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Attack and improve of a group signature scheme based on Chinese remainder theorem

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology 2007年第1期29卷 182-184页

作者： Wang, Feng-He Hu, Yu-Pu Wang, Chun-Xiao Key Lab. of Computer Networks and Information Security Xidian University Xi'an 710071 China Dept. of Mathematics Taishan Collage Taian 271000 China Dept. of Math. and Phys. Shandong Institute of Architecture and Engineering Jinan 250014 China

An attack is mounted on a group signature ncncmc bnscd on Cliincso Remainder Theory (Chen-Scheme). It shows the Chen-Scheme is insecure: A colluding subset of group members can find the private key of other members and forge their signatures;The dishonest group center can produce a valid signature un behalf of group members. Using Schnorr signature scheme this paper proposes an improve scheme, which has good characteristics: A colluding subset of group members can not produce a valid signature that the group manager can not open;A dishonest group center can not sign messages on behalf of other members;The revocation of the membership is efficient.

关键词： Cryptography

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Study on user's identification system in insider threats

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Tongxin Xuebao/Journal on Communications 2009年第11 A期30卷 121-126页

作者： Pei, Qing-Qi Zhao, Peng Zhang, Hong-Bin Wang, Chao Yin, Hao Institute of China Electronic System Engineering Corporation Beijing 100141 China Key Lab. of Computer Networks and Information Security of Ministry of Education Xidian University Xi'an 710071 China Institute of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang 050018 China

Monitoring user's abnormal behaviors, which is an effective method to detect impersonation, is used for impersonation detection in insider threats. A model is built by using TAN-based Bayesian network to reflect the characteristics of user's behavior. When the deviation from the model is found, the system can determine the identity of the user. As a result, experiments show that the monitoring numbers of processes called by users can be very effective on detecting impersonation and can identify the identity of the attacker.

关键词： Bayesian networks

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Single focused-beam acoustical tweezers: Trapping cells in 3‐D

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The Journal of the Acoustical Society of America 2023年第4_SUPPLEMENT期154卷 A263-A263页

作者： Zhixiong Gong Shiyu Li Zhichao Ma Key Lab. of Marine Intelligent Equipment and System State Key Lab. of Ocean Eng. School of Naval Architecture Ocean and Civil Eng. Shanghai Jiao Tong Univ. Dongchuan Rd. #800 Minhang District Shanghai 200240 China zhixiong.gong@*** Key Lab. of Marine Intelligent Equipment and System State Key Lab. of Ocean Eng. School of Naval Architecture Ocean and Civil Eng. Shanghai Jiao Tong Univ. Shanghai China Inst. of Medical Robotics School of Biomedical Eng. Shanghai Jiao Tong Univ. Shanghai China

Single focused beams have shown great potential in acoustofluidics and medical ultrasound because of their large pressure gradient and acoustic-thermal effect, such as acoustical tweezers and high-intensity focused ultrasound (HIFU) technique. However, the three-dimensional trapping of typical cells is still challenging since cells in water have a positive acoustic contrast factor and will be repulsive from the focus [Gong and Baudoin, Phys. Rev. Applied 18, 044033 (2022)]. In this work, to reverse this positive acoustic contrast factor into negative, we propose to use a cell-friendly medium (made of iodixanol with water) [Augustsson et al., Nat. Commun. 7, 11556 (2016)]. This enables a single focused beam to selectively trap and control cells in 3‐D, and more importantly, keeps the manipulated cells with good viability. This work will extend the applications of acoustical tweezers, which may be beneficial to single cell analysis, cellular phenotyping, precise assembly of different cells in tissue engineering, and controlled drug delivery.

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Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer

arXiv

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arXiv 2023年

作者： Luo, Anwei Cai, Rizhao Kong, Chenqi Ju, Yakun Kang, Xiangui Huang, Jiwu Kot, Alex C. The School of Information Technology Jiangxi University of Finance and Economics Nanchang330013 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Lab. School of Electrical and Electronic Engineering Nanyang Technology University Singapore The Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The China-Singapore International Joint Research Institute Singapore

