计算机科学最新文献_第3页

Movable Antenna Enabled Near-Field Communications: Channel Modeling and Performance Optimization

IF 8.3 2区计算机科学

IEEE Transactions on Communications Pub Date : 2025-03-04 DOI: 10.1109/tcomm.2025.3547783

Lipeng Zhu, Wenyan Ma, Zhenyu Xiao, Rui Zhang

引用次数: 0

Distributed Routing and Data Scheduling in IPNs With GNN-Based Multi-Agent DRL

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-03-04 DOI: 10.1109/jiot.2025.3547341

Xixuan Zhou, Ruoyu Xu, Xiaojian Tian, Yueyue Zhang, Yu Liang, Xiaoliang Chen, Zuqing Zhu

引用次数: 0

Control-Aware Energy-Efficient Transmission for Satellite Internet of Things Systems

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-03-04 DOI: 10.1109/jiot.2025.3547921

Qingming Wang, Hua Zhang, Xiao Liang

引用次数: 0

Control Synthesis of Singular PDT Switched Systems with State Constraints and Application to HyTAQs

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2025-03-04 DOI: 10.1109/tase.2025.3545069

Lixian Zhang, Yuting Ma, Jianan Yang, Tong Wu, Yimin Zhu

引用次数: 0

U-control-based active disturbance-rejection control revealed by the sliding mode for USV heading control

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-03-04 DOI: 10.1016/j.conengprac.2025.106288

Jiabin Yu , Yuxiang Wei , Yang Chen , Huiyan Zhang , Jiping Xu , Zhiyao Zhao

引用次数: 0

Dynamic multi-objective evolutionary algorithm based on dual-layer collaborative prediction under multiple perspective

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-03-04 DOI: 10.1016/j.swevo.2025.101876

Yaru Hu , Yana Li , Junwei Ou , Jiankang Peng , Jun Li , Jinhua Zheng

{"title":"Dynamic multi-objective evolutionary algorithm based on dual-layer collaborative prediction under multiple perspective","authors":"Yaru Hu , Yana Li , Junwei Ou , Jiankang Peng , Jun Li , Jinhua Zheng","doi":"10.1016/j.swevo.2025.101876","DOIUrl":"10.1016/j.swevo.2025.101876","url":null,"abstract":"<div><div>Prediction-based strategies become increasingly prominent in addressing dynamic multi-objective optimization problems (DMOPs). However, challenges remain in selecting predictive models and effectively utilizing historical solutions. In this paper, we propose a multiple perspective dual-layer collaborative prediction strategy to efficiently tackle both challenges. The multi-perspective approach is further divided into a search perspective and a spatial perspective and realized through the collaboration of three sub-strategies. From the search perspective, we employ a dual-layer prediction strategy that focuses on both global and local information. Specifically, the first layer utilizes Gaussian process regression (GPR) to predict centrality, which serves as a measure of the population’s collective intelligence. This layer effectively captures global insights into population dynamics, identifying overarching movement trends over time. Building on these global insights, the second layer employs a knee-point interval partitioning strategy that combines vector partitioning with knee-point-based predictions. This layer provides localized insights that complement the broader movement trends identified by the first layer. From the spatial perspective, we implement dual-layer historical similarity detection across non-dominated solutions in both decision and objective spaces. Specifically, the historical Pareto-similarity selection strategy identifies populations in these spaces that demonstrate the greatest similarity to the current population’s non-dominated solutions. The spatial perspective complements the search perspective, forming a coherent framework that systematically integrates global, local, and historical information. Experimental results indicate that the proposed algorithm performs better than previous state-of-the-art methods.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"94 ","pages":"Article 101876"},"PeriodicalIF":8.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Ohio Child Speech Corpus

IF 2.4 3区计算机科学

Speech Communication Pub Date : 2025-03-04 DOI: 10.1016/j.specom.2025.103206

Laura Wagner , Sharifa Alghowinhem , Abeer Alwan , Kristina Bowdrie , Cynthia Breazeal , Cynthia G. Clopper , Eric Fosler-Lussier , Izabela A. Jamsek , Devan Lander , Rajiv Ramnath , Jory Ross

{"title":"The Ohio Child Speech Corpus","authors":"Laura Wagner , Sharifa Alghowinhem , Abeer Alwan , Kristina Bowdrie , Cynthia Breazeal , Cynthia G. Clopper , Eric Fosler-Lussier , Izabela A. Jamsek , Devan Lander , Rajiv Ramnath , Jory Ross","doi":"10.1016/j.specom.2025.103206","DOIUrl":"10.1016/j.specom.2025.103206","url":null,"abstract":"<div><div>This paper reports on the creation and composition of a new corpus of children's speech, the Ohio Child Speech Corpus, which is publicly available on the Talkbank-CHILDES website. The audio corpus contains speech samples from 303 children ranging in age from 4 – 9 years old, all of whom participated in a seven-task elicitation protocol conducted in a science museum lab. In addition, an interactive social robot controlled by the researchers joined the sessions for approximately 60% of the children, and the corpus itself was collected in the peri‑pandemic period. Two analyses are reported that highlighted these last two features. One set of analyses found that the children spoke significantly more in the presence of the robot relative to its absence, but no effects of speech complexity (as measured by MLU) were found for the robot's presence. Another set of analyses compared children tested immediately post-pandemic to children tested a year later on two school-readiness tasks, an Alphabet task and a Reading Passages task. This analysis showed no negative impact on these tasks for our highly-educated sample of children just coming off of the pandemic relative to those tested later. These analyses demonstrate just two possible types of questions that this corpus could be used to investigate.</div></div>","PeriodicalId":49485,"journal":{"name":"Speech Communication","volume":"170 ","pages":"Article 103206"},"PeriodicalIF":2.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Abnormal Behaviour Detection in Crowd for Video Surveillance: Advances and Trends, Datasets, Opportunities and Prospects

