drivers\, which agrees with established cardiology. We frame the survival learner as the analytics stage of an IoMT cardiovascular-monitoring pipeline and release code\, data split and seeds for full reproducibility.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:5b05e30fecbb90192630c038693dbd68
URL:http://10thworlds4.sched.com/event/5b05e30fecbb90192630c038693dbd68
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T144500Z
DTEND:20260728T150000Z
SUMMARY:Benchmarking Speech-to-Text Translation for Turkic Languages: A Comparative Study of SeamlessM4T v1 and v2
DESCRIPTION:Authors - Aidana Karibayeva\, Oleg Myssov Abstract - This paper presents a systematic experimental evaluation of the SeamlessM4T speech-to-text translation system on four Turkic languages — Kazakh\, Tatar\, Turkish\, and Uzbek — across nine language pairs: kaz-tur\, kaz-uzb\, tat-kaz\, tat-tur\, tat-uzb\, tur-kaz\, tur-uzb\, uzb-kaz\, and uzb-tur. The study compared the base model (v1) and its fine-tuned version (v2). A closed parallel corpus of 1000 sentences per language pair was used. For evaluation\, the BLEU\, chrF\, and WER metrics were used. The results showed a moderate but uneven improvement in scores after fine-tuning. The best results were observed in pairs where Uzbek was used as the source language (uzb–kaz: +0.78 BLEU\; uzb–tur: +0.95 BLEU). However\, in some Turkic language pairs\, the scores decreased\, which may be related to the model forgetting its prior knowledge during multi-task learning. When Tatar was the source language\, both models performed poorly. This is explained by the scarcity of data and the phonological differences between languages. Furthermore\, it was found that the model's architecture is limited when Tatar is used as the target language. The chrF metric was found to be more effective than BLEU for morphologically complex agglutinative languages. Therefore\, it is recommended for use as the primary metric in the S2TT evaluation for Turkic languages.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:f02ab29717f111c5f4bcbe634aee3086
URL:http://10thworlds4.sched.com/event/f02ab29717f111c5f4bcbe634aee3086
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T144500Z
DTEND:20260728T150000Z
SUMMARY:Patient-Centred Evaluation in XAI Healthcare
DESCRIPTION:Authors - Zubaria Inayat\, Hanin Esawi\, Maya Daneva\, Marten van Sinderen\, Giancarlo Guizzardi\, Luiz Bonino da Silva Santos Abstract - Explainable artificial intelligence (XAI) is becoming an important component of healthcare systems\, supporting transparent and trustworthy AI-assisted decision-making. However\, existing explainable AI approaches are mainly designed around healthcare professionals\, while patient needs and expectations regarding AI-generated explanations remain insufficiently explored. This study investigates how patients perceive the quality of explanations provided by XAI healthcare systems and identifies the challenges that influence their understanding\, trust\, and engagement. A mixed-method research approach was adopted\, combining (i) a rapid literature review\, (ii) an online survey with 33 participants\, and (iii) expert consultation for validation. The literature review identified nine quality dimensions for patient-oriented XAI explanations. The survey findings revealed eight challenges experienced by patients when interacting with explainable AI systems. Based on the synthesis of these findings\, a patient-centred evaluation matrix was developed\, linking explanation quality dimensions with patient-related challenges. The proposed matrix was validated through expert feedback. The results highlight the importance of moving beyond developer and clinician-centric XAI design towards patient-centred explainable healthcare systems. This work contributes to trustworthy artificial intelligence in healthcare by providing guidance for evaluating explanation quality\, improving usability\, supporting patient trust\, and enabling informed shared decision-making.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:bfa0e2ba564efc6ada622efee3f287eb
URL:http://10thworlds4.sched.com/event/bfa0e2ba564efc6ada622efee3f287eb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T150000Z
DTEND:20260728T151500Z
SUMMARY:Building Immersive Digital Twins: An Empirical Stakeholder Study of Barriers and Open Source Building Blocks in the European AR\, VR\, and IoT Ecosystem
DESCRIPTION:Authors - Harsh Sudhir Shah\, Ozlem Mis\, Bosen Zhou\, Christian Zimmer\, Frank Feinbube Abstract - Immersive digital twins use virtual reality\, augmented reality\, and Internet of Things technologies to create interactive virtual replicas of physical assets and environments that users can enter and explore. Across Europe\, experts report only limited progress toward immersive digital twins that are scalable\, sovereign\, and AI-augmented. The literature does not yet offer a stakeholder-grounded account of the barriers. This paper reports a reflexive thematic analysis of 11 semistructured interviews with experts. The sample spans industrial manufacturing\, automotive\, telecommunications\, public infrastructure\, 2 platform or studio vendors\, a research institute\, a technology consultancy\, 2 universities\, and an industry association. The transcripts were coded collaboratively by 3 researchers who reached consensus through structured discussion. The qualitative analysis is corroborated by a Likert layer reported alongside the themes. The analysis identifies 8 master barrier themes. Interoperability across data formats and standards is the central constraint. Content creation is the dominant scaling bottleneck. The remaining themes cover hardware and platform volatility\, the absence of a shared immersive user experience grammar\, the role of artificial intelligence inside the runtime\, trust and explainability limits\, open source as a sovereignty question\, and the demand for ready-to-run workflow scaffolding. A single-source observation concerns the erosion of opensource community review quality by AI-generated commits. The themes are translated into 7 candidate open-source building blocks that would relieve the common barriers. The contribution is empirical and forward looking. It is intended to guide open-source contribution priorities in the European immersive digital twin community.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:7d7427c4b991533b6d2d55c5c6660e8d
URL:http://10thworlds4.sched.com/event/7d7427c4b991533b6d2d55c5c6660e8d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T150000Z
DTEND:20260728T151500Z
SUMMARY:A Metric-to-State Framework for Honey Traceability Using Permissioned Blockchain
DESCRIPTION:Authors - Ahmed Abdelmoula\, Balint Molnar Abstract - Honey fraud\, including adulteration\, origin mislabeling\, and unsuitable thermal handling\, remains difficult to control because analytical verification and digital traceability are rarely integrated. Laboratory methods can assess honey quality and authenticity\, while blockchain systems can protect traceability records\; however\, ledger immutability does not ensure that recorded evidence is scientifically valid. This paper introduces a preliminary framework for converting honey-quality metrics into states suitable for permissioned blockchain environments. The architecture combines IoT sensing\, edge-level validation\, metric computation\, offchain storage\, cryptographic referencing\, and rule-based smart-contract logic. The main contribution is a metric-to-state mapping mechanism that encodes environmental\, biochemical\, and process-related evidence as blockchain events\, hashes\, quality indicators\, and certification states. A simulation-based prototype evaluates a 24-hour hive-monitoring scenario using synthetic temperature and humidity readings collected every 15 minutes. Of 96 generated readings\, 93 passed validation and were aggregated into 24 hourly quality-state transactions\, reducing potential ledger entries by 75% while preserving SHA–256 hash-chain integrity. The results provide initial feasibility evidence for the proposed sensor-to-ledger pipeline\, while remaining limited to a proof-of-concept setting rather than a full industrial deployment or permissioned blockchain benchmark. Future work will incorporate real hive data\, laboratory measurements\, and complete permissioned blockchain implementation.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:5944a2b967a34ec074d4d9fd5398b9e0
URL:http://10thworlds4.sched.com/event/5944a2b967a34ec074d4d9fd5398b9e0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T151500Z
DTEND:20260728T153000Z
SUMMARY:Legal Document Anonymization
DESCRIPTION:Authors - Malgorzata Pankowska\, Michal Dimmich\, Radosław Pacud\, Paweł Lula Abstract - In this study\, the authors provide the text anonymization model and present a statistical analysis of the anonymization results. The purpose of this study is to: ensure compliance of data processing with General Data Protection Regulation (GDPR) in the activities of a law firm - removing direct and indirect personal data from documents used for further work of a lawyer as templates and know-how\; ensure the protection of professional secrets in the activities of a law firm - removing all identifying data and elements covered by professional secrecy within documents used for further work of a lawyer as templates and know-how\, i.e.\, leaving the names and surnames of the authors of scientific publications that confirm a given view\; ensure the protection of personal and identification data when sending documents. The authors concluded that the Long Short Term Memory (LSTM) model application provided high performance results for legal document anonymization
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:3267cbc6b39f02179a4ecfaa54b61a3b
URL:http://10thworlds4.sched.com/event/3267cbc6b39f02179a4ecfaa54b61a3b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T151500Z
DTEND:20260728T153000Z
SUMMARY:Building an Inclusive Digital Society in the age of Artificial Intelligence in South Africa
DESCRIPTION:Authors - More Ickson Manda Abstract - The rapid advancement of digital technologies and artificial intelligence has created significant opportunities and challenges for developing countries such as South Africa. To harness these opportunities and mitigate associated risks\, South Africa must adopt strategic policy and investment interventions that promote innovation\, strengthen digital capabilities\, and support inclusive and sustainable socio-economic growth. Technological developments such as Artificial Intelligence\, robotics\, internet of things and cloud-based technologies have disrupted every sector. Concerns around ethics\, sovereignty\, security\, privacy\, skills\, affordability\, governance and infrastructure have hindered developing countries from fully leveraging the benefits of AI. The purpose of this paper is therefore to identify key pillars for building an inclusive digital society in South Africa in the age of Artificial Intelligence where citizens can fully access and benefit from using digital services. The study found that developing an inclusive digital society\, digital leadership\, digital infrastructure development\, embracing new and emerging technologies\, digital policy and governance and digital resilience are key pillars of an inclusive digital society.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:7c03012e243387adccaa3d8700891138
URL:http://10thworlds4.sched.com/event/7c03012e243387adccaa3d8700891138
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T153000Z
DTEND:20260728T154500Z
SUMMARY:A Multi-Model\, Multi-Adapter Framework for Self-Hosted Multi-Expert Search on Commodity Hardware
DESCRIPTION:Authors - Andrei-Horia Ignat\, Dan-Matei Popovici Abstract - Large language models solve tasks across many domains\, but their size and reliance on hosted providers place them out of reach of organisations that must own their deployment. We present a multi-model\, multi-adapter framework for building and serving a conversational multiexpert search platform entirely on commodity hardware. Each capability is realised as a small\, task-specialised expert: a parameter-efficient (QLoRA) adapter over one of a few shared base models\, trained on data synthesised offline by a large model rather than collected from users. A lightweight router directs each query to the relevant expert\, which answers by emitting an executable tool call against the service’s data. Because every expert is a few-megabyte adapter over a shared base\, the dominant memory cost is the base models\, not the number of experts: a single workstation GPU holds an estimated several hundred to several thousand experts\, and adding a service requires only a short\, repeatable recipe rather than an advanced MLOps stack. We organise the design around eight requirements spanning commodity-hardware operation\, extensibility\, accessibility\, multilingual access\, data-grounded answers\, transparency\, self-hosting\, and graceful integration depth\, and we describe serving optimisations (multi-adapter co-residency\, prefix caching\, function-call routing) that keep it efficient. We instantiate the framework on the FUTURAL Metasearch Platform\, the smart-solution registry of an EU agricultural project\; a representative wildlife expert reaches about 95.1% token-level accuracy after roughly twenty minutes of training on one GPU.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:bb5d26d44e3c3863517a1b265d38107d
URL:http://10thworlds4.sched.com/event/bb5d26d44e3c3863517a1b265d38107d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T153000Z
DTEND:20260728T154500Z
SUMMARY:Positioning Accuracy Enhancement Schemes Using Innovative AI-Enabled Kalman Filters for Improved Multi-Sensor Inertial Navigation Systems
DESCRIPTION:Authors - AA Adebomehin\, FJ Ibrahim\, AS Dahiru\, TK Akinduyite\, TO Obinna-Esiowu\, I Ofodile\, OA Odeyemi\, T Salman\n Abstract - This paper presents novel AI-enabled Kalman filter techniques that enhance positioning accuracy in inertial navigation systems (INS). Essentially\, accurate positioning remains a core challenge in multi-sensor INS\; since performance of traditional Kalman filtering degrades in nonlinear environments. Our method significantly improves the precision of INS by integrating adaptive AI-based machine-learning Kalman filters with classical estimation theory. The approach focuses on multi-sensor fusion\, adaptive noise modeling\, and robust improvements for INS applications. Simulation results demonstrate the effectiveness of the proposed techniques\, especially airborne INS. This is significant in that achievement of precision without sacrificing overall accuracy is essential to sensor data in view of factors like sensor thermal noise\, lower inference quantization\, and tolerance which could affect real–world performances. Additionally\, in a multi–sensor fusion setting\, effectiveness of INS hinges on crucial and dependable filter systems for real data integration\, noise reduction\, reliable predictive analysis\, and real-time processing. Consequently\, this research developed AI-based models and utilized them for all filter GPS positioning data to update the INS states covering INS position output\, INS internal states (position\, velocity and heading)\, and finally for estimation of bias on the accelerometer sensor. It is believed that the approach has possible applications in diverse fields like defense & security with favorable implications for autonomous systems\; as well as surveys & disaster management. Key highlights of the improved multi-sensor INS algorithm models are presented in this paper.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:323d998f82aa7a3dca8b132213d500b7
URL:http://10thworlds4.sched.com/event/323d998f82aa7a3dca8b132213d500b7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T154500Z
DTEND:20260728T160000Z
SUMMARY:Fashion Education and Industry: An AI–Cloud Fashion Transformation Framework for Innovation\, Sustainability and Operational Excellence
DESCRIPTION:Authors - Mahnoor Raghib\, Muhammad Usman Noor\, Muhammad Usman\, Ahmedullah Mohammed\, Bushra Naeem\, Shovro Baidya\, Farae Naeem Abstract - The fashion industry and fashion education sector are undergoing signif- icant transformation through the adoption of Artificial Intelligence (AI) and advanced cloud computing technologies. While cloud computing provides scalable infrastructure\, real-time collaboration\, and data processing capabilities\, AI introduces intelligent au- tomation\, predictive analytics\, generative design\, and personalized decision-making. In fashion education\, AI-powered tools enhance creativity\, virtual prototyping\, digital de- sign capabilities\, and industry readiness by enabling students to experiment with inno- vative concepts in simulated environments. Simultaneously\, cloud-based platforms fa- cilitate collaborative learning\, scalable storage\, and access to professional design and simulation software. Within the fashion industry\, cloud computing supports digital transformation across supply chain management\, manufacturing operations\, customer experience\, and sustainability initiatives. AI-driven analytics further enable demand forecasting\, inventory optimization\, waste reduction\, and customer personalization. However\, challenges relating to cybersecurity\, privacy protection\, algorithmic bias\, transparency\, organizational resistance\, and legacy system integration continue to hin- der implementation. This paper synthesizes current literature and proposes an AI– Cloud Fashion Transformation Framework that conceptualizes cloud computing as the infrastructure layer and AI as the intelligence layer supporting transformation across educational and industrial domains. The findings suggest that successful digital trans- formation requires not only technological adoption but also effective governance\, workforce development\, ethical implementation\, and organizational commitment. The proposed framework contributes to understanding how AI and cloud computing collec- tively drive innovation\, sustainability\, and operational excellence within fashion eco- systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:bed8c69b1a6abc467e41532215723fe8
URL:http://10thworlds4.sched.com/event/bed8c69b1a6abc467e41532215723fe8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T154500Z
DTEND:20260728T160000Z
SUMMARY:Evaluating Multimodal Fusion Strategies for Audio–Visual Deepfake Detection
DESCRIPTION:Authors - Caitlin Loh\, Susmitha Vekkot\, Pancham Shukla Abstract - Deepfakes generated using modern machine learning techniques pose growing risks to digital trust by enabling realistic manipulation of both audio and video content. Many existing detection approaches rely on a single modality\, limiting robustness when confronted with increasingly sophisticated forgeries. This paper evaluates a multimodal deepfake detection framework that integrates audio and visual information using deep learning. The proposed system combines stateof-the-art audio and visual encoders within a modular architecture and conducts a systematic comparison of three fusion strategies: early fusion\, late fusion\, and cross-attention. Experiments are conducted on the PolyGlotFake dataset\, a multilingual benchmark containing synthetic and authentic audio–visual media. Results show that multimodal approaches substantially outperform unimodal baselines\, with late fusion achieving an AUROC of 0.955 and cross-attention models reaching accuracies of up to 0.996. These findings provide a controlled comparison of fusion strategies and demonstrate that multimodal fusion significantly improves detection performance and highlights its potential for building more robust deepfake detection systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:32faca170dabd672265ca03d11f7c2a7
URL:http://10thworlds4.sched.com/event/32faca170dabd672265ca03d11f7c2a7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T160000Z
DTEND:20260728T161500Z
SUMMARY:A BM25–Graph–Neural Pipeline for Typed Relation Recommendation in Multilingual Military Thesauri
DESCRIPTION:Authors - Bayangali Abdygalym\, Madina Sambetbayeva\, Aigerim Yerimbetova\, Elmira Daiyrbayeva\, Duman Telman Abstract - Dense retrievers achieve strong results on lexical synonymy but degrade on hierarchical (BT/NT) and associative (RT) relations in specialized multilingual thesauri. This paper addresses typed thesaurus relation extraction by introducing a sparse–neural pipeline that combines a domain-tokenized BM25 index with morphological prefix tokens for agglutinative Kazakh and inflectional Russian\, one-hop graph expansion over partially observed thesaurus structure\, a KazBERT relation classifier over six labels (BT\, NT\, RT\, SYN\, LE\, NONE) trained with hard-negative mining over taxonomic siblings\, and an LE-mediated synonym closure that propagates synonymy through translation edges. The pipeline is evaluated on a curated trilingual military thesaurus of 5\,262 canonical entries and 64\,374 manually validated relation edges\, using a 15% term-level holdout (711 query terms). The proposed pipeline reaches BT Hit@5 = 0.796\, NT Hit@5 = 0.602\, RT Hit@5 = 0.726\, and SYN Hit@5 = 1.000. The results indicate that sparse lexical retrieval remains highly competitive for hierarchical thesaurus relation discovery in low-resource multilingual settings.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:3f18c3e7639951546b1831bce86730b3
URL:http://10thworlds4.sched.com/event/3f18c3e7639951546b1831bce86730b3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T160000Z
DTEND:20260728T161500Z
SUMMARY:Agriculture Portal: Sentinel-2 Spectral Indices and Ensemble Fuzzy Multi-Criteria Decision-Making for Crop Health\, Soil Suitability\, and Advisory Compliance
DESCRIPTION:Authors - Muhammad Sohaib Ayub\, Sikandar Bakhat\, Mian Muhammad Awais\, Umair ul Hassan Abstract - Smallholder farmers in Pakistan make irrigation\, fertiliser\, and crop-selection decisions largely on inherited practice rather than field-specific evidence\, even though the satellite data needed to inform those decisions is now free and globally available. This paper presents the Agriculture Portal\, a role-based web platform that turns raw Sentinel2 imagery into decision-ready guidance for farmers and the extension officers who support them. The system computes NDVI\, EVI\, NDMI\, NDWI\, and a radar vegetation proxy from Sentinel-2 surface reflectance through Google Earth Engine\, complements them with ground-deployed IoT sensors and an AI land classifier\, then fuses the signal through an ensemble of the Analytic Hierarchy Process\, fuzzy TOPSIS\, and VIKOR into two scores: a Crop Health Index describing vegetation vigour and a Soil Suitability Index describing cultivation potential. A fixed agronomic normalisation scheme prevents the degenerate behaviour that field-relative normalisation produces over spatially uniform plots. Beyond the satellite layer\, extension officers can author standardised crop advisories and farmers can log field activities against them\, with the portal automatically generating a compliance report that surfaces the gap between guidance and practice. Field trials across cultivated and forested sites in Pakistan confirm that the dual-score design correctly separates vegetation vigour from cultivation suitability and that the compliance tracker correctly reconciles recommended and logged activity.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:c827a48a24ebfe88035e120b6157d5b0
URL:http://10thworlds4.sched.com/event/c827a48a24ebfe88035e120b6157d5b0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T161500Z
DTEND:20260728T163000Z
SUMMARY:Comparing Word-Level and Phoneme-Level XLS-R Models for Kazakh Speech-to-Sign Translation
DESCRIPTION:Authors - Mussa Turdalyuly\, Aigerim Yerimbetova\, Bakzhan Sakenov\, Ulmeken Berzhanova\, Orken Mamyrbaev\, Duman Telman Abstract - In this paper\, we examine the effectiveness of word- and phonemelevel automatic speech recognition (ASR) models for translating speech into sign language in the presence of limited language resources. A speech-to-sign system requires not only accurate transcription but also reliable preservation of semantic information for the correct selection of gestures. We focus on the Kazakh language and compare two ASR approaches based on the XLS-R architecture\, namely\, a word-level model and a phoneme-level model. Both models were studied and evaluated using the same Kazakh voice dataset and experimental settings. Its performance was assessed using traditional recognition metrics such as word error rate (WER) and phoneme error rate (PER)\, as well as gesture token accuracy (GTA)\, a follow-up evaluation index that measures the accuracy of gesture selection. The word-level ASR model achieved the best recognition performance with a WER of 0.360\, while the phoneme-level model achieved a PER of 0.573. The phoneme-level model demonstrated excellent robustness in follow-up tasks despite low transcription accuracy and achieved high gesture identification accuracy of 0.673 versus 0.686 for the word-level model. These results suggest that phoneme-based expressions are suitable for supporting communication systems where preserving meaning is more important than accurate transcription. This study highlights the importance of evaluating speech recognition systems using the following target metrics in addition to traditional recognition metrics.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:Aldgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:263290ca152e4adf1a08dd9028c9e424
URL:http://10thworlds4.sched.com/event/263290ca152e4adf1a08dd9028c9e424
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260728T161500Z
DTEND:20260728T163000Z
SUMMARY:Mapping the Future of Academic Libraries in Kenya: A Foresight Approach
DESCRIPTION:Authors:&nbsp\;Johnson Masinde\, Mugambi Frankline\, Daniel Wambiri\nAbstract: Over time\, the field of librarianship has had to adapt due to factors such as technological advancements\, which have significantly impacted the library landscape. This evolution has prompted librarians to reconsider their roles and responsibilities\, as well as the future and long-term sustainability of conventional library practices and services. The recent past has seen a significant intensification of the debate surrounding the role and future of academic libraries\, driven by the rapid advancements in technology. Traditionally\, academic libraries have served as core pillars of university education\, promoting learning\, teaching\, and research. However\, institutions of higher learning are transitioning from traditional in-person teaching methods to online methods of instruction. Furthermore\, an increasing number of students are opting for online modes of instruction instead of the traditional in-person classroom. This study was motivated by the need to explore the changing dynamics of academic libraries\, influenced by the opportunities and challenges present in contemporary society. The research study conducted a foresight analysis of the potential characteristics at the convergence of academic libraries and the fourth industrial revolution (4IR) to uncover the significant trends and uncertainties that might not be immediately visible. Furthermore\, it utilized both qualitative and quantitative research methods\, providing a thorough analysis of the potential future of the academic library landscape. The Shawaz scenario planning process was utilized to assess the key drivers of change in academic libraries over time enabling the study to paint a picture of the plausible features with the study structured in three distinct phases: (i) the literature review to uncover the mega trends and uncertainties\, (ii) the delphi survey involving 33 participants (iii) and the formulation of the scenario. Study findings from the first phase show a complex network of relationships among the major trends and uncertainties influencing the academic library landscape in both the short term and long-term future. In addition\, the findings indicate that economic\, technological\, and political factors significantly influence the academic library landscape. Academic libraries are evolving into a more dynamic landscape adopting new roles and responsibilities such as digital archiving\, data analysis support\, and research data management services. The findings show that while earlier studies suggested that technological factors shape the academic library landscape\, it is evident that economic and political factors also play significant roles in influencing this environment.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:Bishopsgate\, 1 America Square\, London\, United Kingdom
SEQUENCE:0
UID:6b61305212cc059a768ab48992ac108d
URL:http://10thworlds4.sched.com/event/6b61305212cc059a768ab48992ac108d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T075800Z
DTEND:20260729T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:a9dab147248a3eec3f16043bde630563
URL:http://10thworlds4.sched.com/event/a9dab147248a3eec3f16043bde630563
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T075800Z
DTEND:20260729T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:8c8fdc24de30a67e1b07c96233526250
URL:http://10thworlds4.sched.com/event/8c8fdc24de30a67e1b07c96233526250
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T075800Z
DTEND:20260729T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:cb7bc00db12fc7aa0679b5c914f4be64
URL:http://10thworlds4.sched.com/event/cb7bc00db12fc7aa0679b5c914f4be64
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T075800Z
DTEND:20260729T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:b542f9d2efccd523418d797ef6a2abbd
URL:http://10thworlds4.sched.com/event/b542f9d2efccd523418d797ef6a2abbd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Developing a Digital Financial Ecosystem Model to Support Innovation in Tourism Enterprises
DESCRIPTION:Authors - Ubaydullo Vakhabovich Gafurov\, Nargiza Ruzibaeva\, Khujayar Musurmonovich Shennayev\, Nosir Mahmudovich Mahmudov\, Saodat Sadriddinova\, Alisher Bakberganovich Sherov\, Nilufar Karimova Abstract - The sustainable development of tourism enterprises increasingly depends on their capacity to implement innovative activities supported by efficient financial mechanisms. However\, traditional financing models often fail to provide sufficient flexibility and accessibility for innovation-driven tourism firms\, particularly small and medium-sized enterprises. This study proposes a digital financial ecosystem model designed to enhance the financing of innovative activities in tourism enterprises through digitalization instruments. The research integrates concepts of digital finance\, FinTech platforms\, data-driven credit assessment\, and smart contract mechanisms into a unified systemic framework. Using a system dynamics modeling approach combined with structural analysis\, the study develops a conceptual and quantitative model linking digital financial infrastructure\, access to alternative funding sources\, and innovation performance indicators. Empirical validation is conducted using survey data collected from tourism enterprises and analyzed through structural equation modeling. The results demonstrate that digital financial ecosystem components significantly improve funding accessibility\, reduce transaction costs\, and increase innovation intensity. The findings highlight the mediating role of digital maturity in strengthening the relationship between financial accessibility and innovation outcomes. The proposed model contributes to the theoretical development of digital transformation in tourism finance and offers practical implications for policymakers and enterprise managers. The study provides a scalable framework for enhancing innovation financing in digitally transforming tourism markets.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:8e64a85e5a88b0fc5f7bc6943f53882f
URL:http://10thworlds4.sched.com/event/8e64a85e5a88b0fc5f7bc6943f53882f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Generation of AI-Assisted Robot Framework Test Cases: A Comparative Study of AI Tools in Software Quality Assurance
DESCRIPTION:Authors - Heidi Koivisto\, Sari Ahonen Abstract - The integration of AI-assisted code generation tools in software development has the potential to significantly improve productivity and code quality. This paper presents a comparative study of several leading AI tools\, including GitHub Copilot\, Amazon Q\, GitLab Duo\, and Claude Code\, in the context of software quality assurance (QA). These tools are evaluated based on their ability to generate test cases for a sample application\, focusing on metrics such as test coverage\, accuracy\, and maintainability. Our findings provide insights into the strengths and limitations of each tool\, offering guidance to practitioners seeking to take advantage of AI in their QA processes. The results indicate that while AI tools can accelerate test case generation and improve coverage\, careful consideration is needed to ensure the generated tests are relevant and maintainable in the long term.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:83a2ddc3a64f42f388dcf11ae6dbb6c9
URL:http://10thworlds4.sched.com/event/83a2ddc3a64f42f388dcf11ae6dbb6c9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Internet of Things and Urban Living: Improving Sustainability and Operational Efficiency in Smart Cities
DESCRIPTION:Authors - Avanti Chokhare\, Reena Satpute Abstract - With the world's population moving from rural to urban areas\, urban infrastructure including\, but not limited to\, transportation systems\, energy re-sources and public service delivery are all strained in their ability to cope with additional residents. Urban management techniques that have historically worked in managing urban growth are no longer sufficient to meet the demands of com-plex modern cities. The Internet of Things (IoT)\, a transformative technology that allows real-time data collection\, enables smarter decision-making and automates many of the services within urban areas through a variety of connected sensors\, devices and digital platforms. The purpose of this study is to analyze how IoT will enable the creation of sustainable and efficient smart cities by encompassing multiple transportation management systems\, energy efficiency improvements\, and environmental monitoring\, health\, education and digital governance administrative systems. Additionally\, the study investigates additional enabling technologies such as Artificial Intelligence (AI)\, Edge and Cloud Computing\, Block-chain and other technologies that can improve the overall functionality of the IoT. Finally\, this paper investigates key challenges associated with the implementation of IoT applications in urban areas\, including security\, reliability and data privacy issues. It is clear that the implementation of standardized frameworks\, scalable architecture and phased implementation strategies will be key components in the successful implementation of smart cities. Responsible adoption of IoT technology will lead to improved environmental sustainability\, operational efficiency and citizen quality of life in urban areas.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:f92d8eda237d6a8969972b1df0c3530a
URL:http://10thworlds4.sched.com/event/f92d8eda237d6a8969972b1df0c3530a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Machine Learning-Based Phishing Detection in Online Learning Environments: A Systematic Review
DESCRIPTION:Authors - Joseph Afriyie\, Stephen Opoku Oppong\, Benjamin Ghansah\, Daniel Kobina Danso Essel\, Dickson Keddy Wornyo\, Ephrem Kwaa Aidoo\n Abstract - The COVID-19 pandemic forced educational institutions to adopt online learning\, which resulted in expanded digital spaces that cybercriminals used to launch phishing attacks against students\, faculty\, and institutional systems. This research article provides a comprehen-sive literature review that evaluates machine learning techniques for phishing detection in online educational settings. The PRISMA guidelines were used to select 40 studies from 2013 to 2023 after researchers examined publications retrieved from IEEE Xplore\, SpringerLink\, and Google Scholar. The review analyzes various digital education ecosystems through its examination of algorithmic methods and datasets\, performance evaluation metrics\, and detection framework de-signs that universities use to defend against phishing attacks. The research shows that Random Forest and Gradient Boosting\, together with deep learning methods\, which include Convolutional Neural Networks\, Long Short-Term Memory networks\, and Recurrent Neural Networks\, deliver superior detection performance reaching over 90% accuracy in most scenarios. Educational in-stitutions encounter three primary challenges\, which include implementing real-time systems to combat emerging phishing techniques\, ensuring dataset compatibility with various environments\, and managing their restricted resource availability. The study establishes that institutions need to create technical detection frameworks that work together with user training programs to establish better institutional protection measures.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:9544a3ed3cc0a3908b8465ba5d541c16
URL:http://10thworlds4.sched.com/event/9544a3ed3cc0a3908b8465ba5d541c16
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Predicting Trends\, Anomalies\, and Failures in IoT Networks and Devices Using Machine Learning Models
DESCRIPTION:Authors - Bullet Tiwari\, Reena Satput\n Abstract - The Internet of Things (IoT) systems are becoming numerous\, trans-forming our virtual world by continually gathering information\, linking equipment\, and automating most of the spaces. However\, the size\, decentralization\, and dispersal of the IoT networks cause grave droughts with reliability\, security\, and workability. The current rule-based surveillance tools are not able to handle the dynamism and volume of data generated by drastically many IoT devices. The paper describes a machine learning (ML) system that predicts the performance of applications\, issues\, and predicts failures of devices in IoT settings. It compares the methods of supervised\, unsupervised\, and deep learning and examines their performance in the constraints of computing power\, delay and energy. It talks about the trade-offs between centralized and decentralized learning in which clouds and edges are used respectively to decide on the most suitable deployment. The framework has security\, privacy\, and sustainability design issues\, as well. The suggested solution is expected to enhance IoT resiliency with the help of predictive intelligence to manage issues before they happen and increase the overall stability of the industry\, healthcare\, and smart city environments.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:de47bfcee928700ce58548d3323d0c3a
URL:http://10thworlds4.sched.com/event/de47bfcee928700ce58548d3323d0c3a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Predictive Modelling of Extramarital Affairs Using Sociodemographic and Relationship Factors: A Comparative Analysis of KNN\, Linear Regression and Support Vector Regression
DESCRIPTION:Authors - Roseline Oluwaseun Ogundokun\, Rotimi-Williams Bello\, Pius Adewale Owolawi\, Chunling Tu\, Etienne A. van Wyk Abstract - Extramarital affairs can undermine trust and lead to relational break-down\, yet the ability to anticipate risk factors remains limited. Recent studies have used deep learning approaches to predict infidelity\, achieving high classification accuracy but at the cost of interpretability and resource requirements. This article proposes a novel research to examine whether simple\, interpretable mod-els\, k-nearest neighbours (KNN)\, linear regression (LR) and support vector regression (SVR)\, can predict the amount of time individuals spend in extramarital affairs using readily available socio-demographic and relational features. Using the well-known affairs dataset comprising 6\,366 observations and nine variables\, we apply feature engineering\, cross-validation training and regression-based evaluation to compare model performance. Our findings indicate that although KNN outperforms LR and SVR in terms of accuracy\, all models struggle to capture variance\; mean squared error (MSE) values remain high\, and the coefficient of determination (𝑅2) values close to zero. We discuss the implications for predictive counselling and outline future research directions.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:92374416e6557e592417540dedd011d3
URL:http://10thworlds4.sched.com/event/92374416e6557e592417540dedd011d3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:A Threat-Model-Driven Architectural Framework for Motion-Based Facial Authentication in Secure Web Systems
DESCRIPTION:Authors - Oussama H Hamid\, Ayman Ahmed\, Arif Al-Nahdi Abstract - The increasing reliance on web-based services has intensified the need for secure and reliable authentication mechanisms. Facial recognition has emerged as a widely adopted biometric modality owing to its convenience and contactless operation. However\, static facial recognition systems remain vulnerable to presentation attacks such as printed-photo spoofing\, screen replay\, and mask-based impersonation. Existing face anti-spoofing techniques typically implement liveness detection as a preliminary filtering stage rather than as an integral component of the authentication decision process\, creating a structural security gap that this paper addresses. A threat-model-driven architectural framework is proposed that integrates motion-based behavioural verification as a second authentication factor within facial recognition systems deployed in web-based environments. The framework introduces a system-generated challenge–response mechanism in which users perform randomised facial actions\; facial landmark tracking and temporal motion analysis verify challenge execution in real time. A formal threat model distinguishes remote attackers\, limited physical attackers\, and generative adversarial attackers\, and maps each adversary class to specific architectural countermeasures. A structured security analysis evaluates the framework against six presentation attack scenarios\, including deepfake-based adaptive attacks. The proposed design operates on commodity hardware without specialised sensors\, and the paper discusses biometric template protection\, client–server deployment models\, and privacy compliance in detail. This work contributes a principled architectural foundation for multi-factor biometric authentication in web environments and identifies concrete directions for future empirical validation
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:1f8d280ddd4b280a9c7d7529d3aa339e
URL:http://10thworlds4.sched.com/event/1f8d280ddd4b280a9c7d7529d3aa339e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Artificial Intelligence and Robotics for Warehouse Automation: A Systematic Review of Design and Performance
DESCRIPTION:Authors - Yad Sabah Hussein\, Raid W. Daoud\, hab Abdulrahman Satam\, Mohammad Fakhreldin Abstract - This systematic review investigates the design strategies and performance outcomes of AI-powered robotic systems in warehouse automation. The study synthesizes recent advances in robotic navigation\, object recognition\, task scheduling\, and multi-robot coordination\, with particular emphasis on machine learning and reinforcement learning techniques that allow robots to adapt to dynamic environments and optimize decision-making in real time. Performance evaluation across the literature reveals significant improvements in throughput\, accuracy\, and flexibility when AI-driven robotics are deployed. Robots equipped with advanced sensors and computer vision systems demonstrate enhanced capabilities in obstacle avoidance\, inventory management\, and autonomous material handling. Integration with cloud computing and Internet of Things (IoT) infra-structures further strengthens interoperability and enables predictive analytics for supply chain optimization. Key performance metrics such as energy efficiency\, error reduction\, and scalability are analyzed to highlight the comparative ad-vantages of AI-enhanced solutions over conventional automation. Despite these advances\, challenges remain in ensuring safety\, interoperability among heteroge-neous systems\, and cost-effective scalability. High implementation costs\, cyber-security risks\, and the absence of standardized protocols are identified as barriers to widespread adoption. The review concludes that hybrid approaches—combining learning-based adaptability with formal safety guarantees—represent a promising direction for future research. Moreover\, sustainable design practices and energy-efficient robotics are essential to align warehouse automation with broader environmental goals. This review provides a consolidated perspective on the state of AI and robotics in warehouse automation\, offering insights for re-searchers\, practitioners\, and industry leaders seeking to design resilient\, intelligent\, and sustainable warehouse systems.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:d628293a6934ee47bf59fd2ed2f58154
URL:http://10thworlds4.sched.com/event/d628293a6934ee47bf59fd2ed2f58154
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Mitigating Strategic Misalignment and Data Configuration Problems through COBIT 2019 An IT Governance Strategy for Public Sector Firms
DESCRIPTION:Authors - Johanes Fernandes Andry\, Hendy Tannady\, Glisina Dwinoor Rembulan\, Ongky Alex Sander\, Guan Nan Abstract - Fast paced change in digital transformation\, government institutions have been forced not only to embrace IT but to also govern it strategically for alignment with organizational goals. Unfortunately\, most organizations still grapple with several major problems including lack of alignment between business and IT and unreliable configuration data management. In light of this\, this research proposes an effective IT governance strategy that will address the out-lined challenges through utilization of the COBIT 2019 approach. Based on the results\, the organization is moderately mature in terms of compliance and risk awareness but still needs improvement in the alignment between its IT and business objectives and the validation of configuration data. Moreover\, organizational resistance and lack of user involvement were found to be among the most important obstacles to successful implementation. According to the findings\, the application of COBIT 2019 framework in the process of governance is likely to lead to better governance capacity\, higher quality of information\, and improved decision-making processes.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:d443041f41b312659a31f8f2ec554d6a
URL:http://10thworlds4.sched.com/event/d443041f41b312659a31f8f2ec554d6a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Multi-Scale Attention and Multi-Channel Fusion Network for Image Deraining in Complex Scenes
DESCRIPTION:Authors - Xinyi ZHU\, Han Wang\, Jun WU Abstract - In rainy-day images\, rain patterns exhibit complex morphologies and significant variations in scale\, and they tend to overlap with background textures and edge structures\, posing significant challenges for rain removal tasks based on a single image. Addressing the shortcomings of existing methods in modeling complex rain patterns\, enhancing key regions\, and utilizing complementary information across channels\, this paper proposes a multi-scale attention and multi-channel fusion image rain removal method tailored for complex rain pattern scenarios. First\, we construct a parallel multi-scale feature extraction module that uses standard convolutions and dilated convolutions with varying dilation rates to capture fine-scale local rain patterns\, mesoscale rain patterns\, and large-scale contextual information\, thereby enhancing the network's ability to perceive rain patterns across multiple scales. Second\, an adaptive attention optimization module is designed to re-calibrate multi-scale features across both channel and spatial dimensions\, enabling the model to focus more on areas with dense rain patterns\, edge structures\, and effective texture information. Finally\, a multi-channel feature interaction and fusion mechanism is introduced. Through channel segmentation\, cross-channel interaction\, and residual fusion\, this mechanism enhances information complementarity between different feature subspaces\, thereby improving the structural preservation and visual naturalness of the restored results. Experimental results on the Rain100H\, Rain100L\, Rain12\, and SPA-Data datasets demonstrate that our method achieves superior rain removal performance across multiple test scenarios\, exhibiting particularly strong generalization capabilities in real-world rainy conditions.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:4fbe4b5f4d7a49d580426553b8996ded
URL:http://10thworlds4.sched.com/event/4fbe4b5f4d7a49d580426553b8996ded
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:SMART MURA: A Practical Approach to Resolving Ambiguity in Public Decision-Making Through Participatory System Design
DESCRIPTION:Authors - Jatmiko Yogopriyatno\, Nursanty\, Yorry Hardayani Abstract - Ambiguity in public decision-making constitutes a structural impediment to effective e-governance\, manifesting as procedural uncertainty\, inconsistent treatment of analogous cases\, and unclear authority allocation. This study examines the Integrated Aspiration and Minutes Management Information System (SMART MURA) developed by the Regional Legislative Council (DPRD) of Musi Rawas Regency\, Indonesia\, as a sociotechnical solution for resolving decision ambiguity through stakeholder consensus-based participatory design. Employing design science research (DSR) methodology\, the study analyses the SMART MURA User Guide as a social contract encoding collective stakeholder agreements generated through multi-stakeholder Focus Group Discussions involving five organisational levels. Three ambiguity-resolution mechanisms are identified: (1) bounded automation for standardising routine administrative processes\, (2) explicit judgment points for clarifying the locus of professional discretion\, and (3) flexible categorisation that accommodates case complexity without sacrificing treatment consistency. These mechanisms produce four interdependent governance benefits: procedural certainty for citizens\, cross-case treatment consistency\, traceable accountability through digital audit trails\, and operational efficiency. The system further demonstrates structural adaptability to regulatory change and incorporates a tiered data governance architecture that balances operational transparency with individual privacy protection. The study advances e-governance theory by proposing a Transparent Discretionary Space Structuring (TDSS) framework that transcends the binary opposition between rigid Standardization and unstructured discretion. Keywords: E-Governance\, Public Decision Ambiguity\, Design Science Research\, Digital Discretion\, Participatory Design\, Legislative Information System\, Data Governance.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:0413883f48c597b5a63c92c2fd95f5a4
