Search Results (389)

Traditional book recommendation methods predominantly rely on collaborative filtering and context-based approaches. However, existing methods fail to account for the order of users’ book borrowings and the duration they hold them, both of which are crucial indicators reflecting users’ book preferences. To address this challenge, we propose a book recommendation framework called DPBD, which disentangles preferences based on borrowing duration, thereby explicitly modeling temporal patterns in library borrowing behaviors. The DPBD model adopts a dual-path neural architecture comprising the following: (1) The item-level path utilizes self-attention networks to encode historical borrowing sequences while incorporating borrowing duration as an adaptive weighting mechanism for attention score refinement. (2) The feature-level path employs gated fusion modules to effectively aggregate multi-source item attributes (e.g., category and title), followed by self-attention networks to model feature transition patterns. The framework subsequently combines both path representations through fully connected layers to generate user preference embeddings for next-book recommendation. Extensive experiments conducted on two real-world university library datasets demonstrate the superior performance of the proposed DPBD model compared with baseline methods. Specifically, the model achieved 13.67% and 15.75% on HR@1 and 15.75% and 12.90% on NDCG@1 across the two datasets. Full article

The integration of virtual reality, digital twins, and spatial behavior-tracking technologies is reshaping cultural exhibition architecture, shifting the design focus from functional efficiency to immersive, user-centered experiences. However, the behavioral dynamics within these interactive environments remain insufficiently addressed. This study proposes a behavior-oriented spatial typology grounded in Bitgood’s attention–value model, which maps the psychological stages—Attraction, Hold, Engagement, and Exit—onto four spatial categories: Threshold Space, Transitional Space, Narrative Focus Space, and Closure Space. Each represents a distinct phase of perceptual and behavioral response along the exhibition sequence. A mixed-method approach was employed, combining eye-tracking experiments with structured questionnaires to capture both physiological reactions and subjective evaluations. Key spatial variables—enclosure (X₁), visual corridors (X₂), spatial scale (X₃), and light–shadow articulation (X₄)—were analyzed using multiple regression to assess their impact on interest and dwell time. The results show that enclosure (α = −0.094; β = −0.319) and light–shadow articulation (α = −0.057; β = 0.156), respectively, decreased interest and increased dwell time, while spatial scale (α = 0.042; β = 0.186) positively affected both. Visual corridors had minimal influence (α = −0.007; β = 0.022). These spatial effects align with the proposed typology: Threshold Spaces support rapid orientation and exploratory behavior, while Transitional Spaces aid navigation but reduce sustained attention. Narrative Focus Spaces enhance cognitive engagement and decision making, and Closure Spaces foster emotional resolution and extended presence. These findings validate the proposed typology and establish a quantifiable link between spatial attributes and visitor behavior, offering a practical framework for optimizing immersive exhibition sequences. Full article

Biometric authentication using human physiological and behavioral characteristics has been widely adopted, with speaker verification attracting attention due to its convenience and noncontact nature. Conventional speaker verification systems remain vulnerable to spoofing attacks, however, often requiring integration with separate spoofed speech detection models. In this work, the authors propose an emotion-dependent speaker verification system that integrates speaker characteristics with emotional speech characteristics, enhancing robustness against spoofed speech without relying on additional classification models. By comparing acoustic characteristics of emotions between registered and verification speech using pretrained models, the proposed method reduces the equal error rate compared to conventional speaker verification systems, achieving an average equal error rate of 1.13% for speaker verification and 17.7% for the anti-spoofing task. Researchers additionally conducted a user evaluation experiment to assess the usability of emotion-dependent speaker verification. The results indicate that although emotion-dependent authentication was initially cognitively stressful, participants adapted over time, and the burden was significantly reduced after three sessions. Among the tested emotions (anger, joy, sadness, and neutral), sadness proved most effective, with stable scores, a low error rate, and minimal user strain. These findings suggest that neutral speech is not always the optimal choice for speaker verification and that well-designed emotion-dependent authentication can offer a practical and robust security solution. Full article