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (ViT) based models can achieve some promising results after fully fine-tuning on the Deepfake dataset, their generalization performances are still unsatisfactory. One possible reason is that fully fine-tuned ViT-based models may disrupt the pre-trained features [1], [2] and overfit to some data-specific patterns [3]. To alleviate this issue, we present a Forgery-aware Adaptive Vision Transformer (FA-ViT) under the adaptive learning paradigm, where the parameters in the pre-trained ViT are kept fixed while the designed adaptive modules are optimized to capture forgery features. Specifically, a global adaptive module is designed to model long-range interactions among input tokens, which takes advantage of self-attention mechanism to mine global forgery clues. To further explore essential local forgery clues, a local adaptive module is proposed to expose local inconsistencies by enhancing the local contextual association. In addition, we introduce a fine-grained adaptive learning module that emphasizes the common compact representation of genuine faces through relationship learning in fine-grained pairs, driving these proposed adaptive modules to be aware of fine-grained forgery-aware information. Extensive experiments demonstrate that our FA-ViT achieves state-of-the-arts results in the cross-dataset evaluation, and enhances the robustness against unseen perturbations. Particularly, FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation. The code and trained model have been released at: https://***/LoveSiameseCat/FAViT. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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The design and implementation of network attack and defense platform based on cloud desktop

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Journal of Physics: Conference Series 2017年第1期887卷

作者： Zining Yan Yunhan Wang Shijiao Shao Boquan Li Embedded System Lab. College of Computer Science and Technology Harbin China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Key Laboratory of Network Assessment of Technology Chinese Academy of Sciences Beijing China

This paper discusses the cloud desktop technology, virtualization technology and penetration testing technology used in the network attack and defense training platform, and introduces the design and implementation process of the network attack and defense training platform. This paper focuses on the cloud desktop construction scheme based on B/S structure, and aims to enhance the flexibility and convenience of network attack and defense training platform.

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Corrigendum to “Fast and scalab.e lock methods for video coding on many-core architecture” [J. Vis. Commun. Image R. 25(7) (2014) 1758–1762]

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Journal of Visual Communication and Image Representation 2015年 27卷 57-57页

作者： Weizhi Xu Hui Yu Dianjie Lu Fenglong Song Da Wang Xiaochun Ye Songwei Pei Dongrui Fan Hongtao Xie Institute of Microelectronics Tsinghua University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Information Science and Engineering Shandong Normal University Jinan China Department of Computer Science and Technology Beijing University of Chemical Technology Beijing China Institute of Information Engineering Chinese Academy of Sciences National Engineering Laboratory for Information Security Technologies Beijing China

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Corrigendum to “Fast and scalab.e lock methods for video coding on many-core architecture” [J. Vis. Commun. 25 (7) (2014) 1758–1762]

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Journal of Visual Communication and Image Representation 2015年 26卷 350-350页

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5G entering into a new era [Guest Editorial]

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China Communications 2017年第12期14卷 iv-vi页

作者： Feifei Gao Changle Li Jun Fang Kaizhi Huang Zhi Quan Chengwen Xing Jianhua Zhang Zhongshan Zhang Liqiang Zhao Yulong Zou Department of Automation Tsinghua University Beijing China State Key Laboratory of Integrated Services Networks Xidian University University of Eletronic Science and Technology of China National Digital Switching System Engineering & Technological Research Center Zhengzhou China College of Information Engineering Shenzhen University School of Information and Electronics Beijing Institute of Technology Beijing University of Posts and Telecommunication School of Computer and Communication Engineering in the University of Science and Technology Beijing State Key Lab. of Integrated Service Networks Xidian University Nanjing University of Posts and Telecommunications (NUPT) Nanjing China

The ever increasing wireless data services, such as imaging, video, audio, multimedia, etc., have demands for the very high speed wireless communications and network, which are unfortunately a bottleneck when combining with the wireline core network. Users' are now expecting high quality of experience with low-cost devices, ubiquitous connectivity, energy efficiency, high reliability, or even ultra-low latency if a vehicle terminal is applied.

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Publisher Correction: Twisted moiré conductive thermal metasurface

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Nature communications 2024年第1期15卷 2876页

作者： Huagen Li Dong Wang Guoqiang Xu Kaipeng Liu Tan Zhang Jiaxin Li Guangming Tao Shuihua Yang Yanghua Lu Run Hu Shisheng Lin Ying Li Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. Wuhan National Laboratory for Optoelectronics and State Key Laboratory of Material Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China. Smart Materials for Architecture Research Lab Innovation Center of Yangtze River Delta Zhejiang University Jiaxing 314100 China. State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan 430074 China. Chongqing 2D Materials Institute Chongqing 400015 China. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. eleying@***. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. eleying@***. Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. chengwei.qiu@nus.edu.sg.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第Sup1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Kings County Hospital Center Brooklyn NY 11203 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics

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