Search Results (1,733)

Review text in recommender systems provides rich insights into user preferences and experiences that cannot be fully captured by numerical ratings alone. While recent studies have increasingly leveraged review text to enhance recommendation accuracy, most have primarily focused on improving model performance, with limited attention to quantitatively examining how specific textual elements influence rating prediction. To address this gap, this study empirically investigates the impact of review text characteristics on prediction performance in review-based recommender systems. Specifically, we employ the Unstructured Context-Aware Model (UCAM), where contextual information is replaced with review text embedded using a pre-trained BERT model. Three key textual factors are examined: review length, aspect, and emotion type. Review length is divided into quartiles, and results show that removing shorter reviews significantly degrades performance, indicating their critical role. For analysis, reviews are categorized into food, service, price, atmosphere, and location, with service and food contributing most to performance improvements, while location shows relatively low influence. Emotion types are classified based on Plutchik’s framework, revealing that removing joy, trust, and anticipation reduces performance, whereas excluding sadness slightly improves it. Overall, this study highlights the differential importance of textual features and demonstrates their potential for enhancing recommender system design. Full article

Since coming into full operation in 2020, the BeiDou-3 Navigation Satellite System (BDS-3) has provided global users with positioning, navigation and time-synchronization services. Satellite clock bias is a key factor that affects real-time precise point positioning (PPP), precise orbit determination and the optimization of navigation message parameters; high-precision prediction of clock bias is therefore critical for improving the accuracy and reliability of BDS-3. To further enhance the prediction accuracy and stability of satellite clock bias, we propose a hybrid model based on Mamba-LSTM. This combined model leverages the strengths of the Multimodal Adaptive Model Building Algorithm (Mamba) and the Long Short-Term Memory neural network (LSTM) to predict satellite clock bias. Using precise BDS-3 satellite clock bias data from the International GNSS Service (IGS), we carried out prediction experiments. First, we compared the proposed model’s predictive performance with that of the Mamba and LSTM models. In short-term (6 h) and long-term (24 h) prediction scenarios, the average prediction RMSE of Mamba-LSTM improved by approximately 41.7% and 48% relative to Mamba, and by approximately 50.4% and 54.7% relative to the LSTM results, respectively. Next, we ran comparison experiments against traditional neural networks—the BP model and the CNN model. In mid-term (12 h) and long-term (24 h) prediction scenarios, the average prediction RMSE of Mamba-LSTM improved by approximately 59.6% and 63.1% compared with BP, and by approximately 52.4% and 56.2% compared with CNN, respectively. The results indicate that the Mamba-LSTM hybrid model can significantly improve the accuracy and stability of satellite clock bias prediction. Full article

Under growing urbanization and environmental challenges, sustainable urban mobility has become a critical priority for cities worldwide. Public Transport (PT) systems play a central role in reducing car dependency, lowering emissions, increasing network capacity, and promoting more equitable and efficient access to urban spaces for all users. Hence, the present paper aims to investigate PT preferences in the city of Larissa, Greece. Larissa is a medium-sized city currently serviced only by buses, and is currently focusing on the potential introduction of a new tram system to operate in parallel with existing bus services. To this end, a SP survey was designed and implemented, resulting in 972 observations that were collected for further statistical analysis. Survey results show a slight preference for trams over buses, with 54.63% selecting the tram and 45.37% favoring the buses. Moreover, a context-based segmentation pipeline was established using PCA, DBSCAN and t-SNE algorithms, aiding the visualization of existing clusters for transport choice approaches. Afterwards, a series of mixed logit models was applied, and statistically significant variables influencing mode choice were determined. The study also examines Value of Time (VoT) metrics and finds that respondents assign lower VoTs to trams than to buses, especially in out-of-vehicle segments of the journey, such as waiting and walking, and therefore consider trams as more pleasant and less burdensome. The findings also indicate that passengers place a high value on the quality of infrastructure related to access and waiting times, underlining the need to improve the overall user experience beyond the vehicle itself. In summary, the present research offers valuable insights into how the introduction of a tram system could possibly reshape PT usage patterns when compared with the legacy existing bus services. Full article

