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.