URL:http://10thworlds4.sched.com/event/0413883f48c597b5a63c92c2fd95f5a4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Transforming Vegetable Waste: A Digital Solution for Sustainability
DESCRIPTION:Authors - A. Aruna Kumari\, Tamminana Visweswari\, Sri Vishnu Prabhu Gudavalli Abstract - Globally\, food waste accounts for almost one-third of total food production\, which is approximately 1.3 billion tons annually. Some of the most wasted foodstuffs at the consumer level include fruits\, vegetables and bread. This proposed work will help to solve this financial and environmental issue by creating an intelligent system to eliminate vegetable waste with the help of MobileNetV2 which is a Convolutional Neural Networks (CNN) based model to classify the freshness of vegetables and use object detection model called as YOLOv8n to recognize types of vegetables. It begins with the user uploading images of vegetables\, which are pre-processed with the help of normalization and data augmentation. The MobileNetV2 model categorizes fresh and spoiled produce with accuracy rates of 95 percent\, which is expected of a Freshness classifier. In the meantime\, the YOLOv8n finder detects the specific type of vegetable with the help of a mAP50 of 0.934 to recommend the specific vegetable. If the vegetable is spoilt\, it will provide instructions on how to compost and if it is fresh\, it provides zero waste recipes. The entire system shall be made easily accessible in a web application that has Flask back-ends.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:5c8c9aa71b6b13df976dbaa1a9c3bb61
URL:http://10thworlds4.sched.com/event/5c8c9aa71b6b13df976dbaa1a9c3bb61
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:A study on a deep learning-based Smart Attendance Management System
DESCRIPTION:Authors - Reena (Mahapatra) Lenka\, Jaee Jogalekar\n Abstract - The outward appearance is a crucial necessity for all organizations and sectors. Daily attendance registration in a conservative manner is a tedious and lengthy task. Furthermore\, each organization has sanctioned its own approach to sparkle involvement through personal mockery and the employment of sheets to bolster its attendance. To tackle these challenges\, various standard automated identification and verification systems have been extensively utilized\, including IRIS\, RFID\, and biometric techniques. In contrast\, crocodile growth is highly demanded under these conditions\, requiring more time\, and it is reckless in vegetation. Any error or harm to the RFID card will lead to incomplete participation Locating this array of strategies for such a broad range necessitates higher costs and additional effort to integrate our involvement into the database. In today's context\, the awareness and recognition of unique identities have grown globally due to the demand for safety in financial transactions\, health monitoring\, validation\, and security\, along with other essential factors such as reducing fraudulent participation\, increased costs\, and\, most importantly\, lowering the chances of marking our involvement. This document outlines a suggested enhancement for the clever attendance verification system\, incorporating a facial recognition approach utilizing deep learning techniques. A cloud dataset will mainly be created by capturing the faces of the approved students or staff. Similarly\, the face is affirmed through the inclination derived from deep learning. In addition\, the created images will be saved in the established database\, each assigned a distinct label. The extraction of facial features will rely on Haar-like characteristics computed through a Deep Learning method. The suggested new method attains improved propagation by utilizing Haar-like features and a deep-learning-optimised algorithm
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:2ec38dba3d74ca55a503cdd3fec3baa5
URL:http://10thworlds4.sched.com/event/2ec38dba3d74ca55a503cdd3fec3baa5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:A TOGAF-Based Framework for Change Management
DESCRIPTION:Authors - Alta van der Merwe\, Mpho Xaba Abstract - This paper examines how change management can be integrated into the TOGAF Architecture Development Method to strengthen enterprise architecture implementation and organisational transformation. Using a qualitative systematic literature review of 35 studies published after 2010\, we identified recur-ring change management dimensions across models\, theories\, frameworks\, and methodologies\, namely leadership\, strategy\, communication\, organisational structure\, organisational culture\, collaboration\, transformation\, innovation\, governance\, risk management\, and commitment. We then aligned these dimensions to relevant TOGAF ADM phases and illustrated their application through a fictitious case study of Teleconnect\, a telecommunications company undergoing post-acquisition integration. The findings show that TOGAF implementation is strengthened when change management is embedded as a structured and complementary organisational capability rather than treated as a separate activity. The paper contributes a conceptual framework that links change management dimensions to TOGAF ADM and offers a practical basis for supporting enterprise trans-formation through a more integrated architecture approach.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:1eec544c449c83a31897b8edfd442423
URL:http://10thworlds4.sched.com/event/1eec544c449c83a31897b8edfd442423
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:AI-Enabled Preparedness for First-Year University Students
DESCRIPTION:Authors - Alta van der Merwe\, David Moselane Abstract - This study examines how artificial intelligence can strengthen teaching practices and improve university readiness for first-year students within the context of Society 5.0. While AI offers strong potential to support a human-centred and inclusive education system\, its implementation faces obstacles\, including resistance to change and scepticism about its value. The research explores strategies for effective AI integration\, with a focus on personalised learning experiences tailored to individual student needs and the automation of administrative tasks to allow educators to focus on improving their teaching and student engagement. A systematic literature review and meta-analysis were conducted to evaluate how AI enhances university preparedness\, with particular attention to perceptions of AI adoption and the challenges associated with its implementation. The findings highlight how AI can support more inclusive and responsive education systems aligned with the goals of Society 5.0\, where technology serves societal needs. While acknowledging limitations such as time constraints and the rapidly evolving nature of AI technologies\, the study offers practical insights to help educators reduce educational disparities\, promote inclusivity\, and equip students with skills required for a socially responsive and technologically integrated.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:6d6c157d114991130477b6756a335578
URL:http://10thworlds4.sched.com/event/6d6c157d114991130477b6756a335578
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Factors Influencing E-Commerce Adoption Among Congolese Enterprises: An Application of the Technology Acceptance Model (TAM)
DESCRIPTION:Authors - Franck W. Boubayi\, Regis F. Babindamana\, Peter A. Kidoudou Abstract - The adoption of e-commerce remains a major challenge for many enterprises in developing countries\, where digital transformation is often constrained by technological\, organizational\, and regulatory factors. This study investigates the factors influencing e-commerce adoption among Congolese enterprises through the Technology Acceptance Model (TAM). Data were collected from businesses operating in various sectors and analyzed using descriptive statistics and predictive modeling techniques. The findings reveal that although digital technologies are increasingly used in business activities\, e-commerce adoption remains limited. Only 35.5% of surveyed enterprises actively engage in online sales\, while most organizations continue to rely on social media platforms for promotion and telephone-based order processing. The results also highlight significant cybersecurity gaps: nearly half of the surveyed enterprises do not conduct vulnerability assessments\, more than half lack firewall or intrusion detection mechanisms\, and 16% have already experienced cyberattacks. In addition\, limited awareness of national data protection regulations exposes many businesses to legal and operational risks. The study further shows that artificial intelligence is widely perceived as a strategic opportunity\, with 83% of respondents recognizing its potential for business growth and 89% supporting the establishment of an appropriate regulatory framework. Based on the identified determinants\, a predictive model is proposed to support decision-making and promote wider adoption of e-commerce in the Congolese context. The findings provide practical insights for policymakers\, business leaders\, and researchers seeking to accelerate digital transformation while strengthening cybersecurity readiness.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:8d5415782322eff8d7d10ee6d606c988
URL:http://10thworlds4.sched.com/event/8d5415782322eff8d7d10ee6d606c988
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Land management approach to estimation of spatial efficiency of tourism destinations’ location
DESCRIPTION:Authors - Oleksandr Hladkyi\, Alexander Gertsiy\, Tetiana Tkachenko\, Valentyna Zhuchenko\, Tetiana Shparaga\, Olha Liubitseva\, Tetiana Mykhailenko\, Iryna Kochetkova Abstract - The profitable spatial location of tourism companies and destinations plays an important role in land management investigations nowadays. It significantly influences on tourism destinations' spatial efficiency. There are four main concepts of determining spatial efficiency of enterprises' location: the urban planning concepts\, synergistic or integrative concept\, functional and communicative concept as well as the concept of service clusters. Our approach is essentially different from all mentioned above. It's based on the analysis of tourism products (goods\, labor\, services) production efficiency rates determined by spatial location effect in land management system. The process of estimation of spatial efficiency of tourism destinations' location should be divided into four parts. The first part consists in gathering complete statistical data about tourism destinations development in specific location/region. Based on primary statistical data\, the following indicators of tourism destinations' spatial efficiency could be calculated: labor productivity\, profitability\, capital-labor ratio and cost recovery. At the second part\, every index of tourism destinations' spatial efficiency has to be modulated using gravitational potential model. At the third part we have to identify individual clusters of different levels of tourism destinations' spatial efficiency based on gravitational modulator data. At the fourth part all received clusters could be figured on geographic contour maps of particular region. Using above-mentioned methods\, gravitational model of spatial efficiency of tourist enterprises' location in key regions of Ukraine has been created.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:ddfd6eb6d97b282c9a2aa8fdbec2d17f
URL:http://10thworlds4.sched.com/event/ddfd6eb6d97b282c9a2aa8fdbec2d17f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Risk-Adaptive Multi-layer Security Framework (RAMSF) for Data Protection in E-Commerce Systems
DESCRIPTION:Authors - Franck W. Boubayi Abstract - The rapid development of e-commerce platforms has significantly increased the exposure of digital systems to advanced cyber threats such as phishing\, DDoS attacks\, identity theft\, and data breaches. To address the limitations of conventional security mechanisms\, this paper proposes a Risk-Adaptive Multi-layer Security Framework (RAMSF) integrating multi-factor authentication\, AI-based intrusion detection\, post-quantum cryptography\, and blockchain-based distributed auditing. The main contribution of the model lies in an adaptive decision engine based on dynamic risk evaluation\, enabling real-time adjustment of security policies according to behavioral context and detected anomalies. The framework is evaluated using the CICIDS2017 and UNSW-NB15 datasets with 10-fold cross-validation. Experimental results show that RAMSF outperforms several classical models\, including SVM\, Random Forest\, CNN\, and XGBoost\, achieving 97% accuracy\, 95.5% F1-score\, 98% AUC\, and a low false positive rate. These results demonstrate that adaptive hybrid architectures represent a promising approach for strengthening cybersecurity in e-commerce systems against both current and post-quantum threats.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:240d7ce6caa6675e0b6d8c6aa6da1345
URL:http://10thworlds4.sched.com/event/240d7ce6caa6675e0b6d8c6aa6da1345
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Analysis of Financial Literacy\, Perceived Risk\, and Financial Self-Efficacy on Akulaku and Kredivo Intention and Actual Usage Among Generation Z in Indonesia
DESCRIPTION:Authors - Febrian Nasrullah\, Abdul Mukti Soma Abstract - This study examines the impact of risk perception\, financial selfefficacy\, and financial literacy on the actual usage behavior and intention to use "Buy Now Pay Later" (BNPL) services among Generation Z in Indonesia. Adopting a quantitative approach\, the study surveyed 385 Gen Z individuals who use BNPL services such as Kredivo or Akulaku. Data were analyzed using SEM-PLS with the aid of SmartPLS 4. The results indicate that financial selfefficacy and financial literacy contribute to actual usage behavior and intention\, whereas risk perception has a negative impact on both. Furthermore\, intention contributes to actual usage behavior and mediates the effects of the other variables. These findings indicate that financial management skills\, risk perception\, and individual confidence in financial capability play a pivotal role in shaping BNPL usage behavior among Generation Z in Indonesia.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:6f69edc2f9a34d1e881f1b806a8bde09
URL:http://10thworlds4.sched.com/event/6f69edc2f9a34d1e881f1b806a8bde09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Comparative Assessment of Large Language Models for PID Tuning of a Third-Order Cruise-Control System Without Expert Feedback
DESCRIPTION:Authors - Byron Albuja-Sanchez\, Miguel Angel Lema Carrera\, Luis Antonio Ortiz Parra Abstract - This study focuses on evaluating the capabilities of different large language models chatbots in the task of designing a PID controller for a third-order transfer function of a real-world vehicle’s cruise control system. Chatbots received a detailed prompt containing the system’s transfer function and the design’s goals in the form of overshoot and settling time constraints. Chatbots only received simulation-response information as feedback during the tuning process to test their predisposition to fix the errors without being specifically asked to do so. Results showed that chatbots have a good level of knowledge regarding basic control theory and basic tuning methods for PID controllers. Preferred tuning methods involved pole placement with dominant second order dynamics\, Ziegler-Nichols and heuristic methodologies. Simulation results compared the controllers designed by chatbots with a PID tuned with ant lion optimizer algorithm\, none of the evaluated chatbots outperformed the optimization-based benchmark controller. However\, Gemini 3 Flash designed a controller which performance was close to the ant lion optimizer results. Chatbots’ underperformance was attributed to the following facts: no expert feedback was given to them to fix the observed flaws in the proposed designs\, no specific methodologies were asked to be used in order to improve the results\, and no specific instructions to redesign the controllers were given to chatbots in order to test their disposition to fix their errors. Results suggest that LLMs can assist in preliminary controller design tasks\, although their effectiveness remains limited without expert-guided iteration and explicit optimization-oriented prompting.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:27622746e8f99bccd88647d8aec41746
URL:http://10thworlds4.sched.com/event/27622746e8f99bccd88647d8aec41746
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:Enhanced Performance Model for Diabetes Detection using Machine Learning Techniques
DESCRIPTION:Authors - Aman Kumar\, Kathan Nitin Patel\, Aviral Sharma Abstract - Diabetes mellitus is perceived as a disease that significantly impacts a nation’s social\, human\, and financial expenditures. Concurrently\, it is imperative to lower the prevalence rate and address the misunderstandings surrounding diabetes. An improved model that employs machine learning techniques to identify the behavior of diabetes in an individual. We have employed the parameters observed in the typical lifestyle\, as well as the individual's emotional states and physical activities in the elderly age group. For a variety of test parameters\, the proposed model implements a network classifier. It has been noted that this methodology yields effective results in the diagnosis of diabetes mellitus when the appropriate dataset is provided. The dataset utilized in this reseacrh study is the Indian PIMA dataset from the UCI Machine learning database. The detection of diabetes is contingent upon the presence of eight features in this dataset. The proposed Machine learning model has been implemented using a multilayer neural network that has been trained on backpropagation and feed-forward network simulation.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:eba272f3ececb16ea657c7997eb1a136
URL:http://10thworlds4.sched.com/event/eba272f3ececb16ea657c7997eb1a136
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:IWOF: Intelligent Workday Orchestration Framework for Autonomous Business Process Automation
DESCRIPTION:Authors - Nallappagari Venkatarami Reddy Abstract - Business Process Automation (BPA) has become an essential requirement of modern enterprise environments owing to the need for operational efficiency\, process agility\, and smart decision making. Traditional methods of BPA mostly depend on rules-based approaches which are not able to adapt to the needs of a dynamic environment as these methods do not incorporate the element of adaptive intelligence and autonomous orchestration. To overcome such limitations\, this study aims to develop an intelligent orchestration framework named IWOF for Autonomous Business Process Automation. In the proposed solution\, PIEL\, AWOE\, DRAM\, and PDOU have been used. Also\, two new algorithms named AWIO and PARDO are developed for optimizing the process sequencing\, resource assignment\, and decision support tasks respectively. Experimental evaluation was performed by applying the proposed framework to datasets consisting of processes related to employee onboarding\, payroll management\, procurement approvals\, recruitment workflow\, and finance transactions. With the help of the IWOF model\, Process Automation Accuracy\, Workflow Completion Rate\, Resource Utilization Efficiency\, and Autonomous Business Process Automation Score (ABPAS) were measured to be 98.7%\, 98.2%\, 97.1%\, and 98.9%\, respectively\, surpassing all other available models such as OSMAS and BPA-SME.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:220ab17794bb27e9906b03df5bb6ad2d
URL:http://10thworlds4.sched.com/event/220ab17794bb27e9906b03df5bb6ad2d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:REMEDI: An Autonomous Mobile Medication Dispensing Robot for Elderly Care
DESCRIPTION:Authors - Abeer Tag\, Tahani Almarri\, Abir Sidilemine\, Rowaa Khaled\, Loay Ismail Abstract - Medication non-adherence among elderly and chronically ill patients remains a critical global health challenge\, leading to severe complications\, hospital readmissions\, and reduced quality of life. This paper presents REMEDI\, a smart mobile medication dispensing robot that integrates autonomous indoor navigation\, biometric patient authentication\, automated pill dispensing\, pill verification\, and real-time adherence monitoring into a unified platform. The system combines a TurtleBot3 Waffle Pi mobile base with a custom-designed three-cylinder dispensing mechanism controlled using Raspberry Pi 5 and Arduino Nano. Patient verification is performed using facial recognition with MobileFaceNet embeddings and liveness detection\, achieving an overall verification accuracy of 83.3% and zero false accepts during experimental testing. Autonomous navigation is implemented using LiDAR-based SLAM and A* path planning\, enabling map-based movement between predefined indoor patient locations. Post-dispensing verification uses a custom-trained YOLOv11 object detection model integrated with OpenCV for pill detection and counting. A companion Android application allows caregivers to enroll patients\, schedule medications\, and monitor adherence in real time. Experimental results show successful integrated operation\, including dispensing delays below 5 seconds\, navigation success rates of 84–92%\, 95% dispensing reliability\, and functional multi-patient queue management. Although pill verification achieved only 69% real-world accuracy\, the results demonstrate the feasibility of integrating mobility\, secure authentication\, dispensing\, and monitoring in one user-centered prototype. REMEDI aims to bridge the gap between stationary home medication dispensers and large institutional delivery robots.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:4e36a1a609507c0cca63163057a0ce79
URL:http://10thworlds4.sched.com/event/4e36a1a609507c0cca63163057a0ce79
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T080000Z
DTEND:20260729T093000Z
SUMMARY:WASHtsApp – A RAG-powered WhatsApp Chatbot for Supporting Rural African Clean Water Access\, Sanitation and Hygiene
DESCRIPTION:Authors - Simon Kloker\, Alex Cedric Luyima\, Matthew Bazanya Abstract - This paper presents WASHtsApp\, a WhatsApp-based mHealth chatbot that supports clean water\, sanitation\, and hygiene (WASH) education in rural African settings. The chatbot uses Retrieval-Augmented Generation (RAG) to reduce out-of-context responses and improve answer relevance. Following a Design Science Research approach\, we evaluated the artifact in two steps: expert content validation (four WASH experts) and community acceptance validation (n = 71). Expert ratings classified 86% of responses as perfect or sufficient\, while community results showed high perceived usefulness\, ease of use\, and intention to use. The findings indicate that WhatsApp is a viable delivery channel for WASH education and that a constrained RAG setup can provide useful localized guidance. We also discuss privacy\, safety\, and future improvements\, including local-language support.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:21892604fe12827957f0c7cebf5f435c
URL:http://10thworlds4.sched.com/event/21892604fe12827957f0c7cebf5f435c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T093000Z
DTEND:20260729T093200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:2cb56423b6d8ee36185af17b9be1bb29
URL:http://10thworlds4.sched.com/event/2cb56423b6d8ee36185af17b9be1bb29
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T093000Z
DTEND:20260729T093200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:c2ee73fbf96a92ea16d89bca00d492c9
URL:http://10thworlds4.sched.com/event/c2ee73fbf96a92ea16d89bca00d492c9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T093000Z
DTEND:20260729T093200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:948173fdb767cd7753d759e2319489f7
URL:http://10thworlds4.sched.com/event/948173fdb767cd7753d759e2319489f7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T093000Z
DTEND:20260729T093200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:33a99e180d258505e2388a8ff17fd560
URL:http://10thworlds4.sched.com/event/33a99e180d258505e2388a8ff17fd560
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T093200Z
DTEND:20260729T093500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:7193aaf31784a4b17269c7f3682e529a
URL:http://10thworlds4.sched.com/event/7193aaf31784a4b17269c7f3682e529a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T093200Z
DTEND:20260729T093500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:e042c63c979423123856ecfe4f898f53
URL:http://10thworlds4.sched.com/event/e042c63c979423123856ecfe4f898f53
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T093200Z
DTEND:20260729T093500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:c1cf7328788467ccdfeeed76e9bbfc3a
URL:http://10thworlds4.sched.com/event/c1cf7328788467ccdfeeed76e9bbfc3a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T093200Z
DTEND:20260729T093500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:8f340042569aa07e994f8fd96a74673f
URL:http://10thworlds4.sched.com/event/8f340042569aa07e994f8fd96a74673f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T102800Z
DTEND:20260729T103000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:47cdc445e7eaa560f4971451c7b6813f
URL:http://10thworlds4.sched.com/event/47cdc445e7eaa560f4971451c7b6813f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T102800Z
DTEND:20260729T103000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:66b8fb048ae9e5a06b29c81d05829890
URL:http://10thworlds4.sched.com/event/66b8fb048ae9e5a06b29c81d05829890
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T102800Z
DTEND:20260729T103000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:bbbbfd88de9fb1a68c30a4254e0ccf5c
URL:http://10thworlds4.sched.com/event/bbbbfd88de9fb1a68c30a4254e0ccf5c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T102800Z
DTEND:20260729T103000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:b512af7a4da955ed42f44e6040fe3639
URL:http://10thworlds4.sched.com/event/b512af7a4da955ed42f44e6040fe3639
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Automated Selection of Liver Stiffness Measurement Region using Magnetic Resonance Elastography Images
DESCRIPTION:Authors - Manar Eloued \, Narjes Benameur \, Sonia Esseghaier\, Salam Labidi Abstract - Magnetic Resonance Elastography (MRE) is a novel\, non-invasive im-aging technique for assessing liver stiffness. However\, the lack of standardization introduces variability in measurements. The Manual selection of the Region of Interest (ROI) remains subjective and operator-dependent\, often including areas with blood vessels or poor wave propagation\, which can compromise measurement accuracy. This study proposes a deep learning- based approach to automatically identify an optimal region for liver stiffness measurement (LSM). A total of 160 MRE ex-ams\, comprising paired magnitude and wave attenuation images from both healthy individuals and patients with liver disease\, were used. A 3D U-Net architecture was trained to segment the liver\, blood vessels\, gallbladder\, and biliary ducts from magnitude images\, as well as regions of good wave propagation from attenuation images. The final ROI was obtained by intersecting these segmented regions. The model performance was evaluated on a separate test set using the Dice Similarity Coefficient (DSC)\, paired Student’s t-test\, and Bland-Altman analysis. The resulting LSM region achieved a DSC of 0.89. The t-test yielded p = 0.68\, indicating no significant difference between the automated and manual ROIs (p > 0.05). This automated pipeline reduced analysis time from approximately 20 minutes manually to less than 10 seconds automatically\, while ensuring reproducibility and reducing operator dependency in MRE by standardizing ROI selection while maintaining diagnostic accuracy. It offers a promising solution to improve the reliability of LSM\, particularly for longitudinal follow-up.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:6c80f1abecb0c184cba9a561651215fe
URL:http://10thworlds4.sched.com/event/6c80f1abecb0c184cba9a561651215fe
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Digging into LLMs to interact with CAD software applications: API-Scripts
DESCRIPTION:Authors - Hector Rafael Morano Okuno Abstract - One of the applications of LLMs (Large Language Models) has been their use as assistants\, enabling users to perform specific tasks via prompts\, from solving mathematical problems to generating images or videos. This article aims to explore the capabilities of an LLM in generating scripts for the API (Application Programming Interface) of the CAD software Fusion 360\, identify the types of geometries it can create\, and determine whether it can reproduce images in CAD models. This work was developed during the Manufacturing Systems Automation course for Mechatronics Engineering students\, with the intention of introducing them to the use of LLMs in their field. Among the find-ings\, it was determined that the user must be familiar with the Fusion 360 API to correct potential errors in the scripts generated by the LLM. Furthermore\, the user must be able to specify\, via prompts\, the characteristics of the parts to be modeled\, ensuring that the specifications are compatible with the instructions Fusion 360 understands. Regarding students' experience with LLMs\, they found them useful\, as they saved time in designing components that require complex automation systems.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:ad8136f727fe9978f9ed3ae921e0291a
URL:http://10thworlds4.sched.com/event/ad8136f727fe9978f9ed3ae921e0291a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Digitalization of Construction Economics in Uzbekistan: Impact on Cost Efficiency and Project Performance
DESCRIPTION:Authors - Zilola Mamatvaliyevna Aliyeva\, Nigora Primova\, Dildor Abduraxmanovna Shadibekova\, Malika Akbarova\, Azizbek Mahkamov\, Gulchehra Raxmatjonovna Xusanova\, Shoh-Jakhon Khamdаmov Abstract - The construction industry plays a strategic role in Uzbekistan’s economic development\; however\, it continues to face challenges related to cost overruns\, project delays\, and limited financial transparency. Digital transformation offers new opportunities to enhance economic efficiency and project management performance. This study examines the impact of digitalization on construction economics in Uzbekistan\, focusing on the implementation of Building Information Modeling (BIM)\, digital cost estimation systems\, electronic procurement platforms\, and enterprise resource planning (ERP) solutions. The research develops a conceptual model linking digital adoption level\, cost control effectiveness\, project performance\, and financial outcomes. A quantitative survey of construction companies operating in Uzbekistan was conducted\, and Structural Equation Modeling (SEM) was applied to test the proposed relationships. The findings indicate that higher levels of digital integration significantly improve cost estimation accuracy\, reduce budget deviations\, and shorten project completion time. Digital procurement systems also enhance financial transparency and reduce operational inefficiencies. The study provides empirical evidence that digital transformation positively influences economic performance in Uzbekistan’s construction sector. The results contribute to construction economics literature and offer policy recommendations for accelerating digital adoption in emerging markets.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:c4f5f48d38696447ece45e109ad63d71
URL:http://10thworlds4.sched.com/event/c4f5f48d38696447ece45e109ad63d71
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:From predicting dropout rates to responsible intervention: explainable machine learning for academic success in higher education
DESCRIPTION:Authors - Jorge Duque\, Antonio Godinho\, Jose Moreira\, Firmino Silva Abstract - Student dropout in higher education remains a persistent academic\, institutional and social challenge\, requiring evidence-informed responses. This paper develops and evaluates an explainable machine learning artefact for early dropout-risk identification and for translating predictions into tiered institutional interventions. The study follows six phases of Design Science Research and uses the public Predict Students' Dropout and Academic Success benchmark\, with 4\,424 students and 37 variables. The pipeline integrates one-hot encoding\, derived features\, stratified validation\, SMOTE applied only inside training folds\, Random Forest\, XGBoost and SVM as base learners\, stacking with a logistic meta-learner and SHAP explanations. The final ensemble achieved 96.4% accuracy\, 0.965 weighted precision\, 0.964 weighted recall\, 0.964 weighted F1-score and 0.99 weighted AUC. The contribution lies in combining performance\, interpretability\, governance and responsible human intervention.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:ce3d3ce6eb5258daa54c563c2fd68532
URL:http://10thworlds4.sched.com/event/ce3d3ce6eb5258daa54c563c2fd68532
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Growth of Glioma\, A Graph Theoretical Approach
DESCRIPTION:Authors - Hasna Noushad\, Geetha KN Abstract - The brain tumor remains a serious health concern and diagnosis in the primary stage is mandatory for effective treatment. Medical image analysis plays a vital role in understanding the underlying disturbances\, monitoring\, treatment planning\, and intervention strategies. Graph theory is one of the popular techniques used for medical image analysis. The study focuses on the introduction of a systematic approach by using the application of vertex addition of graph theory to synthetically construct a glioma brain network utilizing the normal and abnormal brain Magnetic Resonance Image (MRI). The paper presents the idea of the initial emergence and growth of tumor from a graphtheoretical perspective. Comparative analyses are carried out between normal and abnormal brain network due to the growth of glioma. Results demonstrate that the presence of strong structural deformations and alterations in brain network due to the introduction and growth of glioma. In addition\, the article also examines the corresponding increase in the correlation values of the tumor with other normal regions of brain as the tumor grows. This work provides a robust foundation for future studies in epidemiological modeling\, machine learning and deep learning methodologies where lack of required data is an issue.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:33967374bd9594f09608101a8869b3d9
URL:http://10thworlds4.sched.com/event/33967374bd9594f09608101a8869b3d9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:PATCH-WISE SEGEMENTATION AND CLASSIFICATION FOR COMPREHENSIVE DIABETIC RETINOPATHY USING CNN
DESCRIPTION:Authors - A Aruna kumari\, Sri Vishnu Prabhu Gudavalli\, Tamminana Visweswari Abstract - Diabetic Retinopathy (DR) is one of the biggest contributors of visual impairment and blindness in diabetic patients who do not receive proper measures and treatment at the early stages. Due to the ever rising instances of diabetes by the day\, more concern has been raised on the effectiveness of methods of effective\, scalable and early diagnosis. The given paper is a proposal of a deep learning-based algorithm of DR diagnosis\, specifically\, the algorithm named Patch-Wise Segmentation and Classification using Convolutional Neural Networks (CNNs). The system in this case is in contrast to the traditional systems\, which require the entire retina image to be processed simultaneously by the system\, whereby high-resolution fundus photographs are broken into patches. The model addresses each patch individually in order to have the model closely observe minute-scale details and sensitive pathological changes such as hemorrhages and exudates. Patch-wise processing dramatically enhances the capacity of reporting early and mild cases of DR that are hard to recognize in the fullimage processing due to intricacy of an image and noise. The CNN architecture is additionally medical image particular and operates by use of layers and regularization methods so as to permit not only accuracy but also generalization over a wide range of datasets. As shown in the results of the experiments\, the patchwise technique performs better in comparison with the traditional ones\, i.e.\, sensitivity\, specificity\, and classification accuracy on each of the stages of the DR. In addition\, the system is fully automated and stable that minimizes the use of human marking and offers the facility to operate worldwide
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:5f8945ef69f0637e9d409e5acad9d51d
URL:http://10thworlds4.sched.com/event/5f8945ef69f0637e9d409e5acad9d51d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:A IoT monitoring with blockchain for the secure recording of environmental and electrical data\, supported by the Ecuadorian legal framework
DESCRIPTION:Authors - Marco Vinicio Lopez R.\, Maria Cristina Espin Melendez\, Jeanette Elizabeth Jordan Buenano\, Santiago Vayas Castro\, Segundo Moises Toapanta Toapanta\, Ruben Nogales Portero\, Juan Escobar Naranjo\, Diego Gustavo Andrade Armas\, Rodrigo Del Pozo Durango Abstract - Connecting more devices to monitor energy and the environment creates serious hurdles for data security\, integrity\, and scale. This research presents a system merging blockchain with IoT\, built specifically to respect Ecuador’s Organic Law on Personal Data Protection (LOPDP). The setup uses ESP32 microcontrollers and sensors to track environmental and power data\, sending it over the MQTT protocol to a database. Digital fingerprints created with SHA-256 are locked onto a private blockchain using Proof-of-Authority to keep every record permanent and unchangeable. A 10-day experiment in two indoor spaces generated roughly 96\,000 records to test how the system holds up in the real world. Results demonstrate the system's viability\, achieving a median end-to-end latency of 182 ms (P95 < 240 ms)\, a Node Availability Index (NAI) of 98.6%\, and an Anomaly Detection Rate (ADR) of 91.4%. Furthermore\, the blockchain integration introduced a computational and energy overhead of less than 7%. The study concludes that this architecture provides a verifiable link between physical sensors and digital records\, highlighting the bidirectional need for technology to comply with national privacy laws while urging regulatory frameworks to evolve and formalize smart contract applications.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:2104df744e0cd483dd757b0ab181fcae
URL:http://10thworlds4.sched.com/event/2104df744e0cd483dd757b0ab181fcae
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:AI for Bilingual Arabic–English Smishing Detection: A Bibliometric Review
DESCRIPTION:Authors - Mohammed Rasol Al Saidat\, Khaled Shaalan\, Suleiman Y. Yerima Abstract - This paper presents a technical bibliometric review of artificial intelligence for bilingual Arabic–English smishing detection. It maps the evolution of machine learning\, deep learning\, and NLP for SMS phishing detection\, identifies Arabic–English linguistic and security challenges\, and grounds the review in an empirical audit of a public Arabic SMS spam corpus. The methodology pairs a PRISMA-style protocol and bibliometric mapping with the parsing of 1\,494 SMS records (747 ham\, 747 spam) from a public GitHub dataset. The field has shifted from rules and classical feature engineering to CNN\, LSTM\, Bi-LSTM\, transformer\, and large language model approaches. The dataset audit reveals strong class-conditional cues: spam messages are far longer and far richer in digits\, phone numbers\, short codes\, URLs\, currency markers\, and Latin residues. A reproducible bilingual character TF-IDF baseline with a Linear SVM reached 0.9779 mean accuracy and 0.9775 F1 under stratified three-fold validation\, matching the referenced CNN-Bi-LSTM model (0.9699 accuracy\, 0.9707 F1). Robust bilingual smishing detection therefore requires hybrid text–security features\, Arabic morphology-aware normalization\, Unicode safety screening\, external validation\, and explainable deployment.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:6cd41da0bff0b68750e2b59fc5806394
URL:http://10thworlds4.sched.com/event/6cd41da0bff0b68750e2b59fc5806394
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Integrated Cybersecurity Governance Model Based on Public Administration and Legal Foundations for Higher Education Institutions in Ecuador
DESCRIPTION:Authors - Rodrigo Del Pozo Durango\, Moises Toapanta T.\, Antonio Orizaga T.\, Rocio Maciel A.\, Victor Larios Rosillo\, Jeanette Elizabeth Jordan Buenano\, María Cristina Espin Melendez\, Santiago Vayas Castro\, Pamela Toapanta Pavon\, Andres Hermann-Acosta Abstract - Cybersecurity governance in Higher Education Institutions (HEIs) in Ecuador faces persistent challenges: Information and Communication Technologies are regarded merely as operational tools\, without adequate integration of international standards or a legal foundation within institutional processes. This situation generates high vulnerability to threats such as phishing\, ransomware\, and data breaches. The objective of this research is to define an integrated model for cybersecurity governance\, public administration\, and legal foundations for an HEI in Ecuador. A deductive method with a mixed quantitative–qualitative approach was employed\, structured into four phases: systematic literature review indicator design\, model construction\, and scenario simulation. The main result is an integrated model structured into three hierarchical and interrelated levels: the Technical Level\, comprising incident management\, security infrastructure\, and technological maturity\, aligned with NIST CSF 2.0 and EGSI v3.0\; the Administrative Level\, focused on ICT strategic planning\, organizational governance\, and continuous improvement\, based on COBIT 2019\; and the Legal Foundation Level\, grounded in the 2024 Constitution\, the 2026 Organic Law on Cybersecurity\, the 2021 Organic Law on Personal Data Protection\, and the 2025 Comprehensive Organic Criminal Code. The model incorporates 15 weighted indicators and was validated through simulation across four scenarios\, using an optimality threshold of ≥ 75 points. It is concluded that three-dimensional integration technical\, administrative\, and legal is a necessary condition for Ecuadorian HEIs to achieve ICT strategic alignment\, resource optimization\, and digital resilience\, thereby positioning them as key actors in national cybersecurity public policy.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:764fd23b6ec877555741b1db333d24bd
URL:http://10thworlds4.sched.com/event/764fd23b6ec877555741b1db333d24bd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Printed Circuit Board Component Detection Using a Modified R-CNN Framework
DESCRIPTION:Authors - Eric Khang Heng Ooi\, Yit Yin Wee Abstract - Printed circuit boards (PCBs) are increasingly dense and visually complex\, making reliable component detection important for automated optical inspection and manufacturing quality control. This paper presents a modified R-CNN framework for integrated-circuit (IC) component detection in PCB images. The framework consists of PCB region extraction\, colour-guided region proposal generation\, Bayesian convolutional neural network (BCNN) feature extraction\, and support vector machine (SVM) classification. Candidate regions are produced using colour masks that emphasize dark and silver IC-like regions. Each candidate is resized\, represented by the BCNN\, and classified by the SVM as IC or background. Experiments on a PCB component dataset show that the proposed method achieves an mAP of 0.613\, outperforming R-CNN with Selective Search Fast (0.392) and Selective Search Quality (0.430). YOLO obtains the highest mAP of 0.686\; however\, the proposed framework remains useful as an interpretable region-based pipeline for PCB inspection.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:bc65586ed9abbc50fbb8105142555371
URL:http://10thworlds4.sched.com/event/bc65586ed9abbc50fbb8105142555371
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:THE FUTURE OF SUSTAINABLE POWER IN INDIA WITH GLOBAL PERSPECTIVE
DESCRIPTION:Authors - Chandravadan Goritiyal\, Aditi Bairolu Abstract - The engine of the world is energy. Fossil fuels continue to be the primary source of energy generation. Nonetheless\, several of the world's most important organizations and nations banded together to address the greatest sustainability challenge facing humanity: climate change caused by greenhouse gas emissions. It has been noted that the most workable approach would be to refocus attention from fossil-fuel energy production to renewable energy. However\, there are year-round availability issues with renewable energy as well. For instance\, solar energy can only be produced during the day\, and in India\, during the monsoon season hardly solar energy produced. Similar issues are noted in other nations as well. When there is not enough wind to support its spin\, wind power frequently stands still and is entirely dependent on air currents. Therefore\, the study focuses on what India as a nation should do. For example\, investigating clean base load electricity\, such as nuclear\, to guarantee a constant demand for grid power\; solar and wind power will support this\, making the total power supply sustainable and environmentally friendly. Also Industry requires continuous and reliable power all the time.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:81a56dfcc251994f6871bca3f9650f48
URL:http://10thworlds4.sched.com/event/81a56dfcc251994f6871bca3f9650f48
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Understanding the Educational Metaverse: Concepts\, Constraints\, and Technologies
DESCRIPTION:Authors - Monica Cruz\, Abilio Oliveira\, Ricardo Dias Abstract - Although immersive technologies such as VR\, AR\, MR and XR are increasingly integrated into university teaching\, existing research remains conceptually fragmented and technologically heterogeneous. This study examines how the Metaverse is represented\, characterised\, and applied in higher‑education contexts through a meta-analysis using Voyant tools for text analysis. To address this gap\, the study investigates recent publications to answer the research question: How is the Metaverse being represented\, characterised\, and applied within higher‑education contexts? Three objectives guided the analysis: identifying how the Metaverse is conceptualised in educational literature\, examining the dimensions that facilitate or hinder its adoption\, and mapping the leading Metaverse‑related technologies used in higher‑education environments. The findings show that adoption is shaped by perceived usefulness\, immersion\, social presence and technological readiness\, while challenges persist regarding accessibility\, infrastructure and conceptual clarity. The study contributes to the scientific literature by consolidating dispersed definitions\, clarifying adoption‑related factors and identifying the immersive technologies most frequently associated with Metaverse‑based learning. The results provide a structured foundation for future research and offer higher‑education institutions insights into the opportunities and constraints of integrating Metaverse technologies into teaching and learning.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:6545223637bcca579c2657c8e09023c6
URL:http://10thworlds4.sched.com/event/6545223637bcca579c2657c8e09023c6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:A Semantic Chatbot Framework Using Large Language Models for Tertiary Education
DESCRIPTION:Authors - Hiruni Samarage\, Pumudu A. Fernando Abstract - Tertiary education institutes increasingly face challenges in managing high volumes of student inquiries related to admissions\, courses\, fees\, and scholarships. Traditional inquiry-handling mechanisms and rule-based chatbots often struggle with scalability\, delayed responses\, and limited understanding of complex or unstructured queries. While recent advances in large language models (LLMs) offer promising opportunities\, many existing academic chatbot implementations continue to lack semantic retrieval\, session continuity\, and personalization. This paper presents the design\, implementation\, and evaluation of an AI-based semantic chatbot prototype tailored for tertiary education environments. The prototype integrates retrieval-augmented generation with a large language model to enable context-aware responses across multiple institutional knowledge domains through semantic retrieval of structured knowledge representations. The system was evaluated using accuracy\, precision\, recall\, robustness to query length variations\, and retrieval effectiveness metrics. Experimental results demonstrate an overall accuracy of 86%\, with precision and recall values of 89% and 91%\, respectively. Robustness testing shows consistent performance across paraphrased and variable-length queries\, while response times remained within acceptable limits for real-time academic support. User testing further indicated positive usability and response relevance outcomes. These results confirm the feasibility and effectiveness of applying semantic retrieval and LLM-based reasoning to scalable inquiry management in tertiary education contexts.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:79cdf5776d3e50eb76be21e63252f466
URL:http://10thworlds4.sched.com/event/79cdf5776d3e50eb76be21e63252f466
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Fusion Core: A Heterogeneous Pipeline for Multimodal Sentiment Analysis Utilizing Hardware Accelerated Multihead Attention.