To improve vehicle user service quality and address data privacy and security issues in intelligent transportation vehicle networking systems, a three-tier communication architecture with cloud-edge-end collaboration was designed in this paper. A Bayesian decision criterion was utilized to divide user data segments into fine-grained slices based on their privacy levels, and differential privacy techniques were applied to protect the offloaded data. To achieve multi-objective optimization between user service quality and data privacy and security, the problem was formulated as a constrained Markov decision process. A communication model, a caching model, a latency model, an energy consumption model, and a data-fragment privacy protection model were designed. Additionally, a deep reinforcement learning algorithm based on the actor–critic approach was proposed for the collaborative and centralized training of multiple intelligent agents (CTMA-AC), enabling multi-objective optimization decision-making for the protection of offloaded private user data. Simulation experiments demonstrate that the proposed multi-agent collaborative privacy data offloading protection strategy can effectively safeguard private user data while ensuring high service quality. Full article

Promoting the green development of large public buildings is a crucial pathway toward environmental sustainability. As a type of public building characterized by both high energy consumption and high public engagement, green stadiums provide an important setting for examining whether building-embedded green features are visible, understandable, and valued by users. In this sense, green stadium consumption intention is treated in this study as a building-related outcome that reflects user acceptance of green building spaces and services rather than as a generic green marketing preference alone. This study examines the effects of Green Facility Visibility, Perceived Green Communication, and Green Interactive Experience on Millennials’ Green Stadium Consumption Intention, while investigating the parallel mediating roles of Green Self-Efficacy and Future Orientation. A sample of 976 millennial users was surveyed. The hypothesized model was tested using covariance-based structural equation modeling (CB-SEM), and Bootstrapping was employed to validate the significance of the mediating effects. Findings reveal that: (1) Green Facility Visibility and Perceived Green Communication significantly and positively influence Green Stadium Consumption Intention, whereas the direct effect of Green Interactive Experience is insignificant; (2) Green Self-Efficacy mediates the relationships between Green Facility Visibility, Perceived Green Communication, and consumption intention; and (3) Future Orientation similarly mediates the relationships between Green Facility Visibility, Perceived Green Communication, and consumption intention. Rather than proposing a major theoretical breakthrough, this study offers a context-specific extension of green consumption research by introducing Green Self-Efficacy and Future Orientation as parallel mediators in a stadium setting. The findings show how building-related green cues and user cognition jointly shape the acceptance of green stadiums, thereby providing evidence relevant to the design, operation, and evaluation of public-facing green buildings. Full article

Electric Vehicle Charging Systems (EVCSs) are increasingly connected with the Internet of Things (IoT) and smart grid infrastructure, yet they face growing cyber risks due to expanded attack interfaces. These systems are vulnerable to various attacks that potentially impact both charging operations and user privacy. Intrusion Detection Systems (IDSs) are essential for identifying suspicious activities and mitigating risks to protect EVCS networks, but conventional ML-based IDSs are often unable to achieve optimal performance due to imbalanced datasets, complex traffic distributions, and human design limitations. In practice, EVCS traffic is typically multi-class, imbalanced, and safety-critical, where both missed attacks and false alarms can lead to denial of charging, service interruption, unnecessary incident escalation, financial loss, and reduced user trust. Automated ML (AutoML) and Generative Artificial Intelligence (GAI) have emerged as promising solutions in cybersecurity. Existing GAI and augmentation methods are mostly class-frequency-driven, but this does not necessarily improve the error-prone regions where IDSs actually fail. In this paper, we propose a GAI and an AutoML-based IDS that incorporates a Conditional Generative Adversarial Network (cGAN) with the optimized XGBoost model to improve the effectiveness of intrusion detection in EVCS networks and IoT systems. The proposed framework involves two techniques: (1) a novel cGAN-based error-guided generative augmentation (EGGA) method that extracts misclassified samples and generates a more robust training set for IDS development, and (2) an optimized IDS model that automatically constructs an optimized XGBoost model based on Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE). The main algorithmic novelty lies in EGGA, which uses model errors to guide generative augmentation toward difficult decision regions, while the overall pipeline represents a practical system-level integration of EGGA, XGBoost, and BO-TPE. To the best of our knowledge, this is the first work that combines GAI and AutoML to specifically improve detection on hard samples, enabling more autonomous and reliable identification of diverse cyber attacks in EV charging networks and IoT systems. Experiments are conducted on two benchmark EVCS and cybersecurity datasets, CICEVSE2024 and CICIDS2017, demonstrating consistent and statistically meaningful improvements over state-of-the-art IDS models. This research highlights the importance of combining automation, generative balancing, and optimized learning to strengthen cybersecurity solutions for EV charging networks and IoT systems. Full article