Despite increasing attention to inclusive education, the spatial and environmental requirements of individuals with Down syndrome remain insufficiently addressed within architectural research. This study investigates how educational environments can be redesigned to betteraccommodate the developmental, sensory, and behavioral needs of this user group, utilizing the interdisciplinary lens of building biology that emphasizes occupant health, well-being, and environmental quality. Employing a case study methodology, this study focuses on Gülseren Özdemir Special Education Practice School in Turkey. Fieldwork was conducted through structured qualitative spatial analysis based on principles derived from building biology and universal design. While the facility meets several baseline accessibility criteria, qualitative observations indicate areas for improvement, particularly in lighting quality, acoustic conditions, tactile stimuli, and spatial adaptability. These findings demonstrate the potential of building biology to serve as a comprehensive, health-centered design approach for inclusive educational settings. This study concludes by proposing spatial strategies applicable to both new construction and retrofit projects, offering a knowledge base that may inform future architectural practices aimed at fostering inclusive and supportive learning environments. Full article

This article presents an interaction-aware motion planning framework that integrates a graph neural network (GNN) based multi-modal trajectory predictor with a model predictive control (MPC) based planner. Unlike past studies that predict a single future trajectory per agent, our algorithm outputs three distinct trajectories for each surrounding road user, capturing different interaction scenarios (e.g., yielding, non-yielding, and aggressive driving behaviors). We design a GNN-based predictor with bi-directional gated recurrent unit (Bi-GRU) encoders for agent histories, VectorNet-based lane encoding for map context, an interaction-aware attention mechanism, and multi-head decoders to predict trajectories for each mode. The MPC-based planner employs a bicycle model and solves a constrained optimal control problem using CasADi and IPOPT (Interior Point OPTimizer). All three predicted trajectories per agent are fed to the planner; the primary prediction is thus enforced as a hard safety constraint, while the alternative trajectories are treated as soft constraints via penalty slack variables. The designed motion planning algorithm is examined in real-world intersection scenarios from the INTERACTION dataset. Results show that the multi-modal trajectory predictor covers possible interaction outcomes, and the planner produces smoother and safer trajectories compared to a single-trajectory baseline. In high-conflict situations, the multi-modal trajectory predictor anticipates potential aggressive behaviors of other drivers, reducing harsh braking and maintaining safe distances. The innovative method by integrating the GNN-based multi-modal trajectory predictor with the MPC-based planner is the backbone of the effective motion planning algorithm for robust, safe, and comfortable autonomous driving in complex intersections. Full article

Exhibition design in museum environments serves as a vital mechanism for enhancing cultural engagement, enriching visitor experience, and promoting heritage preservation. Despite the growing number of museums, improvements in exhibition quality remain limited. In this context, understanding exhibition visual content becomes fundamental to shaping visitor experiences in cultural heritage settings, as it directly influences how individuals perceive, interpret, and engage with displayed information. However, due to individual differences in cognitive processing, standardized visualization strategies may not effectively support all users, potentially resulting in unequal levels of knowledge acquisition and engagement. This study presents a quasi-experimental eye-tracking investigation examining how visualizer–verbalizer (V–V) cognitive styles influence content comprehension in a historical museum context. Participants were classified as visualizers or verbalizers via standardized questionnaires and explored six artifacts displayed through varying information modalities while their eye movements—including fixation durations and transition patterns—were recorded to assess visual processing behavior. The results revealed that participants’ comprehension performance was strongly associated with their visual attention patterns, which differed systematically between visualizers and verbalizers. These differences reflect distinct visual exploration strategies, with cognitive style influencing how individuals allocate attention and process multimodal exhibition content. Eye movement data indicated that visualizers engaged in broader cross-modal integration, whereas verbalizers exhibited more linear, text-oriented strategies. The findings provide empirical evidence for the role of cognitive style in shaping visual behavior and interpretive outcomes in museum environments, underscoring the need for cognitively adaptive exhibition design. Full article

Traditional GCN based methods capture the propagation structure between posts, but do not fully model dynamic semantic information, such as the role of specific users on the propagation path and the context of post content that changes over time, leading to a decrease in the accuracy of rumor detection. Therefore, we propose an innovative path attention graph convolution network (PAGCN) framework, which effectively solves this limitation by integrating propagation structure and semantic representation learning. PAGCN first uses the graph neural network (GNN) to model the information transmission path, focusing on the differences between rumor and fact information in communication behavior, such as the differences between depth first and breadth first dissemination modes. Then, in order to enhance the ability of semantic understanding, we design a multi head attention mechanism based on convolutional neural network (CNN), which extracts deep contextual relationships from text content. Furthermore, by introducing the comparative learning technology, PAGCN can adaptively optimize the representation of structural and semantic features, dynamically focus on the most discriminative features, and significantly improve the sensitivity to subtle patterns in rumor propagation. The experimental verification on three benchmark datasets of twitter15, twitter16, and Weibo, shows that the proposed PAGCN performs best among the 17 comparison models, and the accuracy rates on twitter15 and Weibo datasets are 90.9% and 93.9%, respectively, which confirms the effectiveness of the framework in capturing propagation structure and semantic information at the same time. Full article

