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检索条件"机构=Provincial Key Laboratory of Data-Intensive Computing"
421 条 记 录,以下是181-190 订阅
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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement  25
Multi-Channel Hypergraph Convolution Group Recommendation wi...
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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023
作者: Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China
Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With th... 详细信息
来源: 评论
Sparse Representation Optimization of Gaussian Mixed Feature of Image Based on Convolution Neural Network
Research Square
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Research Square 2021年
作者: Ye, Yuguang Quanzhou Normal University Fujian Quanzhou362000 China Fujian Provincial Key Laboratory of Data Intensive Computing Fujian Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Quanzhou362000 China
With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Based on this, this paper studies a new pattern o... 详细信息
来源: 评论
Social-enhanced recommendation using graph-based contrastive learning  25
Social-enhanced recommendation using graph-based contrastive...
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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023
作者: Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China
The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation alg... 详细信息
来源: 评论
AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing
arXiv
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arXiv 2023年
作者: Chen, Zhaoliang Wu, Zhihao Lin, Zhenghong Wang, Shiping Plant, Claudia Guo, Wenzhong The College of Computer and Data Science Fuzhou University Fuzhou350116 China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China The Faculty of Computer Science Austria The research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria
Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious... 详细信息
来源: 评论
A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction
A Triangular Stable Node Network based on Self-supervised Le...
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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
作者: Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China
In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study ai... 详细信息
来源: 评论
Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation
SSRN
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SSRN 2024年
作者: Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China
Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical ima... 详细信息
来源: 评论
ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks
arXiv
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arXiv 2024年
作者: Chen, Zhaoliang Wu, Zhihao Sadikaj, Ylli Plant, Claudia Dai, Hong-Ning Wang, Shiping Cheung, Yiu-Ming Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science and with the research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria
Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by po... 详细信息
来源: 评论
PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomposition for Single View RGB-D Videos
PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomp...
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Jinwen Li Weixing Xie Junfeng Yao Shaoqi Wu Youhong Peng Mengyuan Ge Xiao Dong Center for Digital Media Computing School of Film School of Informatics Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Jiujiang Research Institute of Xiamen University Jiujiang China Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China
Surface reconstruction of dynamic scenes from single view videos is a challenging task due to the highly ill-posed and under-constrained nature. Existing single view reconstruction methods suffer from severe quality i... 详细信息
来源: 评论
Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation
arXiv
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arXiv 2023年
作者: Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus
We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data t... 详细信息
来源: 评论
A Defense Framework for Backdoor Attacks in Federated Learning Based on Client-Server Detection
A Defense Framework for Backdoor Attacks in Federated Learni...
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Industrial Automation, Robotics and Control Engineering (IARCE), International Conference on
作者: Ding Chen Tianrui Li Cai Yu Rasha Al-Huthaifi Wei Huang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Sichuan Provincial Key Laboratory of Cyberspace Security School of Cybersecurity Chengdu University of Information Technology Chengdu China School of Cybersecurity Chengdu University of Information Technology Chengdu China College of Computer and Data Science Fuzhou University Fuzhou China
Addressing the challenge of backdoor attacks in federated learning (FL), various defense schemes are examined, resulting in the proposal of a comprehensive strategy that incorporates both client-side and server-side m... 详细信息
来源: 评论