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Consensus control of stochastic multi-agent systems: a survey

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science China(Information sciences) 2017年第12期60卷 5-19页

作者： Lifeng MA Zidong WANG Qing-Long HAN Yurong LIU School of Automation Nanjing University of Science and Technology College of Electrical Engineering and Automation Shandong University of Science and Technology Department of Computer Science Brunel University London School of Software and Electrical Engineering Swinburne University of Technology Department of Mathematics Yangzhou University

In this article, we provide a review of the consensus control problem for stochastic multi-agent systems(MASs). Recent advances are surveyed according to the method of occurrence of the stochasticity of the MASs. First, the consensus problem is discussed for MASs, wherein individual agents are corrupted by random noises, i.e., the dynamics of agents involve stochasticity in process and/or measurement equations. Both additive noises and multiplicative noises are surveyed in detail and special attention is paid to the MASs whose dynamics are governed by It?o differential equations. Moreover, particular effort is devoted to presenting the latest progress on the consensus problem for a special type of stochastic MAS with Markovian jump parameters. Subsequently,the relevant research is summarized for MASs with noisy communication environments and stochastic ***, we provide a systematic review of the consensus problems for MASs whose communication topology varies randomly in the process of data propagation among agents. Finally, conclusions are drawn and several potential future research directions are outlined.

关键词： stochastic multi-agent systems consensus control stochastic noises Markovian jump systems random topology

Local contrast learning

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arXiv 2018年

作者： Xu, Chuanyun Zhang, Yang Feng, Xin Ge, Yong Xing Zhang, Yihao Long, Jianwu College of computer science and engineering Chongqing University of Technology Chongqing China College of Computer and Information Science Chongqing Normal University Chongqing China School of Software Engineering Chongqing University Chongqing China

Learning a deep model from small data is yet an opening and challenging problem. We focus on one-shot classification by deep learning approach based on a small quantity of training samples. We proposed a novel deep learning approach named Local Contrast Learning (LCL) based on the key insight about a human cognitive behavior that human recognizes the objects in a specific context by contrasting the objects in the context or in her/his memory. LCL is used to train a deep model that can contrast the recognizing sample with a couple of contrastive samples randomly drawn and shuffled. On one-shot classification task on Omniglot, the deep model based LCL with 122 layers and 1.94 millions of parameters, which was trained on a tiny dataset with only 60 classes and 20 samples per class, achieved the accuracy 97.99% that outperforms human and state-of-the-art established by Bayesian Program Learning (BPL) trained on 964 classes. LCL is a fundamental idea which can be applied to alleviate parametric model's overfitting resulted by lack of training samples. Copyright © 2018, The Authors. All rights reserved.

关键词： Sampling

Synthesis and Recognition of Internet Celebrity Face Based on Deep Learning 12th

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Synthesis and Recognition of Internet Celebrity Face Based o...

12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Zhou, Jiancan Zeng, Guohang He, Jia Jia, Xi Shen, Linlin College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783319699226

The similarity among Internet Celebrity Faces brings a big challenge to the recognition and verification of faces. To study this problem, more than 20,000 Internet Celebrity Face pictures are collected from the Internet. We utilize these faces to train the Variational Auto-Encoder (VAE) to synthesize the fake Internet Celebrity Faces and compare the faces with real samples. Results show that the performance of the deep network in Internet Celebrity Face greatly decreases. 20 pairs of the same or different Internet Celebrity Faces are selected to test the human’s ability to recognize Internet Celebrity Faces by questionnaire. The comparison with the VGG deep network shows that the deep learning algorithm performs much better than human in terms of recognition accuracy. © 2017, Springer International Publishing AG.

关键词： Face recognition

Impact of temperature and relative humidity on the transmission of COVID-19: A modeling study in China and the United States

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arXiv 2020年

作者： Wang, Jingyuan Tang, Ke Feng, Kai Lin, Xin Lv, Weifeng Chen, Kun Wang, Fei School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Social Sciences Tsinghua University China State Key Laboratory of Software Development Environment Beihang University China Department of Statistics University of Connecticut United States Center for Population Health University of Connecticut Health Center United States Department of Population Health Sciences Weill Cornell Medical College Cornell University United States

Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.;an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

A New Anti-Spam Model Based on E-mail Address Concealment Technique

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Wuhan University Journal of Natural sciences 2018年第1期23卷 79-83页

作者： ZHANG Yuqiang HE Jingsha XU Jing Beijing Institute of Aerospace Control Devices Beijing100039 China College of Computer Science and Technology Beijing University of Technology Beijing 100124 China School of Software Engineering Beijing University ofTechnology Beijing 100124 China Department of Automation Tsinghua University Beijing100084 China

To deal with the junk e-mail problem caused by the e-mail address leakage for a majority of Internet users, this paper presents a new privacy protection model in which the e-mail address of the user is treated as a piece of privacy information concealed. Through an interaction pattern that involves three parties and uses an e-mail address code in the place of an e-mail address, the proposed model can prevent the e-mail address from being leaked, thus effectively resolving the junk e-mail problem. We compare the proposed anti-spam method with the filtering technology based on machine learning. The result shows that 100% spams can be filtered out in our scheme, indicating the effectiveness of the proposed anti-spam method.