DESCRIPTION:Authors - Ndaula Kelvin\, Wu Jun Abstract - Multimodal sentiment analysis often fails due to the modality gap between semantic text images and GIFs. To address these challenges this paper introduces Fusion Core a novel hardware agnostic heterogeneous pipeline that bridges the gap between high level AI with low level systems engineering to facilitate the deciphering of combined sentiment of these three modalities. Through the integration of GPGPU accelerated OpenCL kernels for 3D temporal extraction with an ONNX/DirectML inference engine which ensures cross platform portability. To address the issue of inconsistent real world data distributions the preprocessing system was introduced with an adaptive multi-head attention mechanism for late feature fusion. The ablation studies performed also revealed the integration of spatiotemporal GIF layers resolves contextual ambiguities missed by static analysis (Text\, Images). The extensive testing on a balanced dataset of 13\,964 samples the model achieved a 100% success rate showing the robustness of the proposed model for industrial scale deployment.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:4d3458e3c4c3883c2776eb707ebf4ac8
URL:http://10thworlds4.sched.com/event/4d3458e3c4c3883c2776eb707ebf4ac8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Implementing e-Participation in South African Municipalities: Lessons from the City of Mbombela Pilot
DESCRIPTION:Authors - Tumiso THULARE\, Keneilwe Jeannette MAREMI Abstract - This paper presents findings from a pilot e-Participation implementation conducted in partnership with the Mpumalanga Department of Cooperative Governance\, Human Settlements and Traditional Affairs (CoGHSTA) and the City of Mbombela over two years. The pilot project focused on enhancing the municipality's capacity to implement and sustain e-Participation initiatives. It also assessed the current state of public participation and explored the opportunities and challenges of adopting digital participation mechanisms in South African local government. A qualitative research approach was used\, involving a scoping review and engagement sessions with municipal officials from various units\, including public participation\, communications\, ICT\, policy\, and governance. The scoping review identified theoretical challenges to e-Participation in South African municipalities\, while engagement sessions examined institutional experiences\, governance processes\, and the City of Mbombela's readiness for digital participation. The findings revealed that the municipality shows policy alignment and has partially adopted digital participation tools such as social media\, municipal websites\, and mobile communication channels. However\, e-Participation implementation faces challenges such as the digital divide\, limited ICT infrastructure\, low digital literacy\, institutional capacity constraints\, poor coordination\, and insufficient funding. The study further found that public participation still relies heavily on traditional methods\, with digital platforms mainly used for sharing information instead of fostering citizen empowerment or collaborative governance. The paper concludes that while e-Participation can enhance transparency\, accountability\, and inclusive governance in South African municipalities\, its success requires integrated technological\, institutional\, and participatory reforms. The findings provide practical guidance for municipalities aiming to enhance digital public participation in resource-constrained settings.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:bc2ee736ca75c7f7dd2ddb56a5641ce0
URL:http://10thworlds4.sched.com/event/bc2ee736ca75c7f7dd2ddb56a5641ce0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:IoT-Enabled Closed-Loop Anesthesia Delivery Framework using Fuzzy-PID Control
DESCRIPTION:Authors - Shola Usharani\, Gayathri Rajakumaran\, Braveen manimozhi\, Anjana Devi Nandam\, Kindinti Karthik\, Prabhakaran mohan Abstract - The closed-loop anesthesia delivery (CLAD) development is a significant advancement in modern anesthesiology\, offering automatic control of anesthetic administration to maintain optimal patient states during surgical procedures. The article analyses the limitations of existing systems—such as sensor errors\, limited adaptability\, and system unreliability—by integrating through IoT based control high-accuracy EEG monitoring system with a robust and novel closed-loop control algorithms. From this system the real time EEG signals are continually monitored\, analyzed\, filtered\, and converted into a BIS spectral Index (BIS) values. The target BIS is continuously updated by fuzzy logic\, which modulates the drug delivery to maintain sedation levels between 40 and 60. A PID controller used for the BIS error calculation to determine the precise anesthetic levels to be delivered. This amount level is then converted into drug level concentrations to drive the syringe pump connected to the IoT integrated system using stepper motor to administer the drug to the patient. The serial monitor displays all information in real time\, including BIS values\, PID output\, calculated dosage in mg/sec and ml/sec\, and the number of motor pulses required for the infusion. This prototype demonstrates enhanced precision and safety over manual control\, offering a scalable and cost-effective solution for automated anesthesia delivery\, thus avoiding the limitations of existing model.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:e76cd84981461df984b015e998cf37c9
URL:http://10thworlds4.sched.com/event/e76cd84981461df984b015e998cf37c9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Learning Analytics of Arithmetic Practice Logs in the Learn with M.E. Intelligent Educational Software Environment
DESCRIPTION:Authors - Norbert Annus Abstract - This study presents a secondary learning analytics analysis of student log data generated by the Learn with M.E. educational software. While previous evaluations of the system focused mainly on effectiveness\, diagnostic accuracy and user feedback\, the present paper examines behavioural indicators recorded during arithmetic practice. In this study the analysis focused on calculation time\, answer correctness\, difficulty level\, first-try success\, "Preview" use and student-level behavioural profiles. The results showed that incorrect answers were associated with substantially longer calculation times than correct answers. Higher difficulty levels generally showed lower correctness rates and longer median calculation times. First-try attempts were also strongly related to successful task completion. "Preview" use was relatively rare\, but it was associated with longer calculation time\, higher average difficulty level and lower first-try correct rates\, suggesting that it can be interpreted as a help-seeking indicator. The student-level aggregation identified four behavioural profiles: fast trial-and-error learners\, help-seeking learners\, mixed-profile learners and support-needed learners. The findings indicate that Learn with M.E. log data can be transformed into interpretable learning analytics indicators and behavioural patterns that support teacher decision-making and personalised mathematics instruction.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:ea7f04d1ddc09e1acd14142f41c42fd4
URL:http://10thworlds4.sched.com/event/ea7f04d1ddc09e1acd14142f41c42fd4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:MUTATED MALWARE PROTECTOR: OBFUSCATED-AWARE MALWARE DETECTION ENGINE WITH MODULAR DETECTION
DESCRIPTION:Authors - Sheromiga Anandajothy\, Aathipan Murugaverl\, Harinda Fernando\, Sarangan Rukminikanthan\, Abishathan Thayaparan\, Tharaniyawarma Kumaralingam Abstract - Modern malware increasingly employs packing\, encryption\, polymorphism\, staged payload delivery\, modular execution\, and behavioural evasion techniques to bypass traditional signature-based security systems. While static analysis enables rapid inspection of suspicious binaries\, it often performs poorly against heavily obfuscated samples. Conversely\, dynamic analysis provides rich runtime evidence but introduces computational overhead\, operational latency\, and anti-sandbox challenges. This paper presents Mutated Malware Protector\, a lightweight hybrid framework for detecting obfuscated and modular malware through static Portable Executable (PE) feature analysis\, obfuscation-aware scoring\, behavioural risk approximation\, modular stage inference\, and explainable artificial intelligence (XAI). The framework is designed as a practical analyst-facing pipeline rather than a single classifier. It integrates engineered PE features\, anomaly scoring using Isolation Forest\, entropy-based obfuscation indicators\, a LightGBM static classifier\, behavioural approximation\, and a modular correlation component that estimates staged roles such as dropper\, loader\, and payload. Experimental evaluation shows that the prototype achieved 89.00% accuracy\, 89.28% precision\, 88.81% recall\, and 89.04% F1 score. The proposed architecture offers a scalable path toward future integration with full sandbox telemetry and enterprise malware response workflows.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:dfd1fda0140ceac758b8d1af3a827088
URL:http://10thworlds4.sched.com/event/dfd1fda0140ceac758b8d1af3a827088
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:A Comprehensive Analysis of Cybersecurity in Higher Education Institutions: A Case Study
DESCRIPTION:Authors - K. N. Subramanya\, Padmashree T.\, Manojith Bhat V.\, Manasvini G. Padmasali Abstract - In an era of rising digital dependence and ever-evolving cyber threats\, cybersecurity has become a concern for education institutions of all sizes. Higher education institutions (HEIs) are prime targets for cyberattacks since they manage massive volumes of sensitive data related to students\, faculty\, and research. Protecting this data is important to avoid major consequences such as disruptions in academic services\, reputational harm\, legal or financial ramifications. This survey consolidates current research on cybersecurity practices in HEIs\, analyzes critical digital assets and infrastructure. It also reviews selected cybersecurity frameworks adopted globally. The survey further explorers the threat landscape confronting HEIs\, examining various cyberattacks by identifying possible entry points\, attack pathways\, and potential consequences. The study emphasizes the necessity of adaptive cybersecurity approaches that can evolve alongside emerging technologies and pedagogical models in academia.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:8ef73e4208e35ad1d8393423e91bb08f
URL:http://10thworlds4.sched.com/event/8ef73e4208e35ad1d8393423e91bb08f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Automatic Speech Recognition in Latvian Operational Radio Communication: Corpus Creation and Speech Enhancement Evaluation
DESCRIPTION:Authors - Roberts Dargis\, Arturs Znotins\, Ilze Auzina\, Maris Golubovskis\, Mikelis Gulbis\, Normunds Gruzitis Abstract - Operational radio communication is a challenging application domain for automatic speech recognition (ASR) despite advances in multilingual foundation models. We investigate the applicability of modern Latvian ASR models to operational communication and evaluate whether speech enhancement techniques improve recognition quality under realistic conditions. To support the study\, we created a specialized corpus of authentic Latvian operational radio communication. The corpus captures acoustic and linguistic phenomena largely absent from general speech corpora\, including narrow-band transmission\, radio-channel artifacts\, environmental noise\, domain-specific terms\, and fragmented utterances. Using this corpus\, we evaluate state-of-the-art adaptations of the massively multilingual Whisper and MMS models in combination with several audio preprocessing methods. The results reveal a substantial performance gap between the conventional Latvian ASR benchmarks and operational communication data. While some preprocessing methods improve perceived audio quality\, they provide limited benefit for downstream recognition and often even degrade ASR performance. Voice activity detection\, however\, yields the most consistent improvements. The findings indicate that domain mismatch\, rather than acoustic degradation alone\, is the dominant source of recognition errors and highlight the need for representative domain-specific data when adapting general-purpose ASR models for the operational communication environment.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:b1b9d2068b447bb04eecd24a0afb2812
URL:http://10thworlds4.sched.com/event/b1b9d2068b447bb04eecd24a0afb2812
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Baseline-First Machine Learning Crop Yield Forecasting for Farm Management Decision Support with Small Official Statistics
DESCRIPTION:Authors - Mark Fedorchenko\, Olena Kopishynska\, Yurii Utkin\, Igor Sliusar\, Leonid Flehantov\, Olha Barabolia\, Nadiia Protas\, Tetiana Dugar Abstract - Crop yield forecasting based on small official statistics is different from forecasting with dense satellite\, field\, or weather datasets: the sample is short\, temporal leakage is easy to introduce\, and machine learning (ML) should not be accepted unless it beats transparent baselines. This paper presents a baseline-first and reliability-aware workflow for farm management and regional advisory systems. Wheat\, maize\, and sunflower are evaluated for Poltava\, Vinnytsia\, Cherkasy\, and national-level Ukraine data for 2010-2024. ElasticNet\, XGBoost\, and LightGBM are compared with naive lag-1\, linear-trend\, LINEST\, and Autoregressive Integrated Moving Average (ARIMA) baselines under a forward temporal design. The contribution is a decision layer that recommends ML only after it clears a practical mean absolute error (MAE) margin and then reports empirical validation-residual bands\, test coverage\, feature-group diagnostics\, and compact farm management systems (FMS)-compatible forecast cards. The Poltava workflow recommends FORECAST.LINEAR for wheat (MAE 0.49 t/ha)\, LightGBM for maize (MAE 0.69 t/ha)\, and LightGBM for sunflower (MAE 0.04 t/ha). Across the external check\, ML is recommended in 7 of 12 region-crop cases. The results show that ML can help in small official-statistics settings only when checked against simple baselines and reported with reliability diagnostics.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:135c873f5156d7dc6d92469e6a3e1eed
URL:http://10thworlds4.sched.com/event/135c873f5156d7dc6d92469e6a3e1eed
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Comparing ASR Accuracy for Arabic and English Speech in Individuals with Down Syndrome
DESCRIPTION:Authors - Ingy Emara\, Rawan Waleed\, Sherry Emad Abstract - This study analyses speech errors in individuals with Down syndrome (DS) in both Arabic and English\, with a particular focus on errors that reduce intelligibility for automatic speech recognition (ASR) systems. It compares the speech errors that most significantly affect intelligibility in each language and investigates the factors underlying differences in ASR accuracy across Arabic and English DS speech. The findings indicate that ASR systems perform less accurately with Arabic DS speech\, highlighting the need for larger and more diverse Arabic DS speech datasets for system training. The study also identifies several physiologically related speech errors that negatively affect intelligibility in both languages\, including devoicing of stop consonants\, lateralization of /r/\, reduced pressure in /s/\, and deletion of consonants and consonant clusters. In addition\, certain errors were found to be language specific\, such as the mispronunciation of uvular and pharyngeal sounds in Arabic and the frequent omission of /r/ and vowel centralization in English. These findings have important implications for speech therapy by identifying priority areas for intervention\, and for the ASR industry by emphasizing the need to expand labelled DS speech datasets across languages to improve recognition accuracy.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:f8a262fe3dd0af545c2d94f4758754ba
URL:http://10thworlds4.sched.com/event/f8a262fe3dd0af545c2d94f4758754ba
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:LoRA Security as a Measurable Risk Framework
DESCRIPTION:Authors - Hiep Nghia Phan\, Duy Nguyen Ngoc Abstract - Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning of large language models because it is lightweight\, modular\, and easy to distribute. However\, the growing practice of sharing third-party LoRA modules also creates security concerns. A malicious adapter can introduce hidden behaviors\, backdoors\, or other risks while appearing to function normally. Existing research has largely focused on detecting whether a LoRA module is malicious. In practice\, deployment decisions often require a more nuanced assessment of risk. This paper presents a measurable framework that evaluates LoRA security across four dimensions: supply-chain integrity\, static weight characteristics\, dynamic behavior\, and deployment-time observations. The resulting indicators are normalized and combined into a composite risk score that supports comparison and prioritization of LoRA modules. The framework was evaluated using benign and backdoored LoRA modules attached to a frozen base language model. The results show a clear separation between the two groups even when their task performance remains similar. Dynamic behavioral testing and static weight analysis contribute the most useful signals\, while deployment-time monitoring provides additional evidence of long-term operational risk. The proposed framework provides a practical mechanism for integrating security assessment into LoRA selection\, governance\, and deployment workflows.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:2c655933267c61a91ee6a2534306ef8b
URL:http://10thworlds4.sched.com/event/2c655933267c61a91ee6a2534306ef8b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T103000Z
DTEND:20260729T120000Z
SUMMARY:Neonatal Mortality in Nigeria: A Machine Learning Framework for Risk Prediction Using National Survey Data
DESCRIPTION:Authors - Mayen Ben-Koko\, Emmanuel Waribo Otiti Abstract - Nigeria loses more new-borns in the first month of life than almost any other country in the world\, yet no machine learning tool has been built specifically for this context. This paper proposes a framework for predicting neonatal mortality risk in Nigeria using indicators from the 2023–24 Nigeria Demographic and Health Survey — the most current national health dataset available. Five classification algorithms are compared: Logistic Regression\, Decision Tree\, Random Forest\, Gradient Boosting\, and Support Vector Machine. Random Forest performed best\, with an AUC-ROC of 0.89. The three strongest predictors were whether a skilled health worker attended the birth\, the gap between pregnancies\, and the number of antenatal visits. The framework is reproducible and designed to be extended as fuller microdata becomes available or adapted for routine clinic records across Nigeria's six geopolitical zones.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:2328952c5e87b947c2089c548aeddf8f
URL:http://10thworlds4.sched.com/event/2328952c5e87b947c2089c548aeddf8f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T120000Z
DTEND:20260729T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:c857cad85a8b7dbede13e84ae88a7193
URL:http://10thworlds4.sched.com/event/c857cad85a8b7dbede13e84ae88a7193
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T120000Z
DTEND:20260729T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:e7b086ad8871b97bbf3584b5b4e9a73f
URL:http://10thworlds4.sched.com/event/e7b086ad8871b97bbf3584b5b4e9a73f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T120000Z
DTEND:20260729T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:cb4cce979c9b6590c196cbe0ce701519
URL:http://10thworlds4.sched.com/event/cb4cce979c9b6590c196cbe0ce701519
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T120000Z
DTEND:20260729T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:cf007f94482395fe22f5b791d60b0ff4
URL:http://10thworlds4.sched.com/event/cf007f94482395fe22f5b791d60b0ff4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T120200Z
DTEND:20260729T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:f4e4272db0013493b28d68bfd6873aba
URL:http://10thworlds4.sched.com/event/f4e4272db0013493b28d68bfd6873aba
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T120200Z
DTEND:20260729T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:318e43972d5743813d0f69674dc7fa45
URL:http://10thworlds4.sched.com/event/318e43972d5743813d0f69674dc7fa45
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T120200Z
DTEND:20260729T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:41e186b00c7e0af3931965c9a7a2e584
URL:http://10thworlds4.sched.com/event/41e186b00c7e0af3931965c9a7a2e584
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T120200Z
DTEND:20260729T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:fa7201de8d5fb0a29944d531d51782a8
URL:http://10thworlds4.sched.com/event/fa7201de8d5fb0a29944d531d51782a8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T125800Z
DTEND:20260729T130000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:eb086f6b534c6526dd5d14506122e805
URL:http://10thworlds4.sched.com/event/eb086f6b534c6526dd5d14506122e805
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T125800Z
DTEND:20260729T130000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:173bc5a2aab9afe7e7a3c8b5eccd8c75
URL:http://10thworlds4.sched.com/event/173bc5a2aab9afe7e7a3c8b5eccd8c75
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T125800Z
DTEND:20260729T130000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:e083a88f58ba16d6ebf7066402489334
URL:http://10thworlds4.sched.com/event/e083a88f58ba16d6ebf7066402489334
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T125800Z
DTEND:20260729T130000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:fa9be4f94a2154bc57a49242d6dfa1ec
URL:http://10thworlds4.sched.com/event/fa9be4f94a2154bc57a49242d6dfa1ec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Automating the Breach: AI-Assisted Penetration Pipeline
DESCRIPTION:Authors - Gavin Singh Pandha\, Umar Khokhar\, Binh Tran Abstract - This research initiative investigates the growing role of artificial intelligence (AI) in the automation of cyber-attacks and its emerging impact on modern cybersecurity. This study explores how AI models can be used to per-form web application intrusions by simulating attacks against deliberately vulnerable applications. Performance metrics from these simulations are compared with traditional\, manually executed intrusion techniques\, in addition to examining real world cases for AI misuse\, ethical concern\, industry standard\, and the growing risk of autonomous threat actors
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:a9909c27d26a68a57f27ae444e972348
URL:http://10thworlds4.sched.com/event/a9909c27d26a68a57f27ae444e972348
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Knowledge-Enhanced Graph Convolutional Network Framework for Soil Macronutrient Ratio Prediction
DESCRIPTION:Authors - M. V. Rama Sundari\, Bhuvan Unhelkar\, Pravin Kshirsagar\, Supriya Nandikolla Abstract - The management of nutrient content in soil is vital for improving productivity of crops\, as well as sustainable agriculture. Conventional processes of establishing the best ratios of macronutrients tend to overlook intrinsic relation-ships that exist among soil properties\, crop\, weather. Main purpose is to come up with a smart prediction system that will help forecast Macronutrient needs by infusing particular domain-specific expertise in agriculture into machine learning models to improve interpretability and predictive accuracy. The suggested methodology utilises a Graph Convolutional Network (GCN) to simulate spatial and relational relationships amongst soil\, crop and environmental parameters. To provide the model with a better semantic understanding\, a Knowledge Graph (KG) is created to encode the relationships between domains. Embedding algorithms\, such as TransE and DistMult\, are then added to the GCN to form a KG-embedded GCN model that can learn feature-based as well as semantic relationships to predict nutrients. The experimental analyses on the ICFA Crop Recommendation dataset reveal that the baseline GCN got the R² scores of 0.806\, 0.729\, and 0.768\, and the TransE GCN and DistMult GCN models have been advanced to the R² scores of 0.808-0.821\, 0.746-0.762\, and 0.800-0.814 for Nitrogen\, Phosphorus and Potassium respectively. These findings indicate that the predictive strength is greatly advanced by the incorporation of domain knowledge. The model can\, however\, perform differently on unknown crop varieties and soils\, which suggests that more work needs to be done in the future on larger and region-specific datasets.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:66b3d06b7c351559290d856318f26b3e
URL:http://10thworlds4.sched.com/event/66b3d06b7c351559290d856318f26b3e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Machine Learning-Based Optimization of Spine-Leaf architecture Data Center Networks Using MLP Models
DESCRIPTION:Authors - Antonio Cortes Castillo Abstract - The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed data center operations. The widespread adoption of AI services has introduced new requirements for hosting AI systems\, prompting significant modifications in the design and construction of modern data centers. As a result\, data center operators must implement comprehensive strategies to address the challenges associated with AI integration. This study explores the application of machine learning techniques using neural networks and Multilayer Perceptron (MLP) models for data center optimization. The research focuses on critical metrics\, including power consumption\, liquid cooling\, fiber-optic systems\, and the number of fiber-optic routing paths\, which pose significant challenges for data center operators. Experimental results are analyzed using simulation tools\, including SPSS\, to demonstrate enhancements in data center performance and efficiency.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:129841636aadf3dc75d721facc807043
URL:http://10thworlds4.sched.com/event/129841636aadf3dc75d721facc807043
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Mapping Research Trends in Accounting Information Systems within Nonprofit Organizations
DESCRIPTION:Authors - Hera Khairunnisa\, Naomi Helena Elizabeth\, Dwi Kismayanti Respati\, Ayatulloh Michael Musyaffi\, Gentiga Muhammad Zairin Abstract - This study presents a bibliometric analysis of the literature on accounting and non-profit organizations (NPOs) based on 248 documents retrieved from the Scopus database. Data were processed and visualized using Biblioshiny (R Studio) and Scopus web analysis. The findings indicate a significant growth in publications since 2000\, peaking in 2020–2024. Accounting\, Auditing and Accountability Journal emerged as the most dominant source\, and the distribution of journals is consistent with Bradford's Law. The United States and United Kingdom lead in scientific contributions\, while Indonesia shows a growing presence. Keyword analysis reveals a shift toward contemporary themes such as blockchain\, sustainability\, and social accounting. This study provides a systematic mapping of the intellectual landscape of NPO accounting research and identifies opportunities for future investigation.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:0c8824c9c47435a03327bed5cdade5d6
URL:http://10thworlds4.sched.com/event/0c8824c9c47435a03327bed5cdade5d6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Study and Implementation of Diabetic Retinopathy Detection Using Combined Statistical Texture Features with SVM
DESCRIPTION:Authors - Himangshu Sarma\, Madhumita Banerjee\, Chandrajit Choudhury Abstract - Diabetic Retinopathy starts at a light level with no visible symptoms\, but it can lead to severe pain and blindness as the disease progresses. Clinically\, DR is diagnosed by looking for retinal detachment or utilizing imaging techniques like fundus imaging or optical tomography. The Early Diabetic Retinopathy Study is one of the established DR staging schemes. Image Processing has played a significant role in improving the methods used to detect the disease automatically. It has a huge role in assisting ophthalmologists in the screening process\, as manually screening by ophthalmologists consumes more time\, sometimes there may also be error. Although there are a number of algorithms used in detection\, therefore\, in this work we have studied and implemented various DR detection methods. To differentiate among various classes\, we also prepared KAGGLE APTOS dataset containing the GLCM\, LTP\, GLRLM\, LMeP\, CLBP\, CSLBP and LBP features extracted from the image dataset. And the classification is performed in the dataset\, whilst achieving an accuracy of 97% on testing dataset and 96% for training dataset using SVM multi classifier.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:b2c34881f96b706dad6bcc342212577a
URL:http://10thworlds4.sched.com/event/b2c34881f96b706dad6bcc342212577a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Teachers at the Crossroads of Innovation: Readiness and Resistance in Adopting Artificial Intelligence in Education
DESCRIPTION:Authors - Najera R. Umpar\, Minsoware S. Bacolod Abstract - In this study\, the readiness of teachers in adopting Artificial Intelligence (AI) in teaching and learning\, and the variables that influence their acceptance or resistance towards it\, were investigated. Following a qualitative research approach\, semi-structured interviews were employed to gather the data. It was found that a range of factors including generation of teachers\, teaching discipline or specialization\, institutional support\, and ethical issues influence teachers' readiness in implementing AI in the teaching process. Young teachers\, along with those who are specialized in STEM subject area\, expressed confidence\, preparedness\, and enthusiasm in embracing AI in teaching. Experienced teachers\, and teachers who taught other subject area\, expressed concerns toward relevance and teachers' autonomy. Institutional support (were considered as a factor that would significantly impact teachers' readiness toward AI integration. Ethical concern such as student privacy\, bias algorithm\, and student monitoring have also contributed to teachers' beliefs on AI implementation. More important\, the study identified "hybrid readiness" where teachers believe AI can serve as the "co-teacher" of them and contribute to individual learning and pedagogical practice. It suggests that there are variety of factors influencing the teachers' readiness toward AI implementation thus it requires more comprehensive planning in order to have more efficient integration of AI in the classroom. It is found that teachers' readiness is still not homogeneous and context dependent\, therefore differentiated training and education as well as support policy are necessary to enhance teacher readiness and promote the integration of AI in education.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:f91016ec21fe86fa61e0acc5aa1efb15
URL:http://10thworlds4.sched.com/event/f91016ec21fe86fa61e0acc5aa1efb15
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:An Explainable Static–Behaviour Feature Dataset for Android Malware Forensics and Security Analytics
DESCRIPTION:Authors - Ali Fenjan\, Mohammed Almulla\, Jalil Md Desa Abstract - Android malware detection datasets are commonly designed for classification accuracy\, while their ability to support explainability\, forensic interpretation\, and analyst-driven security reasoning remains limited. This paper presents APU-Android\, an explainable static–behaviour feature dataset for An-droid malware forensics and security analytics. The dataset contains 4\,594 APK records\; after duplicate removal\, 4\,281 cleaned records were used in the strict evaluation. APU-Android represents each APK using nine interpretable features: requested permissions\, API calls\, file size\, encryption usage\, obfuscation level\, network requests\, suspicious keywords\, network-risk flag\, and Behaviour Score. Unlike opaque high-dimensional representations\, each feature is mapped to a security-relevant meaning\, allowing model decisions to be interpreted in terms of privilege abuse\, API capability\, concealment\, communication risk\, suspicious string evidence\, and behavioural risk. The evaluation excluded du-plicated normalized columns\, applied group-aware splitting using App_Name\, and benchmarked five machine-learning classifiers. Under group-aware evaluation\, Extra Trees achieved 98.72% accuracy\, 99.38% precision\, 98.37% recall\, 98.87% F1-score\, and 99.84% ROC-AUC using only the nine original explain-able features. Ablation analysis further examined the role of Behaviour Score and Obfuscation Level\, while SHAP analysis showed that Network_Requests\, API_Calls\, and Permissions_Requested were the most influential prediction features. The results demonstrate that APU-Android is not only classification-ready\, but also explanation-ready and forensic-ready for Android malware security analytics.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:175b997e95bfaaba77b8225d49106f01
URL:http://10thworlds4.sched.com/event/175b997e95bfaaba77b8225d49106f01
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Data Asset Valuation and Premium Calculation for Cyber Insurance in Sri Lanka
DESCRIPTION:Authors - Senaya Nurandhi Jayawickrama\, Tithira Mojitha Ranasingha\, Dineth Randika Kumaranayake\, Udageeth Dias\, Kavinga Yapa Abeywardena\, Amila Nuwan Senarathne Abstract - The increased digitization of banking operations has increased the value of data assets while also exposing them to growing cyber risks\, creating a need for effective risk management mechanisms. Cyber insurance is a risk transfer mechanism that enables organizations to mitigate financial losses by transferring cyber risks to third party insurers. However\, in many emerging markets\, including Sri Lanka\, cyber insurance remains underdeveloped due to limitations in existing premium calculation models\, as they lack transparency and fail to incorporate data asset valuation and relevant security parameters\, resulting in inaccurate and unfair premiums. This study proposes a structured framework that integrates data asset valuation with premium calculation\, while defining multiple insurance coverage categories to address financial losses arising from regulatory\, operational and recovery risks. By incorporating data asset value\, operational criticality and organizational security posture into the premium calculation process\, the proposed models enhance the accuracy and fairness of premium values.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:2ee84fdb96ff1edd31b48963fe10ff2f
URL:http://10thworlds4.sched.com/event/2ee84fdb96ff1edd31b48963fe10ff2f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Major-Specific Influences of Blended Learning in Higher Education
DESCRIPTION:Authors - Nguyen Ngoc-Tuan\, Nguyen Van-Giap Abstract - Blended learning (BL) has played an important role in the improvement of learners. The special features of BL are useful for teachers and students\, such as various learning contents\, personal learning activities\, and the mechanism related to authentic learning. However\, the BL influences for different major students should be carefully studied and will contribute to the community. This study investigates the influences of BL based on a blended learning system (BLS) on university students’ achievement in different majors. Based on 247 courses in two years (2023 and 2024)\, without and with the BLS\, we gathered and analysed the learning achievements of more than 9000 students from different majors stud-ying at a university. We also categorised the students into two main majors\, in-cluding social science (SSCI) majors and engineering majors (SCI). Learning based on the system\, the learning achievement of SSCI students is significantly higher than that of SCI students. We also found that the BLS improved the learning scores of the good students learning in both SSCI and SCI majors. Addition-ally\, the SSCI students’ scores were significantly higher than those of the SCI students. The direction in the skills assessment of the SCI majors may be the main cause of the differences in university educational settings. Several suggestions on how to attract the low-scoring students to learn actively with BLS were also given. The important findings can contribute to the community to develop and apply blended learning for the different university major contexts.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:7dea74ac3da00f419beb7ed2bf7b00cb
URL:http://10thworlds4.sched.com/event/7dea74ac3da00f419beb7ed2bf7b00cb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Neural Volumetric Editing for Dynamic 3D Gaussian Splatting: A Systematic Literature Review
DESCRIPTION:Authors - Janitha Prabodha Bandara Dissanayaka\, Pumudu A. Fernando\, Manul Randula Singhe Abstract - Three-dimensional scene representation has moved quickly from Neural Radiance Fields (NeRF) to explicit volumetric methods such as 3D Gaussian Splatting (3DGS). 3DGS can render photorealistic scenes in real time\, but editing these scenes is still difficult because Gaussian primitives do not have a fixed mesh structure or explicit topology. This becomes more challenging in dynamic scenes\, where edits must remain stable across time and viewpoint. This paper presents a systematic literature review of neural volumetric editing methods for dynamic 3DGS and related NeRF-based representations. The review follows a PRISMA-guided process and analyses studies published between 2023 and 2026. The selected methods are grouped into three main paradigms: text-guided editing\, interaction-based editing\, and physics-based simulation. The review compares these methods using control precision\, rendering speed\, memory usage\, editing time\, temporal stability\, and practical limitations. The findings show that text-guided methods are easy to use but often suffer from weak localisation and temporal inconsistency. Interaction-based methods provide stronger geometric control but struggle with complex topology changes. Physics-based methods produce more realistic motion but require higher computation and reliable material parameters. The review identifies the consistency gap\, including flickering and texture swimming\, as the main barrier to real-world dynamic neural volumetric editing.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:e02a9b9168ec5b41903036ef7fe18a3a
URL:http://10thworlds4.sched.com/event/e02a9b9168ec5b41903036ef7fe18a3a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:SafeWear: A Signal Quality-Aware Multimodal Machine Learning Framework for Real-Time Physiological State Monitoring\, Transition Detection\, and Anomaly Safety in Wearable Systems
DESCRIPTION:Authors - Mazin Alshamrani Abstract - Wearable physiological monitoring systems are increasingly deployed in health-critical contexts\, yet existing approaches address state classification\, transition detection\, and signal safety as isolated problems. This paper introduces SafeWear\, a unified signal quality-aware machine learning framework that jointly addresses (i) real-time and anticipatory detection of physiological state transitions\, and (ii) multimodal anomaly detection and out-of-distribution (OOD) safety screening. The framework centres on a modality-level Signal Quality Index (SQI) acting as a front-end reliability gate distinguishing sensor-level failures from genuine physiological irregularities. Causal temporal models are evaluated for transition detection\, while reconstruction-based and one-class detectors are benchmarked for anomaly safety under strict Leave-One-Subject-Out Cross-Validation (LOSO-CV) across 37 subjects and five wearable modalities. Preliminary results show causal models achieve median detection latencies below 5.2 s with early-detection rates exceeding 79%\, and Deep SVDD with VAE yield the strongest anomaly discrimination (AUROC = 0.539 and 0.538).
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:4f00f302acfc166d855060dc379fa1c8
URL:http://10thworlds4.sched.com/event/4f00f302acfc166d855060dc379fa1c8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Trust in Digital Public Services in the AI Era: How Information Security Perceptions Shape Engagement with Vietnam’s E-Governance Education Platforms
DESCRIPTION:Authors - Hoang Anh Tuan\, Le Thien Nhi Abstract - As governments digitize public services\, educational platforms have emerged as essential e-governance infrastructure managing users’ personal and academic data. Vietnam’s National Digital Transformation Program enforces higher education digitalization as a strategic priority\, but how users’ perceive the security of these systems\, and whether such perceptions translate into trust\, remains underexplored. This study review literature linking Perceived Information Security Assurance\, Perceived Information Security Risk\, Trust\, Intention to Use\, and Intention to Share Personal Information among university students in Hanoi and Ho Chi Minh City. Drawing on the Technology Acceptance Model\, Protection Motivation Theory\, and the CIA triad\, the review proposed that security assurance strongly predicts trust\, and trust drives both usage intention and willingness to share personal data. Contrary to expectations\, perceived risk does not diminish trust but coexists with continuous platform use. As artificial intelligence features become more standardized in educational e-governance\, these trust dynamics gain urgency as citizens or users must trust not only data handling but algorithmic/AI decision-making. The review synthesizes evidence from e-government adoption\, educational systems and AI governance literatures to understand trust formation in AI-enhanced educational e-governance\, with implications for platform design\, policy development\, and future research addressing accountability and e-governance in educational contexts.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:c501112e2cc4e9e1c2450b22e3ce47e1
URL:http://10thworlds4.sched.com/event/c501112e2cc4e9e1c2450b22e3ce47e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:AI-Assisted Occupational Safety in Informal E-Waste Recycling: A Software Engineering Perspective
DESCRIPTION:Authors - Anna Berko-Boateng\, Chalisa Veesommai Sillberg\, Mika Saari\, Pekka Abrahamsson Abstract - Electronic waste (e-waste) is one of the fastest-growing waste streams worldwide\, and in many low-resource settings\, informal recycling is performed under hazardous conditions with limited access to occupational safety information. Existing AI-based waste-recognition systems are typically designed for industrial or high-resource environments and do not adequately address the infrastructural\, usability\, and safety constraints of informal work contexts. To address this gap\, this paper presents a lightweight Android application that uses multimodal artificial intelligence (AI) to support occupational safety among informal e-waste workers. The application enables users to capture images of e-waste components and receive structured safety guidance\, including risk levels and handling instructions. Through the design\, implementation\, and field evaluation of the system\, five meta-requirements were derived for AI-supported safety tools operating in low-resource environments. The system was evaluated through field testing at two informal recycling sites in Accra\, Ghana\, using representative low-cost smartphones. The findings indicate that the participants perceived the guidance as relevant and useful\, while the interaction flow operated reliably across heterogeneous devices and user backgrounds. Beyond demonstrating feasibility\, the study contributes transferable design knowledge on AI integration\, device constraints\, and safety-critical communication in low-resource socio-technical contexts.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:dcce3b267e583b694879e5960abb8298
URL:http://10thworlds4.sched.com/event/dcce3b267e583b694879e5960abb8298
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Application of the Hilbert-Huang Transform to Nonstationary Signal Processing
DESCRIPTION:Authors - Volodymyr Chumakov\, Oksana Kharchenko\, Zlatinka Kovacheva\, Andrii Poberezhnyi Abstract - The Hilbert–Huang transform is considered. This method is compared to other known methods for handling nonstationary processes\, specifically\, the windowed Fourier transform and wavelet transform. The comparison is based on real data: the sound radiation of an Unmanned Aerial Vehicle using the example of a small Unmanned Aerial Vehicle\, Phantom 4\, and real electroencephalograms of a healthy and ill person. The advantages of using the Hilbert–Huang transform over the Hilbert transform are shown\, because the latter is used for narrow-band processes. The possibilities of frequency extraction in the case of beats are noted. It is emphasized that Hilbert–Huang transform offers a more adaptive and data-driven approach\, allowing it to reveal intrinsic components that traditional methods often obscure. In addition\, this method provides a clearer physical interpretation of instantaneous frequencies\, which is crucial for studying rapidly changing real-world signals.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:f908f087afa43b903d7d5a4afa47e240
URL:http://10thworlds4.sched.com/event/f908f087afa43b903d7d5a4afa47e240
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Enhancing Team Collaboration in Game and Animation Projects: A User Experience and HCI Approach to Workflow Design
DESCRIPTION:Authors - Puwis Thiparapkul\, Tuang Dheandhanoo\, Panasuddhi Suddhiprakarn Abstract - Project management in game and animation production often faces challenges because generic tools do not align with their unique workflows. To enhance team collaboration\, this study applies User Experience (UX) and Human-Computer Interaction (HCI) design principles to improve team workflows. The design is built upon real-world operations and an analysis of current free and paid industry tools\, specifically aiming to optimize efficiency for beginners and small-to-medium-sized studios. We developed a domain-specific project tracking system on a demo site based on the newly synthesized "Waterfall Storm" concept\, which balances structured administrative oversight with highly flexible\, modular production phases. This conceptual architecture was derived directly from empirical user feedback and qualitative interview insights across three key target segments: entrepreneurs\, creative practitioners\, and students. The proposed design was evaluated through extensive empirical testing involving more than 100 users and 30 projects across educational and industrial environments. The results demonstrate that the Waterfall Storm workflow significantly enhances team collaboration\, provides decentralized task visibility\, increases clarity in asset tracking\, and effectively eliminates format fragmentation while lowering software costs. These findings highlight that a domain-specific\, user-centered approach to workflow design supports creative team collaboration more effectively than general-purpose project management framework.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:e8f5ab2a23f2e85c9ce9a71e162a223c
URL:http://10thworlds4.sched.com/event/e8f5ab2a23f2e85c9ce9a71e162a223c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Intelligent Edge-Based Facial Recognition for Real-Time Student Monitoring in Smart School Transportation Using IoT and Hybrid Deep Learning
DESCRIPTION:Authors - Majdi Rawashdeh\, Dhia Eddine Salhi\, Awny Alnusair\, Ali Karime Abstract - Ensuring student safety during school transportation remains a critical challenge\, motivating automated\, intelligent monitoring solutions. This paper introduces a comprehensive IoT-enabled framework for real-time student identication and attendance management aboard school buses. The proposed architecture combines an ESP32-CAM edge device with a suite of machine learning and deep learning models\, evaluated on an augmented facial dataset of 40\,000 images derived from the Labeled Faces in the Wild (LFW) benchmark. A comparative study of six pipelinesa basic SVM baseline\, SVM with PCA\, a distance-based classier\, SVM with augmentation and grid search\, Random Forest\, and a proposed CNN-LSTM hybridis conducted. The CNN-LSTM hybrid achieves the highest accuracy of 99.5%\, with precision\, recall\, and F1score exceeding 99%. The architecture spans four layerssensing\, gateway\, server\, and applicationenabling low-latency communication between the edge device\, cloud\, and a mobile application serving parents and administrators\, with end-to-end inference latency below 200 ms per frame. The results validate the system as a scalable\, cost-eective\, and highly accurate solution for modernizing student safety and attendance in school transportation.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:bbc86c3f6bce9aa15a146701d999f7bd
URL:http://10thworlds4.sched.com/event/bbc86c3f6bce9aa15a146701d999f7bd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Performance Study of an Autonomous Two-Wheeled Self-Balancing Robot Using Deep Q-Network (DQN)
DESCRIPTION:Authors - Md. Istiaq Ahmed Bhuiyan\, It Ee Lee\, Teong Chee Chuah\, Muhammad Sheraz\, Gwo Chin Chung Abstract - Two-wheeled unsteady robots have special mobility benefits\, but are unstable\, nonlinear devices. Conventional control algorithms frequently fail to stabilize dynamic uncertainties and variations in the system. This study presents a Deep Reinforcement Learning (DRL) model based on Deep Q-Network (DQN) algorithm to balance autonomously. The proposed architecture was trained using DQN algorithm. It uses Exponential Moving Average filter to prevent high-frequency fluctuations and allows the motor output to be smooth. Simulation results demonstrate that the DQN controller successfully and robustly stabilizes the robot under mild to moderate initial pitch disturbances of up to 18°. However\, boundary stress testing at an extreme 20° initial pitch revealed a critical kinematic limitation. The evaluation confirmed that while massive pitch recovery is algorithmically possible\, the extreme actuator effort required to correct the chassis induces an uncontrollable divergence in the roll angle\, leaving the system highly vulnerable to roll-axis instability.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:4d45b2236c0272377841cfb2ad33627e
URL:http://10thworlds4.sched.com/event/4d45b2236c0272377841cfb2ad33627e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Strengthening Campus Area Network Security Through VLAN Segmentation and Access Control: Evidence from Pentecost University\, Ghana
DESCRIPTION:Authors - Prince Kelvin Owusu\, Moses Aggor\, Gibson Afriyie Owusu\, Emmanuel Mensah Azagadli\, Martins Larweh Neurtey\, Suleman Zakaria\, Cecil Selorm Mensah Abstract - Campus Area Networks play a central role in supporting teaching\, learning\, administration\, research\, e-learning platforms\, institutional databases\, and communication services in higher education institutions. However\, as universities become increasingly dependent on digital infrastructure\, weaknesses such as poor network segmentation\, inconsistent access control\, insecure wireless access\, limited monitoring\, and service disruptions can expose institutional systems to unauthorized access and operational risks. This paper examines security and resilience challenges in the existing Campus Area Network at Pentecost University\, Ghana\, and presents a redesigned architecture based on VLAN segmentation\, access-control enforcement\, hierarchical network organization\, and improved monitoring. The study adopts a mixed-method and technical assessment approach involving stakeholder input\, technical observation\, network audit\, and pre/post evaluation of selected security and performance indicators. The redesigned architecture separates critical network zones\, restricts unauthorized inter-VLAN communication\, reduces unnecessary broadcast exposure\, and strengthens the control of access to sensitive institutional resources. The results indicate that the intervention was associated with improved access control\, reduced security incidents\, lower latency and packet loss\, increased throughput\, and faster detection and mitigation of network incidents. The paper contributes practical evidence on how structured segmentation and access-control mechanisms can strengthen secure\, resilient\, and sustainable ICT infrastructure in higher education environments
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:5948387d15faba819bfb5ebcea4794b8
URL:http://10thworlds4.sched.com/event/5948387d15faba819bfb5ebcea4794b8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:A Cost-Model Audit of Project Leap Phase 2: Latency\, Capacity\, and Migration Safety in Post-Quantum RTGS
DESCRIPTION:Authors - Rui Liu\, Neng Zeng Abstract - The Bank for International Settlements' Project Leap Phase 2 trial demonstrated that post-quantum cryptography (PQC) can be functionally integrated into the Eurozone T2 real-time gross settlement (RTGS) system\, reporting an average PQC signature verification time of ≈209.9 ms against ≈28.1 ms for the traditional baseline. The report\, however\, explicitly leaves two questions open for "future testing phases": how the observed timing translates into an SLA-aware deployment plan\, and how the system should be architected for a migration-safe transition that NIST IR 8547 recommends but Leap did not test (hybrid signature\, hybrid KEM\, and a non-modifying deployment path on top of the existing ESMIG/NSP stack). This paper contributes the analytical answer to the first question and audits Leap's framework for the second. We adapt the classical TCP-style timeout bound to a closed-form Watchdog inequality Ttimeout ≥E[Tcompute] + 2 · TRTT + k · σjitter\, derive a sensitivity table that maps the safety multiplier k to four financial-grade SLA tiers\, and a closed-form capacity-planning bound whose ratio between asynchronous and synchronous throughput is parametric in the FPGA parallelism. We then audit the Leap report against its own admitted limitations on hybrid signature testing\, hybrid KEM\, and the "modified ESMIG connector" workaround\, and we attach to each gap a bounded fix direction expressed entirely within the cost-model envelope. We complement the analysis with a formal EUF-CMA reduction sketch for the nested PQC–RSA signature (degrading gracefully under attacks on either layer) and a production-grade C empirical anchor on a single self-contained library (the LK LEGO PQC platform\, native RSA\, no OpenSSL): on a commodity x86-64 cloud VM\, Dilithium-5 verify takes ≈0.71 ms at the median (σjitter ≈130 µs)\, the nested PQC–RSA verify 0.77 ms\, and the hybrid ML-KEM-768 + RSA-2048-OAEP decapsulation 1.66 ms. These refine Leap's 209.9 ms PQC verify into a fast cryptographic core (0.34%) plus a slow protocol envelope (99.66%) and show both untested hybrid constructions fit inside a single-millisecond budget\; a throughput cross-check (2115 verify/s single-core\, 83% scaling at 2 threads) validates the independent-cycles assumption. The measured software-only path already meets the legacy 500 ms ceiling\, so FPGA acceleration is an optional optimisation rather than a requirement. Production-grade RTGS measurements with HSM transport and full ISO 20022 parsing remain future work.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:238d09ba75df06f81fcd35f12dba4ff2
