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An Overview of General Theory of Security

China Communications 2017年第7期14卷 1-10页

作者： Min Lei Yixian Yang Xinxin Niu Yu Yang Jie Hao Information Security Center Beijing University of Posts and Telecommunications Guizhou University Guizhou Provincial Key Laboratory of Public Big Data Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology

Cyber security lacks comprehensive theoretical guidance. General security theory, as a set of basic security theory concepts, is intended to guide cyber security and all the other security work. The general theory of security aims to unify the main branches of cyber security and establish a unified basic theory. This paper proposal an overview on the general theory of security, which is devoted to constructing a comprehensive model of network security. The hierarchical structure of the meridian-collateral tree is described. Shannon information theory is employed to build a cyberspace security model. Some central concepts of security, i.e., the attack and defense, are discussed and several general theorems on security are presented.

关键词： cyber security network attack and defense meridian-collateral theory

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Visual Tracking Algorithm Based on Color Name Histogram

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IOP Conference Series: Materials Science and engineering 2020年第1期790卷

作者： Chenchen Yue Wangsheng Yu Zhiqiang Hou Sugang Ma College of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China Information and Navigation Institute of Air Force Engineering University Xi'an 710077 China

Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.

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An obstacle avoidance design of UAV based on genetic algorithm

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Journal of Physics: Conference Series 2020年第1期1633卷

作者： Xusheng Hu Juan Liu Key Laboratory of Unmanned Aerial Vehicle Development & Data Application of Anhui Higher Education Institutes Wanjiang University of Technology Ma'anshan 243031 China Ma'anshan Engineering Technology Research Center for Wireless Sensor Network and IntelliSense Ma'anshan 243031 China Ma'anshan Technical College Ma'anshan 243030 China

In order to improve the effect of obstacle avoidance, a PID control algorithm combined with genetic algorithm is proposed. By establishing the mathematical model of UAV, the auxiliary control system of UAV is analyzed, and then the control system is optimized by genetic algorithm. Taking STM32 as the control platform, the obstacle avoidance experiment was carried out by building the experimental platform. The experimental results show that the algorithm can avoid obstacles effectively.

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Quantized frank-wolfe: Faster optimization, lower communication, and projection free

arXiv

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arXiv 2019年

作者： Zhang, Mingrui Chen, Lin Mokhtari, Aryan Hassani, Hamed Karbasi, Amin Department of Statistics and Data Science Yale University New HavenCT06511 United States Yale Institute for Network Science Department of Electrical Engineering Yale University New HavenCT06511 United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical Engineering and Computer Science Yale University New HavenCT06511 United States

How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and has been mainly studied in the unconstrained setting. In this paper, we propose Quantized Frank-Wolfe (QFW), the first projection-free and communication-efficient algorithm for solving constrained optimization problems at scale. We consider both convex and non-convex objective functions, expressed as a finite-sum or more generally a stochastic optimization problem, and provide strong theoretical guarantees on the convergence rate of QFW. This is accomplished by proposing novel quantization schemes that efficiently compress gradients while controlling the noise variance introduced during this process. Finally, we empirically validate the efficiency of QFW in terms of communication and the quality of returned solution against natural baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Self-Evolutionary Neuron Model for Fast-Response Spiking Neural networks

TechRxiv

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TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

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A global overview of marine heatwaves in a changing climate (vol 5, 701, 2024)

COMMUNICATIONS EARTH & ENVIRONMENT

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COMMUNICATIONS EARTH & ENVIRONMENT 2024年第1期5卷 701页