Attention Deficit Hyperactivity Disorder (ADHD) is a lifelong condition; however, traditional treatment focuses on hyperactivity and inattention, which is largely a manifestation of pediatric ADHD. Studies are limited regarding cognitive difficulties, as seen in adult ADHD, as well as treatment strategies for this population. This review of the literature examines multiple recent studies that discuss various novel treatment strategies for cognitive impairment in adults with ADHD. A targeted literature review was conducted using PubMed to identify recent studies on cognitive dysfunction in adults with ADHD, with an emphasis on emerging treatment strategies. Data collected included sample size, intervention strategies, cognitive function, and side effects. Studies on non-invasive brain stimulation revealed significant effects on executive function in adult ADHD patients. Other studies revealed statistically significant improvements in cognitive flexibility and response inhibition in modafinil users. Another study demonstrated significant improvement in working memory with off label use of viloxazine for adults. This review of the literature describes the effectiveness of novel treatment strategies of adult ADHD including non-stimulant medications, behavioral therapies and neurofeedback. This highlights the need for treatment modalities that enhance cognitive outcomes and further research into long-term efficacy and safety of these novel interventions and implementing psychological treatment into medical management of adult ADHD. Full article

On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user behavior spans heterogeneous long- and short-term patterns. To address this, we propose a context-driven recommendation framework that integrates a hybrid sequence modeling architecture with a Large Language Model for post hoc reasoning and reranking. Specifically, the solution tackles several key issues: (1) integration of multimodal features to achieve explicit context fusion through a hybrid fusion strategy; (2) introduction of a context capture layer and a context propagation layer to enable effective encoding of implicit contextual states hidden in the heterogeneous long and short term; (3) cross attention mechanisms to facilitate context retrospection, which allows implicit contexts to be uncovered; and (4) leveraging the reasoning capabilities of DeepSeek-R1 as a post-processing step to perform open knowledge-enhanced reranking. Extensive experiments on a real-world dataset show that our approach significantly outperforms strong baselines in both prediction accuracy and Top-K recommendation quality. Case studies further demonstrate the model’s ability to uncover nuanced, implicit contextual cues—such as family roles and holiday-specific behaviors—making it particularly effective for personalized, dynamic recommendations in high-frequency scenes. Full article

Spatial environments influence users’ behavioral patterns and psychological perceptions, affecting health outcomes—a professional consensus in architecture, particularly within healthy buildings. Growing attention to spatial design’s health benefits has rapidly increased quantitative research. Relationships between spatial elements (e.g., green spaces, water features, facilities) and health indicators (e.g., emotional state, mental health, physical activity) are increasingly clear. Due to collective behavior patterns on campuses, the space–health relationship is particularly pronounced. This paper examines campus open spaces via meta-analysis to explore spatial elements’ relative influence on health outcomes. After a chronological review of qualitative research, it cross-sectionally extracts quantitative data. The independent variable (“campus open space”) is categorized into natural landscapes, service facilities, and built environment (design organization). The dependent variable (“health”) is subdivided into physical health, mental health, and positive social adaptation. The main conclusions of the study are as follows: Campus open spaces significantly impact student health, with the built environment exerting the strongest influence. Combining landscape/facility elements with spatial guidance yields more significant results. Furthermore, based on the calculated impact factor data for each element, this study has developed an evaluation scale that could serve as an empirical foundation for future assessments of campus health benefits, thereby guiding health-oriented campus spatial design. Full article

Edge computing builds relevant services and applications on the edge server near the user side, which enables a faster service response. However, the lack of large-scale hardware resources leads to weak defense for edge devices. Therefore, proactive defense security mechanisms, such as Intrusion Detection Systems (IDSs), are widely deployed in edge computing. Unfortunately, most of those IDSs lack causal analysis capabilities and still suffer the threats from Advanced Persistent Threat (APT) attacks. To effectively detect APT attacks, we propose a heterogeneous graph neural networks threat detection model based on the provenance graph. Specifically, we leverage the powerful analysis and tracking capabilities of the provenance graph to model the long-term behavior of the adversary. Moreover, we leverage the predictive power of heterogeneous graph neural networks to embed the provenance graph by a node-level and semantic-level heterogeneous mutual attention mechanism. In addition, we also propose a provenance graph reduction algorithm based on the semantic similarity of graph substructures to improve the detection efficiency and accuracy of the model, which reduces and integrates redundant information by calculating the semantic similarity between substructures. The experimental results demonstrate that the prediction accuracy of our method reaches 99.8% on the StreamSpot dataset and achieves 98.13% accuracy on the NSL-KDD dataset. Full article