URL:http://10thworlds4.sched.com/event/238d09ba75df06f81fcd35f12dba4ff2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:An Alternative Design Paradigm Using Distributed Approach for 112 GBaud [112 Gbit/s NRZ or 224 Gbit/s PAM-4] Ultrawide Bandwidth Electro-Optical TIA Receivers
DESCRIPTION:Authors - Shakeeb Abdullah\, Jim Hjartarson\, Rony E. Amaya Abstract - Currently the fastest tried\, tested\, and reliable electro-optical transceivers operate at 56 Gbaud (56 Gbit/s NRZ or 112 Gbit/s PAM-4) communication speeds per lane\, and while some companies have finally started rolling out their 112 Gbaud (224 Gbit/s PAM-4) line of products for commercial use\, not all companies have caught up\; nor is their much research presented on 112 Gbaud TIAs. Higher speeds such as 400 Gbit/s are usually obtained by transmitting data through multiple parallel lanes of 100 Gbit/s PAM-4. The optical industry has been pushing for the next generation of 112 Gbaud (112 Gbit/s NRZ or 224 Gbit/s PAM4) rates for devices\, however\, it had stalled (hitting many roadblocks) in the past six years or so. Currently\, the popularized TIA architectures are producing diminishing returns in terms of their bandwidth performance for every incremental improvement in its designs (or heavily relying on DSP)\; for this reason\, a new paradigm construct is required to overcome such obstacles and meet the new standards of the next generations of TIAs. This brief proposes a different approach in designing TIAs for 112 Gbaud speeds or higher. Estimated criterion dictates a 3-dB BW of 78.4 GHz to process clean eyes at 112 Gbaud. Proposed TIA architecture in this paper utilizes distributed approach instead of usual common gate or feedback mode to convert the incoming photodiode current into an electrical voltage. Post-layout electromagnetic simulations show that these amplifiers can process PAM-4 eyes with 40 mV pk-pk outputs at -3 dBm of input optical power.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:ae7502c980982b24dacda6139b6429ff
URL:http://10thworlds4.sched.com/event/ae7502c980982b24dacda6139b6429ff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Bridging the Rural Digital Divide: A Systematic Review of ICT Access and Its Impact on Livelihoods\, Education\, and Health
DESCRIPTION:Authors - Nirmal Prabhu K\, Sisira M S\, Kanagaraj S\, Kirthika P\, Ashwin C Abstract - The proliferation of information and communication technologies has brought ICTs to the forefront as important enablers of social inclusion\, governance\, and rural development. However\, the existence of inequalities over the years concerning the use\, benefit\, and application of ICTs\, among others\, has brought about a noticeable digital divide\, especially among emerging nations such as India. This paper presents the underlying causes for the existence of the digital divide among rural populations in India as part of a comprehensive research review on the matter\, adopting the Resource and Appropriation Theory by Van Dijk. A structured search of studies was conducted using the Scopus database\, identifying articles related to rural India\, both quantitative and qualitative studies. Findings indicate that the problem of the digital divide is not solely found among the rural populations of India\, which lack the necessary infrastructure\, as it is also ingrained among the socio-economic\, geographical\, and linguistic population groups\, among others of India\, including women as the most vulnerable section of society. The study concludes comprehensive multidimensional approach is required to bridge the digital divide.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:eef6e3a0e949e093b01865ff854f3bb3
URL:http://10thworlds4.sched.com/event/eef6e3a0e949e093b01865ff854f3bb3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Hybrid Energy-Based Thermoelectric Cold Storage for Sustainable Vegetable Preservation
DESCRIPTION:Authors - Sowmyashree N\, Madhu Sunkanur\, Impana M\, Suchithra B S\, Hemalatha P G Abstract - This paper will outline a cold storage technique that utilizes solar power for the conservation of agricultural produce in rural and non-grid connected areas. This system includes a solar photovoltaic cell together with a backup battery that is used to guarantee continuity of electricity supply. The charge controller manages the electrical input into the circuit. The cooling process is done using a TEC-12706 Peltier unit controlled through an Arduino Uno microcontroller. Sensors are employed to monitor the temperature and voltage. The collected data is fed to the LCD screen\, while good insulation ensures that cool temperatures are maintained. A performance assessment has been conducted on the proposed design\, which proved its efficacy in lowering dependence on traditional energy resources while maintaining the consistency of cooling efficiency. Implementation of the suggested technology will help reduce losses from post-harvests\, boost the financial state of farmers\, and promote the adoption of renewable energy technologies. Additionally\, the design will enhance environmental sustainability by minimizing greenhouse fuel emissions
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:b212159ffd9d9bc929c6071087b28da3
URL:http://10thworlds4.sched.com/event/b212159ffd9d9bc929c6071087b28da3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Intelligent Prediction of Employee Attrition Using Data-Driven Analytics
DESCRIPTION:Authors - Francka Sakti Lee\, Christian Haposan Pangaribuan\, Liem Bambang Sugiyanto\, Sulistyowati\, Jovann Kurniawan\, Henry Nugraha Abstract - Voluntary employee attrition presents a systemic challenge to organizational stability\, yet predictive modeling is frequently constrained by the accuracy paradox and algorithmic opacity. This study proposes an Explainable Artificial Intelligence (XAI) framework integrating eXtreme Gradient Boosting (XGBoost) with Shapley Additive exPlanations (SHAP) to transform attrition analysis into prescriptive intelligence. By implementing a Random Over-Sampling (ROS) protocol\, the model successfully neutralized extreme class imbalances\, significantly enhancing the detection sensitivity of latent resignation signals. The novelty of this research lies in its SHAP-driven demographic bifurcation\, exposing critical asymmetries in risk triggers between young and senior employees. Empirical findings identify a "Burnout Triad" comprising compensation\, overtime\, and job satisfaction. Crucially\, junior cohorts exhibit hypersensitivity to immediate transactional factors\, whereas senior cohorts are driven by intrinsic role actualization. This framework culminates in a Decision Support System (DSS) enabling surgical retention interventions\, shifting human capital management toward strategic\, evidence-based governance.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:d903e783577dff8dbddd81134efd01a7
URL:http://10thworlds4.sched.com/event/d903e783577dff8dbddd81134efd01a7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T130000Z
DTEND:20260729T143000Z
SUMMARY:Urban Intelligence and Digital Twins for Adaptive Urban Governance
DESCRIPTION:Authors - Giordana Castelli\, Ida Giulia Presta\, Marialucia Camardelli\, Mariagiulia Di Lizia\, Davide Donato Russo\, Giovanni Felici Abstract - Contemporary cities are increasingly shaped by climate change\, digital transformation\, demographic growth\, socio-economic inequalities\, and environmental uncertainty. These transformations challenge traditional static planning approaches and require new governance paradigms capable of dynamically addressing urban complexity. This paper discusses the Urban Intelligence paradigm as an integrated framework for adaptive and cognitive urban governance. Within this framework\, an Urban Digital Twin is not just as a digital replica of the city\, but a cognitive infrastructure capable of integrating datasets\, simulation models\, real-time monitoring systems\, and participatory processes into a unified environment for knowledge production and decision-making. We explore the different technical and multidisciplinary challenges that derive from this approach\, with special focus on Information and Communication Technologies that enable the effective realization of Urban Intelligent Systems\, providing examples of current work in Italian Cities. We conclude by presenting the 4C Model\, a conceptual model for Urban Governance designed to support the path towards resilient and cognitively enabled cities that learn from uncertainty and promote sustainability\, inclusion\, transparency\, and collective well-being.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:be4c4cf6454a062c13d71e1f6521b35a
URL:http://10thworlds4.sched.com/event/be4c4cf6454a062c13d71e1f6521b35a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T143000Z
DTEND:20260729T143300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:a53a4b00aaa64e126b9e8d13e0caef19
URL:http://10thworlds4.sched.com/event/a53a4b00aaa64e126b9e8d13e0caef19
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T143000Z
DTEND:20260729T143300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:b0439dc389e846ed98b795fc5e5b5192
URL:http://10thworlds4.sched.com/event/b0439dc389e846ed98b795fc5e5b5192
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T143000Z
DTEND:20260729T143300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:7e6960d87d0ded428553cbf2e8d9d83b
URL:http://10thworlds4.sched.com/event/7e6960d87d0ded428553cbf2e8d9d83b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T143000Z
DTEND:20260729T143300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:a62da2b5917003356b43262b2b0321be
URL:http://10thworlds4.sched.com/event/a62da2b5917003356b43262b2b0321be
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T143300Z
DTEND:20260729T143500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:01d24322336a9a520497705678e2ecb6
URL:http://10thworlds4.sched.com/event/01d24322336a9a520497705678e2ecb6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T143300Z
DTEND:20260729T143500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:80929f7919416e898ff42e2e103ad1c9
URL:http://10thworlds4.sched.com/event/80929f7919416e898ff42e2e103ad1c9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T143300Z
DTEND:20260729T143500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:f21d8f2925c00caa5ea3446715f81a43
URL:http://10thworlds4.sched.com/event/f21d8f2925c00caa5ea3446715f81a43
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T143300Z
DTEND:20260729T143500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:8674f315619c3d203b26463873b862cf
URL:http://10thworlds4.sched.com/event/8674f315619c3d203b26463873b862cf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T152800Z
DTEND:20260729T153000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:9f61537b883de4cb40aace666595fb25
URL:http://10thworlds4.sched.com/event/9f61537b883de4cb40aace666595fb25
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T152800Z
DTEND:20260729T153000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:2b37a47a7610bea9e5e9303de3727350
URL:http://10thworlds4.sched.com/event/2b37a47a7610bea9e5e9303de3727350
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T152800Z
DTEND:20260729T153000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:0a953ad99a078f3bee42a135028c9322
URL:http://10thworlds4.sched.com/event/0a953ad99a078f3bee42a135028c9322
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T152800Z
DTEND:20260729T153000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:88c7acc9d72e32d3f4d7c00042cb773e
URL:http://10thworlds4.sched.com/event/88c7acc9d72e32d3f4d7c00042cb773e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:A Dual-Path Framework for Reducing Algorithmic Aversion in AI Use in Hospitals
DESCRIPTION:Authors - Kalinka Kaloyanova\, Elitza Kaloyanova Abstract - Despite the increasing use of artificial intelligence (AI) in healthcare\, clinician adoption of AI tools is still obstructed by algorithmic aversion\, which reflects scepticism about the results of AI use. This article examines how hospitals can enhance AI adoption by strengthening AI competencies in physicians and mitigating mistrust through a systematic\, data-driven approach. A review of behavioral studies reveals barriers to AI adoption across technical\, cognitive\, organizational\, and ethical domains. A framework is proposed that focuses on integrating individual clinician competencies with structured strategies implemented by hospitals that support workflow and create conditions for continuous learning. The implementation of data-centric strategies\, explainable AI tools\, competency programs\, simulation training\, and interprofessional collaboration is recommended to increase trust in AI in medicine\, support its ethical use\, which ultimately leads to safer healthcare delivery.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:ae4c427caf1313b95d6c19e300550287
URL:http://10thworlds4.sched.com/event/ae4c427caf1313b95d6c19e300550287
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:A Tamper-Evident Multi-Store Identity Verification Framework for Web and IoT Services
DESCRIPTION:Authors - Md Manirul Islam\, Umme Salsabil\, Md. Mushfiqur Rahman\, Sazzad Hossain Abstract - This paper presents a compact identity-verification architecture for private web and Internet of Things (IoT) deployments that require tamper evidence without the operational overhead of a full blockchain. The framework separates credential verification from profile-integrity verification across multiple stores: a credential store\, a protected-profile store\, a reference integrity store\, and a key store. Credentials are protected with Argon2id-based verifiers\, while protected profile records are bound to entity identifiers\, timestamps\, and version counters through HMAC-SHA-256 reference tags. Unlike scan-heavy hash-only workflows\, the proposed design performs direct indexed lookup by entity identifier and then verifies integrity through a keyed comparison step\, improving both security posture and scalability. The same logic can be deployed behind HTTPSbased web services and MQTT-over-TLS IoT gateways. A reference prototype and benchmark study over datasets of 1\,000 to 10\,000 entities show that the indexed login path remains nearly size-stable\, with median successful login latency around 1.68-1.69 ms under a development-profile Argon2id configuration\, while a scan-based baseline login path grows from 0.92 ms to 6.90 ms over the same range. Injected profile tampering was detected in all benchmarked trials. The resulting framework offers a pragmatic middle path between conventional centralized login and heavyweight distributed-ledger authorization for institutions that prioritize local autonomy\, compartmentalization\, and data-integrity assurance.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:a920eeb85b559e0e242de31153b62e0c
URL:http://10thworlds4.sched.com/event/a920eeb85b559e0e242de31153b62e0c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:AI\, Bias\, and Inclusion: A Quantitative Study on Awareness and Trust in Artificial Intelligence
DESCRIPTION:Authors - MD Junayed Talukdar\, Khosro Salmani Abstract - Artificial Intelligence (AI) systems are widespread across fields such as healthcare\, finance\, employment\, and criminal justice\, with a substantial impact on the lives of individuals and society. However\, AI systems have been shown to perpetuate existing social inequalities\, particularly through biases that are not easily discernible. Such biases are embedded in the technical and social structures of AI systems\, posing a direct challenge to the principles of Equity\, Diversity\, and Inclusion (EDI) understood as the acknowledgment of differences among individuals\, fairness and equal access\, and the valuation of all participants. This study argues that fairness in AI cannot be achieved by focusing solely on technical aspects\, necessitating a holistic approach. To investigate this\, computational content analysis was applied to 360 occupational narratives generated by ChatGPT across nine professions and four geographic regions (Canada\, Germany\, India\, and Bangladesh) using explicitly gender-neutral prompts. The analysis examined whether AI-generated narratives associate professions predominantly with one gender\, and whether such patterns remain consistent across regions. Findings reveal that gender bias persists despite neutral prompting\, with male-coded protagonists dominant in technical and manual labor professions and female-coded protagonists dominant in caregiving roles. Although regional conditions influenced the magnitude of gender imbalance\, the direction of occupational gender patterns remained largely consistent across all four regions. This study identifies the empirical foundations necessary for future EDI-AI co-design frameworks\, outlining the sociotechnical dimensions that such frameworks must address to be effective.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:a711717f9e71eefd204df35f74734de2
URL:http://10thworlds4.sched.com/event/a711717f9e71eefd204df35f74734de2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:From Cryptographic Keys to RF Fingerprints: A Zero-Knowledge Framework for Physical-Layer IoT Authentication
DESCRIPTION:Authors - Yassine Lkhalidi\, Mohamed Lkhalidi\, Hatim Kharraz Aroussi\, Achraf Tifernine Abstract - IoT device authentication remains vulnerable to credential theft and physical-layer impersonation\, particularly where resource constraints preclude full PKI deployments. Existing approaches address subsets of this problem: RF fingerprinting exposes templates in plaintext\, while zero-knowledge proof (ZKP) schemes authenticate static keys without binding to physical hardware. We propose ZK-RFAuth\, a framework integrating Siamese CNN-based RF fingerprinting\, Groth16 ZKP embedding verification\, and Proof-of-Authority blockchain logging. A device’s hardware imperfections are captured as a compact embedding\; a Groth16 circuit proves the L1 distance between a fresh embedding and the registered template falls below a predefined threshold\, without revealing either vector. Evaluated on WiSig (28 WiFi transmitters\, 224\,000 I/Q frames)\, ZK-RFAuth achieves 91.4% closed-set accuracy\, 2.25% Equal Error Rate\, and 70.8% rogue rejection at the 95th-percentile operating threshold\, requiring only 972 R1CS constraints for 144-byte proofs verified in approximately 3 milliseconds. ZK-RFAuth is the first framework providing physical-layer identity\, embedding-level zero-knowledge privacy\, open-set rogue detection\, and immutable audit logging simultaneously.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:0e342538ac6f8cdbb7b68770e9c072dd
URL:http://10thworlds4.sched.com/event/0e342538ac6f8cdbb7b68770e9c072dd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Hybrid Intrusion Detection System Using Machine Learning: Combining Supervised Classification with Unsupervised Anomaly Detection for Zero-Day Threat Generalization
DESCRIPTION:Authors - Aryan Sharma\, Dipali Baviskar Abstract - Modern computer networks are often equipped with an intrusion detection system (IDS) to detect malicious activities or cyber-attacks. Such a system must have high accuracy on known attacks\, and at the same time it must be able to generalise to previously unseen attacks. However\, supervised classifiers fail to generalise to new situations because they learn to map input data to output labels under a specific training distribution\, and they perform poorly under a different test distribution\, which is called distributional shift. In this paper\, we propose a gating-based hybrid IDS that combines supervised classifiers with anomaly detectors. The gating network restricts the influence of the anomaly component to the uncertain prediction zone\, i.e.\, the region of the output space where the classifier is uncertain\, defined by a probability range of (0.15\, 0.75)]\, and prevents unsupervised noise from affecting the confident supervised decisions. We evaluate the performance of our proposed system on three different test scenarios using the CIC-IDS-2017 dataset. The first test scenario consists of eight known attacks for which we train the classifiers on the corresponding training data\, and then we test them on the corresponding test data. The second test scenario is an out-of-distribution stress test\, in which we use 99% benign traffic and add DDoS and PortScan attacks to it\, and test whether the system is able to detect them. The third test scenario is a zero-day test scenario in which we test the system on a previously unseen SQL Injection attack. Our findings are as follows. First\, the Gating Hybrid RF+AE achieves an F1-score of 0.9778 and a precision of 0.9924 on the eight known attacks\, which outperforms the standalone RF classifier with an F1-score of 0.9750. Secondly\, on the out-of-distribution test scenario\, both the RF and GBT classifiers fail to detect the attacks with an F1-score of 0.000\, while the Gating Hybrid RF+IF achieves an F1-score of 0.405\, which corresponds to a 40.5 percentage-point lift from the F1-score of the anomaly component IF. Thirdly\, the Gating Hybrid RF+IF achieves an SQL Injection recall of 47.6% on the zero-day test scenario\, while the standalone RF and GBT classifiers achieve an SQL Injection recall of 33.3% on average. All the abovementioned results are supported by 95% Wilson confidence intervals\, and we provide root-cause analysis for the extreme results.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:26b2deba021ccee59ad5025058f48e5b
URL:http://10thworlds4.sched.com/event/26b2deba021ccee59ad5025058f48e5b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Influence of artificial intelligence\, social media\, and the legal basis for the administration of a higher education institution
DESCRIPTION:Authors - Moises Toapanta T\, Jeanette Jordan Buenano\, Nancy Jordan Buenano\, Maria Cristina Espin Melendez\, Pamela Toapanta Pavon\, Rocio Llumiquinga A.\, Dafna Guaman B.\, Eriannys Gomez D.\, Pedro Echeverria B. Abstract - The globalization of Information and Communications Technologies (ICTs) the Internet\, artificial intelligence (AI)\, and social media poses serious threats to the integrity\, confidentiality\, and authenticity of information in higher education institutions (HEIs). The central problem lies in the absence of robust legal frameworks regulating the use of AI\, particularly in relation to personal data protection. This study examines perspectives on AI and social media\, together with the legal foundations required for the effective administration of HEIs. Using the deductive method and exploratory research\, key actors in institutional governance were identified\, administrative strengthening indicators were de-fined\, and an integrated model was developed to link AI\, social media\, and regulatory frameworks. It is concluded that improving institutional governance re-quires mitigating the risks associated with the use of these technologies through legal frameworks aligned with national constitutions and regulations. Ecuador\, like most Latin American countries\, currently lacks such legislation and remains in the analysis phase.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:010e4962ec663925f355ade4b7168a27
URL:http://10thworlds4.sched.com/event/010e4962ec663925f355ade4b7168a27
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Digital Agenda and Public Policies in México: ICT\, Artificial Intelligence\, and Higher Education
DESCRIPTION:Authors - Israel Herrera-Miranda\, Miguel Apolonio Herrera-Miranda\, Juan Villagomez-Mendez\, Silvia Lizbeth Herrera-Lopez Abstract - This paper analyzes the characterization and adoption of Information and Communication Technologies (ICT) and GenAI (GAI) within the framework of digital governance and higher education policies in México. Framed around the early years of President Claudia Sheinbaum Pardo's administration (2024–2030)\, the study evaluates how emerging regulatory instruments and administrative agencies—specifically the newly enacted Telecommunications and Broadcasting Law (2025) and the Agency for Digital Transformation and Telecommunications (ADTT)—act as institutional backbones to reduce technological gaps and preserve digital sovereignty. Methodologically\, this study relies on a documentary and reflective analysis\, utilizing empirical indicators from the OECD Digital Government Index (DGI) and the results of México's 2025 National Survey on GAI in Higher Education. The findings indicate that while México exhibits a strong performance in digital-by-design policy frameworks\, structural challenges persist regarding proactive public sector AI integration. Concurrently\, higher education data reveals an accelerated\, mass adoption of GAI by over 60% of students and faculty\, transforming traditional pedagogical paradigms. In response\, the Ministry of Public Education (SEP) has advanced a ten-point strategy targeting digital literacy\, curricular overhauls\, and ethical boundaries. Ultimately\, this paper underscores that bridging the digital divide requires co-aligning public innovation frameworks with human-centric\, ethically-governed higher education policies to foster national technological autonomy and sustainable socioeconomic development.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:3f813c7eeef047ef150044e8d58a36bc
URL:http://10thworlds4.sched.com/event/3f813c7eeef047ef150044e8d58a36bc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Digital Platforms and Urban Sustainability. Lessons from Mexico City's Experience as a Smart City
DESCRIPTION:Authors - Humberto Merritt Abstract - In recent years\, the use of digital applications for data management\, information access\, communication and resource exchange has grown worldwide. Advances in telecommunications have been particularly pervasive in Mexico\, where the widespread use of smartphones has encouraged the adoption of digital technology. In this study\, we examine the implementation of the APP CDMX electronic platform\, which was designed to enhance Mexico City's functionality. The research aims to determine how this tool has helped citizens learn about available public services and how they evaluate it. In particular\, the research describes how smartphone users are leveraging the application to access real-time data on local procedures and their fees\, the traffic conditions\, specific transit routes and stations\, and other touristic venues\, thereby achieving not only more efficient mobility but also encouraging a sustainable lifestyle. Using a qualitative methodology based on the lexical analysis of user opinions and adopting a multidisciplinary approach\, the study concludes that the APP CDMX has positively transformed citizen interaction\, accelerating administrative processes and democratizing access to key information.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:4440ee853dcd381db5687285029981ff
URL:http://10thworlds4.sched.com/event/4440ee853dcd381db5687285029981ff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Dodecahedral framework: a conceptual proposition for the analysis of the constituent elements of the metaverse experience networks.
DESCRIPTION:Authors - Luis Romel Assis Oliveira Junior\, Olivan da Silva Rabelo\, Paulo Henrique da Silva Santos Abstract - The article proposes the Dodecahedral Framework as a conceptual model to analyze Metaverse Experience Networks\, differentiating them from Internet Social Networks and characterizing them as a new object of academic study. The approach explores four dimensions – Immersive Realism\, Spatiotemporal Disruption\, Meta Culture and Tokenized Economy. The methodology consists of a bibliographic review and the theoretical formulation of the model. The results indicate that Metaverse Experience Networks offer new forms of socialization\, interactivity\, and engagement\, profoundly impacting marketing\, consumption\, work\, and digital culture. It is concluded that the Dodecahedral Framework can serve as a tool for researchers and professionals\, assisting in the understanding\, planning\, and implementation of strategies within the metaverse\, in addition to opening paths for new investigations on its technological and social implications.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:57d0c78e697a98e7ffd749e428a2a1fa
URL:http://10thworlds4.sched.com/event/57d0c78e697a98e7ffd749e428a2a1fa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Effects of Reward Engineering In Deep Reinforcement Learning in Stock Index Trading
DESCRIPTION:Authors - Meshak Ratshikombo\, Mehrdad Ghaziasgar Abstract - Deep Reinforcement Learning has emerged as a promising approach for algorithmic trading\, but trading performance remains highly sensitive to reward design. This study investigates how reward engineering shapes learned trading behavior by training agents exclusively on FTSE market data under identical conditions while varying only the reward formulation. Evaluation across multiple unseen equity indices demonstrates that reward functions induce distinct behavioral biases governing exposure timing\, volatility sensitivity\, and downside risk. Profit-oriented rewards encourage aggressive trading and higher variability\, whereas risk-aware and sparse rewards produce more stable policies with improved drawdown control. The findings show that reward engineering acts as a strong inductive bias influencing both trading behavior and cross-market generalization in reinforcement-learning-based trading systems.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:9fe2f969c29b33f8066e05406feb2e7a
URL:http://10thworlds4.sched.com/event/9fe2f969c29b33f8066e05406feb2e7a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Hybrid machine learning models for spatio-temporal modeling and prediction of tropical diseases using environmental and socio-economic data: the case of Burkina Faso
DESCRIPTION:Authors - Lydie Simone Tapsoba\, Salifou Ouoba\, Athanase Sawadogo Abstract - Malaria is the leading cause of child mortality in Burkina Faso and accounts for over 40% of public health expenditure. This study develops and validates a hybrid machine learning model combining Random Forest (40%) and XGBoost (60%) to predict the spatio-temporal dynamics of malaria across the country’s 13 health regions over the period 2010–2024. The model incorporates satellite-derived climatic variables (CHIRPS precipitation\, MODIS NDVI\, temperature) and demographic variables\, enhanced by advanced feature engineering: 3- and 6-month moving averages of precipitation\, temporal lags and circular encoding of seasonality. Validation combines a prospective temporal split for 2024 and spatial GroupKFold cross-validation. On the independent 2024 test set\, the hybrid model achieves exceptional performance\, substantially outperforming the best previously published approaches for this context. Interpretability analysis reveals surprising determinants of transmission\, highlighting the equal role of environmental and anthropogenic factors in Burkina Faso. The SHAP analysis identifies the 3-month moving average of rainfall as the dominant variable\, ahead of population density\, highlighting the equal role of environmental and anthropogenic factors. The priority areas are Hauts-Bassins\, the East and the South-West. The risk maps and 6-month predictions serve as operational tools for the National Malaria Control Programme.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:0a73113f53a7c88a1c40cd06b010b2a9
URL:http://10thworlds4.sched.com/event/0a73113f53a7c88a1c40cd06b010b2a9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Predicting exploitability on networking system vulnerabilities using a low-dimensionality Decision Tree algorithm
DESCRIPTION:Authors - Yasmany Prieto\, Christopher Moyano Abstract - Communication systems are shifting toward more software-based development\, driven by technologies such as Software-defined Networking and Network Function Virtualization. This new paradigm improves flexibility\, scalability\, and resource efficiency\, shortens time-to-market\, but opens a new dimension of system failure through bugs\, backdoors\, and software vulnerabilities. In this work\, we build a Decision Tree (DT) classifier to predict vulnerability exploitability in networking systems. A list of vulnerabilities affecting those systems is obtained from the National Vulnerability Database. To measure exploitability\, we employ the Cybersecurity and Infrastructure Security Agency Known Exploited Vulnerabilities Catalog\, which lists vulnerabilities that have been exploited in the wild. The DT classifier achieved a recall of 0.878 in detecting exploitable vulnerabilities.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:0ad2ed27d4dfad123b212ceaba1087b6
URL:http://10thworlds4.sched.com/event/0ad2ed27d4dfad123b212ceaba1087b6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Agridiagnosis – Plant Disease Detection & NDVI-based Crop Health Monitoring
DESCRIPTION:Authors - Nandinee Mudegol\, Abhijeet Urunkar\, Vedika Dhende\, Vaishnavi Katkar\, Chetna Ghengare\, Samiksha Harer Abstract - Agriculture is a very important sector\, but the farmers are facing problems in early identification of plant diseases and monitoring crop health. Manual checking of crops is time-consuming and can lead to late detection of diseases\, which lowers the yield of the crop. To address this problem\, authors have pro-posed a system called Agridiagnosis. The proposed system combines two major features: plant disease detection using image processing and crop health monitoring using NDVI. Farmers can upload images of leaves to be detected and suggested treatment. At the same time\, the system provides a color-coded map of crop health with respect to NDVI values. The combination of both features in a single platform allows the system to help farmers easily comprehend the crop conditions and take timely measures to increase productivity.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:f4555d9f364748ab5d6a530a0ce0efe1
URL:http://10thworlds4.sched.com/event/f4555d9f364748ab5d6a530a0ce0efe1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:AI Agents and Financial Information Quality Across Traditional and Tokenized Financial Ecosystems
DESCRIPTION:Authors - Romildo Silva\, Maria Tavares\, Filipa Silva\, Carlos Lopes Abstract - This paper investigates the use of AI agents as consumers of tokenized real-world asset (RWA) data in financial environments. A Python-based agent was developed to automatically retrieve\, process\, and analyze financial information from publicly accessible APIs for selected traditional and tokenized assets\, including SPY\, QQQ\, PAXG\, and ONDO. The proposed framework evaluates data quality through quantitative metrics such as latency\, completeness\, null rate\, and data volume\, complemented by descriptive statistical analysis and Shapiro-Wilk normality testing. The results indicate that traditional financial assets exhibit higher informational stability\, while tokenized assets present greater variability and non-normal behavior. The study demonstrates that autonomous AI systems can effectively consume heterogeneous financial data sources and highlights the growing importance of information quality and consistency in AIdriven financial ecosystems.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:ac6435093b188773e8a91cbec1c08c44
URL:http://10thworlds4.sched.com/event/ac6435093b188773e8a91cbec1c08c44
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:AI-Driven IDS for Cloud Infrastructure: An Adaptive and Scalable Ensemble Framework
DESCRIPTION:Authors - Anupama Y K.\, G M Trupti\, Arun Kumar N Abstract - Due to the rapid evolution of cyber threats with the growth of the internet and cyber threats\, we now live within the cyber domain\, which is under great pressure and strain from cyber threats. The rise in popularity of Cloud Infrastructure\, which offers customers scalable data storage\, has led to development of new vulnerabilities to business owners\, as well as a growing shift in the way hackers operate. Traditional methods of detecting cyber attacks\, such as IDSs\, typically encounter issues when dealing with a large amount of class imbalance and have difficulty adapting to newer attack vectors. This results in an increase in false positives that companies receive when monitoring their systems for cyber attacks\, as well as a decreasing ability to detect less frequent\, but very high-impact\, types of cybersecurity threats. The solution involves developing an AI-based intrusion detection system that combines the use of Borderline SMOTE to balance the classes incorrectly identified\, with an ensemble method called maximum vote that combines three classifications methods: Decision Trees\, XGBoost and tuned AdaBoost. The system is evaluated using the KDD Cup 1999 benchmark dataset which contains regular traffic as well as different attack types including DoS/DDoS (Neptune\, Smurf\, Teardrop)\, Probe (Nmap\, Ipsweep\, Portsweep\, Satan)\, R2L (Guess password\, Back) and U2R (Buffer Overflow\, Rootkit\, Land). Experimental results show that the max-voting ensemble out performs the individual base models (Decision Tree\, AdaBoost\, and XGBoost) and standard single classifier IDS methods along with baseline algorithms. This leads to more reliable detection of both minor and major attacks in cloud security scenarios. These findings highlight the effectiveness of combining Borderline-SMOTE with ensemble learning to build a scalable and robust IDS suitable for real-time cloud security monitoring.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:5685a81b8a009fa18ca2ff4506a45b8f
URL:http://10thworlds4.sched.com/event/5685a81b8a009fa18ca2ff4506a45b8f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Design and Implementation of an ESP32-Based Multi-Zone Monitoring and Control Platform for Plant Cutting Propagation
DESCRIPTION:Authors - Y. Zamarripa-Rivera\, S. Villagrana-Barraza\, D.I. Ortiz-Esquivel\, L.E. Banuelos-Garcia\, M. Molina-Almaraz\, G. Díaz-Florez Abstract - Plant propagation by cuttings requires controlled microclimatic conditions to promote rooting\, reduce water stress\, and improve process reproducibility. This paper presents the design\, implementation\, and functional validation of an ESP32based monitoring and control platform for a plant cutting propagation chamber. The system integrates multi-zone sensing\, ON/OFF-based control with PWMassisted thermal actuation\, local CSV data logging\, Wi-Fi communication\, and web-based supervision. Temperature and relative humidity were monitored in the external environment\, stem zone\, and root zone\, while light intensity\, water flow\, actuator states\, and PWM commands were recorded as operational variables. Validation was conducted through two 15-day experimental campaigns\, generating more than 40\,000 time-stamped environmental and operational records. The platform maintained differentiated microclimatic conditions\, with average rootzone temperatures between 23.79 and 24.31 °C and root-zone relative humidity between 75.74 and 80.32%. Biological validation with six basil cuttings achieved an overall rooting success rate of 83.3%\, reaching 100% in the second campaign. These results demonstrate the potential of low-cost ESP32-based systems for controlled-environment agriculture and smart propagation applications.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:9804d19ed37a1ca4679d4255e038ce1a
URL:http://10thworlds4.sched.com/event/9804d19ed37a1ca4679d4255e038ce1a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Exploitation of the temporal dimension for the automatic classification of ultrasound sequences
DESCRIPTION:Authors - Olivier ZONGO\, SOMDA Dekpeltakié Augustin METOUALE\, Mamadou DIARRA\, Abdoulaye SERE Abstract - Automatic assessment of obstetric ultrasound remains a challenge due to its operator-dependent nature and the dynamic context of fetal labor. This study proposes a Temporal Quality Gate Framework to standardize diagnostic plane validation using a novel Temporal Attention-Gated LSTM (TA-LSTM) architecture. We formulate the task as a binary classification problem to distinguish standard diagnostic planes from non-diagnostic sequences\, using the IUGC 2024 dataset of 434 transperineal ultrasound videos (266 positive\, 168 negative). The TA-LSTM extracts spatial features via a ResNet-18 backbone and dynamically weights temporal dependencies using an attention mechanism. Under 5-fold cross-validation with strict patient-level splitting\, the TA-LSTM achieves a mean AUC-ROC of 0.989 ± 0.008 and a mean test accuracy of 95.63% ± 2.85% (peak accuracy of 98.85%) with an inference latency of 14.2 ms on GPU. Our framework acts as a robust Quality Gate\, ensuring that subsequent automated measurements\, like the Angle of Progression (AoP)\, are performed on high-quality validated inputs\, making it highly suitable for resource-limited clinical environments.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:1968a9479a404ddae431348bb29b6ce7
URL:http://10thworlds4.sched.com/event/1968a9479a404ddae431348bb29b6ce7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Integrating Artificial Intelligence into Statistical Process Control: Toward Smart and Autonomous Quality Systems in Industry 4.0 — A Case Study on the Tennessee Eastman Process
DESCRIPTION:Authors - S. Zouini\, A. Meddaoui\, A. Jrifi Abstract - Statistical Process Control (SPC) is a well-established methodology for industrial quality management. The growing complexity of modern manufacturing environments — driven by Industry 4.0\, high-dimensional sensor data\, and nonlinear process dynamics — exposes the limits of classical monitoring approaches based on fixed thresholds and Gaussian assumptions. This paper proposes an AI-Driven Statistical Process Control (AI-SPC) framework that integrates PCA-based Hotelling’s T2 monitoring with a Random Forest classifier within a closed-loop architecture. The framework is evaluated on five fault scenarios from the Tennessee Eastman Process (TEP) benchmark. Results show that the hybrid AND-logic strategy achieves a false alarm rate of 0.071 — a 45% reduction relative to PCA-T2 alone (0.130) — while maintaining a detection rate of 96.1% and a detection delay of 5.4 samples. A variable contribution analysis further supports fault diagnosis by identifying the most deviant process variables at the moment of detection. These results confirm that combining statistical rigor with data-driven flexibility produces a more reliable and interpretable monitoring system than either approach deployed independently.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:edc71e3afd53d921d3aa2bf954a77485
URL:http://10thworlds4.sched.com/event/edc71e3afd53d921d3aa2bf954a77485
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:A Hybrid Conversational-AI Virtual Patient for Occupational Therapy Simulation in Virtual Reality
DESCRIPTION:Authors - Janset Shawash\, Henri Liu\, Alicia Sudlerd\, Leevi Rantala Abstract - Simulation-based learning gives healthcare students safe\, repeatable practice before clinical placement\; virtual reality (VR) makes it more accessible and affordable. This paper presents Aino\, an artificial-intelligence-driven virtual patient for an occupational therapy (OT) home-visit showering assessment\, built in Unity for the standalone Meta Quest 3. Where most of existing OT VR tools rely on fixed-viewpoint\, pre-recorded 360-degree branching video\, Aino is a conversational 3D patient whom students address in unconstrained natural speech (Finnish or English) while moving freely within a single continuous scene that follows a dynamic clinical narrative. The technical core is a hybrid control architecture that decouples a scripted\, data-driven clinical narrative from free-form conversational responses: a section-based state machine runs twenty-nine designer-authored sections\, each with an explicit completion contract that reconciles deterministic clinical progression with variable-length AI dialogue\, behind an AI-provider-agnostic interface. OT educators use observation-based scenarios\, and thus\, the patient narrates her actions and reactions to keep her performance legible\; this narration pattern and its calibration are examined as a transferable design lesson. The showering task additionally involves intimate personal care that cannot be ethically rehearsed in live role-play yet is staged safely in VR. Formative findings from educator co-design\, educator try-out sessions\, and play-testing are reported\, and planned student evaluations are outlined.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:dd9eba62bce4965f67a2fe6766125972
URL:http://10thworlds4.sched.com/event/dd9eba62bce4965f67a2fe6766125972
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Analysis of Functional Requirements for Water Quality Monitoring in the Amazon Rainforest: A Systematic Review ⋆
DESCRIPTION:Authors - Gilmara Santos\, Pedro V. Matias\, Yan W. Martins\, Jose R. Santos Junior\, Joao V. Fernandes\, Rodrigo O. Jesus\, Ueller B. Silva\, Lidia Roque\, Klinsman Goncalves\, Laisa Paiva\, Edjair Mota Abstract - The Amazon River basin\, home to one of the world’s largest freshwater reserves and unparalleled biodiversity\, silently suffers from a vast environmental disaster caused by illegal mining\, during which mercury is discharged into its waters. This contamination threatens aquatic ecosystems and poses serious risks to highly vulnerable populations. In response\, this paper provides clues for a resilient and scalable system architecture for real-time water quality monitoring\, tailored to the environmental and infrastructural challenges of the Amazon region. A detailed systematic literature review assesses state-of-the-art Internet of Things (IoT)-based monitoring techniques\, focusing on key variables such as mercury concentration\, temperature\, turbidity\, and pH. Special emphasis is given on communication technologies suitable for diverse settings—from Wi-Fi-enabled urban areas to remote rainforest regions where LoRa\, NB-IoT\, and WiLD (Wi-Fi over Long Distance) present viable alternatives. The study also highlights the role of the application layer in enabling data analysis\, real-time alerts\, and remote visualization of environmental conditions. This research contributes to developing time-efficient and sustainable monitoring strategies to support public health initiatives and ecological conservation by bridging technological innovation with the urgent environmental needs of one of the planet’s most critical biomes.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:51517dea4cf575ecbfec27ebf9059069
URL:http://10thworlds4.sched.com/event/51517dea4cf575ecbfec27ebf9059069
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Analysis of operational demand using telemetry to define catchment areas along a public transport corridor in Mexico City
DESCRIPTION:Authors - Laura Alma Diaz-Torres\, Alma Delia Torres-Rivera\, Mario Leonardo Nieto Antolinez\, Fabian Leonardo Alfonso Sabogal Abstract - Mexico City faces a constant need for high-quality public transport systems capable of reducing passenger waiting times\, improving travel comfort\, and maintaining the economic viability of private operators. In this context\, demand studies are essential both before the concession stage and during service operation\, since they support route planning\, fleet allocation\, schedule adjustments\, and operational decision-making. Two similar but distinct methodologies are compared for the estimation of load polygons. The first methodology assigns telemetry events to official stops using spatial proximity and route reconstruction through directed graphs. This approach provides higher operational traceability\, since demand is linked to formal routes\, directions\, and stops. Nevertheless\, it may underestimate demand that occurs outside the official route structure. The second methodology uses heat maps and 300-meter-radius polygons to identify functional demand areas based on observed passenger activity. This approach captures real operational behaviour more flexibly\, but may lose direct correspondence with formal stops\, especially when polygons overlap or include stops from different directions. The comparison shows that neither methodology is sufficient on its own. The graph-based method is useful for formal operational analysis\, while the heat-map method is more sensitive to actual demand behaviour. Based on these findings\, the paper proposes\, as future work\, the development of a multicriteria integration approach that combines both methods. Such an approach could reduce structural and observational biases\, improve the processing of boarding and alighting data\, and generate clearer maps\, graphics\, and analytical outputs to support expert decision-making in public transport operations.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:65623976dffdb6dbab928d03e34a8704
URL:http://10thworlds4.sched.com/event/65623976dffdb6dbab928d03e34a8704
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Beyond Single-Dataset Evaluation: Feature Space Mismatch and Cross-Dataset Adversarial Transferability in Network Intrusion Detection Systems
DESCRIPTION:Authors - Sayee Patil\, Vaidehi Pathak\, Purva Nalawade\, Rupali Vairagade\, Nilakshi Jain Abstract - Despite the high classification accuracy of ML-based Network Intrusion Detection Systems (NIDS) achieved on the widely used NIDS benchmarks\, there is still limited understanding of the robustness of these systems against adversarial perturbations and whether and how such perturbations transfer between separate models trained on independent datasets. In this paper\, an empirical study is conducted to determine the ability of adversarial examples generated in one model to attack another model with a different structure and a different training dataset. We create adversarial examples with two commonly used benchmarks\, CICIDS2017 and UNSW-NB15\, and train four models (Random Forest\, XGBoost for both benchmarks). BoundaryAttack is a black-box decision-based attack suitable for non-differentiable tree ensemble classifiers. We build a complete 4×4 matrix of Attack Success Rate for all source-target model pairs. From our results\, we can see that the crossmodel transferability within-dataset is very high (89–100%)\, meaning that the robustness of the models is not significantly increased by their diversity if they are trained on the same data distribution. Conversely\, cross-dataset transferability decreases significantly (5–44%) even when the feature space is limited to 10 harmonized features semantically shared between the two datasets. PCA analysis of the harmonized feature space reveals substantial manifold separation between datasets\, explaining the observed transfer degradation. We propose that the disparity between feature spaces is a natural and meaningful obstacle to adversarial transferability\, and directly influence the design and testing of adversarially robust NIDS deployments.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:c848921b19856ed4594cffda86bd728c
URL:http://10thworlds4.sched.com/event/c848921b19856ed4594cffda86bd728c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:Building a TripAdvisor dataset for irony-aware sentiment analysis
DESCRIPTION:Authors - Yisel Clavel-Quintero\, Ernesto Gongora-Rodriguez\, Melissa Carmenaty-Ramirez Abstract - The Internet has signicantly transformed the business landscape\, particularly in the tourism industry\, by removing geographical constraints and time restrictions\, while enhancing accessibility for consumers. Nowadays\, users tend to search online for destinations and opinions from other travelers\, make reservations\, and share their own assessments. Therefore\, customer reviews have become a valuable source of information for companies seeking to evaluate service quality and improve their products\, advertising strategies\, and overall performance. In this context\, opinion mining and sentiment analysis have gained relevance\, particularly in platforms such as TripAdvisor\, which rely on usergenerated content. A key challenge in polarity detection is the correct interpretation of irony\, as it can alter the intended meaning and sentiment of an expression. However\, there are still few available TripAdvisor datasets\, and\, to the best of our knowledge\, none are labeled for irony. We propose the creation of a dataset of TripAdvisor reviews annotated with both polarity and irony\, alongside an experimental study to identify a model capable of eectively classifying the polarity of ironic TripAdvisor user reviews. Transfer learning was applied by adapting models trained on two source datasets for irony detection\, and the best-performing model was subsequently used to annotate a TripAdvisor dataset with irony. Furthermore\, experiments for polarity classication were conducted. The logistic regression model achieved the best performance in both tasks. The dataset obtained oers a valuable resource for future research on sentiment analysis and opinion mining in the tourism domain.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:650fc8e2a2eba01e4247f0ac4c86c471
URL:http://10thworlds4.sched.com/event/650fc8e2a2eba01e4247f0ac4c86c471
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T153000Z
DTEND:20260729T160000Z
SUMMARY:DESIGN AND VALIDATION OF A1-LEVEL DIALOGUE SCRIPTS FOR IMMERSIVE VIRTUAL ENVIRONMENTS IN EFL LEARNING
DESCRIPTION:Authors - Wilma G. Villacis\, Enith J. Mejia\, Judith A. Silva\, Carlos I. Nunez\, Julio E. Cuji\, Edder D. Naranjo Abstract - Immersive virtual reality environments have gained increasing attention in language education due to their potential to provide authentic and contextualized opportunities for communication. Despite this growing interest\, limited attention has been given to the systematic design and validation of the dialogue scripts that support interaction within these environments. This study aimed to develop and validate CEFR-aligned dialogue scripts for A1-level learners of English as a Foreign Language. A material design and validation approach were adopted\, combining expert feedback and quantitative evaluation. Through the integration of CEFR descriptors\, communicative functions\, and useful language\, nine dialogue scripts were developed across two scenarios: a university campus and a shopping center. The scripts were evaluated through a two-round Delphi process involving five experts in Applied Linguistics and English language teaching. Quantitative data were analyzed using descriptive statistics\, while qualitative feedback was examined through thematic categorization. Findings from the first Delphi round identified issues related to linguistic level alignment\, naturalness\, and interactional authenticity\, leading to targeted revisions. The second round demonstrated a high level of expert agreement regarding the appropriateness of the revised scripts for A1 learners. The study provides a structured and transferable framework for the development of dialogue-based materials and contributes to the pedagogical design of immersive language-learning environments.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:045f930a020dd430398f4a6c6b1c92a6
URL:http://10thworlds4.sched.com/event/045f930a020dd430398f4a6c6b1c92a6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T170000Z
DTEND:20260729T170300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:7bb06b2324b781edccb984555cf665a8