During pregnancy, women are more inclined to modify their habits and lifestyle to find a new balance and promote well-being for themselves and the child-to-be. However, the availability of nutritional and psychological support is often limited by stigma, geographic barriers, and a lack of services. Digital health tools are emerging as possible solutions to cover these needs. This study explores the acceptability, feasibility, and user experience of Demetra, a virtual coach based on Acceptance and Commitment Therapy (ACT), designed to promote healthy lifestyles and mental well-being. Fifty pregnant women will be enrolled in the feasibility study of the intervention. It starts with an educational part on the foundations of healthy eating and suggestions about lifestyle habits, followed by a six-week psychoeducational module. Content is delivered through text, audio, and video formats. User experience and engagement will be measured through validated questionnaires and semi-structured interviews. Psychological well-being will be evaluated both before and after the program. The intervention is expected to be well-received, with high levels of satisfaction and engagement, leading to a greater awareness of healthy behaviors, improved psychological flexibility, and enhanced overall well-being. Demetra offers an accessible solution to support women through the transformative experience of motherhood with a multidisciplinary and innovative approach. Full article

Telerehabilitation—the remote delivery of rehabilitation services—is undergoing a paradigm shift with the convergence of immersive virtual reality (VR) and wearable biosensor technologies. This perspective article outlines a vision for home-based motor and cognitive rehabilitation that is engaging, personalized, and data-driven. We describe how immersive VR environments (for example, simulations of home settings or supermarkets) coupled with wearable sensors can address current challenges in rehabilitation by increasing patient motivation, enabling real-time biofeedback, and supporting remote clinician supervision. Gamification mechanisms and rich sensory feedback in VR are highlighted as key strategies to enhance user engagement and adherence to therapy. We discuss conceptual innovations such as multi-sensor data integration, dynamic difficulty adaptation, and AI-driven personalization of exercises, derived from recent research and our development experience, and consider their potential benefits for patients with neuro-cognitive-motor impairments (e.g., stroke, Parkinson’s disease, and multiple sclerosis). Implementation scenarios for home-based therapy are presented, emphasizing scalability, standardized digital metrics for monitoring progress, and seamless involvement of clinicians via telehealth platforms. We also critically examine the current limitations of VR and telehealth rehabilitation and how an integrative model could overcome these barriers. More specifically, this perspective defines the engineering requirements of a closed-loop VR-based telerehabilitation framework, including multimodal data synchronization, calibration, signal-quality management, interpretable adaptive control, digital biomarker validation, and practical strategies to improve accessibility, privacy, and scalability in home-based neurological rehabilitation. Full article

Experience-based co-design emphasizes understanding service-users’ experiences to inform service improvement, yet little research has explored domestic abuse survivors’ perspectives within this framework. This study examined survivors’ accounts of their interactions with the police and organizations that support domestic abuse survivors. We aimed to identify aspects of practice experienced as either helpful or in need of improvement. Semi-structured interviews with six survivors in one area of England were analyzed using reflexive thematic analysis. Survivors described obstructive and supportive responses from formal services. Four interrelated themes were developed: The Importance of Being Understood, Believed, and Cared For; It Is Important That There Is Good Communication Between the Survivor and Formal Services; Survivors Want a Victim-Centered, Rapid, and Meaningful Response; and Specific Circumstances Sometimes Influence Opportunities for Help-Seeking. Survivors described being dismissed and disbelieved, which contributed to negative help-seeking experiences and heightened feelings of vulnerability. In contrast, empathic and timely responses validated survivors’ experiences and supported their sense of safety. The findings highlighted the importance of practice that recognizes the different forms abuse can take, provides timely, victim-centered support, and responds equitably to survivors in diverse circumstances. This study demonstrates the valuable insights gained through applying an experience-based co-design approach in this setting. Full article