作者： Capotondi, Antonietta Rodrigues, Regina R. Sen Gupta, Alex Benthuysen, Jessica A. Deser, Clara Froelicher, Thomas L. Lovenduski, Nicole S. Amaya, Dillon J. Le Grix, Natacha Xu, Tongtong Hermes, Juliet Holbrook, Neil J. Martinez-Villalobos, Cristian Masina, Simona Roxy, Mathew Koll Schaeffer, Amandine Schlegel, Robert W. Smith, Kathryn E. Wang, Chunzai University of Colorado Boulder Cooperative Institute for Research in Environmental Sciences Boulder USA (GRID:grid.266190.a) (ISNI:***) NOAA/Physical Sciences Laboratory Boulder USA (GRID:grid.511342.0) Federal University of Santa Catarina Florianópolis Brazil (GRID:grid.411237.2) (ISNI:0000 0001 2188 7235) University of New South Wales Climate Change Research Centre and Centre for Marine Science and Innovation Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) University of New South Wales Australian Research Council Centre of Excellence for Climate Extremes Sydney Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432) Australian Institute of Marine Science Crawley Australia (GRID:grid.1046.3) (ISNI:0000 0001 0328 1619) National Center for Atmospheric Research Climate and Global Dynamics Laboratory Boulder USA (GRID:grid.57828.30) (ISNI:0000 0004 0637 9680) University of Bern Climate and Environmental Physics Physics Institute Bern Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157) University of Bern Oeschger Centre for Climate Change Research Bern Switzerland (GRID:grid.5734.5) (ISNI:0000 0001 0726 5157) University of Colorado Boulder Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research Boulder USA (GRID:grid.266190.a) (ISNI:***) NOAA/Physical Sciences Laboratory Boulder USA (GRID:grid.511342.0) South African Environmental Observation Network Cape Town South Africa (GRID:grid.507758.8) (ISNI:0000 0004 0499 441X) University of Tasmania Institute for Marine and Antarctic Studies Hobart Australia (GRID:grid.1009.8) (ISNI:0000 0004 1936 826X) University of Tasmania ARC Centre of Excellence for Climate Extremes Hobart Australia (GRID:grid.1009.8) (ISNI:0000 0004 1936 826X) Universidad Adolfo Ibáñez Faculty of Engineering and Science Santiago Chile (GRID:grid.440617.0) (ISNI:0000 0001 2162 5606) ANID Technology Center No. DO210001 Data Observatory Foundation Santiago Chile (GRID:grid.440617.0) CMCC Foundation—Euro-M

Marine heatwaves have profoundly impacted marine ecosystems over large areas of the world oceans, calling for improved understanding of their dynamics and predictability. Here, we critically review the recent substantial advances in this active area of research, including the exploration of the three-dimensional structure and evolution of these extremes, their drivers, their connection with other extremes in the ocean and over land, future projections, and assessment of their predictability and current prediction skill. To make progress on predicting and projecting marine heatwaves and their impacts, a more complete mechanistic understanding of these extremes over the full ocean depth and at the relevant spatial and temporal scales is needed, together with models that can realistically capture the leading mechanisms at those scales. Sustained observing systems, as well as measuring platforms that can be rapidly deployed, are essential to achieve comprehensive event characterizations while also chronicling the evolving nature of these extremes and their impacts in our changing *** understanding of marine heatwave predictability and impacts requires analysis of these extremes at full ocean depth, using models and observations capturing their key drivers at the relevant scales, according to a broad literature review.

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Comments on cut-set bounds on network function computation

Comments on cut-set bounds on network function computation

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作者： Huang, Cupjin Tan, Zihan Yang, Shenghao Guang, Xuan Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Computer Science University of Chicago ChicagoIL60637 United States School of Science and Engineering Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China School of Mathematical Sciences and LPMC Nankai University Tianjin China Institute of Network Coding Chinese University of Hong Kong Hong Kong Hong Kong

A function computation problem over a directed acyclic network has been considered in the literature, where a sink node is required to compute a target function correctly with the inputs arbitrarily generated at multiple source nodes. The network links are error free but capacity limited, and the intermediate nodes perform network coding. The computing rate of a network code is the average number of times that the target function is computed for one use of the network, i.e., each link in the network is used at most once. In the existing papers, two cut-set bounds were proposed on the computing rate. However, we in this paper show that these bounds are not valid for general network function computation problems. We analyze the reason of the invalidity and propose a general cut-set bound by using a new equivalence relation associated with the inputs of the target function. Moreover, some results in the existing papers were proved by applying the invalid upper bound. We also justify the validity of these results. © 1963-2012 IEEE.