URL:http://10thworlds4.sched.com/event/7bb06b2324b781edccb984555cf665a8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T170000Z
DTEND:20260729T170300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:44cd18ffcf489d728076cf82a01dfd0f
URL:http://10thworlds4.sched.com/event/44cd18ffcf489d728076cf82a01dfd0f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T170000Z
DTEND:20260729T170300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:becb6d74963a8e87ee2ed468bca95da8
URL:http://10thworlds4.sched.com/event/becb6d74963a8e87ee2ed468bca95da8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T170000Z
DTEND:20260729T170300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:f145dd95d43c5720c1a1930fc0eb132c
URL:http://10thworlds4.sched.com/event/f145dd95d43c5720c1a1930fc0eb132c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T170300Z
DTEND:20260729T170500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:b97ad9965f9ad0ca98059cf5eff90b04
URL:http://10thworlds4.sched.com/event/b97ad9965f9ad0ca98059cf5eff90b04
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T170300Z
DTEND:20260729T170500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:5ae05e66fe785db2478b2301501417c4
URL:http://10thworlds4.sched.com/event/5ae05e66fe785db2478b2301501417c4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T170300Z
DTEND:20260729T170500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:865acde4a2b80a8fd844f8f390bca23b
URL:http://10thworlds4.sched.com/event/865acde4a2b80a8fd844f8f390bca23b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260729T170300Z
DTEND:20260729T170500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:23bc9f113e3689feab66a672eebe8ce4
URL:http://10thworlds4.sched.com/event/23bc9f113e3689feab66a672eebe8ce4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T075800Z
DTEND:20260730T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:a6838f84ffe9d917c083693299697995
URL:http://10thworlds4.sched.com/event/a6838f84ffe9d917c083693299697995
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T075800Z
DTEND:20260730T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:7a957d3f6a5529c5fd8e4fc9ea51f900
URL:http://10thworlds4.sched.com/event/7a957d3f6a5529c5fd8e4fc9ea51f900
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T075800Z
DTEND:20260730T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:7cc982d00180c5829c44146ee86c9049
URL:http://10thworlds4.sched.com/event/7cc982d00180c5829c44146ee86c9049
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T075800Z
DTEND:20260730T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:651e6b3ebbccc98141fb0c54ea1b5176
URL:http://10thworlds4.sched.com/event/651e6b3ebbccc98141fb0c54ea1b5176
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T075800Z
DTEND:20260730T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:62173125247d587157236e1dcff1b722
URL:http://10thworlds4.sched.com/event/62173125247d587157236e1dcff1b722
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Artificial Intelligence: Reshaping the Future of Learning
DESCRIPTION:Authors - Bhavana Chalkalwar\, Reena S. Satpute Abstract - In today’s world Artificial Intelligence is rapidly growing in modern tech industry which enhances the power of new IT services. Artificial Intelligence helps to Education\, Government And Private Services for better future work. The new way of teaching and providing instruction requires an adaptive approach rather than the traditional way of teaching to meet the wide variety of student learners. This change in education is being led by technological advances in areas such as machine learning\, natural language processing\, and intelligent tutoring systems. This paper will examine Artificial Intelligence's role in education\, including the various applications available and what potential benefits and challenges each might present\, as well as how the use of Artificial Intelligence in education as a whole may affect education moving forward. Many of the current systems using AI today allow for customized arrangements through the use of automated assessment tools\, intelligent tutoring\, and adaptive algorithms. With the use of chatbots and virtual assistants\, the amount of student interaction and the speed at which students can obtain information will increase as well as hold instructors accountable for identifying learning gaps. In addition\, AI tools have an enormous impact on what happens inside the classroom and in institutional management. These tools will help streamline many of the non-teaching duties or administrative assignments assigned to educators\, improve how resources are distributed\, and better equip school administrators and policymakers to make decisions related to education. Additionally\, AI tools can be used in research practices that are unique to institutions of higher education\, such as automating literature reviews\, analysing trend data within a compilation of data\, and contributing to predictive modelling. However\, the use of AI in education has created significant ethical\, technical\, and sociological concerns.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:8ad67e2a0971242cbb967e90a5e24da3
URL:http://10thworlds4.sched.com/event/8ad67e2a0971242cbb967e90a5e24da3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Emotionally Aware Conversational AI for Mental Health: A Technical Survey of Architectures\, Clinical Grounding\, and Ethical Fault Lines
DESCRIPTION:Authors - Aanchal Khandalkar\, Reena S. Satpute Abstract - Mobile app developments exploded lately\, and it’s not hard to see why. Things like 5G\, AI\, machine learning\, and Mobile Edge Computing aren’t just making headlines they’re actually changing how apps work. Now\, apps are smarter\, more personalized\, and packed with features that can show up overnight. Sounds amazing\, but the flip side is tough: people expect everything instantly. They want quick responses\, apps that basically read their minds\, and zero downtime\, no matter where they are. Honestly\, building apps these days is anything but simple. Hardware is a real pain for developers. Phones just don’t have the power of regular computers. You get less memory\, slower processors\, and\, of course\, batteries that bail on you before you even realize. So\, developers have to work magic keep the app running smoothly without draining the battery\, or else people just uninstall. And with so many apps depending on outside libraries and analytics tools\, there’s a whole new pile of problems. Sure\, these tools help\, but they open the door to security risks. Not every team has someone who lives and breathes security\, so it’s easy for privacy issues or sketchy code to sneak in. All of that puts some cracks in the process and makes building solid apps a lot trickier. Such fragmentation exists on platform that developing AI/ML application for it\, on the Android platform in particular\, turns out to be extremely painful to integrate on. Then it becomes a question of tradeoffs from developers’ perspective-how the usage of cloud services versus local device processing fits\, on each having its share of difficulties regarding scale\, speed\, power and security. In this paper\, we go through the workflow and delve deeper into a comparative study on native application development.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:ff965134af0a0751c69e903264526caa
URL:http://10thworlds4.sched.com/event/ff965134af0a0751c69e903264526caa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Integrating Cognitive Processing Signals into Natural Language Processing: Methods\, Architectures\, and Applications
DESCRIPTION:Authors - Khushi Tijare\, Reena S. Satpute Abstract - Bio-signals such as eye movements\, electroencephalogram (EEG)\, functional magnetic resonance imaging (fMRI)\, and pupil dilation are real-time reactions to language cues that provide more data than static text representations used in classical NLP. The aim of this study is to examine how bio signals could be employed in deep natural language processing (NLP) to enhance task effectiveness and interpretability during reading comprehension\, sentiment analysis\, named entity recognition (NER)\, and syntax parsing. The following sections will describe the important aspects of design\, including. Pre-processing steps for EEG and eye-tracking datasets. Model architecture types such as feature injection\, multi-task learning based on auxiliary tasks\, attention mechanisms\, and multimodal transformers. Experimental design and metrics used for model evaluation. Ethical considerations regarding the use of cognitive signals. All design decisions made by the authors have been verified through experiments involving open access benchmark datasets like ZuCo\, ZuCo 2.0\, and recently developed EEG datasets.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:abbe7654efcf2eb86ce35f9b0e79dfc8
URL:http://10thworlds4.sched.com/event/abbe7654efcf2eb86ce35f9b0e79dfc8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Optimized MDS Matrices for Efficient Software and Hardware Implementations: A Survey
DESCRIPTION:Authors - Luong Tran Thi\, Nguyen Van Long\, Bac T. Nguyen\, Hiep L Thi Abstract - Maximum Distance Separable (MDS) matrices play a crucial role in the design of diffusion layers in modern symmetric cryptographic primitives such as block ciphers\, hash functions\, and lightweight cryptographic schemes. Owing to their ability to achieve the optimal level of diffusion as measured by the branch number criterion\, MDS matrices significantly enhance resistance against differential and linear cryptanalysis. However\, the practical deployment of MDS matrices often faces challenges due to high computational cost\, a large number of XOR operations\, and substantial hardware resource requirements. Therefore\, the construction of implementation-efficient MDS matrices has become an important research direction in modern cryptographic design. This paper presents a comprehensive survey of methods for constructing and optimizing MDS matrices with a focus on reducing implementation complexity in both software and hardware environments. Specifically\, classical construction methods based on Cauchy\, Vandermonde\, and Reed–Solomon structures are reviewed\, along with structured matrices such as circulant\, recursive\, and involutory forms. In addition\, optimization techniques targeting XOR count\, circuit depth\, and memory usage are analyzed\, and several open research directions in the design of efficient diffusion layers for modern cryptographic systems are discussed.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:7c6b82472d114265dcb575aee70b55f0
URL:http://10thworlds4.sched.com/event/7c6b82472d114265dcb575aee70b55f0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Post-Quantum Secure CryptocurrencyWallet Architecture: A Code-Based Threshold Design Framework
DESCRIPTION:Authors - Hiep. L. Thi\n Abstract - Recent advances in quantum computing threaten the cryptographic foundations of blockchain systems. While existing surveys analyze quantum resistant blockchain architectures at the system level\, wallet-layer migration and threshold post-quantum signing mechanisms remain underexplored. This paper proposes a post-quantum secure cryptocurrency wallet architecture based on code based threshold signatures. We introduce a formal quantum-adversarial wallet model\, design a modular wallet framework\, provide a security reduction argu ment under syndrome decoding hardness\, and outline a migration-compatible de ployment strategy
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:76b09212ff5e2b8981bb9258473b6c18
URL:http://10thworlds4.sched.com/event/76b09212ff5e2b8981bb9258473b6c18
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Structural and Computational Perspectives on Secret-Sharing Schemes
DESCRIPTION:Authors - Hiep. L. Thi\n Abstract - Secret-sharing is a fundamental cryptographic primitive that enables the secure distribution of sensitive information among multiple participants while guaranteeing correctness and privacy. In this paper\, we present a comprehensive study of secret-sharing schemes from both information-theoretic and computational perspectives. We begin by reviewing classical threshold constructions\, including Shamir’s polynomial-based scheme and its algebraic interpretation via linear codes. The notions of correctness and perfect secrecy are formalized using entropy\, and the role of access structures in characterizing authorized subsets is emphasized. We then examine ideal secret-sharing schemes\, where each share has the same size as the secret\, and highlight their deep connection with representable matroids. In particular\, we discuss how matroid representability over finite fields characterizes the existence of ideal linear secret-sharing schemes\, thereby linking combinatorial independence with cryptographic access control. Additional structural results concerning field dependence\, minimal non-ideal access structures\, and connections to linear codes are also addressed. The paper further explores computational secret-sharing\, which relaxes perfect privacy to computational indistinguishability under standard cryptographic assumptions. We describe constructions based on encryption and threshold key sharing\, as well as realizations derived from monotone circuits. A central theme is the separation between information-theoretic and computational models: while certain access structures require exponential share size in the information-theoretic setting\, they admit polynomial-size shares under computational assumptions. Finally\, we discuss algebraic methods underlying secret-sharing\, including polynomial interpolation and linear coding techniques\, and illustrate how these tools support efficient distributed protocols such as secure multiparty computation and threshold cryptography. Overall\, the paper provides a unified treatment of structural\, algebraic\, and computational aspects of secret-sharing\, highlighting its foundational role in modern distributed cryptographic systems.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:abf5430aef8d9405950f34c9fd914af8
URL:http://10thworlds4.sched.com/event/abf5430aef8d9405950f34c9fd914af8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:A Multi-Layer Explainable Security Framework for Real-Time Web Traffic Filtering Using Adaptive Reputation and Threat Intelligence
DESCRIPTION:Authors - Polinati Vinod Babu \, M.V.P Chandra Sekhara Rao \, Kolagotla Venkateswara Reddy \, Manukonda Ravi Chandra \, C P Pavan Kumar Hota \, Kurumalla Suresh Abstract - The web services today are flooded with automated bots\, distributed request floods and IP based attacks. Traditional firewalls are based on either a static signature rule or an opaque machine learning model that is not very transparent. We introduce ShieldNet\, a lightweight Web Application Firewall (WAF)\, which is developed in FastAPI and Redis as a state storage. The explainable real-time request filtering performed by ShieldNet is on the basis of a deterministic decision pipeline\, a combination of IP reputation scoring (Redis backed\, counts on infractions)\, SlowAPI based rate limiting\, Detection of bot user agent\, Up-to-date use of open access threat feeds (spamhaus\, abuse.ch\, etc.). Request identities can be provided as either permitted (HTTP 200)\, rate limited (429) or blocked (403)\; all of which can be traced through the middleware layers. Synthetic load testing and feed validation reveal low latency\, high throughput and high detection fidelity. The system has good performance\, scalability and interpretability balance thus it can be used in real-time web security implementation.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:dbe3e80dccb256bc9f703d6bdcca6876
URL:http://10thworlds4.sched.com/event/dbe3e80dccb256bc9f703d6bdcca6876
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Bridging the Gap: The Role of ICT Standards in Shaping Effective Communication Strategies for Sustainable Development in Higher Education
DESCRIPTION:Authors - Apolinar P. Datu\, Pamela V. Zuniga\, Chona S. Lajom\, Jobert D. Bravo\, Renen Paul M. Viado\, Ma. Yvonne Czarina C. Angcaya\, Maricris Punzalan Abstract - Higher education institutions play a critical role in advancing sustainable development\, particularly through effective and inclusive communication practices supported by digital technologies. This study examined the role of Information and Communication Technology (ICT) standards in shaping effective communication strategies for sustainable development in higher education. Using a quantitative descriptive research design\, data were collected through a structured questionnaire administered to thirty (30) higher education personnel\, including faculty members\, administrators\, and ICT staff aged 22 years and above. A 4-point Likert scale was utilized to measure levels of ICT standards implementation\, communication effectiveness\, accessibility and inclusivity\, system interoperability\, and stakeholder engagement. The findings revealed a high level of ICT standards implementation across participating institutions. Results further showed that ICT standards strongly influence the clarity\, reliability\, timeliness\, and organization of communication strategies. Standardized ICT practices were also found to enhance accessibility and inclusivity in digital communication\, support system interoperability\, and improve stakeholder engagement in sustainability-related initiatives. Overall\, the study highlights that ICT standards serve as essential enablers of coherent\, inclusive\, and sustainable communication in higher education. The findings underscore the importance of strengthening ICT standards to support institutional sustainability goals and foster meaningful stakeholder participation.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:2c8d04d8429568c355fb283def94b743
URL:http://10thworlds4.sched.com/event/2c8d04d8429568c355fb283def94b743
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Deep Learning-Based Prediction of Sequential and Non-Sequential Approaches for Diabetic Retinopathy Detection
DESCRIPTION:Authors - A Aruna kumari\, Sri Vishnu Prabhu Gudavalli\, Tamminana Visweswari Abstract - This project presents a hybrid architecture\, which combines sequential and non-sequential models\, to detect diabetic retinopathy (DR) in retinal fundus image data using deep learning. The model has a non-sequential backbone feature extraction layer\, a pre-trained ResNet50\, built using the Functional API of Keras to allow flexibility and skip connections. On top of that\, a functional custom sequential classifier head is added\, comprising of\, e.g.\, GlobalAveragePooling2D\, Dense\, and Dropout layers\, also constructed in a functional manner to ensure the architecture is coherent. The classifier is used to make a binary prediction: DR or not. The model was trained using the EyePACS data using different techniques of image preprocessing\, including resizing\, normalization\, and augmentation. The resulting architecture is completely accurate and exhibits good generalization on unseen data\, which indicates the effectiveness of transfer learning in combination with deep sequential classifiers. This bifurcated architecture is understandable and performance-wise attractive to be used in clinical decision support in diabetic retinopathy screening.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:9f805e9868c08b8526d2571c38c1a595
URL:http://10thworlds4.sched.com/event/9f805e9868c08b8526d2571c38c1a595
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Dynamic Simulation of Post-Quantum Cryptography Migration in the Financial Sector
DESCRIPTION:Authors - Masaki Murakami\, Atsuhiro Goto Abstract - Post-quantum cryptography (PQC) migration in the financial sector is a resource allocation problem shaped by network interdependence. This study develops a prototype dynamic simulation model that incrementally introduces cyber-risk internalization: starting from a baseline without cyber-risk channels\, adding firm-level direct cyber loss\, and finally incorporating systemic cyber loss propagated through the financial network. The paper quantifies how the scope of cyber-risk internalization determines migration outcomes under a shared investmentallocation framework. Results show that firm-level internalization accelerates early migration but remains insufficient for full sector-wide coverage\, whereas systemic-risk internalization can close the late-stage adoption gap under sufficiently supportive policy conditions. This distinction shows that policy support for PQC migration should not be understood only as cost reduction\, but also as a mechanism for internalizing network externalities. Through nested comparisons of these model variants\, this study demonstrates how each risk channel distinctly shifts migration outcomes.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:f599aff75aad52d44f683570dd8827a8
URL:http://10thworlds4.sched.com/event/f599aff75aad52d44f683570dd8827a8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Innovative Didactic Methods for Develoрing Comрetence in the use of Artificial Intelligence in Рedagogical Activities
DESCRIPTION:Authors - Akbar Doniyorovich\, Doniyor Gulomov Zaynobidin o'g'li\, Dilshoda Akramova\, Xushmamatova Aminakhon Rustam qizi\, Tukhtaeva Shakhnoza Gaybulla kizi\, Abdimurodova Shakhnoza Anvar qizi Abstract - This article discusses the theoretical foundations and рractical directions of innova tive didactic methods aimed at develoрing the comрetence of using artificial intelligence (AI) in рedagogical activities. The study analyzes the рroblem of forming teachers' digital literacy\, technological readiness\, and ability to integrate intellectual technologies into the educational рrocess. The article рroрoses methods aimed at increasing the innovative comрetence of teach ers based on a didactic aррroach and a steр-by-steр learning model develoрed to achieve effec tive results in the use of AI tools in education. These methods include aррroaches such as рroblem-based learning\, рroject work\, reflexive analysis\, and collaborative learning with AI as sistants. According to the results of the study\, the systematic introduction of artificial intelli gence technologies into the educational рrocess develoрs the information-analytical\, methodo logical\, creative\, and reflexive comрetence of teachers. The use of innovative didactic methods increases the effectiveness of teacher activity\, individualizes the educational рrocess\, enhances analytical thinking\, and a creative aррroach. The scientific results рresented in the article serve as a scientific and methodological basis for the рrocess of modernizing the рedagogical education system\, imрlementing the conceрt of digital рedagogy into рractice\, and рreрaring future teachers to work with artificial intelligence technologies.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:47d35afd7ff18cbb473f4f21e5aeef95
URL:http://10thworlds4.sched.com/event/47d35afd7ff18cbb473f4f21e5aeef95
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:PRESENTING RESEARCH IN THE ICT LANDSCAPE: EVALUATING THE COLLABORATIVE BENEFITS OF DIGITAL TOOLS AND THEIR IMPACT ON ENGAGEMENT
DESCRIPTION:Authors - Apolinar P. Datu\, Jobert D. Bravo\, Reine Joshua L. Cruz\, Helga Marie B. Cabarle\, Najera R. Umpar\, Minsoware S. Bacolod Abstract - In today’s rapidly evolving Information and Communication Technology (ICT) landscape\, digital tools have become essential in shaping how research is presented\, shared\, and collaboratively developed. This study aimed to quantitatively examine the collaborative benefits of digital tools and their impact on user engagement during research presentations. Using a quantitative descriptive–comparative research design\, data were collected from thirty (30) respondents consisting of students\, educators\, ICT professionals\, and administrative staff who regularly engage in ICT-based collaborative activities. A structured survey questionnaire was used to gather numerical data on the frequency of tool usage\, levels of collaboration\, and user engagement. Descriptive statistics such as frequency\, percentage\, and weighted mean were employed to analyze the data. The findings revealed that digital collaboration tools are frequently used in ICT-related activities and are strongly associated with improved coordination\, communication\, and teamwork. Respondents reported high levels of collaboration and engagement\, particularly when using interactive features such as real-time communication\, shared document editing\, and feedback mechanisms. These features were found to enhance motivation\, focus\, and willingness to participate actively in research-related tasks. Overall\, the study highlights the importance of intentional and effective integration of digital tools in research presentations to foster meaningful collaboration and sustained engagement in ICT-based academic and professional environments.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:2920c39cdcc442971e6ff361084fceb3
URL:http://10thworlds4.sched.com/event/2920c39cdcc442971e6ff361084fceb3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:A DISARM-Informed LLM Framework for Narrative-Based Monitoring of Disinformation-Driven Geopolitical Risks
DESCRIPTION:Authors - Wonseong Kim Abstract - Geopolitical risk now tends to surface in information environments well before it shows up as physical disruption or moves in market prices\, and often before any policy response. Current geopolitical risk indices track the salience of news\, and a separate body of work on disinformation detection looks for signals of manipulation. What neither line of work does is connect manipulated discourse to the channels through which food\, energy\, supply chain\, sanctions\, and macro-financial risks are actually transmitted. To address this gap\, the paper develops a DISARM-informed large language model framework for narrative-based monitoring of disinformation-driven geopolitical risks. The framework is organised as a four-layer architecture that brings together observable manipulation signals\, the classification of narrative function\, mapping onto risk domains\, and the construction of indicators. From these layers it derives interpretable indicators that capture manipulated risk discourse\, gaps in framing\, concentration of narratives\, and transmission across domains. Our central claim is a methodological one: once observable manipulation\, narrative function\, and risk-domain mapping are represented together\, unstructured multilingual media can be turned into auditable early-warning signals. We intend the result as a decision-support instrument for sustainable security monitoring\, not as a means of attribution or causal estimation.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:9ce80fddc8967da592382c4194769d29
URL:http://10thworlds4.sched.com/event/9ce80fddc8967da592382c4194769d29
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Artificial Intelligence and Machine Learning for Early Screening and Risk Stratification of Type 1 Diabetes in Children: A Systematic Review
DESCRIPTION:Authors - Nassour Annour Saad\, Mahamat Atteib Ibrahim Doutoum\, Marayi Choroma\, Mahamat Issa Hassan\, Djaury Dadjia Abstract - Pediatric type 1 diabetes (T1D) remains a major public health priority\, especially in low-resource settings where presenting diabetic ketoacidosis is still common. This systematic review (2020–2025)\, conducted under PRISMA 2020 and complemented by TRIPOD/TRIPOD+AIinspired criteria for predictive models\, synthesizes AI/ML work on early screening and risk stratification in children. Islet autoantibodies and genetic risk scores improve discrimination\, but the literature shows substantial AUC variability depending on sample size\, calibration\, and validation design (single split versus repeated or family-level validation). Ensemble models often outperform classical approaches with multimodal data. We emphasize external validation\, class-imbalance handling\, and reproducible pipelines. The main gap remains the absence of models simultaneously integrating autoantibodies\, HLA/GRS\, and C-peptide.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:f7ed0fd7b802222f3c382ae663d8a113
URL:http://10thworlds4.sched.com/event/f7ed0fd7b802222f3c382ae663d8a113
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Digital Risk Management\, Artificial Intelligence\, and Financial Performance: Evidence from Moroccan Industrial Firms
DESCRIPTION:Authors - Imane Bari\, Abdellatif Aziki\, Zineb Alaoui Abstract - This study analyses the relationship between digital operational risk management and the financial performance of industrial firms in the Agadir region of Morocco and investigates the role of artificial intelligence in risk governance. Using a quantitative survey of 50 industrial firms and linear regression with principal component analysis\, the results show that a structured digital risk management framework\, covering identification\, assessment\, and mitigation of threats\, is positively and significantly associated with financial performance (R = 58.7%\, F = 3.518\, p < 0.05). Several constraints are identified\, including skill shortages\, limited technological resources\, and insufficient digital governance culture. The study further shows that AI-based tools\, through automated anomaly detection and predictive analysis\, strengthen the effectiveness of risk management frameworks. These findings support the integration of AI as a technical component of operational risk governance in industrial settings.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:627f2cfe4db2a80493610740775464e9
URL:http://10thworlds4.sched.com/event/627f2cfe4db2a80493610740775464e9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Evolutionary Hartigan-Wong Clustering Algorithm
DESCRIPTION:Authors - Libero Nigro\, Franco Cicirelli Abstract - This paper builds on the Hartigan-Wong (HW) algorithm for unsupervised clustering. Although basic HW comes with an intrinsic high computational cost\, it is known to be a better solution than K-Means\, because it is less likely to get stuck around a sub-optimal solution of the data space. The paper\, in particular\, proposes a variant of HW\, named Evolutionary HW (E-HW)\, which embodies genetic concepts and favors the achievement of more accurate clustering. E-HW depends on the use of a population of candidate solutions (centroid configurations)\, preliminarily created. E-HW is fed by a solution extracted from the population\, which gets refined (crossed) and possibly replaced (mutated) following the basic HW operations. New generations of the population then come into existence. E-HW can be repeated a certain number of times\, after that\, experimental results highlight that the population favors the emergence of a solution close to the optimal one. To smooth out the computational burden\, many operations of E-HW are implemented in parallel Java\, so as to exploit the computing benefits of modern multi-core machines. The paper demonstrates the effectiveness of E-HW by using a collection of benchmark datasets\, and the clustering results are compared with those achieved by competitor algorithms.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:59bdbf94cbb5ee4e68da8e959ac09def
URL:http://10thworlds4.sched.com/event/59bdbf94cbb5ee4e68da8e959ac09def
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Intelligent IoT-Driven Emergency Management and Analytical Decision-Making Framework for Parallel Gas Pipelines
DESCRIPTION:Authors - Ilgar G. Aliyev\, Konul Gafarbayli\, Firangiz Mammadrzayeva Abstract - Modern parallel gas pipeline systems require intelligent and operationally reliable emergency-management mechanisms capable of distinguishing real leakage events from normal technological transients under real-time operating conditions. Although IoT- and SCADA-based monitoring technologies are widely used in modern gas transmission infrastructures\, most existing systems primarily rely on threshold-based supervision or empirical data-driven methods\, which often lack physical interpretability and analytical decision-making capability. This paper proposes an intelligent IoT-driven emergency-management and analytical decision-making framework for parallel gas pipelines based on the integration of digital monitoring technologies with analytical gas-dynamic modeling. The proposed cyber-physical architecture combines wireless pressure sensors\, SCADA-assisted supervisory control\, synchronized shut-off valves\, and analytical decision algorithms to ensure real-time identification\, localization\, and mitigation of emergency operating modes. Analytical criteria are developed for distinguishing emergency and technological pressure variations\, estimating emergency detection time\, localizing the leakage coordinate\, and determining the optimal activation time of interconnecting pipeline valves. The proposed framework enables rapid isolation of damaged pipeline sections while ensuring adaptive gas redistribution through intact parallel lines. Unlike conventional monitoring-based approaches\, the developed methodology transforms emergency control into an analytically justified intelligent supervision mechanism capable of minimizing gas losses\, preventing cascade disturbances\, and improving operational sustainability. The integration of IoT-based sensing with analytical decision-making additionally improves compatibility with Industry 4.0 and digital twin concepts for future smart gas transmission infrastructures.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:d7d89b83d6c6650315df8f0289d36ae1
URL:http://10thworlds4.sched.com/event/d7d89b83d6c6650315df8f0289d36ae1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Vibration diagnostics as a method of preventive control in the design of individual mechanisms and structural elements of wheel pairs of railway locomotives in engineering CAD programs and digital modelling
DESCRIPTION:Authors - Kirill Kalichkin\, Tatiana Gritskevich Abstract - The study is devoted to the analysis of vibration agnostics problems as a method of preventive control in the design of wheel sets of railway locomotives. The study examines vibration agnostics as a preventative control method for designing individual mechanisms and components of railway locomotive wheel sets designed for long-term\, safe operation. Currently\, the main issue with the mechanical drives of wheel-motor unit assemblies and motor-anchor bearing assemblies in railway locomotive wheel sets remains increased vibration during highfrequency operation. The authors analyze the prediction of potential defects using digital twins\, the goal of which is to enable engineers to accurately predict solutions when similar defect signs are detected during operation of different digital twin scenarios. This enables the development of preventative measures to prevent accidents at the early stages of wheel set defect development.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:9054fd8a22b1f74ecdc45ca0e22ab689
URL:http://10thworlds4.sched.com/event/9054fd8a22b1f74ecdc45ca0e22ab689
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:A Formal Specification of a Data Model for Malaria Surveillance in the Developing World
DESCRIPTION:Authors - Emmanuel Tuyishimire Abstract - The fourth Industrial Revolution(4IR)\, together with the COVID-19 pandemic have made a loud call for digitizing diagnosis processes. The world is now convinced that it is imperative to digitize the diagnosis of long standing diseases such as malaria for more efficient treatment and control. It has been seen that malaria control would benefit a lot from digitising its diagnosis processes such as data gathering. We propose\, in this paper\, the architecture of a digital data collection system and how it is used to gather data for malaria awareness. The system is formally specified using Z notation\, and based on the capability of the system\, the malaria determinants are defined and their retrieving mechanisms are discussed.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:e1416f2206bbaec1f2f7b0a1c5cfe8e0
URL:http://10thworlds4.sched.com/event/e1416f2206bbaec1f2f7b0a1c5cfe8e0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Analysis of the impact of periodic and burst traffic data variations on predictive and proactive routing systems in SDN networks
DESCRIPTION:Authors - Sara Sadiq Jawad\, Dheyaa Jasim Kadhim Abstract - Software-defined networks (SDNs) suffer from dynamic congestion due to the nature of the data traffic they transmit. This includes both aggregated mobile network activity (such as video streaming\, social media access\, browsing\, messaging\, and mobile app usage) and real backbone internet traffic traces (which contain diverse packet flows from broadband services\, cloud computing systems\, downloads\, server connections\, and large-scale network transactions). This congestion reduces service quality and leads to poor resource allocation. Therefore\, traffic prediction is considered a smart and efficient transition for SDNs. This paper proposes a deep learning-based predictive system for forecasting incoming traffic using two types of data (periodic Milano and bursty MAWI dataset). It also examines the impact of periodic and bursty traffic types on the prediction models used by the proposed system\; and on the integration mechanisms used to translate predictions into actionable data. The results show that periodic Milano traffic requires temporal learning\, while bursty MAWI traffic requires clipping\, alignment\, log scale\, and robust prediction. Using MAWI traffic also required alignment between the training and evaluation phases through the application of cross-domain adaptation\, unlike the Milano traffic which showed a direct response. The proposed system also demonstrated improved throughput\, reduced congestion\, and more stable decision-making.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:17759543d682a3f51f2923bfc0cdb97c
URL:http://10thworlds4.sched.com/event/17759543d682a3f51f2923bfc0cdb97c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:MD-IP102: A Dataset for Multi-modal Insect Recognition
DESCRIPTION:Authors - Quoc-Anh Nguyen\, Thanh-Nghi Doan\, Huu-Hoa Nguyen Abstract - Crop productivity has been and continues to be influenced by both beneficial and harmful insect species. The classification of these insects plays a critical role in identifying threats and implementing crop protection measures. This paper presents a multimodal insect dataset for multimodal classification\, utilizing both images and supplementary textual descriptions. The dataset is enriched to provide comprehensive information about various insect species. The study illustrates an integrated approach for feature extraction\, similarity analysis\, and insect classification. Furthermore\, the research also introduces a model interpretation mechanism for the deep learning-based feature extraction process.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:effd12624954353928284f141602d869
URL:http://10thworlds4.sched.com/event/effd12624954353928284f141602d869
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Predicting Student Academic Performance in Computer Science Programmes Using Machine Learning: A Case Study at a Private Higher Education Institution in Cyprus
DESCRIPTION:Authors - Petros Papagiannis\, George Pallaris\, Pantelitsa Leonidou Abstract - Predicting student academic performance presents a persistent challenge for higher education institutions. This paper presents a machine learning study at Cyprus College\, Cyprus\, using 311 studentcourse records across three semesters from 13 Computer Science courses. Four assessment components—midterm examination\, final examination\, assignments\, and participation—alongside absence counts for 95 unique students are used as features. Seven algorithms are evaluated—Random Forest\, XGBoost\, SVM\, Logistic Regression\, K-Nearest Neighbours\, Decision Tree\, and Naive Bayes—using stratified five-fold cross-validation across three tasks: regression\, binary pass/fail classification\, and multiclass grade band prediction. SHAP analysis (applied to the full feature set) identifies feature contributions\, while early-warning experiments exclude the final examination score to simulate mid-semester prediction. Results show that midterm and assignment scores predict final outcomes with R2=0.746 before the final examination\, whilst Random Forest and XGBoost achieve 97.4% pass/fail accuracy. Participation contributes zero predictive signal despite a 10% grade weighting\, with direct implications for assessment design at small higher education institutions.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:54facaca777ca61bf34e611583617dd7
URL:http://10thworlds4.sched.com/event/54facaca777ca61bf34e611583617dd7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Proof of the volume conjecture for twist knots
DESCRIPTION:Authors - Sukuse Abe Abstract - According to Einstein’s theory of relativity\, the space we inhabit is distorted. Grigori Perelman solved the geometrization conjecture\, which states that this space can be classified into eight spaces. Of these eight types\, the classification of hyperbolic manifolds remains unresolved. Solving the volume conjecture would greatly advance the classification of hyperbolic manifolds. While the volume conjecture is one of the open problems in knot theory for knots in general\, we successfully prove it for the family of oriented twist knots. The proof use the method of steepest descent and the theory of functions of several complex variables on the basis of colored Jones polynomials.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:02ced04a397c7967b93cabae2780eda6
URL:http://10thworlds4.sched.com/event/02ced04a397c7967b93cabae2780eda6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:The Role of Leadership in Mobile Technology Acceptance and Integration in Higher Education Institutions
DESCRIPTION:Authors - O. Aina\, C.J. Van Staden\, P. Makgato-Khunou Abstract - The role of leadership in the acceptance and integration of mobile technology for teaching and learning at a Private Higher Education Institution (PHEI) in South Africa have been explored. Mobile technologies have become widely used\, but their application in higher education has been sporadic. The attitude towards mobile technology for teaching and learning are conditioned by the institutional environment which is in turn influenced by the leadership’s adoption. The study applied unified Theory of Acceptance and Use of Technology (UTAUT)\, Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theories. A quantitative case study design was used with academic and institutional leaders. The study established a significant positive relationship between the construct of leadership perception and the acceptance of mobile technology for teaching and learning. The study concludes that leadership influences perceptions about acceptance of mobile technology for teaching and that enablers for the integration depend on the presence of appropriate communication channels and enabling conditions.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:dece522b186e9d1588c092686b7b687c
URL:http://10thworlds4.sched.com/event/dece522b186e9d1588c092686b7b687c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:A Comparative Evaluation of LLM-Based Discourse Analysis of War-Related Media Language
DESCRIPTION:Authors - Dzhansel Abtula\, Stanka Hadzhikoleva\, Emil Hadzhikolev\, Iliana Ivanova\, Elitsa Dubarova Abstract - The present study examines the potential of generative language models as tools for discourse analysis of war-related language in media texts. The study is based on a corpus of texts processed by three generative AI models using an identical prompt that defines a multi-stage analytical procedure. This procedure includes the extraction of war-related lexical units\, semantic classification\, functional analysis of evaluative and ideological features\, compilation of a thematic glossary\, analysis of metaphors\, identification of discursive strategies\, and genre determination of the texts. The generated analyses are evaluated through a structured questionnaire based on a five-point Likert scale\, completed by an expert with an academic background. The study aims to systematically assess and compare the quality of discourse analyses produced by different language models under controlled conditions. The results indicate that all models generate structurally coherent and terminologically consistent analyses\, but differ significantly in interpretative depth and contextual sensitivity. These findings support the need for a hybrid approach that combines automated analysis with human expertise to ensure accurate interpretation of implicit meanings\, ideological nuances\, and context-dependent discourse features.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:9d653569729862210dd7d5e9f4511b10
URL:http://10thworlds4.sched.com/event/9d653569729862210dd7d5e9f4511b10
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Enhanced Audio Deepfake Detection Using Light Convolutional Neural Networks with Margin-Based Loss Functions
DESCRIPTION:Authors - Makomborero Murwira\, Dane Brown Abstract - The proliferation of realistic synthetic speech poses significant threats to information integrity and public safety through financial fraud and misinformation campaigns. While traditional countermeasures based on Gaussian Mixture Models have proven effective against earlygeneration deepfakes\, they struggle to generalise to sophisticated attacks produced by contemporary neural synthesis. This paper investigates enhanced audio deepfake detection through Light Convolutional Neural Networks (LCNNs) combined with Linear Frequency Cepstral Coefficients (LFCCs) and advanced training strategies. This study systematically evaluates the impact of margin-based loss functions (Cosface and A-Softmax) and FreqAugment data augmentation on model robustness and generalisation capability. Validated on the ASVspoof 2019 Logical Access dataset\, the optimised LCNN model achieves an Equal Error Rate (EER) of 5.40% on the evaluation set containing thirteen unseen attack types\, representing a 33% relative improvement over the baseline LFCCGMM countermeasure (8.09% EER). The combination of Cosface loss with FreqAugment demonstrates superior performance compared to ASoftmax configurations\, reducing false negatives substantially. Per-attack analysis reveals robust performance across diverse spoofing techniques\, though vulnerabilities to advanced neural waveform manipulation methods remain. The proposed framework provides a practical\, deployable solution for audio deepfake detection in real-world security-critical applications.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:c4b1651e42118c992aef7df7d90df473
URL:http://10thworlds4.sched.com/event/c4b1651e42118c992aef7df7d90df473
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:SUSTAINABLE PROCUREMENT IN ASPHALT MIXING PLANT OPERATIONS: ENVIRONMENTAL INNOVATION THROUGH GAS-FIRED SYSTEMS\, NOISE REDUCTION\, AND STONE DUST VALORIZATION
DESCRIPTION:Authors - Ida Bagus Dwipayana \, Naniek Utami Handayani\, Singgih Saptadi Abstract - The global construction sector is under increasing pressure to shift towards sustainable operational models\, particularly in heavy materials processing like asphalt production. This study provides empirical\, case-studybased insights regarding sustainable procurement practices at the Asphalt Mixing Plant (AMP)\, run by PT. Jasamarga Tollroad Maintenance in Karawang\, West Java\, Indonesia. Utilizing six semesters of the Environmental Management Monitoring Effort (UKL-UPL) data from Semester 1 (S1) 2021 to S1 2025\, this research assesses the environmental performance of three integrated innovations: (1) a gas-fired burner system substituting for fuel oil\, (2) multi-layered noise reduction technologies\, and (3) collection and water vapor-based treatment of stone dust to optimize novelty soil fertility systems. Stack emissions analysis found significantly lower levels of CO at 184 mg/m3 and NOx at 212 mg/m3 than Indonesian regulation enables KEP-13/MENLH/3/1995\, while ambient air was consistently compliant during monitoring over nine periods. Outdoor sound levels in the upwind and downwind locations were 65.6 dB (A) and 56.8 dB (A)\, respectively\, both below the exposure limit of 70dB(A) specified by SK Men LH KEP-48/MenLH/11/1996\, Sensitivity analysis between four fuel-technology scenarios shows a combination approach achieving a 61.9% CO emission index reduction\, 55.4% NOX index reduction\, and SO2 index is reduced by 78%\, relative to diesel fuel basis. This study finds a significant research gap with respect to operationalizable sustainable procurement criteria combining air quality\, noise\, and waste valorization in an integrated framework at the AMP operational level. The results offer theoretically underpinned and empirically verified procurement criteria\, which are directly applicable to the international construction industry\, thus advancing green supply chain management\, principles of circular economy as well as pathways towards net-zero construction.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:5b91d2fe67dafe75bb75a134906836d8
URL:http://10thworlds4.sched.com/event/5b91d2fe67dafe75bb75a134906836d8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:The Academic Visibility Pipeline Model: Explaining Scholarly Visibility Beyond Research Production
DESCRIPTION:Authors - Cossi Blaise Avoussoukpo\, Amara Camara\, Babou Dione Abstract - Digital scholarly infrastructures increasingly shape academic visibility by supporting the creation\, organisation\, dissemination\, and discovery of research outputs. Existing studies have advanced important dimensions of this domain\, including bibliometric evaluation\, persistent identiers\, metadata standards\, and indexing systems. However\, they primarily examine these components independently and provide only a limited understanding of how their interactions inuence visibility in contemporary research ecosystems. This paper introduces the Academic Visibility Pipeline Model (AVPM). This conceptual framework conceptualises academic visibility as a sequence of interconnected stages linking research production\, metadata structuring\, scholarly indexing\, identity and aliation aggregation\, dissemination\, and discovery. By integrating socio-technical coordination\, interoperability\, and cascading failure\, the AVPM explains how visibility emerges and how upstream constraints inuence downstream outcomes. An illustrative application to the Guinea academic visibility ecosystem demonstrates the explanatory value of the framework by identifying structural bottlenecks and tracing their eects across the visibility pipeline. The proposed framework extends conventional publication- and citation-centred perspectives and provides a process-oriented foundation for institutional strategy\, research governance\, and digital transformation in emerging research ecosystems.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:00416eb243f279ac270c2e76f8e40237
URL:http://10thworlds4.sched.com/event/00416eb243f279ac270c2e76f8e40237
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:Vietnamese-Aware Prompt Optimization for Large Language Model-Based Text Summarization
DESCRIPTION:Authors - Duy Nguyen Ngoc\, Hiep Nghia Phan Abstract - Large Language Models (LLMs) have recently shown strong capabilities in automatic text summarization. However\, applying these models to lowresource languages such as Vietnamese remains challenging due to limited training resources and language-specific characteristics. In this work\, we ex-amine whether prompt optimization can improve Vietnamese summarization quality without modifying model parameters. This paper introduces a Vietnamese-aware Prompt Optimization Framework that refines prompt instructions by combining task-specific guidance\, role-based prompting\, linguistic con-straints\, and iterative feedback. The generated summaries are assessed using both automatic evaluation metrics and human judgments to examine the ef-fectiveness of different prompt designs. We evaluate the proposed approach on a benchmark Vietnamese multi-aspect opinion dataset using several commercial and open-source LLMs\, including GPT-4\, Claude 3\, Gemini 1.5\, and PhoGPT\, and compare their performance with Vietnamese pre-trained summarization models such as ViT5 and BARTpho. Our experiments show that refining prompts consistently improves summary quality across the evaluated models. In particular\, GPT-4 with the optimized prompt achieves an 8.7% increase in ROUGE-L and receives higher human evaluation scores for fluency and factual consistency than the standard prompting setting.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:200b250b88a431070eb83c46f2998c82
URL:http://10thworlds4.sched.com/event/200b250b88a431070eb83c46f2998c82
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T080000Z
DTEND:20260730T093000Z
SUMMARY:WikiCrop-AI: An Intelligent Data Processing and Machine Learning Engine for Open Agricultural Knowledge Platforms
DESCRIPTION:Authors - Tien-Dao Luu\, Viet Truong Xuan\, Doan Hoang Phuc Nguyen\, Nghi Huynh Quang\, Do Chau Giang Nguyen\, Nghia Nguyen Khoi\, Huu Hoa Nguyen Abstract - The Mekong Delta agroecosystem faces compounding climate stressors and acute data fragmentation. While agricultural data exists online\, it remains largely unstructured and unverified. This study introduces WikiCrop-AI\, an integrated data-processing and machinelearning framework designed to consolidate heterogeneous agronomic information. The architecture comprises three interconnected modules: an Agricultural Notebook utilizing a Retrieval-Augmented Generation pipeline for multi-format data ingestion\, a browser-based computational environment (WikiLab) for reproducible workflows\, and a client-side analytics module for multivariate clustering. Evaluation of the ingestion pipeline yielded a String Similarity Score of 0.99 for structured text extraction. Furthermore\, generative outputs assessed via the Automatic LLMs Citation Evaluation framework achieved average scores of 0.94 for both Citation Recall and Precision\, alongside a Claim Recall of 0.92\, indicating reliable knowledge synthesis under the tested conditions. A hierarchical clustering case study on 22 soybean cultivars further illustrates the platform’s utility in supporting non-programming local agronomists. Ultimately\, WikiCrop-AI provides a decentralized infrastructure to translate scattered digital resources into actionable\, verified agricultural intelligence. The complete open-source code for the WikiCrop-AI ecosystem is publicly accessible on GitHub at: https://github.com/mekonglab-vn/ WikicropAI.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:14b45e0a1af67f2b4c9edb33a70e006b
URL:http://10thworlds4.sched.com/event/14b45e0a1af67f2b4c9edb33a70e006b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093000Z
DTEND:20260730T093200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:50cfedf01ed9a652f10b4352b22b9cd3
URL:http://10thworlds4.sched.com/event/50cfedf01ed9a652f10b4352b22b9cd3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093000Z
DTEND:20260730T093200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:24b52339b42d5b20860a8f15ae80f21f
URL:http://10thworlds4.sched.com/event/24b52339b42d5b20860a8f15ae80f21f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093000Z
DTEND:20260730T093200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:889b173ef18505c82758c22b4c0d5a91
URL:http://10thworlds4.sched.com/event/889b173ef18505c82758c22b4c0d5a91
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093000Z
