IEEE Transactions on Services Computing最新文献

A Malicious Information Popularity Prediction Model Based on User Influence

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-20 DOI: 10.1109/tsc.2025.3544122

Tun Li, Yan Tang, Rong Xie, Yuqi Weng, Qian Li, Rong Wang, Chaolong Jia, Yunpeng Xiao

引用次数: 0

Leveraging Consortium Blockchain for Secure Cross-domain Data Sharing in Supply Chain Networks

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-20 DOI: 10.1109/tsc.2025.3544130

Runqun Xiong, Jing Cheng, Xirui Dong, Jiahang Pu, Feng Shan

引用次数: 0

An Adaptively Bias-Extended Non-negative Latent Factorization of Tensors Model for Accurately Representing the Dynamic QoS Data

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-20 DOI: 10.1109/tsc.2025.3544123

Xiuqin Xu, Mingwei Lin, Xin Luo, Zeshui Xu

引用次数: 0

Joint Trajectory Optimization and Resource Allocation in UAV-MEC Systems: A Lyapunov-Assisted DRL Approach

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-20 DOI: 10.1109/tsc.2025.3544124

Ying Chen, Yaozong Yang, Yuan Wu, Jiwei Huang, Lian Zhao

引用次数: 0

Learning Schema Embeddings for Service Link Prediction: A Coupled Matrix-Tensor Factorization Approach

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-18 DOI: 10.1109/tsc.2025.3541552

Jing Yang, Xiaofen Wang, Laurence T. Yang, Yuan Gao, Shundong Yang, Xiaokang Wang

引用次数: 0

User Preference Oriented Service Caching and Task Offloading for UAV-Assisted MEC Networks

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-13 DOI: 10.1109/tsc.2025.3536319

Ruiting Zhou, Yifeng Huang, Yufeng Wang, Lei Jiao, Haisheng Tan, Renli Zhang, Libing Wu

引用次数: 0

A QoS Prediction Framework via Utility Maximization and Region-Aware Matrix Factorization

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-12 DOI: 10.1109/tsc.2025.3541554

Xia Chen, Yugen Du, Guoxing Tang, Fan Chen, Yingwei Luo, Hanting Wang

引用次数: 0

An Active Defense Adjudication Method Based on Adaptive Anomaly Sensing for Mimic IoT

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-07 DOI: 10.1109/TSC.2024.3436673

Sisi Shao;Tiansheng Gu;Yijun Nie;Zongkai Ji;Fei Wu;Zhongjie Ba;Yimu Ji;Kui Ren;Guozi Sun

{"title":"An Active Defense Adjudication Method Based on Adaptive Anomaly Sensing for Mimic IoT","authors":"Sisi Shao;Tiansheng Gu;Yijun Nie;Zongkai Ji;Fei Wu;Zhongjie Ba;Yimu Ji;Kui Ren;Guozi Sun","doi":"10.1109/TSC.2024.3436673","DOIUrl":"https://doi.org/10.1109/TSC.2024.3436673","url":null,"abstract":"Security issues in the Internet of Things (IoT) are inevitable. Uncertain threats, such as known vulnerabilities and backdoors exist within IoT, and traditional passive network security technologies are ineffective against uncertain threats. To address the above issues, we propose an active defense adjudication method based on adaptive anomaly sensing for mimic IoT. The method constructs a mimic IoT active defense architecture, improving system security and reliability despite prevailing security threats. In addition, an intelligent anomaly sensing algorithm is integrated into the adjudication module of the mimic IoT active defense architecture to support arbitration. An adaptive anomaly sensing model based on multi-feature selection is used to determine the anomaly score of the IoT device outputs, and this model fully considers the reliability of the adjudication data and improves the accuracy of the adjudication. Finally, we conduct a comparative analysis of the proposed adjudication algorithm against three others via a mimic power communication IoT system as an application scenario. The experimental results show that our algorithm can improve security and reduce the failure rate of the mimic IoT system.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"57-71"},"PeriodicalIF":5.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Next PoI Recommendation Based on Graph Convolutional Networks and Multiple Context-Awareness

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-07 DOI: 10.1109/TSC.2024.3463500

Wei Zhou;Cheng Fu;Chunyan Sang;Min Gao;Junhao Wen

{"title":"Next PoI Recommendation Based on Graph Convolutional Networks and Multiple Context-Awareness","authors":"Wei Zhou;Cheng Fu;Chunyan Sang;Min Gao;Junhao Wen","doi":"10.1109/TSC.2024.3463500","DOIUrl":"https://doi.org/10.1109/TSC.2024.3463500","url":null,"abstract":"Next Point-of-interest recommendation involves modeling user interactions with Point-of-interests (PoIs) to analyze user behavior patterns and suggest future scenarios. Data sparsity problems in PoI recommendations can significantly impact the performance of the recommendation model. This paper introduces the Graph Convolutional Network and Multiple Context-Aware PoI Recommendation model (GMCA). First, we present a weighted graph convolutional network that aims to capture the optimal representations of users and PoIs within the user-PoI interaction graph. Second, we employ a fine-grained approach to analyze user check-in records and cluster them into multiple user activity centers. Furthermore, we incorporate time, location, and social context information into the matrix decomposition process. Third, User activity centers are constructed by clustering user check-in records, and the geographical influence of PoI location on user behavioral patterns is explored using probabilistic factor decomposition. The evaluation of the GMCA model on the Yelp and Gowalla datasets shows a significant improvement in Precision@10 indicators. Specifically, there is a 13.85% increase in Precision@10 on the Yelp dataset and a 9.01% increase on the Gowalla dataset. The effectiveness of the GMCA model has been confirmed through numerous experiments conducted on two public datasets.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"302-313"},"PeriodicalIF":5.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Behavior Tomographer: Identifying Hidden Cybercrimes by Behavior Interior Structure Modeling

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-02-06 DOI: 10.1109/tsc.2025.3539194

Cheng Wang, Hangyu Zhu

引用次数: 0