关键词： network coding

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Cryptanalysis of novel extended multivariate public key cryptosystem with invertible cycle

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International Journal of network Security 2018年第3期20卷 509-514页

作者： Lu, Gang Xue, Linyuan Nie, Xuyun Qin, Zhiguang School of Information and Software Engineering University of Electronic Science and Technology of China Section 2 North Jianshe Road Chengdu610054 China State Key Laboratory of Information Security Institute of Information Engineering China Network and Data Security Key Laboratory of Sichuan Province China

In 2016, Qiao et al. proposed a novel extended multivariate public key cryptosystem (EMC) to enhance the security of multivariate public key cryptosystem. They applied it on Matsumoto-Imai (MI) encryption scheme and claimed that the enhanced MI scheme can be secure against Linearization Equation (LE) attack. Through analysis, we found that the enhanced MI scheme satisfied Quadratization Equations (QE). After finding all the quadratization equations, we can recover the plaintext corresponding to a valid ciphertext of the enhanced MI scheme. © International Journal of network Security.

关键词： Public key cryptography

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Corrigendum to "Dissemination in time and space in presymptomatic granulin mutation carriers: A spatial chronnectome study" [Neurobiology of Aging Volume 108, December 2021, Pages 155-167]

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Neurobiology of aging 2022年 119卷 140-144页

作者： Enrico Premi Marcello Giunta Armin Iraji Srinivas Rachakonda VinceD Calhoun Stefano Gazzina Alberto Benussi Roberto Gasparotti Silvana Archetti Martina Bocchetta Dave Cash Emily Todd Georgia Peakman Rhian Convery John C van Swieten Lize Jiskoot Raquel Sanchez-Valle Fermin Moreno Robert Laforce Caroline Graff Matthis Synofzik Daniela Galimberti JamesB Rowe Mario Masellis Carmela Tartaglia Elizabeth Finger Rik Vandenberghe Alexandre de Mendonça Fabrizio Tagliavini Chris R Butler Isabel Santana Alexander Gerhard Isabelle Le Ber Florence Pasquier Simon Ducharme Johannes Levin Adrian Danek Sandro Sorbi Markus Otto Jonathan D Rohrer Barbara Borroni Stroke Unit Azienda Socio Sanitaria Territoriale Spedali Civili Brescia Brescia Italy. Centre for Neurodegenerative Disorders Department of Clinical and Experimental Sciences University of Brescia Brescia Italy. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA Departments of Psychology and Computer Science Georgia State University Atlanta GA USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA. Neurophysiology Unit Azienda Socio Sanitaria Territoriale Spedali Civili Brescia Brescia Italy. Neuroradiology Unit University of Brescia Brescia Italy. Laboratory Unit Azienda Socio Sanitaria Territoriale Spedali Civili Brescia Brescia Italy. Department of Neurodegenerative Disease Dementia Research Centre UCL Institute of Neurology Queen Square London UK. Department of Neurology Erasmus Medical Centre Rotterdam the Netherlands. Alzheimer's disease and Other Cognitive Disorders Unit Neurology Service Hospital Clinic University of Barcelona Barcelona Spain. Cognitive Disorders Unit Department of Neurology Donostia University Hospital San Sebastian Gipuzkoa Spain. Clinique Interdisciplinaire de Memoire Department des Sciences Neurologiques CHU de Quebec and Faculte de Medecine Universite Laval Quebec Canada. Center for Alzheimer Research Division of Neurogeriatrics Department of Neurobiology Care Sciences and Society Bioclinicum Karolinska Institutet Solna Sweden. Department of Neurodegenerative Diseases Hertie-Institute for Clinical Brain Research and Center of Neurology University of Tubingen Tubingen Germany. Fondazione Ca' Granda IRCCS Ospedale Policlinico Milan Italy University of Milan

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Balanced distribution adaptation for transfer learning

arXiv

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arXiv 2018年

作者： Wang, Jindong Chen, Yiqiang Hao, Shuji Feng, Wenjie Shenk, Zhiqi Beijing Key Laboratory of Mobile Computing and Pervasive Device 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 Institute of High Performance Computing A*STAR School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution Adaptation (BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation (W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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