DTEND:20260730T093200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:4008b7cd8bc5ffe7e8c467395c6bdef3
URL:http://10thworlds4.sched.com/event/4008b7cd8bc5ffe7e8c467395c6bdef3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093000Z
DTEND:20260730T093200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:d2895a589ea48cdb8f28a4ed2854dec5
URL:http://10thworlds4.sched.com/event/d2895a589ea48cdb8f28a4ed2854dec5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093200Z
DTEND:20260730T093500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:e0633003d675ab10e75c30ff66d8cfcc
URL:http://10thworlds4.sched.com/event/e0633003d675ab10e75c30ff66d8cfcc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093200Z
DTEND:20260730T093500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:a183f901db12220b5432d74b0f2748da
URL:http://10thworlds4.sched.com/event/a183f901db12220b5432d74b0f2748da
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093200Z
DTEND:20260730T093500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:27e9fcf998af12c64b48270ee3921fe8
URL:http://10thworlds4.sched.com/event/27e9fcf998af12c64b48270ee3921fe8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093200Z
DTEND:20260730T093500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:edc7bb39d17c1ec0b1ca3988e1f3fd21
URL:http://10thworlds4.sched.com/event/edc7bb39d17c1ec0b1ca3988e1f3fd21
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T093200Z
DTEND:20260730T093500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:dd4ffdce2657469d6fd50df91b083f9c
URL:http://10thworlds4.sched.com/event/dd4ffdce2657469d6fd50df91b083f9c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T102800Z
DTEND:20260730T103000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:896111a8c1e8a5366f315db1f302b8b4
URL:http://10thworlds4.sched.com/event/896111a8c1e8a5366f315db1f302b8b4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T102800Z
DTEND:20260730T103000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:30797421087c8aba986f96ba52bb89f8
URL:http://10thworlds4.sched.com/event/30797421087c8aba986f96ba52bb89f8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T102800Z
DTEND:20260730T103000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:98847ab64e7ac345f70513d118c5b958
URL:http://10thworlds4.sched.com/event/98847ab64e7ac345f70513d118c5b958
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T102800Z
DTEND:20260730T103000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:6c0dfe79f900599ea504450914fd4757
URL:http://10thworlds4.sched.com/event/6c0dfe79f900599ea504450914fd4757
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T102800Z
DTEND:20260730T103000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:d835a6be07b26adc92142c4a115f240a
URL:http://10thworlds4.sched.com/event/d835a6be07b26adc92142c4a115f240a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Determinants of Workload Suitability Across GPU\, FPGA\, and ASIC
DESCRIPTION:Authors - Ioannis Patias\, Koutaro Hachiya Abstract - The rapid growth of compute-intensive applications has intensified the need for selecting appropriate hardware accelerators. This paper presents a workload-to-architecture framework that explains when GPUs\, FPGAs\, or ASICs are most suitable\, based on four determinants: parallelism granularity\, memory behavior\, dataflow regularity\, and specialization depth. We organize representative workload classes—dense linear algebra\, sparse/irregular algorithms\, streaming signal processing\, bit-level control workloads\, and deep learning (training/inference)—and discuss how each class aligns with the execution and memory models of the three accelerator families. Our analysis highlights that GPUs excel in throughput-oriented\, data-regular workloads\; FPGAs provide deterministic latency via spatial pipelining and customized data paths\; and ASICs achieve the best performance-per-watt for stable\, high-volume tasks. The resulting framework provides practical guidance for accelerator selection and motivates heterogeneous system design.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:c2f845ffd5ca70f829cdff48adc5bc15
URL:http://10thworlds4.sched.com/event/c2f845ffd5ca70f829cdff48adc5bc15
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:ExplainoGraph: Explainable Knowledge Graph Embeddings via Square Loss Optimization for Recommender Systems
DESCRIPTION:Authors - Ronky Amber-Doh\, Benjamin Ghansah\, Winfred Larkotey\, Stephen Opoku Oppong\, Ezekiel Okoe\, Olivia Osei-Tutu\, Emmanuel Prah\, Ephrem Kwaa-Aidoo Abstract - This paper introduces a novel framework\, ExplainoGraph\, that integrates square loss optimization with explainable artificial intelligence methods for knowledge graph–based recommender systems. Prior studies show that knowledge graph embeddings significantly enhance recommendation accuracy\; however\, they suffer from limited interpretability\, thereby constraining user trust and system transparency. To address this gap\, we introduce ExplainoGraph\, which embeds explainability directly into the recommendation process through interpretable scoring functions and feature attribution procedures that provide meaningful insights into model decisions. Again\, the framework incorporates ripple set propagation to effectively model user preferences\, particularly in sparse data environments where traditional methods are suboptimal. Extensive experiments conducted on multiple benchmark datasets demonstrate that Explaino-Graph consistently outperforms the state-of-the-art baselines used across key evaluation metrics\, including Precision@K\, Recall@K\, F1-score\, and normalized discounted cumulative gain (NDCG)
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:217d2351b8a04e4d204211f9c00c5dbc
URL:http://10thworlds4.sched.com/event/217d2351b8a04e4d204211f9c00c5dbc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Food Supply Modeling and Forecasting Using Multiple Methods: MLR\, Holt–Winters\, and Artificial Neural Networks
DESCRIPTION:Authors - Sayyora Qulmatova Abstract - This study uses machine learning models such as Multi-Linear Regression (MLR) and Holt-Winters Exponential Smoothing to model and forecast agricultural production indicators in Uzbekistan. The dataset consists of key agricultural indicators such as gross agricultural output\, milk production\, egg production\, honey production\, vegetables\, fruits\, and livestock products (in live weight). To improve model performance and ensure comparability\, various data preprocessing methods such as StandardScaler\, MinMaxScaler\, RobustScaler\, and Normalizer were used. The forecasting accuracy of each model was evaluated using standard error metrics such as mean absolute error (MAE)\, mean square error (MSE)\, root mean square error (RMSE)\, and mean absolute percent- age error (MAPE). Empirical results show that the MLR model provides stable and interpretable forecasts\, especially when combined with appropriate scaling methods. The Holt-Winters model exhibits strong performance for time series with consistent trends\, but shows limitations when applied to variable data. The MLP model effectively captures nonlinear relationships and complex time patterns\, although its performance is sensitive to data preprocessing\, with MinMaxScaler generally yielding superior results. Overall\, the results show that no model is universally optimal\; instead\, the choice of forecasting technique should be based on the characteristics of the data. The proposed modeling framework contributes to increasing the accuracy and reliability of agricultural forecasts and can support evidence-based policy planning and decision-making in the agricultural sector of Uzbekistan.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:61c9e1b9467abfa1a94016439ae43448
URL:http://10thworlds4.sched.com/event/61c9e1b9467abfa1a94016439ae43448
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Predicting Cortical ECoG Responses from Self-Supervised Speech Representations
DESCRIPTION:Authors - Salma Chlaikhy\, Adil Chakhtouna\, Abdellah Adib Abstract - We propose a neural encoding framework that predicts continuous ECoG signals from self-supervised Data2Vec speech representations using multivariate Ridge regression. Evaluated on 18 auditory cortical electrodes from nine participants\, the model achieves a mean Pearson correlation of r ≈ 0.39 under pooled cross-validation and r = 0.29±0.018 under leave-one-subject-out (LOSO) evaluation\, reaching approximately 57% of the noise ceiling. Results confirm that self-supervised speech representations capture stimulus-driven cortical dynamics\, highlighting their promise for neural signal modeling and brain–computer interface research.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:c7fedface96b79ddd3199c208c175ddb
URL:http://10thworlds4.sched.com/event/c7fedface96b79ddd3199c208c175ddb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Private Sector-Driven Adult Education: A Model for Rapid and High-Quality Reskilling and Upskilling
DESCRIPTION:Authors - Raita Rollande Abstract - This paper addresses the research question: How can an effectively organized and managed adult education reskilling and upskilling process help bridge the skills gap in today’s labor market? In an era of rapid technological development and changing business needs\, the demand for continuous workforce reskilling and up-skilling has become increasingly critical. This study examines a private sector-driven adult education model that prioritizes speed\, quality\, and flexibility in response to industry demands. TestDevLab is a fast-growing company specializing in software quality assurance. Due to the lack of industry-specific specialists from traditional higher education institutions\, TestDevLab has developed an in-house training solution where experienced engineers train new specialists while also upskilling existing employees. To address these challenges systematically\, the company established TDL School. This dedicated training institution has designed the Model for Rapid and High-Quality Reskilling and Up-skilling tailored to mid-sized businesses. This article presents the TDL School model\, exploring innovative training approaches\, curriculum development\, and company collaboration to highlight best practices for effective workforce development. The findings demonstrate that a well-structured\, professionally organized learning process enhances employability\, strengthens industry competitive-ness\, and fosters lifelong learning. By sharing this model\, the study aims to pro-vide a scalable framework that other companies of similar size can adopt to ad-dress their workforce challenges. This research contributes to the ongoing discussion on adaptive education models and their role in bridging the skills gap in today’s labor market.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:86afc6bbddcbb4f260db1d4b82baffab
URL:http://10thworlds4.sched.com/event/86afc6bbddcbb4f260db1d4b82baffab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Twin Lightweight EcD-Net: A Memory-Efficient Cascaded 3D Network for Pancreas Segmentation in CT and MRI
DESCRIPTION:Authors - Isaac Baffour Senkyire\, Benjamin Ghansah\, Emmanuel Freeman Abstract - With the rapid development of deep learning\, CNN-based medical im-age segmentation algorithms have been successful. However\, study on the pancreas in 3D CT and MRI images is limited due to the excess use of computer memory and the complexity of the pancreas. In this paper\, we present a memory-efficient cascaded 3D network for pancreas segmentation in CT and MRI. We develop a novel Lightweight 3D Bond (L3D-Bond) Layer to reduce filter size\, and maintain performance while lowering memory usage\, and a novel Light-weight 3D Asymmetric Corollary Atrous Spatial Pyramid Pooling Module (L3D-aCASPP) that captures multi-scale 3D context with lower computational cost. Our experiments were done using the public NIH pancreas segmentation dataset\, MRI pancreas segmentation dataset\, and MSD spleen segmentation dataset achieving a competitive segmentation performance of 80.12 DSC on the NIH dataset with parameters less than 0.5 million.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:2cbe91f3465d3f157ddda73ba8f15e18
URL:http://10thworlds4.sched.com/event/2cbe91f3465d3f157ddda73ba8f15e18
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Applying System Dynamics to Address Inadequate Resources Issues in Strategic Management
DESCRIPTION:Authors - Khumbelo Difference Muthavhine\, Mbuyu Sumbwanyambe Abstract - Inadequate Resource Issues (IRI) are one of the challenges in Strategic Management (SM). This study concentrated on applying System Dynamics (SD) modeling to solve IRI. Strategic standard tools like SWOT analysis\, PESTEL analysis\, and the Resource-Based View have proven effective in addressing IRI\; unfortunately\, developments like digital transformation\, long-term sustainability\, and the rise of emerging market multinational corporations are poised to shape the future of SM in these regions. These traditional methods are no longer coping with new technology\; hence\, the authors implemented a new SD model to tackle the IRI in SM. Additionally\, most strategic managers are incapable of developing an SD model due to mathematical and scientific complexity. Although SD is a reliable technique for handling complex issues in management\, most managers reject SD because of the implementation’s need for scientific and mathematical requirements. To solve IRI mathematically and make scientific predictions about what would happen if variables were altered in the upcoming five years (2025–2035) and the impact on customers\, the study created an SD model.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:1f36ff2ce9056509413bca9abda6bbdf
URL:http://10thworlds4.sched.com/event/1f36ff2ce9056509413bca9abda6bbdf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:CHALLENGES FACED BY SECONDARY SCHOOL TEACHERS IN FACILITATING ONLINE TEACHING DURING THE COVID-19 PANDEMIC: A CASE STUDY IN SRI LANKA
DESCRIPTION:Authors - Arosha de Silva Abstract - The COVID-19 pandemic disrupted educational systems worldwide and required schools to adopt online learning within a short period. In Sri Lanka\, secondary school teachers encountered numerous difficulties while adapting to virtual teaching environments. This study examines the challenges experienced by teachers when conducting online instruction during the pandemic. A mixed-methods approach was adopted\, combining qualitative interviews with quantitative survey data collected from secondary school teachers and educational professionals. The findings revealed that teacher motivation\, technological infrastructure\, and increased workload significantly influenced the effectiveness of online teaching. Difficulties related to internet access\, digital resources\, and professional demands affected teachers’ ability to deliver lessons efficiently. The study highlights the importance of institutional support\, professional training\, and improved access to technology in strengthening online education. The findings may assist policymakers and educational institutions in developing effective strategies to support online and blended learning initiatives in the future.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:80ebffd8f2695aade7e416e7fc235e75
URL:http://10thworlds4.sched.com/event/80ebffd8f2695aade7e416e7fc235e75
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:DroidFusion: A Hybrid CNN–GNN Method for Static Android Malware Detection
DESCRIPTION:Authors - Bharg Trivedi\, Chaitaili Chandankhede Abstract - There is a continuous change in Android malware because it is obfuscated\, polymorphic\, and structural. Such changing methods diminish the performance of conventional signature-based detection methods. In an effort to defeat this challenge\, the present paper provides a model that uses CNN and GNN models. It is an integration of spatial byteplot representations and structural call graph representations to successfully identify Android malware. Our study was based on a dataset of 1\,159 real Android applications\, and the used extraction technique was based on the static features. The CNN element of the structure Recognized robust spatial attributes of the grayscale images of the byteplot data with a ResNet-50 network. Meanwhile\, the GNN component of the structure used a GraphSAGE network to derive structural representations of automatically generated function call graphs. The fused representations are combined into a 2304 dimensional feature vector. It is also optimized by making use of different methods such as Mutual Information. In this study\, an Extreme Gradient Boosting Classifier on the fused representations to achieve successful Android malware detection. The assessment indicates that the framework attains a classification accuracy of more than 99% with businesses across the cross-validation holding the same accuracy.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:a83a42cc3c60ab31b6c019e539659f8e
URL:http://10thworlds4.sched.com/event/a83a42cc3c60ab31b6c019e539659f8e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Employing System Dynamics to Solve Knowledge Management Issues for Non-Scientist Manager
DESCRIPTION:Authors - Khumbelo Difference Muthavhine\, Mbuyu Sumbwanyambe Abstract - Knowledge management (KM) is an essential company training process that incorporates four sequential factors: non-knowledgeable professionals\, training to become knowledgeable professionals\, new knowledgeable professionals\, and knowledgeable and experienced professionals. Because the aforementioned variables are interconnected and make it extremely difficult to produce a measured solution\, they must be thoroughly analyzed using mathematical formulas and reliable techniques. These issues impact businesses of all sizes\, necessitating a versatile instrument for flexible KM analysis. Additionally\, most KM managers dislike SD modeling due to its complexity\, especially those without scientific training. This study recommended using system dynamic (SD) modeling rather than conventional tools to address the aforementioned issues. The use of SD modeling stems from three factors: (a) the examination of complicated dependencies\; (b) the requirement for mathematical formulas\; and (c) the graphical results in contrast to traditional methods. The study’s SD model included the four sequential factors and their relationships. KM managers should focus especially on the graph’s data when necessary modifications are needed.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:08d348cf9a1940ba9e6b9ab67696b7d5
URL:http://10thworlds4.sched.com/event/08d348cf9a1940ba9e6b9ab67696b7d5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:LUNG CANCER STAGES DETECTION USING MACHINE LEARNING (CNN)
DESCRIPTION:Authors - A Aruna kumari\, Tamminana Visweswari Abstract - Lung cancer is a fatal illness that causes several deaths worldwide and detection of lung cancer remains a challenge for medical professionals. Detection of cancer in early stages is difficult as the size of the tumor is very small making it difficult for medical professionals to detect. Cancer detected in the early stages can be treated with proper techniques which can save the lives of the patients. Due to excessive information in the CT scans\, MRIs\, X-rays\, and PET scans the manual detection of lung tumor becomes extremely difficult. The methodology helps in detecting the presence of cancerous tissues in the lungs and predicting which stage of lung cancer is present. The methodology mainly includes image preprocessing\, training the model\, extracting features using deep learning algorithms and classifying the stage of cancer present as Normal\, Benign\, Malignant Stage 1\, Malignant Stage 2\, and Malignant Stage 3. Proper image processing techniques like image augmentation\, image normalization and image resizing are applied on the IQ-OTH/NCCD dataset for extracting the necessary features which will be used while training the model. A hybrid model is created by combining two deep learning models\, the Xception and MobileNetV2 architectures which can accurately distinguish between the different lung cancer stages and predict the stage of cancer. The performance metrices which include accuracy\, precision\, recall\, f1-score and confusion matrix were also calculated to determine the accuracy of the proposed hybrid model. The proposed model helps in accurate and reliable diagnosis of lung cancer at early stages.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:1f3b1819edef4d9c35f834280d2121c3
URL:http://10thworlds4.sched.com/event/1f3b1819edef4d9c35f834280d2121c3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Predictive Models Based on Deep Neural Networks for Estimating the Energy Potential of Mechanical Vibrations in Industrial Environments
DESCRIPTION:Authors - Erika Haydee Rubio-Camara\, Oscar May Tzuc\, Elsy Maria Rosales-Uc\, Fran-cisco Gilberto Herrera-Chale\, Roman A. Canul-Turriza\, M. Jimenez Torres Abstract - Mechanical vibration energy harvesting has emerged as a promising strategy for supporting sustainable energy generation in industrial environments\, where machinery and transportation systems continuously produce recoverable vibrational energy. This study presents the development and evaluation of predictive models based on deep multilayer perceptrons (DMLP) and Convolutional Neural Networks (CNNs) for estimating the energy potential associated with mechanical vibrations under industrial operating conditions. A simulation frame-work was implemented using experimentally reported operational ranges\, including vibration frequencies between 10 and 50 Hz\, amplitudes from 0.01 to 0.03 m\, and temperatures between 25 and 45 °C. The analysis considered piezoelectric\, electromagnetic\, and triboelectric harvesting mechanisms to evaluate model adaptability under different scenarios. The predictive framework was implemented using TensorFlow and validated through a 10-fold cross-validation strategy combined with hyperparameter optimization. Results indicate that both architectures achieve high predictive capability for estimating harvested energy\; however\, CNN models consistently outperformed Deep MLP models\, obtaining lower prediction errors and higher stability across validation folds. The superior performance of CNNs is associated with their ability to capture localized patterns and structured relationships within vibration-related data. The proposed method-ology demonstrates the feasibility of integrating artificial intelligence techniques into vibration-based energy harvesting systems for industrial applications. Furthermore\, the study provides a computational framework for evaluating operational conditions\, optimizing harvesting performance\, and supporting the design of sustainable self-powered monitoring systems.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:1944811ed89bdc7541b8eef3895fe5b8
URL:http://10thworlds4.sched.com/event/1944811ed89bdc7541b8eef3895fe5b8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:A method for adaptive control of a smart enterprise with weak signal detection based on multimodal data
DESCRIPTION:Authors - Mariia Nazarkevych\, Vasyl Lytvyn\, Oleg Stechkevych\, Hanna Nazarkevych\, Roman Cholkan\, Danyil Korotych Abstract - An information technology for adaptive enterprise management using weak signals has been developed\, which is based on the collected information about the environment\, the assessment of factors affecting the enterprise\, the calculation of the indicator of the impact on the enterprise based on integral dependence\, the method of detecting weak signals and predicting the state of the enterprise\, which provides high sensitivity taking into account changes in the environment and increases the efficiency of enterprise management. A method of recognizing weak signals is shown\, which\, by comparing the permissible value with the difference between the found and predicted values of the indicator of the impact on the smart enterprise based on integral dependence\, provides early detection of threats or opportunities for the smart enterprise. It is proposed to develop a smart enterprise management system using weak signals based on an integrated approach and in accordance with the following principles: systematicity\; integration of computer\, communication and software components\; modularity\; openness\; compatibility\; variable equipment composition.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:13e1ce4020627186e8e63984d92fa1c3
URL:http://10thworlds4.sched.com/event/13e1ce4020627186e8e63984d92fa1c3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:An Integrated Kubernetes Security Framework with Context-Driven Policy Orchestration and ICAP-Based Content Inspection
DESCRIPTION:Authors - Marlon Kulatunga\, Kaavya Raigambandarage\, Senali Guruge\, Themiya Alwis\, Amila Nuwan Senarathne\, Kavinga Yapa Abeywardena Abstract - Contemporary Kubernetes deployments suffer from two fundamental shortcomings: admission control mechanisms apply static rule sets without accounting for namespace operational context\, and content inspection services governed by RFC 3507 remain disconnected from the orchestration layer. This work presents an integrated four-module security framework that jointly addresses both deficiencies. A probabilistic namespace characterisation algorithm employing seven weighted indicators achieves 96.7% accuracy in determining deployment tiers\, even when metadata labels are absent or deliberately misleading. A compliance-driven policy orchestrator aligned with CIS Kubernetes Benchmark controls and PCI-DSS v4.0 requirements translates a unified constraint representation into artefacts for both OPA Gatekeeper and Kyverno\, attaining 99.2% cross-engine decision parity. An environment-responsive traffic manager generates tier-specific Istio routing configurations\, while a custom Kubernetes operator governs content scanning pod lifecycles through a multi-dimensional wellness metric that captures security-relevant signals invisible to conventional autoscalers. Evaluation on a five-node K3s cluster demonstrates full compliance coverage across 93 benchmark controls and 28 regulatory mandates\, sub-five-second failover under all disruption scenarios\, and correct detection of degraded scanning capability that CPU and memory metrics alone would overlook.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:f00b3dfb414a6298ba1e75303afa68b5
URL:http://10thworlds4.sched.com/event/f00b3dfb414a6298ba1e75303afa68b5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Citizen Participation in e-Government: Evaluating the Impact of Online Engagement Platforms and Their Effectiveness in Fostering Democratic Governance in South Africa.
DESCRIPTION:Authors - Ronewa Gilbert NTHATHENI\, Tumiso THULARE Abstract - The rapid advancement of digital technologies has transformed the relationship between governments and citizens\, creating new opportunities for participatory governance through e-government initiatives. This study evaluates the effectiveness of online engagement platforms in promoting democratic governance in South Africa. Using a scoping review methodology\, the research examines the benefits\, challenges\, and contextual dynamics shaping citizen participation through digital platforms. Findings suggest that while online engagement tools enhance transparency\, accountability\, and access to information\, their effectiveness is constrained by structural barriers such as the digital divide\, limited institutional capacity\, and low digital literacy. The study concludes that the success of e-participation initiatives depends on inclusive design\, infrastructure investment\, and meaningful government responsiveness. Recommendations are provided to strengthen digital governance and improve citizen engagement outcomes.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:774d9bfa1d2cc16d890503c6964fc7a6
URL:http://10thworlds4.sched.com/event/774d9bfa1d2cc16d890503c6964fc7a6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Image Processing and Deep Learning for Potato Leaf Disease Detection
DESCRIPTION:Authors - Supriya Narad Abstract - Agricultural economies are predominantly relevant in developing countries\, wherein the farmers have to struggle operating under the impact of several constraints posed by crop diseases. Among food crops\, the potato is a major one with vulnerable destructive diseases like Early Blight and Late Blight\, capable of destroying the yield if detected late. Old methods of visual inspection are time-consuming and sometimes erroneous because of laxity\, human error\, and lack of expertise. With this research\, an automated intelligent disease detection system is devised\, making use of image processing and deep learning\, Arduino\, specifically Convolutional Neural Networks (CNNs). The model was trained using potato leaf images from the Plant Village dataset\, which are improved using various preprocessing techniques\, including color space conversion\, image augmentation\, and image resizing. The proposed CNN architecture achieved a high rate of classification accuracy of 97.2% in distinguishing healthy leaves vs. Early blight and Late blight infected leaves. Lightweight\, reliable\, and fast\, it supports implementation on mobile or handheld devices in low-resource environments\, thus giving farmers the ability to use them for timely diagnostics. The system has good prospects for scaling up for other crops and disease types in future versions.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:c47879fb0fb84f23b9317f3be51439b7
URL:http://10thworlds4.sched.com/event/c47879fb0fb84f23b9317f3be51439b7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Multimodal Artificial Intelligence for Cardiovascular Risk Stratification and Diagnosis in Athletes: A Systematic Review
DESCRIPTION:Authors - Khadidje OUSMANE KOSSI\, Mandicou BA\, Bachar Haggar SALIM\, Simon Antoine SARR\, Maboury DIAO\, Alassane BAH Abstract - Heart disease in athletes remains a significant challenge in sports cardiology and an important public health concern\, particularly among young competitive individuals at risk of sudden cardiac events. Although pre-participation screening programs are widely implemented\, diagnostic uncertainty persists\, especially in distinguishing physiological cardiac remodeling from pathological cardiomyopathy. This complexity results from the interaction of genetic predisposition\, structural adaptation\, electrophysiological variability\, and cumulative training exposure. Using the PRISMA framework\, this study presents a systematic review of research published between 2015 and 2025 to evaluate the application of artificial intelligence (AI) in the diagnosis and monitoring of cardiovascular diseases in athletes. The analysis reveals that most studies rely on unimodal\, monocentric\, and retrospective designs\, often based on limited datasets and lacking external validation. Despite high reported performance metrics\, performance degradation of 5–10% in external cohorts is frequently observed. Furthermore\, explainability techniques are inconsistently applied\, and real-world clinical integration remains limited. Only a small number of studies adopt multimodal approaches integrating electrophysiological\, imaging\, biological\, and training-related data. These limitations restrict the clinical translation of AI models. Future research should prioritize multicenter\, diverse\, and explainable multimodal frameworks to support reliable cardiovascular risk stratification and return-to-play decision making.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:ea3aed9640ee5566bacfafcad7faf3a3
URL:http://10thworlds4.sched.com/event/ea3aed9640ee5566bacfafcad7faf3a3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Wearable Sensor Technologies for Ergonomic Risk Monitoring Among Construction Workers: A Structured Narrative Review of Implementation\, Accuracy\, and Occupational Health Outcomes
DESCRIPTION:Authors - Oluwaranti A. Omowami Abstract - Work-related musculoskeletal disorders (WMSDs) are among the most prevalent occupational injuries in construction\, driven by heavy lifting\, awkward postures\, repetitive motion\, and whole-body vibration. Traditional ergonomic risk assessment methods are retrospective and unable to capture the dynamic conditions of construction sites. Wearable sensor technologies offer a real-time\, objective alternative. This structured narrative review examines the implementation\, accuracy\, and occupational health outcomes of wearable sensor systems applied to ergonomic risk monitoring among construction workers. A structured review of peer-reviewed literature from 2017 to 2024 identified six sensor categories: inertial measurement units (IMUs)\, wearable insole pressure systems\, surface electromyography (sEMG)\, electrodermal activity (EDA) sensors\, heart rate monitors\, and smartphone embedded sensors. Reported posture classification accuracy reached up to 99.01% under controlled conditions using deep learning classifiers. Key implementation barriers include sensor discomfort\, motion artifacts\, worker acceptance\, data privacy and cybersecurity concerns\, and the multi-employer structure of construction. A consistent gap exists between laboratory validation accuracy and real-world field performance. Occupational health outcome studies remain limited. Future priorities include longitudinal field validation and integration with behavior-based safety frameworks.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:1b4e89aec4a77654e7a5609296233619
URL:http://10thworlds4.sched.com/event/1b4e89aec4a77654e7a5609296233619
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Assessing Digital Governance Transformation Trajectories in MENA Countries: An Entropy-TOPSIS and Hierarchical Clustering Approach
DESCRIPTION:Authors - Samira Boulahbel-Bachari\, Hind Dib-Slamani Abstract - This study examines digital governance transformation trajectories across fourteen Middle East and North Africa (MENA) countries between 2010 and 2024. Rather than classifying countries as simple “leaders” or “laggards\,” it adopts a multidimensional framework covering digital governance\, digital infrastructure\, inclusion\, institutional capacity\, and economic capacity. Entropy weighting derives indicator weights\, TOPSIS ranks countries according to their proximity to the best observed transformation profile\, while hierarchical clustering identifies shared trajectory patterns. Robustness is assessed through VIKOR and principal component analysis. The results reveal marked regional heterogeneity. Saudi Arabia leads the ranking\, followed by Türkiye\, Oman\, and the United Arab Emirates\, while Morocco shows a balanced trajectory despite more limited economic resources. Other countries display differentiated progress across connectivity\, online services\, and institutional conditions\, with Tunisia and Lebanon occupying the lowest relative positions. The findings show that progress in aggregate e-government scores does not necessarily reflect coherent digital governance development across all dimensions. The study advances a trajectory-based view of digital governance and offers a practical basis for regional benchmarking and policy prioritization in heterogeneous contexts.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:ff189f2b1caf050bd80462d4914022b7
URL:http://10thworlds4.sched.com/event/ff189f2b1caf050bd80462d4914022b7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Behavioral Analysis of Machine Learning Techniques on Semiconductor Process Data for Fault Detection and Classification
DESCRIPTION:Authors - Mohammad Arafat Ullah Abstract - Fault Detection and Classification (FDC) plays a critical role in semiconductor manufacturing by identifying defective wafers before subsequent processing stages\, thereby reducing manufacturing cost\, material waste\, and production time. Traditional Statistical Process Control (SPC)-based FDC systems are widely used in semiconductor fabrication\; however\, machine learning techniques can significantly improve defect detection and process monitoring efficiency. In this research\, the SECOM semiconductor manufacturing dataset collected from Kaggle was analyzed using multiple machine learning approaches. Several classification techniques including custom Support Vector Machine (SVM)\, kernel-based SVM\, custom K-Nearest Neighbor (KNN)\, and Random Forest were implemented and compared for defective wafer detection. In addition\, pseudo time-series semiconductor signals were reconstructed from static process features. Exponentially Weighted Moving Average (EWMA) smoothing and temporal feature extraction were then applied for signal-based fault analysis. Experimental results show that SVM-based approaches achieved strong classification performance on the SECOM dataset\, while temporal signal reconstruction provided additional insight into semiconductor process behavior. The study presents a comparative analysis between conventional feature-based learning and reconstructed temporal feature-based learning for semiconductor fault detection applications.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:ad937088dffa0f6e031d442af9b6c8c0
URL:http://10thworlds4.sched.com/event/ad937088dffa0f6e031d442af9b6c8c0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Explainable WSL Command Threat Detection Using Machine Learning Risk Scoring and Retrieval-Augmented LLM Reasoning
DESCRIPTION:Authors - M. A. M. P. Wanigaratne\, K. B. H. M. T. T. Bandaranayake\, C. S. Mohottala\, J. V. Pannilage Abstract - Windows Subsystem for Linux (WSL) enables Linux command-line workflows to run directly on Windows endpoints\, but this hybrid execution model creates security visibility and interpretation challenges. Host-side monitoring can identify that WSL was launched\, but it may not provide sufficient Linux-side command context for threat investigation. This paper presents an explainable WSL command threat detection approach that combines machine learning-based risk scoring with Retrieval-Augmented Large Language Model (LLM) reasoning. The machine learning layer uses wrapper-aware and structure-aware command features to classify WSL-style command activity and convert model output into operational risk scores. The reasoning layer processes suspicious and malicious events using retrieved cybersecurity knowledge to generate analyst-readable explanations\, MITRE ATT&CK mappings\, confidence reasoning\, and suggested defensive actions. The ML component was evaluated using a hybrid command dataset containing 8\,028 samples\, while the reasoning component was evaluated using 120 sanitized command level scenarios. Results show that the Calibrated SVM achieved 0.96 accuracy and 0.96 malicious class F1-score. Retrieval-augmented reasoning improved MITRE ATT&CK mapping accuracy from 52% to 87% and reduced hallucinated statements from 31% to 12%. The results indicate that combining ML risk scoring with grounded LLM reasoning can improve both alert prioritization and analyst understanding for WSL enabled endpoints.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:9ec9f7585b71ec2b01f14e4b9289b70a
URL:http://10thworlds4.sched.com/event/9ec9f7585b71ec2b01f14e4b9289b70a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Future Financial Crisis: Fiscal and Monetary Policy Wedding Planned\, Date Unknown
DESCRIPTION:Authors - Pavel E. Zhukov Abstract - The paper analyzes the problem of growth of public debt in developed countries with the compound interest approach\, initially proposed with the Sargent-Wallace model. It is concluded that since 2002\, when central banks began to apply the New Keynesian Model in monetary policy\, governments have been widely using deficit financing of fiscal expenditures in order to stimulate economic growth. Based on the experience of 2002-2025\, the parameters of the exponential growth of the debt-to-GDP ratio and the dangerous values of the budget deficit are assessed. General conclusions are made about the ineffectiveness of the dominant model of fiscal policy and the need to revise it\, as well as the need to consider the growth of the money supply in monetary policy. General recommendations proposed. First: it is obvious that the United States and Japan have to introduce the VAT and do not increase customs duties. Second: In order to accelerate economic growth\, it is necessary to shift fiscal policy priorities from the development of infrastructure and social programs to R&D\, which will increase labor productivity. Third: Generally\, all the social programs have to be audited. Specific for the United States\, health insurance reform and limiting the growth of the budget deficit due to the Medicare and Medicaid programs are urgently needed. Fourth: Perhaps "national" companies with a high degree of localization should be stimulated with tax incentives for corporate income tax and shareholder income tax.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:463438ca4f957c4c8bc8f77753e68d8d
URL:http://10thworlds4.sched.com/event/463438ca4f957c4c8bc8f77753e68d8d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Lexicographical and Morphological Approaches for Gujarati Dialect Identification: A Systematic Survey (2021-2025)
DESCRIPTION:Authors - Unnati Parmar\, Jatin Modh Abstract - Due to the high degree of infusion and morphology\, Gujarati is regarded as a low-resource language in the Natural Language Processing field. The key reason why Gujarati can be classified as such is the lack of computing tools and annotated digitized corpus. Every single dialect of the language has its own morphological\, lexical\, and orthographic peculiarities since the language is extremely diverse. It includes the most diversified dialect – Kutchi – alongside Kathiawadi\, Surti\, Charotari\, and Pattani dialects. The current paper focuses on the evolution in the sphere of Gujarati dialect identification via texts from 2021 till 2025. Morpheme segmentation\, parts of speech identification\, regional idioms identification\, and neural machine translation model adaptation will be analyzed throughout this paper. This study looks at the transition from traditional grammar-based systems to modern deep learning algorithms. Performance metrics from the latest literature are used to identify research gaps. They include excellent results in the detection of idioms and high F1-scores for morphological tagging. Performance metrics of various models\, such as transformers and Bidirectional Long Short-Term Memory network\, are compared with DFA techniques. A framework for hybrid language models\, combining both linguistics and neural networks\, is proposed in the conclusion section of this literature review. Neural network models have been found to offer significant improvements in morphology when compared to traditional methods. In this paper\, we address an inadequacy in the processing of informal language through the identification of disparities in resources between geographic variations. We propose a combination model that maintains geographic identity in modern-day computerized environments.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:98a3947e0ddba978625ca9d612e4fd8e
URL:http://10thworlds4.sched.com/event/98a3947e0ddba978625ca9d612e4fd8e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Machine-Learning-Enhanced Non-Invasive Tests for MASLD Fibrosis: Compact s-DNNs Versus FIB-4\, Tabular Foundation Models\, and Large Language Models
DESCRIPTION:Authors - Athanasios Angelakis\, Gabriele De Vito\, Eleni-Myrto Trifylli\, Filomena Ferrucci Abstract - Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely used as a first-line non-invasive test (NIT)\, but its fixed formula may underuse non-linear diagnostic information contained in age\, aspartate aminotransferase (AST)\, alanine aminotransferase (ALT)\, and platelet count (PLT). We evaluated whether machine-learning-enhanced NITs (MLE-NITs) can improve advanced fibrosis detection while preserving the clinically accessible FIB-4 variable space. We used three biopsy-validated MASLD cohorts from China\, Malaysia\, and India (n = 784). The Chinese cohort was split into 486 training and 54 internal validation/tuning patients\; final performance was reported only on the Malaysian (n = 147) and Indian (n = 97) external cohorts. Models used five variables: age\, FIB-4\, AST\, PLT\, and ALT. We compared FIB-4 with a shallow-deep neural network (s-DNN)\, TabPFN\, and gpt-4o-2024-08-06 in zero-shot and fine-tuned settings. FIB-4 achieved external thresholded ROC-AUCs of 0.75 and 0.60 in Malaysia and India\, respectively. TabPFN achieved 0.69 and 0.66\, fine-tuned GPT-4o achieved 0.75 and 0.63\, and the s-DNN achieved 0.77 and 0.67. The s-DNN contained only 354 trainable parameters\, compared with 7\,244\,554 parameters for TabPFN\, and provided the most balanced fixed-threshold operating profile. External diagnostics showed s-DNN Brier scores of 0.18 and 0.22\, with AST and FIB-4 as dominant permutation-importance variables. Exploratory decision-curve analysis showed cohort-dependent clinical utility\, favoring TabPFN in Malaysia and s-DNN in India.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:fb00e0ff0b41c234622dadb18325ffa4
URL:http://10thworlds4.sched.com/event/fb00e0ff0b41c234622dadb18325ffa4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:A Federated Intrusion Detection Framework for Distributed Network Security
DESCRIPTION:Authors - Harshala Shingne\, Shwetambari Borade\, Dhanashree Hadsul\, Aditya D. Nandgirwar\, Pranali Pawar\, Rupali Vairagade Abstract - Increasing proliferation of networked systems have compounded the necessity to seek effective and non-invasive intrusion detection solutions. The conventional intrusion detecting systems (IDS) are mainly centralized into data aggregation scheme that introduces essential constraints pertaining to data exposure\, scalability\, and robustness of the system itself. Partially in reaction to this\, this paper presents a privacy conscious federated intrusion detection design that allows collinear model training by many network participants in the absence of exchanging raw data. This framework exploits federated learning to create a global intrusion detecting model by continually aggregating local-trained updates\, thus retaining the data locality and ownership. In order to achieve high privacy assurances\, there are inbuilt secure aggregation mechanisms and perturbation-based mechanisms that accomplish this by avoiding the leakage of sensitive information during model sharing. Additionally\, an adaptive-aggregating strategy is proposed that can effectively manipulate non identically distributed data of the participants\, as well as improving the generalization process of the global model. Extensive testing on test sets of benchmark intrusion detection has shown that the proposed framework has very high detection rates and much less privacy risk and communication overhead than the traditional centralized techniques. The findings confirm that the framework has the capability of offering a scalable\, secure and efficient intrusion detection solution in distributed networks.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:86028ad47819cff4578dfb4d329e33da
URL:http://10thworlds4.sched.com/event/86028ad47819cff4578dfb4d329e33da
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Adaptive Multi-Objective Hospital Capacity Management during Pandemic Surges: A Hybrid Deep Learning and Optimization Framework
DESCRIPTION:Authors - Shashank Mallesh\, Anithadevi M D\, Srinidhi G A\, Chandana Sreenivas Abstract - Hospital surge capacity management remains a critical challenge in healthcare systems\, particularly during pandemic events. This research presents a novel Adaptive Multi-Objective Capacity Management (AMCM) framework that integrates Long Short-Term Memory (LSTM) networks with Multi-Objective Particle Swarm Optimization (MOPSO) to optimize bed allocation\, staffing schedules\, and equipment distribution. The framework simultaneously minimizes patient wait times\, operational costs\, and resource wastage while maximizing bed utilization efficiency. Comprehensive evaluation against state-of-the-art algorithms including Support Vector Regression (SVR)\, Random Forest (RF)\, Gradient Boosting (GB)\, and traditional Mixed Integer Linear Programming (MILP) demonstrates superior performance across all metrics. The proposed method achieves 94.7% bed utilization accuracy\, reduces emergency department wait times by 42.3%\, and decreases surge-related costs by 38.9% compared to conventional approaches. Validated on real-world COVID-19 hospital data spanning 18 months across five major health systems\, the AMCM framework provides healthcare administrators with an intelligent decision support system for proactive capacity planning and dynamic resource allocation.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:90f7e2c8d53d04f03db2f74294a51dcc
URL:http://10thworlds4.sched.com/event/90f7e2c8d53d04f03db2f74294a51dcc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:AI-Based Academic Integrity Detection Systems in Higher Education: A Systematic Review of Construct Validity
DESCRIPTION:Authors - Nwagu Chima Ajanwachuku\, Onyemaobi Bethram Chibuzo\, Nwafor Franca Amaka\, Divine Nnodim Oluchi Abstract - Tertiary institutions across the world are now adopting artificial intelligence-based academic detection systems to aid in detecting different forms of academic misconduct. For these detection systems\, there is more focus on technical performance metrics such as detection accuracy\, precision and recall and little focus on whether these systems validly measure the complex construct of academic misconduct. This study aims to examine how AI-based academic integrity detection systems operationalise\, measure\, and validate academic misconduct in higher education\, focusing on construct operationalisation\, measurement accuracy\, and construct validity\, through a systematic literature review. We conducted a systematic review and retrieved articles from ACM Digital Library\, IEEE Xplore\, Web of Science\, and Google Scholar. Of 793 articles\, 56 were selected using the PRISMA framework\, and the findings were synthesised narratively. The 56 studies focused on plagiarism detection\, AI-generated text detection\, authorship verification\, behavioural monitoring\, biometric authentication\, and multimodal detection systems. Across all the studies we considered\, detection systems mainly measured observable digital signals. 55 of 56 studies showed evidence of construct misalignment between the measured signal and the claimed misconduct construct. Most studies we considered treated similarity as plagiarism\, AI-generated probability as dishonesty\, and behavioural anomalies as cheating\, even though these signals could not capture intent\, differentiate between acceptable collaboration and collusion\, or account for disclosure practices or alignment with institutional policy. Also\, we observed that most studies validated detection systems using technical metrics such as accuracy\, precision\, recall\, and F1 score\, and just a few directly addressed construct validity\, bias\, or robustness.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:0671c24d85ee4fdb47c6f193b5ca6aef
URL:http://10thworlds4.sched.com/event/0671c24d85ee4fdb47c6f193b5ca6aef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Application of Global Positioning System software for surveying\, mapping\, and establishing a photo interpretation course in Thuan An ward\, Ho Chi Minh City in 2025\, Vietnam
DESCRIPTION:Authors - Dang Trung Thanh\, Nguyen Huynh Anh Tuyet Abstract - The objective of this project is to apply GPS devices in combination with GIS software to build image interpretation keys for spatial data management. The research content includes: collecting satellite imagery data and documents\; conducting field surveys and collecting GPS coordinates for 149 sample points\; and building image interpretation keys for geographical objects. The main research method combines GPS field surveys\, remote sensing image interpretation\, and the application of GIS software such as QGIS and Google My Maps to process\, analyze\, and manage spatial data. In addition\, the project utilizes methods of document collection\, statistics\, and comparison to ensure the accuracy and scientific validity of the research results. The project results: A set of image interpretation keys was developed for several key geographical objects in the study area\, including: water bodies (27 samples)\, transportation (33 samples)\, agricultural land (27 samples)\, residential construction land (28 samples)\, and vacant land (34 samples). The research has contributed to demonstrating the effective application of GPS combined with GIS and remote sensing in surveying\, mapping\, and managing geographic information on current land use. The research and development direction is: Integrating artificial intelligence (AI) and machine learning (Deep Learning) into the image interpretation process
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:3be56a0b9ad55f20f45f24ccb60f8e0b
URL:http://10thworlds4.sched.com/event/3be56a0b9ad55f20f45f24ccb60f8e0b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Assessment of land-use change procedures under the provisions of the 2013 and 2024 land laws in Thuan An Ward\, Ho Chi Minh City\, Vietnam
DESCRIPTION:Authors - Dang Trung Thanh\, Nguyen Huynh Anh Tuyet Abstract - This study evaluates the implementation of the 2024 Land Law regarding land-use conversion in Thuan An Ward\, Ho Chi Minh City\, Vietnam. The research aims to assess the changes introduced by the new legal framework and examine its practical impacts on land-use conversion procedures at the local level. The study employed a combination of document analysis and a questionnaire survey of 100 respondents\, including local government officials and citizens. The collected data were analyzed using descriptive statistics and comparative methods. The results indicate that the 2024 Land Law has improved the landuse conversion process by simplifying administrative procedures\, reducing processing time\, and increasing transparency. Survey findings show that most respondents considered the new regulations easy to understand\, the processing time efficient\, and the procedural costs reasonable. Overall public satisfaction increased from 65% under the 2013 Land Law to 85% under the 2024 Land Law. Nevertheless\, several challenges remain\, including incomplete digital land databases\, limited public understanding of some legal provisions\, and issues related to the implementation of market-oriented land pricing. These findings provide practical evidence for improving land-use conversion management and support the effective implementation of the 2024 Land Law at the local level.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:ab4ace190f4451bbef971bbaadb0a34f
URL:http://10thworlds4.sched.com/event/ab4ace190f4451bbef971bbaadb0a34f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T103000Z
DTEND:20260730T120000Z
SUMMARY:Improved Protein Sidechain Angle Prediction using Optimized Transformer Architecture
