咨询与建议

限定检索结果

文献类型

  • 161 篇 期刊文献
  • 87 篇 会议

馆藏范围

  • 248 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 192 篇 工学
    • 148 篇 计算机科学与技术...
    • 129 篇 软件工程
    • 61 篇 信息与通信工程
    • 23 篇 电气工程
    • 23 篇 生物工程
    • 18 篇 电子科学与技术(可...
    • 15 篇 控制科学与工程
    • 13 篇 光学工程
    • 11 篇 机械工程
    • 10 篇 生物医学工程(可授...
    • 10 篇 网络空间安全
    • 7 篇 化学工程与技术
    • 5 篇 仪器科学与技术
    • 5 篇 土木工程
    • 5 篇 安全科学与工程
    • 4 篇 动力工程及工程热...
  • 81 篇 理学
    • 44 篇 数学
    • 26 篇 生物学
    • 16 篇 物理学
    • 14 篇 统计学(可授理学、...
    • 9 篇 化学
    • 5 篇 系统科学
  • 53 篇 管理学
    • 31 篇 图书情报与档案管...
    • 24 篇 管理科学与工程(可...
    • 8 篇 工商管理
  • 13 篇 法学
    • 7 篇 社会学
    • 6 篇 法学
  • 9 篇 医学
    • 7 篇 基础医学(可授医学...
    • 7 篇 临床医学
  • 5 篇 经济学
    • 5 篇 应用经济学
  • 3 篇 农学
  • 1 篇 军事学

主题

  • 11 篇 semantics
  • 8 篇 convolution
  • 7 篇 deep neural netw...
  • 7 篇 decoding
  • 7 篇 training
  • 6 篇 object detection
  • 6 篇 feature extracti...
  • 5 篇 cameras
  • 5 篇 optimization
  • 5 篇 machine learning
  • 5 篇 steganography
  • 5 篇 federated learni...
  • 4 篇 image enhancemen...
  • 4 篇 contrastive lear...
  • 4 篇 encoding
  • 4 篇 deep reinforceme...
  • 3 篇 gamma-gamma chan...
  • 3 篇 deep learning
  • 3 篇 task analysis
  • 3 篇 clustering algor...

机构

  • 39 篇 school of data a...
  • 23 篇 key laboratory o...
  • 22 篇 guangdong key la...
  • 21 篇 school of comput...
  • 17 篇 peng cheng labor...
  • 17 篇 guangdong provin...
  • 15 篇 school of comput...
  • 15 篇 school of electr...
  • 15 篇 school of cyber ...
  • 14 篇 hubei key labora...
  • 14 篇 national enginee...
  • 14 篇 hubei engineerin...
  • 14 篇 guangdong key la...
  • 13 篇 services computi...
  • 13 篇 school of inform...
  • 12 篇 cluster and grid...
  • 9 篇 pazhou lab
  • 9 篇 school of data a...
  • 9 篇 school of softwa...
  • 8 篇 college of infor...

作者

  • 17 篇 zheng wei-shi
  • 12 篇 jin hai
  • 10 篇 hu shengshan
  • 10 篇 zhang leo yu
  • 10 篇 xie xiaohua
  • 9 篇 xiao ma
  • 8 篇 kang xiangui
  • 8 篇 wang chang-dong
  • 8 篇 ma xiao
  • 7 篇 wan wei
  • 7 篇 lai jian-huang
  • 7 篇 luo weiqi
  • 7 篇 cao xiaochun
  • 7 篇 xu qianqian
  • 6 篇 fu shenghao
  • 6 篇 huang jiwu
  • 6 篇 huang qingming
  • 6 篇 yang qize
  • 6 篇 zhou ziqi
  • 6 篇 yang jian

