Abstract--Reliable induction motor (IM) fault diagnosis is vital for industrial safety and operational continuity, mitigating costly unplanned downtime. Conventional approaches often struggle to capture complex multimodal signal relationships, are constrained to unimodal data or single fault types, and exhibit performance degradation under noisy or cross-domain conditions. This paper proposes the Multimodal Hypergraph Contrastive Attention Network (MM-HCAN), a unified framework for robust fault diagnosis. T o the best of our knowledge, MM-HCAN is the first to integrate contrastive learning within a hypergraph topology specifically designed for multimodal sensor fusion, enabling the joint modelling of intra-and inter-modal dependencies and enhancing generalisation beyond Euclidean embedding spaces. Evaluated on three real-world benchmarks, MM-HCAN achieves up to 99.82% accuracy with strong cross-domain generalisation and resilience to noise, demonstrating its suitability for real-world deployment. MM-HCAN provides a scalable and robust solution for comprehensive multi-fault diagnosis, supporting predictive maintenance and extended asset longevity in industrial environments. NDUCTION motors (IMs) are essential to modern industrial systems, supporting sectors like manufacturing, energy, and transportation. However, faults in IMs can cause downtime, high maintenance costs, and substantial economic losses. As a result, fault diagnosis in IMs has become a focal point of research, with recent studies highlighting its importance in enhancing operational resilience and minimising financial impacts. IMs faults are broadly classified as either electrical, with stator faults comprising 28-36%, or mechanical, encompassing bearing (42-55%) and rotor (8-10%) failures [1].

Accurate brain tumor segmentation from MRI is vital for diagnosis and treatment planning. Although Monte Carlo (MC) Dropout is widely used to estimate model uncertainty, its effectiveness in identifying segmentation errors -- especially near tumor boundaries -- remains unclear. This study empirically examines the relationship between MC Dropout--based uncertainty and segmentation error in 2D brain tumor MRI segmentation using a U-Net trained under four augmentation settings: none, horizontal flip, rotation, and scaling. Uncertainty was computed from 50 stochastic forward passes and correlated with pixel-wise errors using Pearson and Spearman coefficients. Results show weak global correlations ($r \approx 0.30$--$0.38$) and negligible boundary correlations ($|r| < 0.05$). Although differences across augmentations were statistically significant ($p < 0.001$), they lacked practical relevance. These findings suggest that MC Dropout uncertainty provides limited cues for boundary error localization, underscoring the need for alternative or hybrid uncertainty estimation methods in medical image segmentation.

Automated emotion recognition in real-world workplace settings remains a challenging problem in affective computing due to the scarcity of large-scale, longitudinal datasets collected in naturalistic environments. We present a novel dataset comprising 733,651 facial expression records from 38 employees collected over 30.5 months (November 2021 to May 2024) in an authentic office environment. Each record contains seven emotion probabilities (neutral, happy, sad, surprised, fear, disgusted, angry) derived from deep learning-based facial expression recognition, along with comprehensive metadata including job roles, employment outcomes, and personality traits. The dataset uniquely spans the COVID-19 pandemic period, capturing emotional responses to major societal events including the Shanghai lockdown and policy changes. We provide 32 extended emotional metrics computed using established affective science methods, including valence, arousal, volatility, predictability, inertia, and emotional contagion strength. Technical validation demonstrates high data quality through successful replication of known psychological patterns (weekend effect: +192% valence improvement, p < 0.001; diurnal rhythm validated) and perfect predictive validity for employee turnover (AUC=1.0). Baseline experiments using Random Forest and LSTM models achieve 91.2% accuracy for emotion classification and R2 = 0.84 for valence prediction. This is the largest and longest longitudinal workplace emotion dataset publicly available, enabling research in emotion recognition, affective dynamics modeling, emotional contagion, turnover prediction, and emotion-aware system design.

Feature selection is a fundamental step in model development, shaping both predictive performance and interpretability. Y et, most widely used methods focus on predictive accuracy, and their performance degrades in the presence of correlated predictors. To address this gap, we introduce TangledFeatures, a framework for feature selection in correlated feature spaces. It identifies representative features from groups of entangled predictors, reducing redundancy while retaining explanatory power. The resulting feature subset can be directly applied in downstream models, offering a more interpretable and stable basis for analysis compared to traditional selection techniques. We demonstrate the effectiveness of TangledFeatures on Alanine Dipeptide, applying it to the prediction of backbone torsional angles ϕ and ψ, and show that the selected features correspond to structurally meaningful intra-atomic distances that explain variation in these angles.

Cardiovascular disease and chronic kidney disease are major complications of diabetes, leading to high morbidity and mortality. Early detection of these conditions is critical, yet traditional diagnostic markers often lack sensitivity in the initial stages. This study integrates conventional statistical methods with machine learning approaches to improve early diagnosis of CKD and CVD in diabetic patients. Descriptive and inferential statistics were computed in SPSS to explore associations between diseases and clinical or demographic factors. Patients were categorized into four groups: Group A both CKD and CVD, Group B CKD only, Group C CVD only, and Group D no disease. Statistical analysis revealed significant correlations: Serum Creatinine and Hypertension with CKD, and Cholesterol, Triglycerides, Myocardial Infarction, Stroke, and Hypertension with CVD. These results guided the selection of predictive features for machine learning models. Logistic Regression, Support Vector Machine, and Random Forest algorithms were implemented, with Random Forest showing the highest accuracy, particularly for CKD prediction. Ensemble models outperformed single classifiers in identifying high-risk diabetic patients. SPSS results further validated the significance of the key parameters integrated into the models. While challenges such as interpretability and class imbalance remain, this hybrid statistical machine learning framework offers a promising advancement toward early detection and risk stratification of diabetic complications compared to conventional diagnostic approaches.