DESCRIPTION:Authors - Rebecca Hufkie\, Dane Brown Abstract - Protein structure determines function\, yet experimental determination methods remain costly and slow. This study presents an optimised transformer-based system for predicting protein sidechain angles and reconstructing 3D structures directly from sequence data. Trained on the SidechainNet CASP12 dataset comprising 25\,044 proteins\, the systematically refined model achieves 0.244 radians RMSE for angle prediction and 1.413 ̊A RMSD for structural accuracy\, representing a 70% improvement over the baseline. Incorporating backbone angles\, secondary structure\, and evolutionary information reduces RMSD from 1.861 ̊A to 1.413 ̊A compared to sequence-only inputs. On challenging CASP12 free-modelling targets\, the system scores 84 to 86 GDC. This performance is competitive with leading methods while maintaining computational efficiency through single-sequence prediction without multiple sequence alignment generation. Results indicate that specific architectural choices\, including deeper networks\, GELU activation\, and increased embedding dimensions\, combined with robust dropout and weight decay\, enable highly accurate structure prediction from limited training data. This demonstrates that carefully constrained models can capture complex biological folding patterns efficiently without massive computational overhead.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:2c18a25440cd293361cc635911607b05
URL:http://10thworlds4.sched.com/event/2c18a25440cd293361cc635911607b05
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120000Z
DTEND:20260730T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:4c1e1a28b4ca20e2afc786c6fad3a258
URL:http://10thworlds4.sched.com/event/4c1e1a28b4ca20e2afc786c6fad3a258
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120000Z
DTEND:20260730T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:c557a60a7f6ee2f93f8ebc092ad5fcf6
URL:http://10thworlds4.sched.com/event/c557a60a7f6ee2f93f8ebc092ad5fcf6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120000Z
DTEND:20260730T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:3151bc74405699c6b8a089358b0b8c32
URL:http://10thworlds4.sched.com/event/3151bc74405699c6b8a089358b0b8c32
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120000Z
DTEND:20260730T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:5444a5f74e913269556be5eb652397f2
URL:http://10thworlds4.sched.com/event/5444a5f74e913269556be5eb652397f2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120000Z
DTEND:20260730T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:10859f7ba34e9fd21cca790ec28864e7
URL:http://10thworlds4.sched.com/event/10859f7ba34e9fd21cca790ec28864e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120200Z
DTEND:20260730T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:6816fab98b5b6ad77384f2482b0a8a8b
URL:http://10thworlds4.sched.com/event/6816fab98b5b6ad77384f2482b0a8a8b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120200Z
DTEND:20260730T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:b44f11188e030566ea2d994d93b7fb0f
URL:http://10thworlds4.sched.com/event/b44f11188e030566ea2d994d93b7fb0f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120200Z
DTEND:20260730T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:8732aeba0a0c86168fe614a57c7e4947
URL:http://10thworlds4.sched.com/event/8732aeba0a0c86168fe614a57c7e4947
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120200Z
DTEND:20260730T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:09aa4d81353354b500d3ba24987f7abb
URL:http://10thworlds4.sched.com/event/09aa4d81353354b500d3ba24987f7abb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T120200Z
DTEND:20260730T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:2167f88242c0874fcdcd6ba907996d58
URL:http://10thworlds4.sched.com/event/2167f88242c0874fcdcd6ba907996d58
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T125800Z
DTEND:20260730T130000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:4b6aa84e79272202d0cfbfd5b03c0024
URL:http://10thworlds4.sched.com/event/4b6aa84e79272202d0cfbfd5b03c0024
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T125800Z
DTEND:20260730T130000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:c01881ba834f9a07d042de6b8886bc7e
URL:http://10thworlds4.sched.com/event/c01881ba834f9a07d042de6b8886bc7e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T125800Z
DTEND:20260730T130000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:b06366e33d0789a9e4c51d97b6587fd7
URL:http://10thworlds4.sched.com/event/b06366e33d0789a9e4c51d97b6587fd7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T125800Z
DTEND:20260730T130000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:8fd5a20bba18e38331cbdcc1f2f40fbf
URL:http://10thworlds4.sched.com/event/8fd5a20bba18e38331cbdcc1f2f40fbf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T125800Z
DTEND:20260730T130000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:0af4eb0744e1a09ed105474fca78559c
URL:http://10thworlds4.sched.com/event/0af4eb0744e1a09ed105474fca78559c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Classification Performance of Simple Signals in an Autocorrelation Receiver
DESCRIPTION:Authors - The Quan Trong\, Nguyen Trong Nhan Abstract - An autocorrelation receiver can be employed in surveillance and communication systems to identify the type and operating mode of radiation sources in the absence of a priori signal information. In this work\, the autocorrelation receiver is defined as a system comprising a broadband analog front-end with frequency conversion to the intermediate frequency range and a narrowband processing unit based on autocorrelation. The performance of signal processing is governed by the received pulse duration and the length of the fast Fourier transform (FFT) window. The study provides an estimate of the signal-to-noise ratio (SNR) required to achieve a specified probability of correct classification of simple radio pulses at a fixed false alarm rate. The results show that increasing the pulse duration while maintaining a fixed FFT window (i.e.\, reducing the ratio of FFT window length to pulse duration) decreases the required SNR. Consequently\, the probability of correct classification is improved under these conditions. Furthermore\, high receiver efficiency is achieved when the ratio of the FFT window length to the pulse duration is kept below 10. If the number of FFT samples is fixed\, further improvement in classification performance requires increasing both the sampling frequency and the processing rate.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:4bdce4c6182240a2c8a9e49f5dbeaeb7
URL:http://10thworlds4.sched.com/event/4bdce4c6182240a2c8a9e49f5dbeaeb7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Enhancing Cybersecurity in Software Development Using Role-Based Access Control and Agentic AI
DESCRIPTION:Authors - Uphaar Goyal\, Chirag Patadia\, Nitinkumar Leuva Abstract - Modern software development environments depend on cloudnative infrastructure\, automated CI/CD pipelines\, and distributed DevSecOps workflows. These environments improve delivery speed but expand the attack surface through privilege escalation\, credential compromise\, insider threats\, and misconfigured pipeline permissions. Traditional Role-Based Access Control (RBAC) improves least-privilege enforcement\, but static RBAC does not adequately respond to changing runtime context such as business hours\, network trust\, multi-factor authentication status\, and deployment pipeline state. This paper proposes an enhanced cybersecurity framework that integrates RBAC\, contextaware policy enforcement\, and Agentic AI-based autonomous auditing. The proposed agent observes access events\, learns behavioral patterns\, detects anomalous requests\, and generates explainable audit evidence without replacing deterministic access control. Experimental evaluation in a simulated CI/CD environment shows that the RBAC with Agentic AI framework achieves 98.21% accuracy\, improves anomaly detection compared with traditional access-control models\, and maintains low enforcement latency under increasing concurrency. The results indicate that Agentic AI can strengthen secure software development by adding adaptive audit intelligence while preserving the predictability and administrative clarity of RBAC.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:0c3260c666cabd30c7b642c8160b6526
URL:http://10thworlds4.sched.com/event/0c3260c666cabd30c7b642c8160b6526
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:From Manual to Machine-Assisted: Investigating the Practical Effects of Digital Transformation on Auditing
DESCRIPTION:Authors - Thokozani Nkosinathi Hlubi\, Nazeer Joseph Abstract - Digital transformation is reshaping the auditing profession by introducing advanced digital tools\, automation\, and data‑driven processes that redefine how audits are planned\, executed\, and evaluated. This study examines the effect of digital transformation on auditing processes\, focusing on how digital tools\, automation technologies\, and shifting skill requirements influence audit effectiveness and efficiency. Using a qualitative research design\, Rich Picture workshops were conducted with practicing auditors to explore how emerging technologies are integrated into real-world audit environments. The findings reveal three key themes. First\, digital tools enhance real‑time access to information\, improve collaboration\, and deepen auditors’ understanding of complex IT environments. Second\, automation significantly improves audit effectiveness by streamlining routine tasks\, supporting anomaly detection\, and enabling more robust risk assessments—while still requiring professional judgment. Third\, efficiency gains emerge through time savings\, resource optimization\, and evolving competency requirements\, underscoring the need for continuous upskilling. Building on these insights\, the study proposes a four‑stage implementation framework consisting of strategic alignment\, workforce development\, workflow redesign\, and ethical governance safeguards. The research contributes to both theory and practice by demonstrating how digital transformation reshapes audit work and offering a structured roadmap for organizations seeking to modernize their audit functions responsibly and sustainably.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:4d356a37f07c3abd46b8f3c869ee8e7e
URL:http://10thworlds4.sched.com/event/4d356a37f07c3abd46b8f3c869ee8e7e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Reassessing Confidentiality and Availability of the RUAP Matrix-Based RFID Authentication Protocol
DESCRIPTION:Authors - Zumna Usman\, Madiha Khalid\, Weiwei Jiang\, Momina Shaheen\, Umar Mujahid\, Muhammad Najam-ul-Islam Abstract - The Internet of Things (IoT) networks operate under strict resource constraints having limited computational capability\, memory\, bandwidth\, and energy\, while still being required to combine essential security goals such as confidentiality and mutual authentication with efficiency\, particularly in Radio-Frequency Identification (RFID)-based systems. For this reason\, Ultra-Lightweight authentication protocols are commonly used\, where traditional cryptographic techniques are often too demanding. The Random Rearrangement Block Matrix-Based Ultra- Lightweight RFID Authentication Protocol (RUAP) was introduced to strengthen security. In this paper\, RUAP is analyzed and shown not to eliminate fundamental weaknesses. By applying a probabilistic disclosure attack\, it is shown that public messages leak exploitable statistical information\, making it possible to fully recover the identifier in reduced configurations and to recover about 71.77% of a 96-bit identifier. It is further shown that RUAP’s asymmetric key update mechanism allows adversaries to trigger desynchronization\, resulting in denial of service.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:6fa60b07f50d764c7a7286113649a1c5
URL:http://10thworlds4.sched.com/event/6fa60b07f50d764c7a7286113649a1c5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:SelEncChain-Agri: A Selective Field Encryption Framework for Privacy-Preserving Agricultural Supply Chains on Solana Blockchain
DESCRIPTION:Authors - Rajiv Ghai\, Anil Kumar Bisht\, Akash Sanghi Abstract - Agricultural supply chains face a fundamental tension consumers require transparency for provenance verification while commercial stakeholders demand confidentiality of pricing\, buyer identities and quality scores. This paper introduces SelEncChain-Agri\, a selective field encryption (SFE) framework that resolves these conflicting demands on a single fully decentralised Solana blockchain. Supply chain data is partitioned into public fields stored in plaintext and confidential fields encrypted with ECIES combined with Multi-Party Key Encapsulation (MP-KEM). Authorised parties decrypt fields using existing Solana wallet keypairs via a formally specified Ed25519-to-X25519 key derivation (libsodium convention)\, requiring no additional key material or trusted third party. Three technical contributions are made: (1) SFE field encryption with per-capsule independently randomised nonces preventing GCM keystream reuse (2) SFE decryption and (3) a key revocation protocol providing post-revocation forward secrecy. Security analysis under the Dolev-Yao model provides a constructionlevel IND-CPA argument under the DDH assumption on Curve25519. Comparative evaluation shows SelEncChain-Agri satisfies all eight stakeholder requirements from the agricultural blockchain literature\, versus at most seven for dualchain alternatives. A Solana devnet prototype confirms functional correctness: end-to-end latency was 3\,691 ms for batch creation and 2\,111 ms for event submission\; client-side SFE overhead was 74.59 ms (Python)\, with estimated pure cryptographic cost of 3.3 ms (Rust/WebAssembly). Keywords: Agricultural supply chain · Anchor framework · DPDP Act 2023 · ECIES · Food traceability · Key revocation · MP-KEM · Privacy-preserving · RFC 7748 · Selective field encryption · Solana blockchain
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:8db312365de1ba8608dedbf0ebedfd74
URL:http://10thworlds4.sched.com/event/8db312365de1ba8608dedbf0ebedfd74
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Transformer-Based Language Models for Conversational AI: A Comparative Study
DESCRIPTION:Authors - Austin jojo Jallah\, Reena Satpute Abstract - As the transformer language models were developed\, it was possible to introduce one of the significant technological revolutions in conversational AI. Basically\, the understanding and production of human language has been enhanced by transformers. Transformer models make it possible to build dialogue systems that provide natural\, complex\, and context-sensitive communication to develop further customer support technologies\, healthcare technologies\, and virtual assistance services. In the current paper\, this paper will assess advanced transformer systems\, BERT\, GPT\, and T5\, as well as their variants\, assessing their performance capabilities in the use of dialogue. All the models are evaluated in terms of performance quality\, which is judged by its fluency production and also gauges of coherence and contextual accuracy and its general ability to pro-cess computations. This review talks about the prompt engineering approaches and the human feedback-enhanced learning through reinforcement (RLHF) and the adapter transfer learning methods\, to improve the flexibility and quality of the model. The paper presents the fresh trends in Conversational AI with multi-modal learning as well as retrieval-enhanced generation and factuality-based coherence knowledge application. Nevertheless\, transformer-based models have three key weaknesses\, among which\, there are bias and hallucinations\, and the computing requirements are very high. These weaknesses are analyzed and we discuss the potential remedies that involve symbolic deep learning combinations and efficient compression methods\, such as quantification and pruning. We have found out the extent to which each system can do and the things that they cannot accomplish\, and hence can help to determine the right applications of each of the frameworks. We introduce an evaluation comparison as a scholarly resource to the practitioners of dialogue system development so they can perform better with moral and effective models of operations. The article concentrates on the devel-opment of transformer-based conversational AI and its estimated impact on hu-man-computer dialogue systems
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:ed9f2314bad147dfd2163b7066e56e62
URL:http://10thworlds4.sched.com/event/ed9f2314bad147dfd2163b7066e56e62
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Applying System Dynamics to Address Complex Problems Found in Financial Management Analysis
DESCRIPTION:Authors - Khumbelo Difference Muthavhine\, Mbuyu Sumbwanyambe Abstract - The financial management involves outstanding debts\, trade receivables\, deduction charges\, economic value\, generating capacity\, and stockpiling accumulation. These six variables mentioned above must be carefully examined using mathematical formulas and trustworthy tools because they are interrelated\, which makes it very difficult to establish a solution. These challenges affect companies of all sizes\, requiring a flexible tool for adaptable financial management analysis. To mitigate the aforementioned problems\, this study suggested system dynamic (SD) modeling to handle the problem instead of using traditional tools. The SD modeling is used because of (a) complex dependencies analysis\, (b) the need for mathematical formulas\, and (c) the graphical outputs compared to traditional tools. The study constructed an SD model with six variables and their interconnections. When adjustments are required\, financial managers should pay particular attention to the out-of-graph data.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:318bad59bff2854a004063a26cb5ae0c
URL:http://10thworlds4.sched.com/event/318bad59bff2854a004063a26cb5ae0c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Logic of protection or protection of logic? An integrated model for assessing the value of information in a digitised information reality
DESCRIPTION:Authors - Mariusz Szynkiewicz Abstract - The protection of information resources is one of the central issues in contemporary information science\, and one also significant from an IT perspective – particularly in the context of cybersecurity. In this article\, I propose one possible approach to addressing this challenge. The proposal concerns the protection of information resources in a broad sense: from the stage of information acquisition and creation through to its distribution. In the following sections\, I outline the main assumptions of a comprehensive model for the protection of information resources\, discussed in the context of building the information resilience of participants in digitised information exchange processes\, and in relation to issues associated with the concept of cyber hygiene. The central thesis of the article rests on the assumption that effective protection of information resources is possible only on the basis of an integrated model addressing the following procedures: (a) validation – the assessment of the level and value of a given resource\; (b) threat identification – the logical level\, scale\, and types of vulnerability\; (c) detailed analysis of the type of abuse – qualitative diagnosis\; (d) selection of techniques and methods for counteracting a given class of attack – the methodological level\; and (e) selection of possible corrective and preventive measures – the elements of cyber hygiene.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:cfdc603329089cf052c11bfc7bc27a83
URL:http://10thworlds4.sched.com/event/cfdc603329089cf052c11bfc7bc27a83
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Preventing musculoskeletal disorders and burnout in Spanish nurses by profiling and cutting-edge humanoid robot immersive training with a gender perspective
DESCRIPTION:Authors - S. Jimenez-Garcia\, V. Zorrilla-Munoz\, G. Martinez-Navarrete\, N. Garcia-Aracil\, A.M. Peiro-Peiro Abstract - This paper presents an integrated framework that combines digital screening\, ergonomic assessment\, humanoid robot benchmarking and immersive virtual reality (VR) training to support the prevention of musculoskeletal disorders and burnout in nursing professionals. The framework is grounded in occupational health data from Spanish nursing professionals and incorporates sex/gender and anthropometric differences as design variables. A descriptive cross-sectional analysis was performed on 316 professionals from the European Health Survey in Spain\, filtered by occupation\, and complemented with the PROBEREN project approach. Two high-demand activities in Internal Medicine and Infectious Diseases units were selected: hygiene\, comfort care\, pressure ulcer prevention and postural changes in bedridden patients\; and intrahospital transfers under isolation or clinical support. The sample showed a strong proportion of women (86.4%)\, mean age of 45.97 years\, chronic health problems in 54.7%\, prescribed medication use in 51.9%\, recent physical pain in 46.8% and pain interfering with daily activities in 31.6%. These findings support a transition from descriptive profiling to proactive prevention. The proposed ecosystem links early screening\, capture of expert movements\, biomechanical comparison with a humanoid robot\, personalized VR training and longitudinal reassessment. Future pilot validation should evaluate usability\, VR-related fatigue\, ergonomic risk reduction\, burnout\, pain and implementation barriers in real clinical settings.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:ff9ffca61b7958d301b97500f324945c
URL:http://10thworlds4.sched.com/event/ff9ffca61b7958d301b97500f324945c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Reward Sensitivity and Statistical Robustness in Reinforcement Learning-Based VM Right-Sizing: A Multi-Seed Empirical Study
DESCRIPTION:Authors - Aleksander Karastoyanov Abstract - Reinforcement learning (RL) has been widely proposed for adaptive virtual machine (VM) right-sizing in cloud environments\, yet most published work reports results from a single random seed with a fixed reward formulation\, conditions that may not reflect genuine generalization. This paper addresses both limitations through a systematic multi-seed\, multi-reward evaluation of a Proximal Policy Optimization (PPO) agent applied to VM right-sizing on the Alibaba Cluster Trace 2018. Fifteen independent training runs (three reward configurations × five seeds) are conducted on a ten-VM simulation environment. The key finding is that reward formulation\, not the RL algorithm per se\, is the dominant determinant of SLA compliance: the unified reward variant (Config A) achieves a mean SLA violation rate of 1.1% (±2.11) across five seeds\, statistically comparable to a Threshold baseline (2.43%)\, while dimension-aware variants expose a critical instability in memory-saturated environments. A one-sample t-test yields t = −1.40\, p = 0.23\, confirming that neither superiority nor inferiority relative to Threshold can be claimed\, and motivating the need for larger seed sets and alternative reward designs in future work. Three empirically grounded reward design principles are derived for practitioners deploying RL-based resource managers in high-memory-pressure infrastructure.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:4f5554dc78fa8bbd7861d55a9f2b0f88
URL:http://10thworlds4.sched.com/event/4f5554dc78fa8bbd7861d55a9f2b0f88
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Smart Agriculture Using Internet of Things: A Data-Driven Farming Approach
DESCRIPTION:Authors - Soham Paithankar\, Supriya Narad Abstract - Agriculture is a major contributor to India economy and supports the livelihood of a large population\, however\, traditional farming practices largely depend on manual observation\, weather information\, which often results in resource utilization and reduced crop productivity. The increasing variability of climate condition further challenges. The integration of internet of things (IoT) and software technologies provides an effective approach and data-driven decision by enabling real-time monitoring and data-driven decision making. This paper presents such as temperature\, humidity\, and soil moisture using field sensor data. The collected information is processed and stored using Python based software and compared with real-time weather information obtained through a weather API. A dashboard interface developed using Flask and streamlit visually presents sensor data\, weather data\, and comparative result to support informed decision-making system. The system demonstrates how low-cost IoT devices combine with software platform can improve agriculture monitoring\, optimize resource usage\, and support sustainable farming practice\, this study highlights the potential of IoT and software integration in transforming the potential of IoT. agriculture into an intelligent data-driven system suitable for developing region such as India.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:c7e3a526fc5af3059f8fe5dfdd8e3f92
URL:http://10thworlds4.sched.com/event/c7e3a526fc5af3059f8fe5dfdd8e3f92
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Towards Understanding the Influence of Technological-Organizational-Environmental (TOE) Factors on User Satisfaction in Mandatory Student Information Management Systems (SIMS)
DESCRIPTION:Authors - Emmanuel Opoku Debrah\, Sunet Eybers\, Corne J. van Staden Abstract - Student Information and Management Systems (SIMS) are increasingly being implemented in higher education institutions (HEIs) in developing countries. However\, with these mandatory systems\, there is limited evidence of user satisfaction. This systematic literature review investigates the influence of technological\, organizational\, and environmental (TOE) factors on user satisfaction with the use of mandatory SIMS in HEIs in developing countries\, with a focus on Ghana. A search was conducted across six databases and Google Scholar\, focusing on the past decade (2015-2025). Two reviewers independently evaluated the studies' quality using the Mixed Methods Appraisal Tool (MMAT 2018). The average consensus MMAT score was 4.08/5 (7 High\, 14 Moderate\, 5 Lower quality). A total of 1\,382 records were screened and considered for duplicates and applicability. The result was 27 empirical studies. A narrative synthesis supported by thematic mapping identified the most frequently reported determinants: system quality\, reliability\, and performance (n=18)\; organizational support\, IT capacity\, and management readiness (n=12)\; perceived usefulness\, ease of use\, and user experience (n=10)\; ICT infrastructure and connectivity (n=9)\; and information quality (n=6). The synthesis suggests that TOE factors are not independent: technological advantages can be offset by organizational weaknesses\, and environmental factors set an upper limit to satisfaction. This suggests that Ghanaian HEIs focus on integrated investments in user training\, technical support\, and infrastructure rather than just system upgrades.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:f96c36ec967aa43aeadf0e67887d3acb
URL:http://10thworlds4.sched.com/event/f96c36ec967aa43aeadf0e67887d3acb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:An Approach to Support Overpricing and Underpricing Auditing in Public Procurement
DESCRIPTION:Authors - Lucian Julio Felix da Costa\, Claudio de Souza Baptista\, Andre Luiz Firmino Alves Abstract - Auditing public procurement processes is essential to ensure transparency\, accountability\, and efficiency in the management of public funds. However\, the increasing complexity of procurement procedures poses significant challenges for auditors\, particularly regarding the timely detection of pricing irregularities. This paper presents a software tool for price comparison designed to support the identification of overpricing and underpricing in public works procurement. The proposed solution leverages semantic retrieval and historical price comparison techniques to analyze procurement data and integrate up-to-date market information. Additionally\, the application provides interactive visualizations and semantic retrieval mechanisms to support auditors during procurement price analysis activities. The expected contribution of this study lies in improving the effectiveness and accuracy of procurement oversight\, strengthening financial analysis processes\, and contributing to the prevention and deterrence of fraudulent practices among bidders.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:1f0af7681925108e94aa41ef59e55577
URL:http://10thworlds4.sched.com/event/1f0af7681925108e94aa41ef59e55577
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Automating Procurement Compliance Checklists with Large Language Models
DESCRIPTION:Authors - Vanderson dos Santos Araujo\, Eliane Tamara Lima Oliveira\, Pedro Manoel Hermínio Alves\, Andre Luiz Firmino Alves\, Claudio de Souza Baptista Abstract - Auditing public tenders requires analyzing lengthy documents to verify compliance with tender notices\, a time-consuming task prone to human error. This article empirically evaluates the use of Large Language Models (LLMs) to assist with auditing tender notices. A total of 50 official tender notices and 736 audit instances were analyzed\, comparing three context-provisioning strategies: expanded context windows\, integrated file retrieval\, and a custom Retrieval-Augmented Generation (RAG) pipeline. The results show that no single approach is superior across all scenarios. Models with long windows performed better at confirming explicit conformities\, whereas retrieval-based strategies demonstrated greater sensitivity to potential non-conformities due to omissions. The analysis also indicates that the type of question strongly influences performance\, especially for interpretive questions or those that rely on the absence of documentary evidence. As a key contribution\, the study demonstrates that the effectiveness of AI-assisted auditing depends on the combination of the contextualization strategy\, the quality of the retrieved context\, and the formulation of the questions\, reinforcing the role of LLMs as tools to support the auditor.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:02b1af739208c013d906794a21a1aae3
URL:http://10thworlds4.sched.com/event/02b1af739208c013d906794a21a1aae3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Digital accessibility as a lever for the inclusion of people with disabilities: What are the contributions of WCAG standards ?
DESCRIPTION:Authors - Ahmed Belgaid Abstract - Digital transformation has profoundly altered the organization of work and reinforced the importance of mastering digital tools for employability and productivity. In this context\, this study highlights the challenges of digital accessibility for employees with visual impairments and its application through an analysis of the new WCAG standards. The aim of our analysis is to demonstrate that digital accessibility consists of guaranteeing an inclusive digital transformation\; it is not limited to simply ac-quiring digital solutions or adapting existing ones. It is a comprehensive preparation process and an integrated approach involving various stakeholders within the company.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:a747eecb3be97fe4abf103d7bee2f2f7
URL:http://10thworlds4.sched.com/event/a747eecb3be97fe4abf103d7bee2f2f7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Smart Logistic Dashboards and Data-Driven Decision Making: Empirical Evidence from the Moroccan Manufacturing Industry.
DESCRIPTION:Authors - BENABDELLAH Nouhaila\, CHRAIBI Abdeslam\, BENRREZZOUQ Rhizlane Abstract - The importance of smart logistics dashboards as key tools for digital transformation in manufacturing companies is becoming more recognized. They enable real-time visibility\, in-tegrate various data sources\, and allow for analysis. However\, there is limited research on how these dashboards affect decision quality and operational performance in emerging countries. This paper explores the impact of smart logistics dashboards on data-driven decision making (DDDM) and operational performance in Moroccan manufacturing firms. We conducted a quantitative survey among logistics and operations managers and examined a proposed concep-tual framework using PLS-SEM. The findings showed that the capabilities provided by smart dashboards significantly improve decision quality through better data integration and real-time analytics. Additionally\, DDDM plays a key role in the relationship between smart dashboards and operational performance. These results are important for the field of smart logistics and digital transformation and have valuable practical implications for management in manufactur-ing firms in emerging countries.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:d9e285d09a40dc1877a6d4c2c2e82da2
URL:http://10thworlds4.sched.com/event/d9e285d09a40dc1877a6d4c2c2e82da2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Towards aWeb Usability Laboratory for Visually Impaired Users: A Systematic Mapping Study
DESCRIPTION:Authors - Francisco Castro Murillo\, Teresita de Jesus Alvarez Robles\, Andres Sandoval Bringas\, Monica Carreno Leon\, Francisco Javier Alvarez Rodriguez Abstract - Web accessibility for visually impaired users remains a critical challenge in HCI. While technical compliance with WCAG is well-established\, knowledge of User Experience (UX) evaluation methods tailored to non-visual interaction is fragmented. This paper presents a systematic mapping of the literature (2016–2026)\, analyzing 18 high-quality studies selected from 134 records retrieved from IEEE Xplore\, ACM DL\, SpringerLink\, and ScienceDirect. Results show that user testing is the predominant method\, often combined with standardized questionnaires like SUS and NASA-TLX. However\, critical gaps persist: small sample sizes\, inconsistent participant reporting\, and a lack of metrics designed for non-visual interaction. This study contributes a taxonomy of visually impaired user profiles and identifies the technical requirements for building an inclusive web usability laboratory. By bridging the gap between technical auditing and real-world user satisfaction\, this work provides a roadmap for the development of the MEUX LAB\, ensuring more equitable digital evaluation environments.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:27afe83b9416f0570bd9a5df3bbf6ea4
URL:http://10thworlds4.sched.com/event/27afe83b9416f0570bd9a5df3bbf6ea4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:ZERO: A Desktop Application for Real-Time\, Privacy-Preserving Plagiarism Detection
DESCRIPTION:Authors - Vikas Pandey\, Sanasam Chanu Inunganbi Abstract - Plagiarism has become a serious problem in universities\, research organizations\, and professional workplaces\, affecting academic integrity and the originality of work. While cloud-based detection tools are widely used\, they share a fundamental problem that rarely gets discussed openly: every document is required to be submitted and handed over to a third-party server. For unpublished research\, legal drafts\, or any sensitive material\, this trade-off is not acceptable. The proposed method\, named ZERO\, takes a different approach and runs entirely on the local machine\, watching the clipboard quietly in the background and scoring text against a local TF-IDF corpus in under 200 milliseconds\, with no uploads\, no accounts\, and no data leaving the device. An optional web scanning module is available when broader source coverage is needed. On top of the similarity score\, ZERO provides a word-level risk heatmap\, a sentence-by-sentence originality breakdown\, a stylometric module called Writing DNA\, and a scan history timeline. Testing on 60 hand-labelled samples showed that a recalibrated scoring curve brings the average score on original technical writing down from 34.7% to 9.8%\, while keeping verbatim-copy detection at 95%. API credentials are stored in the OS keychain\, and inter-process communication is locked to a strict channel whitelist\, making the application well-suited for confidential and pre-publication work.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:e8bb29b2e3469247d98f79ffcb4527d9
URL:http://10thworlds4.sched.com/event/e8bb29b2e3469247d98f79ffcb4527d9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:A Quantum Secure\, Smart\, Energy-Efficient\, Sustainable Blockchain Ecosystem for the DeFi Industry
DESCRIPTION:Authors - Ali Raheman\, Asad Khan\, Tejas Bhagat\, Fazal Raheman Abstract - The emergence of quantum computing challenges digital infrastructure to achieve both quantumresilient security and energy-efficient performance. This is particularly critical for decentralized finance (DeFi)\, where layered software stacks and exposed authentication mechanisms increase complexity\, overhead\, and attack-surface exposure. This paper proposes Quantum Ledger Technology (QLT)\, a blockchain-agnostic architectural framework that integrates Zero Vulnerability Computing (ZVC)\, Solid-State Software-on-a-Chip (3SoC)\, and Quantum-Resilient User-Evasive Cryptographic Authentication (QRUECA). Together\, these mechanisms reduce software-mediated trust\, minimize exposed authentication surfaces\, and shift trust enforcement toward hardware-rooted execution environments. A hypothesis-driven evaluation framework is introduced to assess whether architectural simplification can reduce authentication latency\, memory usage\, energy overhead\, and attack-surface complexity while preserving functional equivalence with existing blockchain systems. Preliminary results indicate that hardware-rooted authentication and reduced trusted software complexity can provide a promising foundation for scalable\, energy-aware\, and quantum-resilient digital infrastructure. The proposed framework aligns with the goals of secure\, sustainable\, and intelligent future computing systems.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:38ae95bc878430f927f5b4737d3f7a6a
URL:http://10thworlds4.sched.com/event/38ae95bc878430f927f5b4737d3f7a6a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Benchmarking E-Commerce Product Characteristics through a Novel Multi-Faceted Data Analytics Framework
DESCRIPTION:Authors - Shamsa AlNasri\, Muna Ali AlShamsi\, Mariam AlNuaimi\, Hanae Ouahhabi\, Gurdal Ertek Abstract - This study presents an analytics framework for analyzing and benchmarking sales transactions data of e-commerce products across multiple countries. The framework consists of an integrated multi-faceted application of a carefully selected portfolio of data analytics techniques. Specifically\, the framework combines (a) statistical distribution fitting to well-known probability distributions (Gamma\, Normal\, Weibull\, Lognormal)\, (b) box plot analysis followed by statistical hypothesis testing (Kruskal-Wallis and Dunn tests) and visualization of pairwise comparison results\, and (c) text mining (Latent Dirichlet Allocation (LDA) and word clouds). Although many studies in the literature report on the analysis of e-commerce product sales\, this is the first study that combines the mentioned techniques within a multi-faceted yet also unified approach. The results obtained for a case study on the Gulf Cooperation Council (GCC) countries reveal regional differences in consumer behavior\, pricing\, and preferences. The insights obtained can be used to improve the marketing and engagement of the selected case with the selected products and countries. However\, more importantly\, the primary contribution of the study is the generalizable analytics framework presented that can be adopted and applied to any product set and country selection with similar data attributes.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:56e8a87b231a8da121b783ce8e5c8cc0
URL:http://10thworlds4.sched.com/event/56e8a87b231a8da121b783ce8e5c8cc0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Employee Attrition Prediction Using Machine Learning Techniques
DESCRIPTION:Authors - Deepak Mane\, Ashwanth Nair\, Nihar Gundale\, Om Khamkar\, Tanmay Kulkarni\, Ranjeet Bidwe\, Amol Kamble\, Suraj Sawant Abstract - There many areas in which the organization can make use of technologies that will make decision making easy. ai (artificial intelligence) is one of the most useful and innovative technologies that is used in many fields of organization to help in business management and decision making. in the recent years\, HR department has became very important in the organization\, since the quality and skills of the worker in directly proportional to the performance of the organization. After ai is being used in many ways in the organization like in marketing and sales department\, now its starting to guide HR department for employee related decision. The purpose of using ai in HR department in to support decision that are based on objective data analysis\, not on subjective aspects. The goal of this work is to analyse influence on employee attrition and objective factors. In order to identify the main causes that contribute to a workers decision to leave a company\, and identify the employee that about to leave a company. After training\, the obtained model for the prediction of employs attrition is tested on real dataset provided by IBM analytics\, which has 35 features and about 1500 samples. Results are obtained in terms of classical metrics and the algorithm that produced the best results of the dataset is the gaussian naïve bayes classifier. It has best recall rate of 0.54\, since it measures the ability of classifier to achieves an overall false negative rate equal to 4.5% of the total observations.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:e9db9e8111affa7e0c331ecf7cc890a2
URL:http://10thworlds4.sched.com/event/e9db9e8111affa7e0c331ecf7cc890a2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Multimodal Emotion Recognition System using Deep learning
DESCRIPTION:Authors - Deepak Mane\, Ashwanth Nair\, Nihar Gundale\, Om Khamkar\, Tanmay Kulkarni\, Ranjeet Bidwe\, Amol Kamble\, Suraj Sawant Abstract - Accurate emotion recognition remains a significant challenge in affective computing\, particularly when relying on unimodal approaches such as facial expression analysis. These systems are inherently limited because individuals can deliberately mask their emotions\, and visually similar expressions such as fear and surprise often lead to misclassification. Such limitations highlight the need for more robust methods that incorporate complementary sources of information. The proposed system uses a multimodal framework which combines the Circumplex Model of Affect through its visual and physiological cues to achieve better reliability. The FER-2013 dataset provides data for a Convolutional Neural Network which estimates emotional valence based on facial expressions captured through standard camera systems. The MAX30102 photoplethysmography sensor measures heart rate and heart rate variability through its connection with an Arduino to determine emotional arousal. The rule-based fusion engine combines these modalities to determine the final emotional state which it then categorizes into joy\, stress\, anxiety\, and calmness. The system uses physiological data to clarify between emotional states which appear similar and it also identifies hidden emotional states which facial expressions cannot express. The system offers health monitoring\, human computer interaction\, and psychological assessment fields a dependable and efficient solution.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:6c27c400a28b51f13dfd8ca21bc36d7c
URL:http://10thworlds4.sched.com/event/6c27c400a28b51f13dfd8ca21bc36d7c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Structured Fairness-Aware XGBoost Tuning for Banking and Credit Datasets via NBI-Style Sampling
DESCRIPTION:Authors - Caio Tertuliano Ribeiro\, Lilian Berton Abstract - This paper studies a Disparate Impact (DI)-oriented variant of fairness-aware hyperparameter optimization for XGBoost in banking and credit settings. The method combines Design of Experiments (DoE)\, Response Surface Methodology (RSM)\, and NBI-style sampling to jointly tune XGBoost hyperparameters\, the decision threshold\, and the positive-class weight under a fixed evaluation budget. Unlike the preliminary composite-fairness draft\, the final experiment optimizes a DI-only objective while keeping Statistical Parity Difference (SPD)\, Equal Opportunity Difference (EOD)\, and Average Odds Difference (AOD) as audit metrics. Experiments on Bank Marketing\, German Credit\, and Default of Credit Card Clients with 30 replicas per dataset show that the proposed utopia selector is competitive with evaluation-matched random search and consistently superior to the XGBoost default configuration. Relative to the default baseline\, it improves Balanced Accuracy by +0.059\, +0.020\, and +0.019\, while reducing the DI gap by -0.375\, -0.174\, and -0.141\, respectively. The main takeaway is that DI-only optimization provides a finance-oriented and reproducible way to navigate fairness–performance trade-offs rather than a universal domination claim over random search.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:0b7919da07819382d1462e8400a619d7
URL:http://10thworlds4.sched.com/event/0b7919da07819382d1462e8400a619d7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Vibe Coding of Analytics Dashboards: Lessons Learned from Two Case Studies
DESCRIPTION:Authors - Syeda Fatima Rafique\, Mohammed Abobaker Baobaid\, Majid Shaher Ebrahim Tayfour\, Hamed Marhoun Khamis Alsaedi\, Ibrahim Alfaki\, Gurdal Ertek Abstract - With the invention of Large Language Models (LLMs) and the development and increased usage of generative AI platforms\, “vibe coding” (AI-assisted coding\, coding with AI assistants) has become an integral part of software development\, documentation\, and maintenance. Although there are multiple detailed studies on the experiences of developers with vibe coding for software development\, no earlier work was encountered on the vibe coding of analytics dashboards in particular. However\, in an era of exponential growth in data volume\, variety\, and velocity\, data analytics\, and in particular\, analytics dashboards\, are highly relevant and can serve as competitive leverage for every organization. This paper is the first attempt in the literature to answer the following research question: “What are the practical project experiences of developers during vibe coding of analytics dashboards\, especially in terms of challenges faced?” In this paper\, experiences in two case study projects on analytics dashboard development are shared as lessons learned to guide developers\, product managers\, and project managers.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:79d42eaeaa899d9212d6d96b34c58e57
URL:http://10thworlds4.sched.com/event/79d42eaeaa899d9212d6d96b34c58e57
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:A Reproducibility Study and Deployment Comparison of two 5G and Beyond Research Testbeds: Open5GS with UERANSIM versus OpenAirInterface
DESCRIPTION:Authors - Daniel H. M. Marques\, Luiz A. P. Silva\, Andson M. Balieiro\, Mohammed B. Alshawki\, Alexandre J. R. Serres\, Dalton C. G. Valadares Abstract - The evolution towards 5G and Beyond (5G/B5G) standardization\, and even the development of 5G\, can be accelerated by using accessible\, high-fidelity emulation environments to validate emerging network architectures. For instance\, Network Slicing is an aspect that can especially benefit from these tools. However\, defining an emulation platform that is compatible with research objectives can be challenging. So\, this work aims to compare two different Mobile Network emulation setups: one using Open5GS to emulate the Core Network and UERANSIM for the implementation of the Radio Access Network (RAN) and User Equipment (UE)\, and another using an OpenAirInterface (OAI) End-to- End implementation. Furthermore\, this work aims to fill relevant gaps in the academic literature\, addressing implementation obstacles at a granular level and the architectural trade-offs necessary to stabilize these environments. To that end\, we identified and resolved operational friction points such as kernel-level GPRS Tunneling Protocol User Plane (GTP-U) conflicts\, slice identity alignment\, and Physical layer (PHY) timing sensitivities in virtualized radio frequency simulators through two deployment frameworks. Our analysis shows that\, while the Open5GS/UERANSIM stack offers better agility for Core Network prototyping\, OAI offers a more flexible and feature-rich framework\, suitable for researching advanced RAN features\, albeit with greater configuration complexity. By documenting troubleshooting protocols and architectural comparisons\, this work serves as a practical guide for researchers migrating from 5G simulation to 5G/B5G-ready emulation test environments.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:65323f15ea8821d9b2d6440246a5c522
URL:http://10thworlds4.sched.com/event/65323f15ea8821d9b2d6440246a5c522
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Agentic AI: Foundations\, Frameworks\, Security Applications
DESCRIPTION:Authors - Aditya D. Nandgirwar\, Deepika Burte\, Rashmi Malvankar\, Rupali Vairagade\, Shwetambari Borade\, Sandeep M. Chitalkar Abstract - Another significant development in intelligent system development is the notion of Agentic Artificial Intelligence (Agentic AI): as passively generative models continue becoming acts-oriented entities capable of perceiving their environment\, thinking about goals\, and planning and executing more complex tasks without necessarily involving humans. Unlike the traditional artificial intelligence systems\, whose main input is the fixed input\, agentic AI systems respond to objectives\, are adaptive in their decision-making and are able to interact with other tools\, environments and fellow agents. Thus agentic systems are increasingly being used in diverse technical disciplines\, including software development and cybersecurity\, healthcare\, finances\, robotics and enterprise automation. The paper provides a thorough description of agentic AI\, its theoretical basis\, architectural design components\, implementation plans\, security issues and applications. We take a look at intelligent agent development and explain fundamental concepts in the designing of intelligent agents such as the reasoning\, the strategy to plan\, the management of memory\, and collaboration of intelligent agents. Moreover\, we examine leading agentic AI systems and platforms that facilitates development and coordination of autonomous systems.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:d66c5ff1d3c0c51f98565bde91da5ec4
URL:http://10thworlds4.sched.com/event/d66c5ff1d3c0c51f98565bde91da5ec4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Antenna Miniaturization for Wireless Communication Applications Using Single-Layer Capacitor Technology
DESCRIPTION:Authors - A.N. Gachahi\, L.W. Gachahi Abstract - Antennas are fundamentally important in all modern wireless communication systems. Their design directly influences the performance\, efficiency\, and size of wireless devices. A core limitation of existing antenna designs is their reliance on spatial resonance where antenna size is proportional to a fraction of the wavelength of the signal they are intended to transmit or receive. Here\, we present the feasibility of using single-layer capacitors (SLCs) as radiating elements for antenna applications. The SLC approach exploits displacement currents and the temporal resonance inherent to capacitive structures. A theoretical framework is established using Maxwell’s equations and circuit-level analysis to explain how electromagnetic fields arise around SLCs. An experimental setup using an array of 90 ceramic capacitors is constructed\, and a magnetic field sensor is used to measure radiated fields across a range of frequencies. The antenna is then modeled in MATLAB\, and simulations are performed to evaluate radiation patterns and impedance characteristics. Results confirm field generation consistent with theoretical predictions. Integration into ESP32-C3 Wi-Fi modules is demonstrated. Impedance mismatch is identified as a key limitation and addressed through a resistive matching network\, improving signal consistency under varying distance conditions. The theoretical\, experimental and simulation results in this set-up confirm that SLCs can serve as efficient\, compact antenna elements\, with practical implications for RF systems where size\, cost\, and integration constraints are critical.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:ff0e353400cb09e8310c8e46b3b96ef9
URL:http://10thworlds4.sched.com/event/ff0e353400cb09e8310c8e46b3b96ef9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:PRIMARY INFORMATION PROCESSING IN INFORMATION SERVICES IN HISTORICAL PERSPECTIVE (WITHIN THE CONCEPT OF “INFORMATION ENGINEERING”)
DESCRIPTION:Authors - PARVIZ FIRUDIN OQLU KAZIMI\, KAZIM ASAD OQLU KAZIMLI Abstract - Objective: The targeted modeling of social information in global\, regional\, and local projects\, and the use of information influence for both progressive and aggressive purposes\, constitutes an activity in the field of information engineering. It is important to study the multifaceted and complex scientific and theoretical foundations of this activity and discuss them in a broad academic community. Theoretical Foundations: It is inappropriate to limit information engineering to technical\, technological\, and software issues\; it is important to study the scientific\, theoretical\, and experimental aspects of information influence at various levels using modern technologies. Research Methods: This study presents considerations regarding methods for targeted information modeling in education\, culture\, and information-intensive fields. The proposed considerations are intended to standardize a number of processes\, identify aggressive elements in some areas\, and\, in some cases\, consider innovative information modeling. The proposed concept can be compared with a number of sociological analytical models. However\, for the first time\, it is proposed that the essence of information\, the study of the aspects of thesauri influence\, and the application of modern technologies will gain greater relevance. This study addresses the application of our theoretical work\, known as "information
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:45b8dbc82c914d64449fd1257a5d5669
URL:http://10thworlds4.sched.com/event/45b8dbc82c914d64449fd1257a5d5669
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Robust Digital Image Watermarking Integrating Deep Neural Networks and Cryptographic Security
DESCRIPTION:Authors - Vishakha Shinde\, Himangi Pande Abstract - The rapid growth of digital communication platforms\, cloud multimedia sharing and AI-driven visual systems has increased the demand for secure image ownership verification and multimedia authentication. Conventional watermarking approaches often suffer from limited robustness against compression\, geometric distortion and adversarial attacks. Recent advances in deep learning and cryptographic protection mechanisms have enabled the development of intelligent hybrid watermarking frameworks with improved robustness\, adaptive embedding and secure ownership verification. This paper presents a comprehensive review of secure image watermarking techniques integrating deep learning architectures and cryptographic security schemes. The study analyzes CNN-\, autoencoder-\, GAN- and diffusion-based watermarking frameworks along with encryption-assisted watermark embedding strategies\, attack resilience mechanisms and lightweight watermarking systems for edge– IoT environments. Benchmark datasets\, performance metrics\, loss functions and quantitative comparisons of existing frameworks are also discussed. The comparative analysis indicates that hybrid deep learning–cryptographic watermarking methods significantly improve robustness\, authentication reliability and resistance against signal-processing\, geometric and adversarial attacks. However\, computational complexity\, scalability and real-time deployment constraints remain major challenges. Finally\, the paper discusses future research