With the rapid advancement of location-based services, next Point-of-Interest (POI) recommendation has emerged as a critical task in personalized mobility modeling and recommendation systems. It aims to predict users’ future locations based on their historical trajectories, thereby enhancing the personalization and intelligence of recommendation systems. Despite the promising progress, two key challenges remain insufficiently addressed. First, many existing methods overlook the dynamic evolution of user trajectories across multiple perspectives, resulting in entangled representations that fail to capture user intent accurately. Second, they often ignore the latent synergy across diverse perspectives, which limits the effective utilization of complementary information for recommendation. To address these issues, we propose a novel framework called MRHL. MRHL constructs multiple hypergraphs to represent distinct views of user behavior, including interaction frequency, time decay, and geographical proximity. An enhanced hypergraph convolutional network is employed to effectively model the high-order relationships within them. We propose a cascaded enhancement fusion mechanism that progressively integrates multi-view hypergraph representations to enrich the semantic information of user representations. In addition, a multi-relational contrastive learning strategy is developed to capture the consistent signals across different views, thereby enhancing the robustness and discriminative capability of user and POI representations. Extensive experiments on three public datasets consistently demonstrate that MRHL outperforms a range of strong baselines. Full article

The increasing integration of artificial intelligence (AI) in educational decision-making raises a critical question: how to design AI systems that can effectively support teachers while maintaining an appropriate level of trust. Addressing this question requires not only continuous improvements in the technical capabilities of AI systems but also an examination from a human-AI interaction perspective of how different system designs influence users’ cognitive performance and affective responses, thereby providing guidance for system optimization and design. Therefore, this study conducted a randomized controlled experiment with 120 pre-service teachers to investigate how AI-assisted decision-making paradigms and AI explainability jointly influence teachers’ task performance and trust in AI, and whether these effects transfer to subsequent independent tasks. The results indicate that the effect of explanatory interface on task performance is context dependent and yields an immediate positive impact. Under the concurrent paradigm, the explanatory interface of the AI system significantly improves immediate task performance, whereas no significant effect is observed under the sequential paradigm. Moreover, this improvement is confined to the task execution stage and does not transfer to subsequent independent tasks. In contrast, the effect of explanatory interface on trust exhibits a delayed and negative pattern. The explanatory interface has no significant impact on situational trust, while it exerts a negative effect on learned trust and suppresses the natural development of both cognitive trust and emotional trust. In addition, different AI-assisted decision-making paradigms exhibit distinct patterns of influence on task performance and trust. Although the concurrent paradigm performs worse than the sequential paradigm in terms of immediate task performance, it is more effective in promoting users’ emotional trust. Overall, these findings extend the theoretical understanding of the mechanisms of explainability in human-AI interaction and provide empirical evidence for the joint design of explainable AI systems and human-AI collaboration paradigms. Full article

