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检索条件"机构=CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology"
923 条 记 录,以下是321-330 订阅
排序:
LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback
arXiv
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arXiv 2022年
作者: Zhu, Yunchang Pang, Liang Lan, Yanyan Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China Data Intelligence System Research Center Institute of Computing Technology Cas Beijing China Institute for Ai Industry Research Tsinghua University Beijing China Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Cas University of Chinese Academy of Sciences Beijing China
Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential ... 详细信息
来源: 评论
Policy network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud networks
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China Communications 2024年 第4期21卷 53-73页
作者: Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communication and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China
The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC n... 详细信息
来源: 评论
Ethereum Smart Contract Representation Learning for Robust Bytecode-Level Similarity Detection  34
Ethereum Smart Contract Representation Learning for Robust B...
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34th International Conference on Software Engineering and Knowledge Engineering, SEKE 2022
作者: Tian, Zhenzhou Huang, Yaqian Tian, Jie Wang, Zhongmin Chen, Yanping Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Department of Computer Science and Engineering Wright State University DaytonOH United States
Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before... 详细信息
来源: 评论
Coupled graph neural networks for predicting the popularity of online content
arXiv
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arXiv 2019年
作者: Cao, Qi Shen, Huawei Gao, Jinhua Wei, Bingzheng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China WeChat Tencent Inc
Predicting the popularity of online content in social network is an important problem for the practice of information dissemination, advertising, and recommendation. Previous methods mainly leverage demographics, temp... 详细信息
来源: 评论
Sketch and customize: A counterfactual story generator
arXiv
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arXiv 2021年
作者: Hao, Changying Pang, Liang Lan, Yanyan Wang, Yan Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Tencent AI Lab United States
Recent text generation models are easy to generate relevant and fluent text for the given text, while lack of causal reasoning ability when we change some parts of the given text. Counterfactual story rewriting is a r...
来源: 评论
Adversarial Camouflage for Node Injection Attack on Graphs
arXiv
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arXiv 2022年
作者: Tao, Shuchang Cao, Qi Shen, Huawei Wu, Yunfan Hou, Liang Sun, Fei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China
Node injection attacks on Graph Neural networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates. However, our study indicates that the... 详细信息
来源: 评论
Signed bipartite graph neural networks
arXiv
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arXiv 2021年
作者: Huang, Junjie Shen, Huawei Cao, Qi Tao, Shuchang Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China
Signed networks are such social networks having both positive and negative links. A lot of theories and algorithms have been developed to model such networks (e.g., balance theory). However, previous work mainly focus... 详细信息
来源: 评论
Single node injection attack against graph neural networks
arXiv
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arXiv 2021年
作者: Tao, Shuchang Cao, Qi Shen, Huawei Huang, Junjie Wu, Yunfan Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China
Node injection attack on Graph Neural networks (GNNs) is an emerging and practical attack scenario that the attacker injects malicious nodes rather than modifying original nodes or edges to affect the performance of G... 详细信息
来源: 评论
On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities  24
On the Effectiveness of Function-Level Vulnerability Detecto...
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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024
作者: Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China
Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as ... 详细信息
来源: 评论
PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval  36
PNRITR:Research on Positive and Negative Entity Region Perce...
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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024
作者: Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China
The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various ... 详细信息
来源: 评论