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Guest Editorial Special Issue on Security and Privacy in Cyber-Physical Systems

IEEE Internet of Things Journal 2017年第6期4卷 1798-1800页

作者： Yu, Wei Fu, Xinwen Song, Houbing Economides, Anastasios A. Jo, Minho Zhao, Wei Department of Computer and Information Sciences Towson University TowsonMD21252 United States Department of Computer Science University of Central Florida OrlandoFL32816 United States Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Computer Networks and Telematic Applications Laboratory University of Macedonia Thessaloniki546 36 Greece Department of Computer Convergence Software Korea University Seoul Korea Republic of University of Macau China

A typical cyber-physical system (CPS) refers to a system that features a tight integration of computation, networking, and physical elements for interactions between cyber and physical spaces. The Internet of Things (IoT) is considered to be the networking infrastructure of CPS. applications of CPS cover numerous smart-world research areas that our daily life depends upon, including smart transportation, smart electrical power grid, smart cities, smart medical systems, smart manufacturing systems, and others. While major research on improving the efficiency and reliability of CPS by using advanced information and communication technologies has been conducted, the risks of cyberspace security and privacy breaches in CPS need to be seriously investigated before a massive deployment of CPS technologies can or should be realized.

关键词： Special issues and sections Cyber-physical systems Smart cities Smart grids Manufacturing processes

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2D matrix based indexing with color spectralhistogram for efficient image retrieval

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Journal of Systems engineering and Electronics 2016年第5期27卷 1122-1134页

作者： maruthamuthu ramasamy john sanjeev kumar athisayam Department of Software Engineering RVS College of EngineeringDindigul 624005India Department of Computer Applications Thiagarajar College of EngineeringMadurai 625015India

A novel content based image retrieval （CBIR） algorithmusing relevant feedback is presented. The proposed frameworkhas three major contributions： a novel feature descriptor calledcolor spectral histogram （CSH） to measure the similarity betweenimages; two-dimensional matrix based indexing approach proposedfor short-term learning （STL）; and long-term learning （LTL）.In general, image similarities are measured from feature representationwhich includes color quantization, texture, color, shapeand edges. However, CSH can describe the image feature onlywith the histogram. Typically the image retrieval process starts byfinding the similarity between the query image and the imagesin the database; the major computation involved here is that theselection of top ranking images requires a sorting algorithm to beemployed at least with the lower bound of O（n log n）. A 2D matrixbased indexing of images can enormously reduce the searchtime in STL. The same structure is used for LTL with an aim toreduce the amount of log to be maintained. The performance ofthe proposed framework is analyzed and compared with the existingapproaches, the quantified results indicates that the proposedfeature descriptor is more effectual than the existing feature descriptorsthat were originally developed for CBIR. In terms of STL,the proposed 2D matrix based indexing minimizes the computationeffort for retrieving similar images and for LTL, the proposed algorithmtakes minimum log information than the existing approaches.

关键词： content based image retrieval （CBIR） color spectralhistogram （CSH） short-term learning （STL） long-term learning（LTL） similarity measures.

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Node verification to join the cloud environment using third party verification server

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2017年第4期11卷 55-65页

作者： Aljammal, Ashraf H. Bani-Salameh, Hani Alsarhan, Ayoub Kharabsheh, Mohammad Obiedat, Mamoon Department of Computer Science and its Applications Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan

Currently, cloud computing is facing different types of threats whether from inside or outside its environment. This may cause cloud to be crashed or at least unable to provide services to the requests made by clients. In this paper, a new technique is proposed to make sure that the new node which asks to join the cloud is not composing a threat on the cloud environment. Our new technique checks the node before it will be guaranteed to join the cloud whether it runs malwares or software that could be used to launch an attack. In this way the cloud will allow only the clean node to join it, eliminating the risk of some types of threats that could be caused by infected nodes.

关键词： Network security

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Robust frequent directions with application in online learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2019年第1期20卷

作者： Luo Luo Cheng Chen Zhihua Zhang Wu-Jun Li Tong Zhang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University Beijing China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University Nanjing China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms.

