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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
