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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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ON BED POSTURE RECOGNITION WITH PRESSURE SENSOR ARRAY SYSTEM

ON BED POSTURE RECOGNITION WITH PRESSURE SENSOR ARRAY SYSTEM

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IEEE Sensors

作者： Qingquan Sun Eli Gonzalez Yu Sun School of Computer Science and Engineering California State University San Bernardino San Bernardino CA Computer Science Department California State Polytechnic University Pomona Pomona CA

Automated on bed posture recognition is of major importance to physical rehabilitation exercises. This paper presents a pressure sensor array system to unobtrusively recognize on bed postures for patients recovering from illness, injury and surgery. In this paper, we propose to use limb clusters to recognize on bed postures instead of using the whole body pressure map since the shapes and positions of limbs represent the postures in nature. To measure the similarities of limb clusters, we propose to use a comprehensive distance metric considering both physical distance and pressure difference. In the recognition phase, a weighted limb based recognition method is applied. The experiments are conducted with 15 subjects ranging from various gender, age and weight. The experiments have demonstrated that our proposed system and method can achieve a 97.8% recognition rate for typical on bed postures.

关键词： Sensor arrays Legged locomotion Pressure sensors Head Shape Pressure measurement

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Denoising Auto-Encoders toward Robust Unsupervised Feature Representation

Denoising Auto-Encoders toward Robust Unsupervised Feature R...

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International Joint Conference on Neural Networks

作者： Wei Xiong Bo Du Lefei Zhang Liangpei Zhang Dacheng Tao State Key Laboratory of Software Engineering School of Computer Science Wuhan University China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Centre for Quantum Computation & Intelligent Systems University of Technology Sydney NSW 2007 Australia

ISBN: (纸本)9781509006212

Deep networks like the convolutional neural network and its variants usually learn hierarchical features from labeled images, which is very expensive to obtain. How can we find an unsupervised way to effectively extract deep and abstract features from images without annotations? Even from large qualities of images with noise? In this paper, we propose a robust deep neural network, named as stacked convolutional denoising auto-encoders (SCDAE), which can map raw images to hierarchical representations in an unsupervised manner. Our network is elaborately designed to fit for the visual recognition tasks. It is established by stacking the denoising auto-encoders. Unlike the prior works, in the training phase, the auto-encoders are trained patch-wisely so that the latent features can be applied to powerful regularizers for better representation;in the inference phase, the denoising auto-encoders are stacked convolutionally, hence the generated feature maps in the higher layers can preserve the coherent structures within the features in the lower layers. To achieve better performance, we apply whitening to each layer to sphere the input features. Our network is evaluated on the challenging image datasets MNIST, CIFAR-10 and STL-10 and demonstrates superior performance to the state-of-the-art unsupervised networks.

关键词： Noise reduction Convolutional codes Training Feature extraction Kernel Optimized production technology Robustness

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Let's work together: Fast tag identification by interference elimination for multiple RFID readers

Let's work together: Fast tag identification by interference...

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International Conference on Network Protocols

作者： Xuan Liu Bin Xiao Feng Zhu Shigeng Zhang College of Computer Science and Electronic Engineering Hunan University P. R. China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory for Novel Software Technology Nanjing University P.R. China School of Information Science and Engineering Central South University P. R. China

ISBN: (纸本)9781509032822

Fast tag identification is a fundamental challenging problem in multi-reader RFID systems. The challenge is how to effectively handle Reader-Tag (RT) collisions and Reader-Reader (RR) collisions among adjacent readers. These collisions are caused by interfering signals simultaneously transmitted by the readers, and may disallow adjacent readers to work together. Prior works tackle this problem by scheduling adjacent readers to work in different time slots. The readers that are selected to simultaneously work, however, are usually only a small proportion of the total readers. This greatly restricts the identification throughput. Our insightful investigation on the current tag identification protocol reveals that RT collisions are caused by asynchronous actions of readers. i.e., a reader transmits signal while its adjacent readers receive. We thus develop Slot Splitting, a technique that can completely eliminate RT collisions by synchronizing actions of readers. We also propose a reader selection algorithm that minimizes RR collisions by selecting a reader set with maximum reading efficiency. The combination of these new techniques inspires Federal, a Fast and efficient tag identification protocol with interference-elimination-based reader scheduling. To our knowledge, Federal is the first identification protocol that completely eliminates RT collisions and minimizes RR collisions in both regularly and randomly deployed multi-reader systems. We validate the feasibility of Slot Splitting with experiments on the USRP platform and evaluate the performance of Federal through extensive simulations. The results show that Federal can increase tag identification throughput by up to 218% compared with the state-of-the-art work.