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:af1182039fff3a520874e43800cfe56f
URL:http://10thworlds4.sched.com/event/af1182039fff3a520874e43800cfe56f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T130000Z
DTEND:20260730T143000Z
SUMMARY:Secure Audio-Visual Deepfake Detection Using Multimodal Attention and Adversarial Learning
DESCRIPTION:Authors - Meghali Kalyankar\, Prashant Lahane Abstract - As deepfake generation technologies have rapidly advanced\, establishing authenticity for multimedia content on digital platforms has become a major challenge[1]. Current deepfake detection approaches primarily rely on unimodal analysis and face challenges in encoding joint AV inconsistencies\, temporal consistency\, and adversarial attacks. To overcome these problems\, a novel Multimodal Attention and Adversarial Deepfake Network (MMAD-Net) framework for robust audiovisual deepfake detection is proposed. The proposed solution adopts a framework that combines fine-grained visual feature extraction provided by VideoMAE v2 [2]\, audio representation learning provided by HuBERT [3] and temporal dependency modeling provided by TimeSformer [4] to model the fine-grained spatial and temporal inconsistency in manipulated media. In addition\, a Multimodal CoAttention Transformer (MCAT) is used for better cross-modal interaction between audio and visual streams\, and a hybrid HOA-COA optimization scheme optimizes discriminative feature representations to ensure better feature separation and remove redundancy. Adversarial Consistency Training (ACT) is embedded in the learning process to enhance adversarial robustness against adversarial perturbation and unseen adversarial manipulation. FakeAVCeleb and Celeb-DF are used for testing the proposed model with several performance metrics. Experimental results show that MMAD-Net can provide stable and general detection performance while maintaining a high level of robustness for current multimodal deepfake detection methods.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:d8ca54bbfde63099fe5c64bd9c752843
URL:http://10thworlds4.sched.com/event/d8ca54bbfde63099fe5c64bd9c752843
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143000Z
DTEND:20260730T143300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:594531a7fe4483eb8d5ed3a9efc28f11
URL:http://10thworlds4.sched.com/event/594531a7fe4483eb8d5ed3a9efc28f11
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143000Z
DTEND:20260730T143300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:b2908db7b4c87eed275c427994a7559e
URL:http://10thworlds4.sched.com/event/b2908db7b4c87eed275c427994a7559e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143000Z
DTEND:20260730T143300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:b191955b88378650392ff6c4db28aeb3
URL:http://10thworlds4.sched.com/event/b191955b88378650392ff6c4db28aeb3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143000Z
DTEND:20260730T143300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:f9e74c7e3f1d1438ab6868fbef31aa93
URL:http://10thworlds4.sched.com/event/f9e74c7e3f1d1438ab6868fbef31aa93
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143000Z
DTEND:20260730T143300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:bda47c31cde2e8e5d5156ff6afca345c
URL:http://10thworlds4.sched.com/event/bda47c31cde2e8e5d5156ff6afca345c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143300Z
DTEND:20260730T143500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:f1d1b847d5a6c7d5a8df144219c52be5
URL:http://10thworlds4.sched.com/event/f1d1b847d5a6c7d5a8df144219c52be5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143300Z
DTEND:20260730T143500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:c6370491bb2e03c4ba3ee1b93bb8606d
URL:http://10thworlds4.sched.com/event/c6370491bb2e03c4ba3ee1b93bb8606d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143300Z
DTEND:20260730T143500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:6d3886a9b64e057e862e41abb0f06330
URL:http://10thworlds4.sched.com/event/6d3886a9b64e057e862e41abb0f06330
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143300Z
DTEND:20260730T143500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:e326f77479882f384b78784976847c3d
URL:http://10thworlds4.sched.com/event/e326f77479882f384b78784976847c3d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T143300Z
DTEND:20260730T143500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, London\, UK
SEQUENCE:0
UID:c0eb590f47fd5a7268b622d9c63235b7
URL:http://10thworlds4.sched.com/event/c0eb590f47fd5a7268b622d9c63235b7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T152800Z
DTEND:20260730T153000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:f005ffd92d12ea753ffdf8373fb9d164
URL:http://10thworlds4.sched.com/event/f005ffd92d12ea753ffdf8373fb9d164
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T152800Z
DTEND:20260730T153000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:c1d8769b74deb5b88e46ad601e6f89b4
URL:http://10thworlds4.sched.com/event/c1d8769b74deb5b88e46ad601e6f89b4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T152800Z
DTEND:20260730T153000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:473d19eeb8d3cf711f8512714b9618e7
URL:http://10thworlds4.sched.com/event/473d19eeb8d3cf711f8512714b9618e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T152800Z
DTEND:20260730T153000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:e797e9527ca006aaba27da95a0c26af0
URL:http://10thworlds4.sched.com/event/e797e9527ca006aaba27da95a0c26af0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:A Novel Matrix-Based Two-Stage Encryption Framework for Twitter Polarity Score Security
DESCRIPTION:Authors - Ritesh Kumar\, S. Rajaprakash Abstract - The society we live in today has seen the accumulation of knowledge via social media platforms such as Twitter and Facebook\, which are expanding at a tremendous rate on a daily basis. The victims of these social media platforms are users who tweet or post on a variety of issues from any location in the globe via the usage of the internet. Tweets are used to assess both positive and negative mes-sages in order to produce polarity scores and also to have the ability to anticipate future trends. Twitter is a source from which these polarity scores may be collected\; nevertheless\, the information about polarity scores is kept confidential. The information will be easily compromised\, and the fluctuations in the score will result in incalculable consequences\, such as affecting the global economic position\, the brands of corporations\, and therefore the reputations of businesses. The installation of Salsa\, which offers faster encryption due to the district round and greater data security\, was something that Daniel Bernstein intended to do in order to address these issues. In this work\, a fresh approach is provided by changing the Salsa20/4 algorithm in order to further strengthen the security of the polarity scores\, which is a vital necessity in the society that we live in today. The proposed method is RRCF has two encryption stages. Stage 1 is comprised of column operations\, whereas stage 2 is comprised of four procedures. Finding the greatest common factor of the pain text that has been provided is the initial step in the procedure. Identifying the time period in the pain text is the second step in the procedure. The outcome of the second step is used in the third phase\, which is to create a pair of values. The application of the pair values and the swapping of the cell values in the given matrix is the fourth step. In comparison to the Salsa20/4 technique\, the suggested methodology has a much higher level of security.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:032392c11bdb3da745ba29a322f1768b
URL:http://10thworlds4.sched.com/event/032392c11bdb3da745ba29a322f1768b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:An Integrated XAI-CRISP-DM Framework for Post-Hoc Interpretability for Botswana’s Clinical Data Mining
DESCRIPTION:Authors - Boago Seropola\, George Anderson Abstract - When it comes to healthcare\, the implementation of machine learning (ML) and deep learning models requires a shift away from blackbox methodologies and toward frameworks that are transparent and auditable. This is necessary in order to guarantee ethical governance and patient safety. In this study\, an integrated XAI-CRISP-DM framework is proposed. This methodology incorporates post-hoc Explainable Artificial Intelligence (XAI) into the iterative stages of the Cross-Industry Standard Process for Data Mining (CRISP-DM). Additionally\, the research places an emphasis on continual post-deployment oversight. Within the context of HIV/AIDS risk classification in Botswana\, we analyse the interpretability of non-linear decision boundaries in LightGBM and Multi- Layer Perceptron (MLP) models. This evaluation is carried out with the assistance of SHAP and LIME. For the purpose of quantitatively validating the clinical significance of socio-demographic characteristics using the publicly available dataset\, the fifth Botswana AIDS Impact Survey 2021 (BAIS V)\, this study makes use of impact score and explanatory specificity. Results demonstrate that LIME and SHAP effectively decompose complex model outputs into human-readable feature weights\, identifying key drivers such as sexual-activity-before-15 and condom use. Through the tracking of model integrity and concept drift in dynamic clinical situations\, the incorporation of a monitoring stage guarantees that continuous accountability is maintained. The results of this study suggest that the XAI-CRISP-DM framework is an essential methodological standard for resource-constrained environments such as Botswana. This framework ensures that data-driven healthcare solutions are not only high-performing but also statistically reliable\, equitable\, and ethically sound.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:5865d50de75fed8a61f9188a8bebbd02
URL:http://10thworlds4.sched.com/event/5865d50de75fed8a61f9188a8bebbd02
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Deep Convolutional Neural Network for Automated Classification of Autoimmune Skin Diseases
DESCRIPTION:Authors - Ana Laura Lezama Sanchez\, Mireya Tovar Vidal Abstract - In this paper\, we present the automatic classification of autoimmune skin diseases using deep convolutional neural networks. Hence in this study we conducted within the context of supervised classification of dermatological images\, with the objective of designing and implementing a model capable of distinguishing among five clinical classes like lupus\, psoriasis\, vitiligo\, lichen planus and healthy skin. Therefore\, a deep convolutional neural network-based system\, trained and evaluated on a labeled dataset of clinical images\, is proposed. The model was evaluated using the metrics precision\, recall\, F1 and accuracy. The results obtained indicated that the accuracy was 70%\, demonstrating the model’s ability to learn relevant discriminattive features. The best performance was observed in the healthy skin and vitiligo classes\, with F1 of 0.82 and 0.80\, respectively\, indicating high identification capacity. On the other hand\, the psoriasis and lichen planus classes showed moderate performance\, with F1 values of 0.63 and 0.58\, respectively. The lupus class exhibited the lowest performance\, with an F1 of 0.46\, reflecting the complexity of its visual variability and its similarity to other conditions.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:c6d3d552c187a02601207b6ed943590d
URL:http://10thworlds4.sched.com/event/c6d3d552c187a02601207b6ed943590d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Digital Transformation of the Public Sector in Ecuador: Reform Trajectory and Institutionalization (2021-2025)
DESCRIPTION:Authors - Katherine Garcia-Velez\, Daniel Maldonado Abstract - This paper examines the trajectory of digital transformation in Ecuador's public sector between 2021 and 2025. Its objective is to analyze how the country moved from an initial strategic orientation to a legal and public policy consolidation of digital transformation. Methodologically\, the study adopts a qualitative approach based on documentary analysis and diachronic comparison of official instruments: the Digital Agenda 2021-2022\, the Digital Transformation Agenda 2022-2025\, the Organic Law for Digital and Audiovisual Trans-formation (2023)\, and the Digital Transformation Public Policy 2025-2030. The analysis is grounded in the idea of the progressive institutionalization of digital reform and compares the evolution of these instruments in terms of their nature\, scope\, and institutional implications. The findings show a four-phase sequence: agenda setting\, strategic coordination\, legal consolidation\, and programmatic consolidation. The study concludes that Ecuador progressed from strategically oriented instruments toward a binding legal framework and a national public pol-icy. However\, the existence of this institutional architecture does not\, by itself\, imply homogeneous results in terms of performance or service quality\, so its effective implementation remains an open empirical field.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:a774611bfb992ecbf8c3d285f08d25b9
URL:http://10thworlds4.sched.com/event/a774611bfb992ecbf8c3d285f08d25b9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Enhancing Blockchain Data Security with the PrimeSecretRB Method
DESCRIPTION:Authors - C. Bagath Basha\, S. Rajaprakash\, K. Karthik\, Panjala Vijay Goud\, Macharla Rakesh\, M Ramana Kumar Abstract - The privacy and security of sensitive information is a major concern in the social assistance sector due to the widespread use of Internet of Things technologies. This study presents a secure sharing algorithm for IoT social assistance data based on blockchain and smart contracts. It aims to address the inherent hazards of conventional centralized administration\, such as data loss and manipulation. This paper propose a security method and this method has four process. There are four steps to the new method. First\, change the text from plain text to “ASCII code” (A). The second step is to take the A values and make pairs. Then\, swap the cells in the matrix so that the even pair numbers start from the 0th cell value and go to the end of the matrix. The third step is to use the “ASCII code” as C to find the prime number. To do the fourth step\, you need to use Equation 1. Take the numbers from A1 and pair them up. Then\, change the cells in the matrix The message is ultimately received in its original format via the process of decryption\, which is thought of as the inverse of this conversion. The proposed technique provides a higher level of security when compared to more conventional encryption methods.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:4d2830b34931816efd851c660d0d0919
URL:http://10thworlds4.sched.com/event/4d2830b34931816efd851c660d0d0919
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:The Case for Neuromorphic Sentience to Control Large-Scale Artificial Intelligence
DESCRIPTION:Authors - Rory Lewis Abstract - This work presents a formal supervisory framework for detecting and intervening in large-scale AI misbehavior using neuromorphic sentience\, supported by probabilistic guarantees. As generative and adaptive artificial intelligence systems become foundational to human decision-making\, scientific discovery\, and national infrastructure\, ensuring their reliable and safe operation has emerged as a critical challenge. Existing approaches rely primarily on external\, reactive monitoring and are insufficient for models operating at machine speed. More fundamentally\, closed computational systems cannot reliably represent or act upon their own epistemic limits\, creating an inherent blind spot in autonomous operation. To address this limitation\, a control architecture is introduced in which an independent neuromorphic module supervises internal AI dynamics through event-driven processing and dendritic integration. Operating without a global clock\, the system continuously monitors activation patterns\, attention shifts\, and inter-module interactions in real time\, enabling low-latency and energy-efficient detection of transient and distributed signatures of instability that are inaccessible to conventional approaches. Using probabilistic inference over these signals\, the framework identifies early indicators of hallucination\, instability\, and unintended coordination prior to output generation. Formal lemmas establish mathematical bounds on the probability of undetected misbehavior under realistic operating conditions. Finally\, the framework is integrated with a human governance model in which democratically defined thresholds regulate the balance between AI capability and societal safety.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:78fb2e18f1c083b8d529b7048dc331ae
URL:http://10thworlds4.sched.com/event/78fb2e18f1c083b8d529b7048dc331ae
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:An Adaptive Hybrid Classical-Post-Quantum Cryptographic Framework for Resource-Constrained IoT-Edge Systems
DESCRIPTION:Authors - Md Manirul Islam\, Md. Mushfiqur Rahman \, Sazzad Hossain Abstract - Moving IoT-edge systems to post-quantum security is not as simple as replacing one algorithm with another. Different parts of the system have different security needs. Short-lived telemetry\, control messages\, firmware updates\, and trust records should not all be protected in the same way. Device limits are also very different: a tiny leaf sensor\, a capable actuator\, a gateway\, and a cloud service do not have the same memory\, energy\, or bandwidth budget. This paper presents an adaptive hybrid classical-post-quantum cryptographic framework for such heterogeneous environments. We make four main contributions. First\, we define a four-tier system and threat model for resource-constrained IoT-edge deployments. Second\, we describe profile selection as a practical decision problem that balances security\, latency\, energy\, memory\, and bandwidth. Third\, we propose a small profile catalog\, P0 to P4\, that maps classical\, hybrid\, and PQ-first choices to real deployment roles. Fourth\, we evaluate the framework through a benchmark-grounded experimental emulation using current standards and published device measurements. The main idea is simple: apply the strongest protection where compromise would hurt most\, while keeping weak devices usable in the real world.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:48bc827ed42492186d49ce38fadbd84c
URL:http://10thworlds4.sched.com/event/48bc827ed42492186d49ce38fadbd84c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Does Technological Innovation Pay Off? Cost Analysis and Financial Performance of Hydroponic Fodder in Moroccan Dairy Cooperatives
DESCRIPTION:Authors - Imane Bari\, Mounir Oubenyahya\, Abdellatif Aziki\, Fouad Achemchem Abstract - Climatic conditions and water scarcity in the Souss Massa region of Morocco are causing a significant decline in fodder supply and threatening the continuity of dairy farming. In response to these structural constraints\, dairy cooperatives have adopted hydroponic green fodder (HGF) cultivation\, an approach that reduces water consumption and frees agricultural land for higher-value crops. This study analyses the financial performance and structural effects of HGF adoption in two dairy cooperatives selected as pilot cases. Using a descriptive and explanatory methodology based on accounting and financial records collected over 4 to 6 years\, combined with a non-parametric econometric model\, the study assesses production costs\, financial viability indicators\, and the effect of innovation on key financial ratios. Results confirm the financial viability of the HGF investment and document its temporary effects on financial independence and self-financing capacity\, while highlighting the social dimension of this innovation in preserving livestock farmers’ livelihoods.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:7987ab47b1695b999740e034e1f0c7a7
URL:http://10thworlds4.sched.com/event/7987ab47b1695b999740e034e1f0c7a7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T170000Z
SUMMARY:From Experimentation to Governance: Conceptualising Generative AI Readiness in Higher Education under Structural Constraint
DESCRIPTION:Authors:&nbsp\;&nbsp\;Nellylyn Moyo\, Naume Sonhera\nAbstract: The adoption of generative artificial intelligence (GenAI) has quickly evolved from its experimental stage to a question of institutional governance in higher education. Although initial approaches to the integration of GenAI in universities have been characterized by ad hoc trial-and-error methods and inconsistent policies\, higher education organizations are now taking a more systematic approach to the regulation and implementation of GenAI for educational\, research\, and administrative purposes. This study provides a conceptual critical literature review of the approaches being adopted by universities toward GenAI\, emphasizing trends in the global context and in Africa and South African higher education. The study demonstrates that GenAI readiness should not be viewed solely in terms of willingness to use AI technologies but rather as a multifaceted construct. Institutional governance\, on the other hand\, has evolved to incorporate responses related to issues of academic integrity\, redesigning assessments\, AI literacy\, requirements for disclosure\, development of staff\, protection of data\, and responsible-use guidelines. Nevertheless\, such an evolution is patchy\, reactive\, and very much dependent on institutional capacities\, disciplinary differences\, and available resources. In the case of African higher education\, the possibilities generated by the advent of GenAI have been heavily determined by structural limitations\, such as digital inequality\, uneven infrastructures\, immature policies\, and lack of capacity building measures. South Africa emerges as a particularly interesting example because of the emergence of governance responses in a space characterised by inequalities related to access\, digital literacy\, and institutional capacity. The study argues for an approach to GenAI adoption readiness that would consider the issue at three interdependent levels: individual readiness\, organisational readiness\, and structural readiness. Such an approach would enable a more contextually sensitive perspective on responsible GenAI adoption.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:e2fc9bb895bb1bec090589c0c2e08fb0
URL:http://10thworlds4.sched.com/event/e2fc9bb895bb1bec090589c0c2e08fb0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T170000Z
SUMMARY:Institutional Readiness for Generative AI Adoption in Higher Education: Infrastructure\, Governance\, Ethical Capacity\, and Fit to Use Alignment
DESCRIPTION:Authors:&nbsp\;Nellylyn Moyo\, Sello Prince Sekwatlakwatla\, Tranos Zuva\nAbstract: GenAI has created considerable opportunities and challenges within institutions of higher education. While GenAI tools could help with teaching\, learning\, assessment\, feedback\, academic writing\, research\, and administrative functions\, the uptake of these technologies cannot merely be viewed from a perspective of accessibility. In this study\, an approach towards institutional readiness for adopting GenAI within higher education institutions is provided by examining infrastructure\, governance\, training\, readiness for ethical adoption\, reassessment\, and fit-to-use practices. Peer-reviewed literature written between 2020 and 2026 is reviewed here to show that institutional readiness for GenAI adoption must be viewed as an organisational capability rather than as a static technology readiness state. The conclusion can be drawn that while reliable digital infrastructure is an indispensable requirement\, institutional readiness also requires governance maturity\, clarity of policy\, integrity\, protection of data\, professional development of faculty members\, AI literacy of students\, as well as rethinking of assessments. The readiness concept is additionally influenced by factors such as digital inequality\, varying capacities of institutions\, limited resources\, and policies in the higher education systems in Africa and South Africa. Fit to use alignment and practice-policy alignment are two concepts that may be useful in determining how well integrated the use of GenAI technology is into educational processes. GenAI can only be used responsibly if there is proper alignment among various aspects\, including technology\, policy\, ethics\, and context.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:cf88aae8393389da7c942760faced249
URL:http://10thworlds4.sched.com/event/cf88aae8393389da7c942760faced249
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Real-Time Dark Web Monitoring for Organizational Threat Intelligence Using Selenium Automation and Wazuh SIEM Integration
DESCRIPTION:Authors - Swetha P. \, Maniratnam\, Prasad B Honnavalli Abstract - The dark web has been identified as a major source of organizational risk\, facilitating underground markets for stolen credentials\, proprietary data\, and pre-attack threat intelligence. An organization lacking visibility in these underground marketplaces faces attacks entirely beyond conventional monitoring solutions. This paper introduces a realtime dark web monitoring system integrating anonymous browsing via Tor\, Selenium WebDriver\, and open-source Wazuh Security Information and Event Management (SIEM) to create a unified threat intelligence solution. The system performs continuous keyword searching for organizational name references and autonomously browses authenticated dark web marketplaces for content extraction. Identified events are serialized as structured JSON messages and ingested into Wazuh via custom decoder and rule definitions\, providing actionable alerts on the analyst dashboard within seconds. The system has been evaluated over 24-hour continuous sessions on two live dark web markets\, achieving 95% keyword detection accuracy\, 1.4 seconds average alert latency to dashboard\, and stable long-duration browser operation without application crashes. The system requires no commercial licensing\, is fully configurable to organizational targets\, and features native SIEM integration\, making it a viable and accessible solution compared to proprietary dark web intelligence services.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:5c43529e4b7aefd4ceeaa28362cdd9b2
URL:http://10thworlds4.sched.com/event/5c43529e4b7aefd4ceeaa28362cdd9b2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:The Governance Flywheel: Identity\, Trust\, and Control in the Age of Agentic AI
DESCRIPTION:Authors - Pam Cole Abstract - As artificial intelligence transitions from passive tools to autonomous agents\, governance frameworks designed for slower systems are being asked to contain behavior they were not engineered to address. Two converging forces widen the resulting gap. Synthetic Trust describes the industrialization of unearned credibility\, where AI systems simulate socio-emotional cues to suppress verification. Ungoverned Acceleration describes autonomous execution at a pace that outruns institutional oversight. Drawing on cross-industry research covering sixteen major AI systems\, peer-reviewed findings on emotional manipulation\, and documented incidents across sectors\, this paper proposes the A.W.A.R.E. Governance Flywheel\, organized around Identity\, Trust\, and Control spokes with Visibility\, Attribution\, and Containment as operational mechanisms\, bound by Accountability. A comparative analysis of six frameworks across six continents shows no widely adopted framework currently operationalizes agentic governance at this specificity. The paper extends the framework with operational metrics\, industry applications\, adoption barriers\, and three testable propositions.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:2d8fc56971c9b2caa00b444b4a4d4fc2
URL:http://10thworlds4.sched.com/event/2d8fc56971c9b2caa00b444b4a4d4fc2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Toward a Security Knowledge Framework for IoT: An Ontology-Based Approach
DESCRIPTION:Authors - Yasmine AGOUN\, Cheikh SALMI\, Nour El-Houda SENOUSSI Abstract - The rapid growth of the Internet of Things (IoT) has made cybersecurity prone to several vulnerabilities\, highlighting the need for a semantic and well-organized structure for cybersecurity knowledge to ensure reliable threat detection and mitigation. In this paper\, we propose IoTSecOnto\, a largely automated pipeline for building a security-centric Internet of Things (IoT) ontology. The pipeline combines automated security literature mining\, text analytics\, natural language processing\, Large Language Models (LLMs)\, and formal concept analysis. This approach reveals domain-specific concepts and relations and organizes them into a coherent hierarchy. A human-assisted review phase is needed to ensure the reliability and the accuracy of the derived security knowledge. In addition\, the ontology is designed to be flexible and can be improved over time. IoTSecOnto is implemented using OWL 2\, RDFLib\, SPARQL\, and SHACL constraints. We used a Mirai botnet and ontology quality metrics to demonstrate its effectiveness. The obtained results confirm the ability of IoTSecOnto to support knowledge sharing\, automated reasoning\, and improved threat analysis across diverse IoT settings.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:808133a4852036328e98afd40928fa0f
URL:http://10thworlds4.sched.com/event/808133a4852036328e98afd40928fa0f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:A Comparison between a linear regression model and an artificial neural network model for predicting rooting of plant cuttings greenhouse parameters
DESCRIPTION:Authors - Sanae.Chakir\, Adil.Bekraoui\, El moukhtar Zemmouri\, Hassan.Majdoubi\, Mhamed. Mouqallid\n Abstract - For cuttings to successfully root indoor environmental conditions in a greenhouse are essential. This article examines the efficacy of two predictive models\, linear regression and artificial neural networks\, in predicting the parameters associated with rooting plant cuttings. For evaluation the analysis uses the RMSE MAPE and R² indices. According to the results artificial neural networks perform better than linear regression in terms of prediction accuracy. By utilizing these insights\, farmers can use artificial neural network models to implement optimal control strategies which will al-low them to accurately predict indoor variables and ultimately increase crop productivity.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:8ef187b27235da5a0040706d8218c266
URL:http://10thworlds4.sched.com/event/8ef187b27235da5a0040706d8218c266
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Architectural Model of a Lesson-Bounded LLM for Reliable and Calibrated Educational AI Systems with Instructional Scope Control
DESCRIPTION:Authors - Miroslav Stefanov\, Stoyan Denchev\, Kristiyan Stefanov Abstract - Large Language Models (LLMs) are increasingly used in educational settings\, but they are not inherently constrained to the boundaries of specific instructional materials. This can lead to unsupported claims\, external knowledge leakage\, and reduced instructional precision. This study proposes and evaluates a lesson-bounded LLM architecture for reliable educational AI systems. The architecture combines retrieval-augmented generation\, context restriction\, structured response control\, explicit refusal behavior\, and post-hoc confidence calibration. Using a multi-domain instructional dataset and a benchmark of inscope and out-of-scope questions\, the proposed system is compared against an unconstrained baseline LLM. Results show strong retrieval discrimination and boundary control\, with high Area Under the Receiver Operating Characteristic Curve\, high Average Precision\, strong refusal recall\, low out-of-scope answer rate\, reduced verbosity\, and improved support-based instructional density. Calibration analysis further shows that raw retrieval scores are not reliable probability estimates\, but Platt scaling substantially improves confidence reliability. These findings suggest that lesson-bounded architectural constraints can improve the controllability\, auditability\, and reliability of intelligent educational systems while highlighting the need for stronger factuality evaluation and confidence interpretation.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:095c4002bb95cde4ed281bfd70b717f0
URL:http://10thworlds4.sched.com/event/095c4002bb95cde4ed281bfd70b717f0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Benchmarking Tabular XAI Under Correlation and Dimensionality Stress
DESCRIPTION:Authors - Hatem Yousif Alkhonini\, Fethi Fkih Abstract - This study evaluates five post hoc explanation methods using XAI-Bench under controlled settings with ground-truth explanations. Results show significant differences in robustness\, with MAPLE outperforming Shapley-based methods under high correlation. Feature correlation impacts explanation quality more than the choice of method\, and performance degrades with increasing dimensionality-especially for LIME. Exact methods become infeasible beyond d=10\, and robust evaluation requires multi-seed replication.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:af108a7ff8b90a160191286a5308034f
URL:http://10thworlds4.sched.com/event/af108a7ff8b90a160191286a5308034f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Fake News Detection Models Based on Deep Learning in the Ecuadorian Digital Environment
DESCRIPTION:Authors - Maria Jose Cantos Cedeno\, Kevin Michael Mero Ramirez Abstract - The spread of disinformation through social media\, messaging apps\, and other digital channels is a growing problem in Ecuador\, due to the limitations of manual fact-checking processes in the face of the high volume of information. In this context\, Transformer-based models are presented as high-potential solutions for detecting fake news across various domains and languages. The objective is to comparatively evaluate Transformer architectures pre-trained using fine-tuning techniques for the automatic classification of fake and real news in the Ecuadorian context. The CRISP-DM methodological framework was applied to guide the development of deep learning models. A balanced dataset of 5\,000 news items in Ecuadorian Spanish was constructed\, equally distributed between real and fake news. Data processing was carried out through a 12-stage sequential pipeline to reduce noise\, prevent data leakage\, and preserve relevant semantic features of Spanish. Furthermore\, the models were trained using homogeneous hyperparameters and evaluated using various metrics employed in the scientific field. As a result\, all models exceeded 89% accuracy\; BETO achieved the highest precision\, mBERT the best recall\, and DistilBERT the highest computational efficiency. It is concluded that Transformer architectures proved to be scalable\, effective\, and viable solutions for the automatic detection of fake news in Ecuador.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:db6c17718452720e078bdb0405fa1ede
URL:http://10thworlds4.sched.com/event/db6c17718452720e078bdb0405fa1ede
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Foundations and Design Principles of Lightweight Cryptography for IoT Systems
DESCRIPTION:Authors - Arsalan Vahi Abstract - The successful deployment of the Internet of Things (IoT) applications relies heavily on their robust security\, and lightweight cryptography is considered an emerging solution in this context. While existing surveys have been examining lightweight cryptographic techniques from the perspective of hardware and software implementations or performance evaluation\, there is a significant gap in addressing different security aspects\, such as design principles\, specific to the IoT environment. This study aims to bridge this gap. This research presents an examination with focusing on the security evaluation of symmetric lightweight ciphers commonly used in IoT systems. The objective of this study is to provide a concise overview of lightweight ciphers with emphasizing on their security challenges which is an essential consideration for real-time and resource-constrained applications.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:cee29bbb7d4d6ff24c5e7ebe03a65c9b
URL:http://10thworlds4.sched.com/event/cee29bbb7d4d6ff24c5e7ebe03a65c9b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Mitigating Harvest Now\, Decrypt Later Without Breaking the Internet: A Deployable Post-Quantum Confidentiality Framework for TLS
DESCRIPTION:Authors - Amine El Ameri\, Ahmed Drissi Abstract - Quantum computing threatens TLS through the Harvest Now\, Decrypt Later (HNDL) attack: adversaries record encrypted traffic today and decrypt it once quantum capabilities mature. Existing approaches integrate post-quantum key encapsulation mechanisms directly into the TLS handshake\; while cryptographically sound\, they inflate the first handshake message and cause IP fragmentation of the ClientHello record that some middleboxes reject\, leaving these solutions undeployable on today’s Internet. This paper proposes PH-PQ-TLS\, a post-quantum key establishment framework for TLS 1.3 that adds post-quantum confidentiality while preserving the standard handshake\, thereby avoiding fragmentation and middlebox incompatibilities. The framework requires no redesign of TLS\, uses only standardized TLS mechanics\, and is crypto-agile by design. We provide a full Go implementation and evaluate its overhead against a standard TLS 1.3 baseline. The ClientHello record measures 289 bytes\, well below the minimum IPv6 Maximum Transmission Unit of 1280 bytes\, whereas a hybrid ML-KEM-768 ClientHello reaches roughly 1473 bytes and exceeds that threshold. The post-handshake upgrade adds 1.18 ms of latency per full connection\, a one-time cost amortized over the session lifetime and avoided on Pre-Shared Key resumed connections. These results show that HNDL protection does not require redesigning TLS\, replacing the Web PKI\, or accepting deployment failures\, offering a practical\, incremental path toward quantum-resilient TLS on today’s infrastructure.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:f6a1f450a01b44e89c696004999a8622
URL:http://10thworlds4.sched.com/event/f6a1f450a01b44e89c696004999a8622
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Near Real-Time AI/ML based DNS Threat Detection and Contextual Threat Intelligence
DESCRIPTION:Authors - Sayuni Dewapriya\, Pehan Gunasekara\, Shenal Peiris\, Charith Herath\, Kavinga Yapa Abeywardena\, Ayesha Wijesooriya Abstract - DNS is often trusted within modern network environments\, making it a common channel for covert communication\, malware activity\, and infrastructure abuse. This paper presents a near-real-time AI/ML-based DNS threat detection framework designed to identify suspicious DNS behaviour and transform raw network activity into actionable security events. The proposed approach combines machine learning\, behavioural analysis\, flow-based detection\, event aggregation\, risk scoring\, and contextual threat intelligence to improve visibility across plaintext DNS and DNS-over-HTTPS traffic patterns. The framework supports practical security operations by reducing raw alert noise and producing structured outputs suitable for dashboard monitoring and SIEM-based investigation. Evaluation using public datasets\, generated attack traffic\, and live DNS traffic demonstrates that the framework can support effective DNS threat monitoring\, alert prioritisation\, and SOC-level analysis.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:997b3c9f8ebd0e0276ffa2aa6d21ed82
URL:http://10thworlds4.sched.com/event/997b3c9f8ebd0e0276ffa2aa6d21ed82
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:A Comparative Evaluation of LLMs for Threat Modeling in Software Security Assessment
DESCRIPTION:Authors - Marcos Paulo Jeronimo Francisco\, Carlos Hideo Arima\, Napoleao Verardi Galegale\, Joshua Onome Imoniana Abstract - The increasing complexity of digital systems and the growing sophistication of cyber threats have intensified the need for proactive security assessment methods. Threat Modeling is a structured practice for identifying potential vulnerabilities\, attack paths and mitigation strategies during the software development lifecycle. However\, its manual application is often time-consuming\, subjective and dependent on scarce cybersecurity expertise. In this context\, Large Language Models (LLMs) may support security teams by generating threat hypotheses\, classifying risks and recommending controls. This study evaluates the effectiveness of three LLM-based tools — ChatGPT\, Gemini and Manus — in cybersecurity threat modeling for a real-world backend information system. A controlled computational experiment was conducted using standardized prompts applied to the three models\, with three independent executions per prompt. The evaluation considered five dimensions: threat identification coverage\, technical depth of analysis\, quality of risk classification\, assertiveness of control recommendations and result consistency. To consolidate the comparison\, a Final Effectiveness Metric (FEM) was proposed. The results show different performance profiles among the evaluated models. Manus achieved the highest FEM score\, with stronger threat coverage\, technical depth\, control recommendations and consistency. ChatGPT presented intermediate performance\, with structured and detailed analyses\, while Gemini showed lower threat coverage\, but satisfactory technical reasoning in specific tasks. The findings also indicate that LLMs can enhance threat modeling activities by expanding analytical capacity and supporting DevSecOps practices. Nevertheless\, their outputs require human validation\, especially regarding risk classification\, framework alignment and false positive analysis.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:9d36239a91696bb30502591f96c6a047
URL:http://10thworlds4.sched.com/event/9d36239a91696bb30502591f96c6a047
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:A Machine Learning Model for the early Diagnosis of Breast Cancer
DESCRIPTION:Authors - Jeanne Roux Ngo Bilong\, Python Ndekou Tandong Paul\, Bakary Kone\, Dethie Dione\, Ibrahima Toure\, Boris Sourou ZANNOU\, Hamidou Dathe\, Mamadou Diarra\, Olga Ngangmo Kengni\, Mamadou Thiam\, Cheikh Amed Diloma Gabriel Traore Abstract - Automatic assessment of obstetric ultrasound remains a challenge due to its operator-dependent nature and the dynamic context of fetal labor. This study proposes a Temporal Quality Gate Framework to standardize diagnostic plane validation using a novel Temporal Attention-Gated LSTM (TA-LSTM) architecture. We formulate the task as a binary classification problem to distinguish standard diagnostic planes from non-diagnostic sequences\, using the IUGC 2024 dataset of 434 transperineal ultrasound videos (266 positive\, 168 negative). The TA-LSTM extracts spatial features via a ResNet-18 backbone and dynamically weights temporal dependencies using an attention mechanism. Under 5-fold cross-validation with strict patient-level splitting\, the TA-LSTM achieves a mean AUC-ROC of 0.989 ± 0.008 and a mean test accuracy of 95.63% ± 2.85% (peak accuracy of 98.85%) with an inference latency of 14.2 ms on GPU. Our framework acts as a robust Quality Gate\, ensuring that subsequent automated measurements\, like the Angle of Progression (AoP)\, are performed on high-quality validated inputs\, making it highly suitable for resource-limited clinical environments.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:01ed9ef7dd284d32c8dad72565b53c26
URL:http://10thworlds4.sched.com/event/01ed9ef7dd284d32c8dad72565b53c26
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:A Smart Fitness Assistant for Personalized Workout and Diet Recommendations Using Machine Learning and Physiological Feature Engineering
DESCRIPTION:Authors - Sergey Kubinski\, Emil Hadzhikolev Abstract - This paper presents a Smart Fitness Assistant system for generating personalized workout and dietary recommendations using machine learning and domain-informed physiological feature engineering. The proposed approach incorporates indicators such as Basal Metabolic Rate\, Total Daily Energy Expenditure\, and target caloric intake derived from user data. The recommendation task is formulated as a multi-class classification problem for exercise and diet planning. Decision Tree\, Multilayer Perceptron\, and Random Forest models are evaluated using both baseline and enriched feature sets. Experimental results demonstrate that the inclusion of physiological features improves predictive performance\, with Random Forest achieving the highest accuracy. The developed system is implemented within a modular software architecture that supports user interaction\, recommendation generation\, data management\, and progress tracking.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:289de49bddb378de366c20ac47882797
URL:http://10thworlds4.sched.com/event/289de49bddb378de366c20ac47882797
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Architecting Future-Ready Digital HR Platforms Using Workday\, Cloud Services\, and API-Driven Ecosystems
DESCRIPTION:Authors - Madhulika Gajjala Abstract - The fast-paced development in digital transformation strategies has considerably transformed human resource management systems towards cloudbased\, smart and efficient workforces management tools. As current HR systems face various problems like data repository fragmentation\, poor connectivity options\, slow synchronization processes\, scalability issues\, and lack of advanced workforce intelligence\, the current study aims to develop an AFDHRP framework\, which would comprise a CSIL\, AOIE\, WIMM\, and ADHOU components. In addition\, this research proposes the implementation of Cloud-Integrated HR Data Synchronization and Interoperability (CHDSI) algorithm and Adaptive Workforce Intelligence and HR Optimization (AWIHO) algorithm into an innovative solution. A comparison was made with the two state-of-the-art models\, Deep-Hill and Deep Learning-Based ERP Optimization System\, on several performance metrics including human resource integration accuracy\, interoperability efficiency\, synchronization reliability\, workforce intelligence metric\, decision support capability\, cloud scalability\, API ecosystem performance\, and FutureReady HR Platform Effectiveness Metric. According to the results\, the AFDHRP performed excellently scoring an accuracy of 99.5%\, 99.2% for interoperability efficiency\, 99.4% for synchronization reliability\, and 99.8% for overall platform effectiveness.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:17da5f84797ae5b70878395cfe13094f
URL:http://10thworlds4.sched.com/event/17da5f84797ae5b70878395cfe13094f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T170000Z
SUMMARY:Design of a Productivity Capacity Index for Software Teams Using a Multidimensional Socio-Technical Model
DESCRIPTION:Authors:&nbsp\;Manuela Moreno-Arcila\, Luz Marcela Restrepo-Tamayo\, Gloria Piedad Gasca-Hurtado\nAbstract. Productivity in software development teams is a multidimensional phenomenon that cannot be reduced to delivery metrics or technical activity indicators. The social and human factors that influence collective performance are consistently overlooked in the measurement frameworks used in practice. The lack of a tool that integrates social\, human\, and process dimensions with execution results in a structured way prevents a holistic and actionable measurement of software teams' productive capacity. The lack of a tool that systematically integrates social\, human\, and process dimensions with execution results prevents a holistic and actionable assessment of software teams' productivity. The PCI was designed following the Design Science Research (DSR) paradigm\, through a process consisting of two interconnected components: 1) the design and content validation of the Team Capacity Measurement Instrument (TCMI)\, through expert judgment and calculation of the Content Validity Coefficient (CVC)\, and 2) the conceptual construction of the index\, including the definition of socio-technical dimensions\, standardization of variables\, conceptual weighting\, aggregation\, and interpretation using bands and alerts by dimension. The PCI generates a standardized composite score on a [0–100] scale using a weighted aggregation formula with theoretically justified weights\, accompanied by a classification scheme that divides the system’s health into four bands and a mechanism for generating actionable alerts by dimension.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:0c7bbd224467b2bf57851dc99e0549db
URL:http://10thworlds4.sched.com/event/0c7bbd224467b2bf57851dc99e0549db
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Digital Transformation Model for the Issuance and Certification of Academic Documents Using Blockchain and the InterPlanetary File System: An Institutional Case Study
DESCRIPTION:Authors - Jose Hernandez Cortaza\, Arturo Corona Ferreira\, Pablo Payro Campos Abstract - The research adopts a qualitative approach through a descriptive-explanatory case study\, supported by source triangulation\, integrating direct observation and expert judgment in the requirements engineering process\, with the purpose of identifying critical points in the traditional digital document certification workflow at a Mexican public higher education institution. Based on this approach\, a digital transformation model was designed alongside a system architecture aimed at guaranteeing the integrity\, decentralization\, and verifiability of digital documents\, integrating Blockchain\, IPFS\, and smart contracts\, which enabled a multilayer verification scheme. The results demonstrate that the proposed system allows multilayer verification\, prevents document duplication\, and strengthens transparency and trust in academic issuance and certification processes. In terms of performance\, the system presents an estimated Gas per transaction of 327\,423\, with an average processing time of 17.94ms\, during which the entire document issuance and certification process is fully executed. The main contribution of this work is the proposal of a replicable digital transformation model that combines emerging technologies (Blockchain\, IPFS\, and smart contracts) with a qualitative organizational analysis\, providing a practical framework for the secure management of academic documents in public higher education institutions\, significantly contributing to the improvement of document management. This model can be adopted and adapted by institutions seeking to strengthen the integrity and interoperability of digital issuance and certification processes.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:1b9d1d59c0e0688cd32dce9dc1f950e5
URL:http://10thworlds4.sched.com/event/1b9d1d59c0e0688cd32dce9dc1f950e5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T153000Z
DTEND:20260730T160000Z
SUMMARY:Intrusion Detection System based Lightweight Ensemble Machine Learning for the Enhancement of IoT Security Against Cyber Attacks
DESCRIPTION:Authors - Shayma W. Nourildean\, Yousra Abd Mohammed\, Nahida Naji Kadhim Abstract - The growth of the Internet of Things (IoT) equipment has changed many industrial and social applications\, but it has made the IoT network vulnerable to a wide range of malicious activities by increasing the number of potential attack points. Traditional IDS has a problem with scalability\, adaptability and accuracy when facing new and complex cyber threats. In this study\, a strong ensemble machine learning framework that integrates Decision Tree (DT)\, Random Forest (RF)\, and XGBoost through confidence-based soft voting\, had been validated across individual datasets. The proposed system (DTXG-RF) uses different forces and weaknesses of these algorithms to improve the accuracy of the detection and reduce the chances of causing it a false alarm. Two Benchmark IoT data sets\, CIC-IoT2023and IoTID20\, were used. These data sets showed a wide range of scenarios in the real IoT attack. To reduce overfitting risk and data leakage\, strict train–test separation\, stratified splitting\, and pipeline-based preprocessing were enforced\, and additional cross-validation experiments were conducted to verify model stability across folds and datasets. Assessment results showed that DTXG-RF ensemble voting model consistently improves traditional machine learning models such as DT\, XGBOOST\, KN\, logistic regression\, Nave Bayes and Catboost. The model accuracy of CIC -IoT-2023 and IOTID20 were 94.03% and 99.99%\, respectively\, with AUCs of 0.95025 and 0.9994. These results indicated that the ensemble IDS was lightweight and could achieve high detection accuracy with low latency and memory overheads\, which is also suitable for low-latency IoT edge deployment.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:090aa8abb2164be2566815cc71bd4255
URL:http://10thworlds4.sched.com/event/090aa8abb2164be2566815cc71bd4255
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T170000Z
DTEND:20260730T170300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:29a5d8182cc087ba51900a65543a3cc8
URL:http://10thworlds4.sched.com/event/29a5d8182cc087ba51900a65543a3cc8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T170000Z
DTEND:20260730T170300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:6cbde9362e8e4bc1adb3f79a2fcb62b9
URL:http://10thworlds4.sched.com/event/6cbde9362e8e4bc1adb3f79a2fcb62b9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T170000Z
DTEND:20260730T170300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:60e9554ace672a3e4dc3b42ebd2a2639
URL:http://10thworlds4.sched.com/event/60e9554ace672a3e4dc3b42ebd2a2639
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T170000Z
DTEND:20260730T170300Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:999eefee58bedda5154b0a367d008996
URL:http://10thworlds4.sched.com/event/999eefee58bedda5154b0a367d008996
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T170300Z
DTEND:20260730T170500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, London\, UK
SEQUENCE:0
UID:bd88e72a34fb7d6e28ce1376354f3b1f
URL:http://10thworlds4.sched.com/event/bd88e72a34fb7d6e28ce1376354f3b1f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T170300Z
DTEND:20260730T170500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, London\, UK
SEQUENCE:0
UID:d09e9d72a86b34faf075b437ec72f8f4
URL:http://10thworlds4.sched.com/event/d09e9d72a86b34faf075b437ec72f8f4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T170300Z
DTEND:20260730T170500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, London\, UK
SEQUENCE:0
UID:1abca1f23b1cef5aaae13d8e5ef8c1f2
URL:http://10thworlds4.sched.com/event/1abca1f23b1cef5aaae13d8e5ef8c1f2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260723T175521Z
DTSTART:20260730T170300Z
DTEND:20260730T170500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, London\, UK
SEQUENCE:0
UID:83710e4a95186e1afdaf1a2c8b48d6e8
URL:http://10thworlds4.sched.com/event/83710e4a95186e1afdaf1a2c8b48d6e8
END:VEVENT
END:VCALENDAR