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.
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.
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.
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.
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.
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.
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.
Authors - Almas Temirbekov, Bekdaulet Khudaibergen, Nurlan Temirbekov, Aigul Sagatbekkyzy 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.
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.
Authors - Danial Hooshyar, Yeongwook Yang, Raija Hamalainen, Tommi Karkkainen 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.
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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.