语言

  • 227 篇 英文
  • 20 篇 其他
  • 1 篇 德文
  • 1 篇 法文
  • 1 篇 中文
检索条件"机构=School of Data and Computer Science and Guangdong Key Lab. of Information Security and Technology"
248 条 记 录,以下是71-80 订阅
排序:
Federated Graph Learning via Constructing and Sharing Feature Spaces for Cross-Domain IoT
收藏 引用
IEEE Internet of Things Journal 2025年
作者: Chen, Jiale Zhuo, Shengda He, Jinchun Qiu, Wangjie Zhang, Qinnan Xiong, Zehui Zheng, Zhiming Tang, Yin Chen, Min Wang, Changdong Huang, Shuqiang Jinan University College of Cyber Security Guangzhou China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Zhongguancun Laboratory Beijing China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore Jinan University School of Management Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Computer Guangzhou China Jinan University College of Cyber Security of Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China
The Internet of Things (IoT) collects large volumes of diverse data, with graph data as a critical component, and extensively utilizes Federated Graph Learning (FGL) to process this data while preserving data security... 详细信息
来源: 评论
Cross-Domain Animal Pose Estimation with Skeleton Anomaly-Aware Learning
收藏 引用
IEEE Transactions on Circuits and Systems for Video technology 2025年
作者: Han, Le Chen, Kaixuan Zhao, Lei Jiang, Yangbo Wang, Pengfei Zheng, Nenggan Zhejiang Hangzhou310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310007 China Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou310007 China Institute of Blockchain and Data Security. China Zhejiang University School of Software Technology Ningbo China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou310007 China Zhejiang Provincial Government ZJU Collaborative Innovation Center for Artificial Intelligence by MOE Hangzhou310007 China Bengbu University School of Computer and Information Engineering Bengbu233030 China
Animal pose estimation is often constrained by the scarcity of annotations and the diversity of scenarios and species. The pseudo-lab.l generation based unsupervised domain adaptation paradigm, which discriminates the... 详细信息
来源: 评论
Downstream-agnostic Adversarial Examples
arXiv
收藏 引用
arXiv 2023年
作者: Zhou, Ziqi Hu, Shengshan Zhao, Ruizhi Wang, Qian Zhang, Leo Yu Hou, Junhui Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China School of Information and Communication Technology Griffith University Australia Department of Computer Science City University of Hong Kong Hong Kong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab
Self-supervised learning usually uses a large amount of unlab.led data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning oper... 详细信息
来源: 评论
MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED LEARNING
arXiv
收藏 引用
arXiv 2023年
作者: Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China
Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels...
来源: 评论
Improve the Robustness of Deep Learning Models against Adversarial Attacks in Judicial Field  8
Improve the Robustness of Deep Learning Models against Adver...
收藏 引用
8th International Conference on Digital Home, ICDH 2020
作者: Liao, Yongxian Zhuang, Wenzi Huang, Lifeng Liu, Ning Sun Yat-sen University School of Data and Computer Science China Guangdong Key Laboratory of Information Security Technology China
With the rapid development of deep neural networks (DNNs), they have been applied in various domains including crowd counting, object detection and digital forensic. Consequently, DNN-based forensic process plays an e... 详细信息
来源: 评论
Joint Participation Incentive and Network Pricing Design for Federated Learning  42
Joint Participation Incentive and Network Pricing Design for...
收藏 引用
42nd IEEE International Conference on computer Communications, INFOCOM 2023
作者: Ding, Ningning Gao, Lin Huang, Jianwei Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States Shenzhen Research Institute of Big Data Shenzhen518172 China Harbin Institute of Technology Sch. of Electronics and Info. Eng. and the Guangdong Prov. Key Lab. of Aerosp. Commun. and Networking Technol. Shenzhen China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China
Federated learning protects users' data privacy though sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federa... 详细信息
来源: 评论
Deep Generative Network for Image Inpainting with Gradient Semantics and Spatial-Smooth Attention
SSRN
收藏 引用
SSRN 2023年
作者: Sheng, Ziqi Xu, Wenbo Lin, Cong Lu, Wei School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Applied Laboratory of Dig Data and Education Statistics School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510006 China
As a powerful means of tampering in image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, particularly in boundary... 详细信息
来源: 评论
Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge Computing
收藏 引用
IEEE Transactions on Vehicular technology 2025年
作者: Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China
Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the syste... 详细信息
来源: 评论
Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts
SSRN
收藏 引用
SSRN 2023年
作者: Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States
Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaus... 详细信息
来源: 评论
Design a Proof of Stake Based Directed Acyclic Graph Chain  3rd
Design a Proof of Stake Based Directed Acyclic Graph Chain
收藏 引用
3rd International Conference on Frontiers in Cyber security, FCS 2020
作者: Tian, Haibo Lin, Huizhi Zhang, Fangguo Guangdong Province Key Laboratory of Information Security Technology School of Data and Computer Science Sun Yat-Sen University GuangzhouGuangdong510275 China
The concept of blockchain comes from the Bitcoin system where transactions are organized in blocks. However, blocks are not necessary. Researchers have found ways to use a directed acyclic graph (DAG) to build a chain... 详细信息
来源: 评论