Tourism network attention, defined as the quantifiable measure of public interest toward tourism destinations through online search activities, has become a crucial indicator for understanding tourist behavior in the digital era. This study analyzes the spatiotemporal evolution of tourism network attention for Zhangjiajie National Forest Park using Baidu index data from 2013 to 2023. Results show three temporal phases: rapid rise (2013–2017), fluctuation adjustment (2018–2020), and recovery growth (2021–2023), with a “double-peak” seasonal pattern in July–August and April–May. Spatial distribution exhibits a “high East, low West” pattern with gradually increasing balance (coefficient of variation: 0.6849→0.5382). GDP, internet users, and transportation accessibility are dominant factors influencing spatial patterns. Full article

Urban public transportation systems face increasing pressure from shifting travel patterns, rising peak-hour demand, and the need for equitable and resilient service delivery. While complex network theory has been widely applied to analyze transit systems, limited attention has been paid to behavioral segmentation within such networks. This study introduces a frequency-based framework that differentiates high-frequency (HF) and low-frequency (LF) passengers to examine how distinct user groups shape network structure, congestion vulnerability, and robustness. Using over 20 million smart-card records from Beijing’s multimodal transit system, we construct and analyze directed weighted networks for HF and LF users, integrating topological metrics, temporal comparisons, and community detection. Results reveal that HF networks are densely connected but structurally fragile, exhibiting lower modularity and significantly greater efficiency loss during peak periods. In contrast, LF networks are more spatially dispersed yet resilient, maintaining stronger intracommunity stability. Peak-hour simulation shows a 70% drop in efficiency and a 99% decrease in clustering, with HF networks experiencing higher vulnerability. Based on these findings, we propose differentiated policy strategies for each user group and outline a future optimization framework constrained by budget and equity considerations. This study contributes a scalable, data-driven approach to integrating passenger behavior with network science, offering actionable insights for resilient and inclusive transit planning. Full article

Multi-behavior sequential recommendation (MBSRec) is a form of sequential recommendation. It leverages users’ historical interaction behavior types to better predict their next actions. This approach fits real-world scenarios better than traditional models do. With the rise of the transformer model, attention mechanisms are widely used in recommendation algorithms. However, they suffer from low-pass filtering, and the simple learnable positional encodings in existing models offer limited performance gains. To address these problems, we introduce the context-aware multi-behavior sequential recommendation model (CAMBSRec). It separately encodes items and behavior types, replaces traditional positional encoding with context-similarity positional encoding, and applies the discrete Fourier transform to separate the high and low frequency components and enhance the high frequency components, countering the low-pass filtering effect. Experiments on three public datasets show that CAMBSRec performs better than five baseline models, demonstrating its advantages in terms of recommendation performance. Full article

As ad-supported subscription models proliferate across over-the-top (OTT) media platforms, understanding the psychological mechanisms and perceptual factors that underlie consumers’ transition decisions becomes increasingly consequential. This study integrates the Uses and Gratifications framework with a contemporary motivation-based perspective to examine how users’ media consumption motivations and advertising attitudes predict intentions to adopt ad-supported OTT plans. Data were collected via a nationally representative online survey in South Korea (N = 813). The sample included both premium subscribers (n = 708) and non-subscribers (n = 105). The findings reveal distinct segmentation in decision-making patterns. Among premium subscribers, switching intentions were predominantly driven by intrinsic motivations—particularly identity alignment with content—and by the perceived informational value of advertisements. These individuals are more likely to consider ad-supported plans when ad content is personally relevant and cognitively enriching. Conversely, non-subscribers exhibited greater sensitivity to extrinsic cues such as the entertainment value of ads and the presence of tangible incentives (e.g., discounts), suggesting a hedonic-reward orientation. By advancing a dual-pathway explanatory model, this study contributes to the theoretical discourse on digital subscription behavior and offers actionable insights for OTT service providers. The results underscore the necessity of segment-specific advertising strategies: premium subscribers may be engaged through informative and identity-consistent advertising, while non-subscribers respond more favorably to enjoyable and benefit-laden ad experiences. These insights inform platform monetization efforts amid the evolving dynamics of consumer attention and subscription fatigue. Full article