关键词： frequent directions matrix approximation online Newton algorithm online convex optimization sketching

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Robust frequent directions with application in online learning

arXiv

引用

arXiv 2017年

作者： Luo, Luo Chen, Cheng Zhang, Zhihua Li, Wu-Jun Zhang, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University 5 Yiheyuan Road Beijing100871 China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University 163 Xianlin Avenue Nanjing210023 China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

关键词： Heuristic methods

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Phonetic temporal neural model for language identification

arXiv

引用

arXiv 2017年

作者： Tang, Zhiyuan Wang, Dong Chen, Yixiang Li, Lantian Abel, Andrew Chengdu Institute of Computer Applications Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing100049 China Center for Speech and Language Technologies Tsinghua University Beijing100084 China Tsinghua National Laboratory for Information Science and Technology and the Center for Speech and Language Technologies Tsinghua University Beijing100084 China Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou215123 China

Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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A framework for performance optimisation at software architecture level

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International Journal of Wireless and Mobile Computing 2016年第1期10卷 42-47页

作者： Ye, Peng Ni, You-Cong Du, Xin Li, Song Xie, Da-Tong Xiao, Ru-Liang College of Mathematics and Computer Wuhan Textile University WuhanHubei China Faculty of Software Fujian Normal University FuzhouFujian China Centre of Excellence for Research in Computational Intelligence and Applications School of Computer Science University of Birmingham EdgbastonBirmingham United Kingdom Department of Information Management Engineering Fujian Commercial College FuzhouFujian China

Architecture-based software performance optimisation can significantly not only save time but also reduce cost. A few rule-based performance optimisation approaches at software architecture level have been proposed in recent years. However, the count and the order of each rule usage in the optimisation process have not been fully considered in these approaches. Consequently, the search space for performance improvement is limited so that the optimal solution is hard to find out. Aiming at this problem, firstly, rule-based software performance optimisation is abstracted into a mathematical model called RPOM. The mathematical relation between usage of rules and optimal solution in performance improvement space is precisely characterised in the RPOM. Furthermore, we design a rule sequence execution framework named RSEF which can solve RPOM model. The result of this paper will help those rule-based software performance optimisation approaches to search the larger space in order to improve the quality of optimisation. © Copyright 2016 Inderscience Enterprises Ltd.

关键词： software architecture

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An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks

arXiv

引用

arXiv 2019年

作者： Jacobs, C. Collett, T. Glazebrook, K. Buckley-Geer, E. Diehl, H.T. Lin, H. McCarthy, C. Qin, A.K. Odden, C. Caso Escudero, M. Dial, P. Yung, V.J. Gaitsch, S. Pellico, A. Lindgren, K.A. Abbott, T.M.C. Annis, J. Avila, S. Brooks, D. Burke, D.L. Carnero Rosell, A. Carrasco Kind, M. Carretero, J. da Costa, L.N. de Vicente, J. Fosalba, P. Frieman, J. García-Bellido, J. Gaztanaga, E. Goldstein, D.A. Gruen, D. Gruendl, R.A. Gschwend, J. Hollowood, D.L. Honscheid, K. Hoyle, B. James, D.J. Krause, E. Kuropatkin, N. Lahav, O. Lima, M. Maia, M.A.G. Marshall, J.L. Miquel, R. Plazas, A.A. Roodman, A. Sanchez, E. Scarpine, V. Serrano, S. Sevilla-Noarbe, I. Smith, M. Sobreira, F. Suchyta, E. Swanson, M.E.C. Tarle, G. Vikram, V. Walker, A.R. Zhang, Y. Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia Swinburne University of Technology HawthornVIC3122 Australia Institute of Cosmology & Gravitation University of Portsmouth Portsmouthpo1 3fx United Kingdom Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States School of Software and Electrical Engineering Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia Phillips Academy AndoverMA01810 United States Cerro Tololo Inter-American Observatory National Optical Astronomy Observatory Casilla 603 La Serena Chile Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Instituto de Fisica Teorica UAM/CSIC Universidad Autonoma de Madrid Madrid28049 Spain Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Kavli Institute for Particle Astrophysics & Cosmology Stanford University P. O. Box 2450 StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Madrid Spain Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 Rio de Janeiro RJ20921-400 Brazil Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Barcelona Institute of Science and Technology Campus UAB Bellaterra [Barcelona08193 Spain Observatório Nacional Rua Gal. José Cristino 77 Rio de Janeiro RJ20921-400 Brazil Barcelona08034 Spain Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States California Institute of Technology 1200 East California Blvd MC 249-17 PasadenaCA91125 United States Department of Physics Stanford University 382 Via Pueblo Mall Sta

We search Dark Energy Survey (DES) Year 3 imaging for galaxy-galaxy strong gravitational lenses using convolutional neural networks, extending previous work with new training sets and covering a wider range of redshifts and colors. We train two neural networks using images of simulated lenses, then use them to score postage stamp images of 7.9 million sources from the Dark Energy Survey chosen to have plausible lens colors based on simulations. We examine 1175 of the highest-scored candidates and identify 152 probable or definite lenses. Examining an additional 20,000 images with lower scores, we identify a further 247 probable or definite candidates. After including 86 candidates discovered in earlier searches using neural networks and 26 candidates discovered through visual inspection of blue-near-red objects in the DES catalog, we present a catalog of 511 lens candidates. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Foreword

Proceedings of the 2016 International Conference on Image Pr...

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Proceedings of the 2016 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

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