With the growth of digital financial systems, robust security and privacy have become a concern for financial institutions. Even though traditional machine learning models have shown to be effective in fraud detections, they often compromise user data by requiring centralized access to sensitive information. In IoT-enabled financial endpoints such as ATMs and POS Systems that regularly produce sensitive data that is sent over the network. Federated Learning (FL) offers a privacy-preserving, decentralized model training across institutions without sharing raw data. FL enables cross-silo collaboration among banks while also using cross-device learning on IoT endpoints. This survey explores the role of FL in enhancing financial security and introduces a novel classification of its applications based on regulatory and compliance exposure levels ranging from low-exposure tasks such as collaborative portfolio optimization to high-exposure tasks like real-time fraud detection. Unlike prior surveys, this work reviews FL's practical use within financial systems, discussing its regulatory compliance and recent successes in fraud prevention and blockchain-integrated frameworks. However, FL deployment in finance is not without challenges. Data heterogeneity, adversarial attacks, and regulatory compliance make implementation far from easy. This survey reviews current defense mechanisms and discusses future directions, including blockchain integration, differential privacy, secure multi-party computation, and quantum-secure frameworks. Ultimately, this work aims to be a resource for researchers exploring FL's potential to advance secure, privacy-compliant financial systems.

When working on digital devices, people often face distractions that can lead to a decline in productivity and efficiency, as well as negative psychological and emotional impacts. To address this challenge, we introduce a novel Artificial Intelligence (AI) assistant that elicits a user's intention, assesses whether ongoing activities are in line with that intention, and provides gentle nudges when deviations occur. The system leverages a large language model to analyze screenshots, application titles, and URLs, issuing notifications when behavior diverges from the stated goal. Its detection accuracy is refined through initial clarification dialogues and continuous user feedback. In a three-week, within-subjects field deployment with 22 participants, we compared our assistant to both a rule-based intent reminder system and a passive baseline that only logged activity. Results indicate that our AI assistant effectively supports users in maintaining focus and aligning their digital behavior with their intentions. Our source code is publicly available at https://intentassistant.github.io

Prompt injection attacks deceive a large language model into completing an attacker-specified task instead of its intended task by contaminating its input data with an injected prompt, which consists of injected instruction(s) and data. Localizing the injected prompt within contaminated data is crucial for post-attack forensic analysis and data recovery. Despite its growing importance, prompt injection localization remains largely unexplored. In this work, we bridge this gap by proposing PromptLocate, the first method for localizing injected prompts. PromptLocate comprises three steps: (1) splitting the contaminated data into semantically coherent segments, (2) identifying segments contaminated by injected instructions, and (3) pinpointing segments contaminated by injected data. We show PromptLocate accurately localizes injected prompts across eight existing and eight adaptive attacks.

Abstract--In mixed-traffic environments, where autonomous vehicles (A Vs) must interact with diverse human-driven vehicles (HVs), the unpredictability of human intentions and heterogeneous driving behaviors poses significant challenges to safe and efficient lane change maneuvers. Existing methods often oversimplify these interactions by assuming uniform or fixed behavioral patterns. T o address this limitation, we propose an intention-driven lane change framework that integrates driving-style recognition with cooperation-aware decision-making and motion-planning. First, a deep learning-based classifier is developed to identify distinct human driving styles from the NGSIM dataset in real time. Second, we introduce a cooperation score composed of intrinsic and interactive components, which estimates surrounding drivers' intentions and quantifies their willingness to cooperate with the ego vehicle's lane change. Third, a decision-making module is designed by combining behavior cloning (BC) with inverse reinforcement learning (IRL) to determine whether a lane change should be initiated under current conditions. Finally, a coordinated motion-planning architecture is established, integrating IRL-based intention inference with model predictive control (MPC) to generate collision-free and socially compliant trajectories. Extensive experiments demonstrate that the proposed intention-driven BC-IRL model achieves superior performance, reaching 94.2% accuracy and 94.3% F1-score, and outperforming multiple rule-based and learning-based baselines. In particular, it improves lane change recognition by 4-15% in F1-score, highlighting the benefit of modeling inter-driver heterogeneity via intrinsic and interactive cooperation scores.

This paper introduces a novel methodology for constructing multiclass ROC curves using the multidimensional Gini index. The proposed methodology leverages the established relationship between the Gini coefficient and the ROC Curve and extends it to multiclass settings through the multidimensional Gini index. The framework is validated by means of two comprehensive case studies in health care and finance. The paper provides a theoretically grounded solution to multiclass performance evaluation, particularly valuable for imbalanced datasets, for which a prudential assessment should take precedence over class frequency considerations.