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-03-04 DOI: 10.1111/exsy.70013

A. Jency, K. Ramar

{"title":"A Review of Abnormal Behaviour Detection in Crowd for Video Surveillance: Advances and Trends, Datasets, Opportunities and Prospects","authors":"A. Jency, K. Ramar","doi":"10.1111/exsy.70013","DOIUrl":"https://doi.org/10.1111/exsy.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>The detection of abnormal behaviours with fast and automatic recognising is significant in crowded areas to provide higher security to the public. The adoption of deep learning and machine learning-based abnormal behaviour detection models enhances the influential detection and real-time security monitoring in crowds. The researchers have remotely evaluated the heart rate based on physiological information to detect abnormal activities in various years. Over the past few years, several progress have been made, and there are still some issues concerning processing time, accuracy, and computational complexity. The developed approaches detects the activities of anomalies like traffic rule violations, riots, fighting, and stampede, in addition, several anomalous entities such as abandoned luggage and weapons at the sensitive place automatically in time. However, the identification of video anomalies methods poses several challenges because of various environmental conditions, the ambiguous nature of the anomaly, lack of proper datasets, and the complex nature of human characteristics. In recent days, there have been only a few devoted surveys associated with deep learning related video anomaly identification as the research domain is in its initial stages. In this review work, the abnormal behaviour analysis models using deep learning are reviewed in depth in for security applications. Based on the traditionally used abnormal behaviour analysis models in crowded scenes, we widely categorised the methods into classification using object tracking, classification using handcrafted extracted features, classification using non-contact heart rate variability and blood pressure, analysing motion patterns from the visual frames, and classification using face images. We also discuss the comparative analysis of the previous methods with respect to datasets, computational infrastructure, and performance measures for both qualitative and quantitative analysis. In addition, the open and trending research challenges are analysed for future research.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143533512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fully Asynchronous Neuromorphic Perception for Mobile Robot Dodging with Loihi Chips

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2025-03-04 DOI: 10.1109/tase.2025.3547338

Junjie Jiang, Delei Kong, Chenming Hu, Zheng Fang

引用次数: 0

Social media-induced fear of missing out (FoMO) in the workplace: the influence on job burnout and workplace relations

IF 5.9 3区管理学

Internet Research Pub Date : 2025-03-04 DOI: 10.1108/intr-02-2024-0244

Xiao Deng, Jiayu Li, Yaying Huang

{"title":"Social media-induced fear of missing out (FoMO) in the workplace: the influence on job burnout and workplace relations","authors":"Xiao Deng, Jiayu Li, Yaying Huang","doi":"10.1108/intr-02-2024-0244","DOIUrl":"https://doi.org/10.1108/intr-02-2024-0244","url":null,"abstract":"<h3>Purpose</h3>\u0000<p>This study explores how employees respond to social media-induced fear of missing out (FoMO) in the workplace and examines the subsequent impacts of these responses. Based on the transactional theory of stress and coping, the research focuses on how FoMO affects employees’ adoption of stress-coping behaviors: active work interruption (a problem-oriented strategy) and social network interaction (an emotion-oriented strategy). It further investigates the outcomes of these coping strategies – job burnout (resulting from the problem-oriented strategy) and workplace relationships (resulting from the emotion-oriented strategy). Additionally, the study explores how the perceived competitive climate moderates the effect of FoMO on these coping behaviors.</p>\u0000<h3>Design/methodology/approach</h3>\u0000<p>A two-phase survey yielded 244 responses, which were then analyzed using covariance-based structural equation modeling to validate the relationships among the variables.</p>\u0000<h3>Findings</h3>\u0000<p>The findings revealed that FoMO increases employees’ active work interruption, leading to job burnout. In addition, FoMO leads employees to engage in more social network interaction, which promotes workplace relations. The perceived competitive climate positively moderates the indirect effect of workplace FoMO through active work interruption and job burnout.</p>\u0000<h3>Originality/value</h3>\u0000<p>This study emphasizes that individuals are not merely passive recipients of stress but active agents in coping with it. This shift from “passive impacts” to “active responses” helps uncover the bidirectional impacts of social media-induced FoMO on employees, thereby enriching our understanding of FoMO and expanding the application of the transactional theory of stress and coping. Additionally, we explore the boundary conditions of the FoMO mechanism and advance research related to the perceived competitive climate.</p>","PeriodicalId":54925,"journal":{"name":"Internet Research","volume":"11 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0