关键词： Protocols Throughput Radiofrequency identification Interference Radiation detectors Conferences Monitoring

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URU: A platform for prototyping and testing compatibility of multifunction interfaces with user knowledge schemata 17th

URU: A platform for prototyping and testing compatibility of...

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17th International Conference on Human-computer Interaction, HCI International 2015

作者： Fischer, Sandrine Oelkers, Blaine Fierro, Mitchell Itoh, Makoto White, Eric Computer Science Department California Polytechnic State University San Luis Obispo United States Risk Engineering Department University of Tsukuba Tsukuba Japan Physics Department California Polytechnic State University San Luis Obispo United States

ISBN: (纸本)9783319206110

We present a platform for prototyping and testing the information architecture of multifunctional products. A prototyping component supports the creation of feature outlines and their rendering into clickable prototypes. A user testing component implements an experimental method for characterizing the schemata operations involved during first use of multistate interfaces. Generation of the experimental material is automated via natural language processing techniques. The platform supports remote type of interface testing and fits well the needs for agile and data-informed user-centered design. © Springer International Publishing Switzerland 2015.

关键词： Graphical user interfaces

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POSTER: A hybrid approach for mobile security threat analysis 8

POSTER: A hybrid approach for mobile security threat analysi...

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8th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2015

作者： You, Wanqing Qian, Kai Guo, Minzhe Bhattacharya, Prabir Qian, Ying Tao, Lixin Department of Computer Science and Software Engineering Southern Polytechnic State University MariettaGA United States Department of Electrical Engineering and Computing Systems University of Cincinnati United States Department of Computer Science East China Normal University Shanghai China Dept. of Computer Science Pace University New York United States

ISBN: (纸本)9781450336239

Research on effective and efficient mobile threat analysis becomes an emerging and important topic in cybersecurity research area. Static analysis and dynamic analysis constitute two of the most popular types of techniques for security analysis and evaluation;nevertheless, each of them has its strengths and weaknesses. To leverage the benefits of both approaches, we propose a hybrid approach that integrates the static and dynamic analysis for detecting security threats in mobile applications. The key of this approach is the unification of data states and software execution on critical test paths. The approach consists of two phases. In the first phase, a pilot static analysis is conducted to identify potential critical attack paths based on Android APIs and existing attack patterns. In the second phase, a dynamic analysis follows the identified critical paths to execute the program in a limited and focused manner. Attacks shall be detected by checking the conformance of the detected paths with existing attack patterns. The method will report the types of detected attack scenarios based on types of sensitive data that may be compromised, such as web browser cookie.

关键词： Android application analysis Data path tracing Dynamic analysis Static analysis Symbolic execution

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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SUPPL 1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 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 University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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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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Exploration of Semantic-aware Approach for Contextual Suggestion Using Knowledge from The Open Web 24

Exploration of Semantic-aware Approach for Contextual Sugges...

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24th Text REtrieval Conference, TREC 2015

作者： Wang, Yuan Zhang, Yongfeng Zhang, Yi Zhang, Xintong Liu, Jie Huang, Yalou College of Computer and Control Engineering Nankai University Tianjin300071 China College of Software Nankai University Tianjin300071 China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science Tsinghua University Beijing100084 China School of Engineering University of California Santa Cruz95060 United States

This paper describes our group’s first attempt on the Contextual Suggestion Track of the Twenty-fourth Text REtrieval Conference (TREC 2015). The task aims to provide recommendations on attractions for various kinds of users under different and complex contexts. TREC provides two ways to participate in the track: one is to create a web server that can respond to contextual related queries called "Live Experiment", the other is to submit run files that have all the responses to the released requests called "Batch Experiment". For Live Experiment, due to lack of training data, our approach sticks closely to the defined relevance judgement criteria and context knowledge. We take linear interpolation to combine a variety of factors and contextual related knowledge. For Batch Experiment, we further consider domain preference under user attributes, and take existing Machine Learning based methods in principle. We show that feature engineering is a vital part for attraction suggestions. We find that the performance of suggestions to the provided user profiles and contexts has been improved using domain preference analysis. © 2015 24th Text REtrieval Conference, TREC 2015 - Proceedings. All Rights Reserved.

关键词： User profile

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