Intrusion Detection Systems (IDSs) operate under asymmetric misclassification costs: false alarms (FP) consume analysts’ time and erode trust, whereas missed attacks (FN) carry business risks. This paper presents a complete pipeline for network anomaly detection on the CIC-IDS2017 dataset using Histogram-Based Gradient Boosting (HGB), with a particular focus on cost-aware threshold selection on a validation split for representative operating regimes $w_{FP}:w_{FN}\in \{1:1, 1:2, 1:3, 1:4, 1:5, 1:10\}$—treated as scenario-based proxies for varying risk posture, attack severity, and analyst workload rather than as universally fixed costs—and on the role of isotonic calibration. The results indicate that (i) under 1:1, the cost-optimal operating point aligns with the F1/MCC optimum; (ii) for 1:k cost regimes, the optimum shifts to lower thresholds, reducing FN at the expense of FP and increasing the alert rate; and (iii) isotonic calibration improves PR/ROC (ranking separation), but in the reported 1:5 experiment it did not reduce the final TEST-set operational cost relative to the uncalibrated run, despite using a separately selected post-calibration threshold. The evaluation includes PR/ROC curves, Cost–Threshold and Alert–Threshold sweeps, per-class recall, and permutation importance. In addition, the proposed approach is compared with unsupervised baselines (Isolation Forest, LOF). The results provide practical guidance for SOC decisions on how to choose thresholds consistent with alert budgets and risk profiles. In deployment, these operating points can be indexed to context (e.g., user type, service class, or time of day), yielding a small library of adaptive thresholds rather than one immutable global threshold. Full article

With the rapid growth of e-commerce, recommender systems have been widely adopted across diverse online services by presenting products aligned with user preferences. Moreover, review-based recommender systems have been studied to alleviate the sparsity of interaction data. However, many studies directly use full review texts, which may contain redundant semantics or noise that is irrelevant to recommendations, thereby degrading data quality and recommendation performance. To address this limitation, this study proposes summarized reviews fusion for adaptive recommendation (SuReFAR), which predicts ratings by summarizing reviews into key information using a multi-summarization strategy. Specifically, SuReFAR utilizes TextRank and bidirectional and auto-regressive transformers (BART) to generate extractive and abstractive summaries of user and item review sets, respectively. Subsequently, we apply an attention mechanism to emphasize salient information within each summary representation and fuse multiple summary representations by adaptively controlling their contributions through a gated multimodal unit (GMU) to predict ratings. We conducted experiments on Amazon and Yelp review datasets, demonstrating that the proposed model consistently outperforms baseline models and captures user preferences more effectively via personalized summary representations. Full article

Depression is notable for high rates of disability. The medical model typically characterizes depression as a physiological dysfunction or psychological disorder. However, both views fail to appreciate the phenomenology of depressed experience. Drawing on the existential phenomenology of Merleau-Ponty, this article contends that the lived experience of chronic depression is marked by a disturbance between the body-subject and the world. More specifically, the experience of depression is characterized by alienation from the world, self and others. While anti-depressants have long been the first line of treatment of depression, many governments subsidize cognitive behavioral therapy (CBT) as an adjunct treatment. CBT is said to be the gold standard psychotherapeutic treatment given that it is evidence-based, cost-effective, and short in duration. However, not only are these justifications questionable, but the theoretical underpinnings of CBT have ideological significance. Rather than approaching depressed persons as body-subjects, CBT casts service users as neo-liberal subjects, insofar as depression is characterized as disordered thinking that is independent of a person’s situated life. The emphasis on quickly returning people to work to reduce strain on welfare systems, while a valid economic concern, is not a valid therapeutic concern. The limited choice of subsidized psychotherapeutic options fails to recognize that depression is a heterogenous phenomenon, meaning that the CBT model of disordered thinking is not necessarily representative of the way in which depression manifests. Full article

Background: Artificial intelligence (AI) is increasingly integrated into logistics services, yet limited research explains how AI acceptance translates into relational outcomes among Generation Z consumers. This study investigates the influence of AI acceptance in logistics services on value co-creation behavior and brand loyalty in Thailand. Methods: A quantitative approach was employed using a structured questionnaire administered to 461 Thai Generation Z consumers with experience in AI-enabled parcel delivery services. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results: The findings reveal that AI acceptance significantly and positively affects both value co-creation behavior and brand loyalty. Value co-creation behavior also exerts a strong positive effect on brand loyalty and partially mediates the relationship between AI acceptance and brand loyalty. The measurement and structural models demonstrated satisfactory reliability and validity. Conclusions: The results indicate that AI acceptance enhances brand loyalty both directly and indirectly through customer participation in value co-creation. These findings highlight the importance of designing AI-enabled logistics interfaces that promote user engagement and relational value, particularly for digitally native Generation Z consumers. Full article