关键词： spare e-mail address protection model e-mail address code

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Lockout-Tagout Ransomware: A Detection Method for Ransomware using Fuzzy Hashing and Clustering

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Lockout-Tagout Ransomware: A Detection Method for Ransomware...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Nitin Naik Paul Jenkins Jonathan Gillett Haralambos Mouratidis Kshirasagar Naik Jingping Song School of Computing University of Portsmouth United Kingdom Faculty of Science and Technology Bournemouth University United Kingdom Centre for Secure Intelligent and Usable Systems University of Brighton United Kingdom Department of Electrical and Computer Engineering University of Waterloo Canada Software College Northeastern University China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Ransomware attacks are a prevalent cybersecurity threat to every user and enterprise today. This is attributed to their polymorphic behaviour and dispersion of inexhaustible versions due to the same ransomware family or threat actor. A certain ransomware family or threat actor repeatedly utilises nearly the same style or codebase to create a vast number of ransomware versions. Therefore, it is essential for users and enterprises to keep well-informed about this threat landscape and adopt proactive prevention strategies to minimise its spread and affects. This requires a technique to detect ransomware samples to determine the similarity and link with the known ransomware family or threat actor. Therefore, this paper presents a detection method for ransomware by employing a combination of a similarity preserving hashing method called fuzzy hashing and a clustering method. This detection method is applied on the collected WannaCry/WannaCryptor ransomware samples utilising a range of fuzzy hashing and clustering methods. The clustering results of various clustering methods are evaluated through the use of the internal evaluation indexes to determine the accuracy and consistency of their clustering results, thus the effective combination of fuzzy hashing and clustering method as applied to the particular ransomware corpus. The proposed detection method is a static analysis method, which requires fewer computational overheads and performs rapid comparative analysis with respect to other static analysis methods.

关键词： Ransomware Clustering methods Indexes Cryptography Engines Syntactics

Enhancing Security of FPGA-Based Embedded Systems with Combinational Logic Binding

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Journal of computer science & Technology 2017年第2期32卷 329-339页

作者： Ji-Liang Zhang Wei-Zheng Wang Xing-Wei Wang Zhi-Hua Xia Software College Northeastern University Shenyang 110169 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China Department of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410014 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

With the increasing use of field-programmable gate arrays （FPGAs） in embedded systems and many embedded applications, the failure to protect FPGA-based embedded systems from cloning attacks has brought serious losses to system developers. This paper proposes a novel combinational logic binding technique to specially protect FPGA-based embedded systems from cloning attacks and provides a pay-per-device licensing model for the FPGA market. Security analysis shows that the proposed binding scheme is robust against various types of malicious attacks. Experimental evaluations demonstrate the low overhead of the proposed technique.

关键词： cloning attack reverse engineering FPGA （field-programmable gate array） security hardware security

Bilinear supervised hashing based on 2D image features

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arXiv 2019年

作者： Ding, Yujuan Wong, Wai Kueng Lai, Zhihui Zhang, Zheng Institute of Textiles and Clothing Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Textiles and Clothing Hong Kong Polytechnic University Hong Kong School of Information Technology & Electrical Engineering University of Queensland BrisbaneQLD4072 Australia

Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost. Most of the existing hashing methods focus on learning the low-dimensional vectorized binary features based on the high-dimensional raw vectorized features. However, studies on how to obtain preferable binary codes from the original 2D image features for retrieval is very limited. This paper proposes a bilinear supervised discrete hashing (BSDH) method based on 2D image features which utilizes bilinear projections to binarize the image matrix features such that the intrinsic characteristics in the 2D image space are preserved in the learned binary codes. Meanwhile, the bilinear projection approximation and vectorization binary codes regression are seamlessly integrated together to formulate the final robust learning framework. Furthermore, a discrete optimization strategy is developed to alternatively update each variable for obtaining the high-quality binary codes. In addition, two 2D image features, traditional SURF-based FVLAD feature and CNN-based AlexConv5 feature are designed for further improving the performance of the proposed BSDH method. Results of extensive experiments conducted on four benchmark datasets show that the proposed BSDH method almost outperforms all competing hashing methods with different input features by different evaluation protocols. Copyright © 2019, The Authors. All rights reserved.

关键词： Binary codes