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32nd International Conference on Machine Learning, ICML 2015

作者： Long, Mingsheng Cao, Yue Wang, Jianmin Jordan, Michael I. School of Software TNList Lab for Info. Sci. and Tech. Institute for Data Science Tsinghua University China Department of Electrical Engineering and Computer Science University of California BerkeleyCA United States

ISBN: (纸本)9781510810587

Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multi-kernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.

关键词： Network architecture

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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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Cover Image, Volume 84, Issue 7

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Proteins: Structure, Function, and Bioinformatics 2016年第7期84卷

作者： Wei Wang Juan Liu Lin Sun Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Xinxiang 453007 China Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining Henan Province China Institute of Computer Software School of Computer Wuhan University Wuhan 430072 China

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Tournament selection based artificial bee colony algorithm with elitist strategy

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial intelligence and Lecture Notes in Bioinformatics) 2014年 8916卷 387-396页

作者： Zhang, Meng-Dan Zhan, Zhi-Hui Li, Jing-Jing Zhang, Jun Department of Computer Science Sun Yat-sen University 510275 China Key Lab. Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software MOE China Key Lab. Software Technology Education Department of Guangdong Province China School of Computer Science South China Normal University China

Artificial bee colony (ABC) algorithm is a novel heuristic algorithm inspired from the intelligent behavior of honey bee swarm. ABC algorithm has a good performance on solving optimization problems of multivariable functions and has been applied in many fields. However, traditional ABC algorithm chooses solutions on the onlooker stage with roulette wheel selection (RWS) strategy which has several disadvantages. Firstly, RWS is suitable for maximization optimization problem. The fitness value has to be converted when solving minimization optimization problem. This makes RWS difficult to be generally used in real-world applications. Secondly, RWS has no any parameter that can control the selection pressure. Therefore, RWS is not easy to adapt to various optimization problems. This paper proposes a tournament selection based ABC (TSABC) algorithm to avoid these disadvantages of RWS based ABC. Moreover, this paper proposes an elitist strategy that can be applied to traditional ABC, TSABC, and any other ABC variants, so as to avoid the phenomenon that ABC algorithm may abandon the globally best solution in the scout stage. We compare the performance of traditional ABC and TSABC on a set of benchmark functions. The experiment results show that TSABC is more flexible and can be efficiently adapted to solve various optimization problems by controlling the selection pressure. © Springer International Publishing Switzerland 2014.

关键词： Optimization

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Load balance aware genetic algorithm for task scheduling in cloud computing

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial intelligence and Lecture Notes in Bioinformatics) 2014年 8886卷 644-655页

作者： Zhan, Zhi-Hui Zhang, Ge-Yi Ying-Lin Gong, Yue-Jiao Zhang, Jun Department of Computer Science Sun Yat-sen University Guangzhou510006 China Key Lab. Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software MOE China Key Lab. Software Technology Education Department Guangdong Province China School of Sofware Engineering Sun Yat-sen University Guangzhou510006 China Department of Psychology Sun Yat-sen university 510275 China

This paper proposes to solve the task scheduling problem in cloud computing by using a load balance aware genetic algorithm (LAGA) with Minmin and Max-min methods. Task scheduling problems are of great importance in cloud computing, and become especially challenging when taking load balance into account. Our proposed LAGA algorithm has several advantages when solving this kind of problems. Firstly, by introducing the time load balance (TLB) model to help establish the fitness function with makespan, the algorithm benefits from the ability to find the solution that performs best on load balance among a set of solutions with the same makespan. More importantly, the interaction between makespan and TLB helps the algorithm to minimize makespan in the same time. Secondly, Min-min and Max-min methods are used to produce promising individuals at the beginning of evolution, leading to noticeable improvement of evolution efficiency. We evaluated LAGA on several task scheduling problems and compared with a Min-min, Max-min improved version of genetic algorithm (MMGA), which does not use the TLB strategy. The results show that LAGA can obtain very competitive results with good load balancing properties, and outperform MMGA in both makespan and TLB objectives. © Springer International Publishing Switzerland 2014.

关键词： Genetic algorithms

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An exploration on long-distance communication between left-behind children and their parents in China

An exploration on long-distance communication between left-b...

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2013 2nd ACM Conference on computer Supported Cooperative Work, CSCW 2013

作者： Pan, Lu Tian, Feng Lu, Fei Zhang, Xiaolong Liu, Ying Feng, Wenxin Dai, Guozhong Wang, Hongan Intelligence Engineering Lab Chinese Academy of Sciences Institute of Software China State Key Lab. of Computer Science Chinese Academy of Sciences Institute of Software China Pennsylvania State University United States Intel Corporation China Computer Science Department Boston University United States

ISBN: (纸本)9781450313315

In China, hundreds of millions of migrant workers have moved to cities or coastal regions for more or better-paid jobs and have left their children behind at their rural homes. Separated by thousands of kilometers, these "left-behind" children and their migrant parents use mobile phones as their primary-and often only-method of maintaining family connections. To better understand the use of technology in this long-distance communication, we conducted a multi-phased study using interviews and surveys in three different Chinese rural areas. In this paper, we report our findings on how these children communicate with their migrant parents and what information they exchange. We also discuss design implications derived from these findings that may improve communication between left-behind children and their parents. Copyright 2013 ACM.

关键词： Rural areas

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The biometric menagerie-A fuzzy and inconsistent concept

The biometric menagerie-A fuzzy and inconsistent concept

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5th International Workshop on Soft Computing Applications, SOFA 2012

作者： Popescu-Bodorin, Nicolaie Balas, Valentina Emilia Motoc, Iulia Maria Artificial Intelligence and Computational Logic Lab. Mathematics and Computer Science Department Spiru Haret University Bucharest Romania Automatics and Applied Software Department Faculty of Engineering Aurel Vlaicu University Arad Romania

ISBN: (纸本)9783642339400

This paper proves that in iris recognition, the concepts of sheep, goats, lambs and wolves-as proposed by Doddington and Yager in the so-called Biometric Menagerie, are at most fuzzy and at least not quite well defined. They depend not only on the users or on their biometric templates, but also on the parameters that calibrate the iris recognition system. This paper shows that, in the case of iris recognition, the extensions of these concepts have very unsharp and unstable (non-stationary) boundaries. The membership of a user to these categories is more often expressed as a degree (as a fuzzy value) rather than as a crisp value. Moreover, they are defined by fuzzy Sugeno rules instead of classical (crisp) definitions. For these reasons, we said that the Biometric Menagerie proposed by Doddington and Yager could be at most a fuzzy concept of biometry, but even this status is conditioned by improving its definition. All of these facts are confirmed experimentally in a series of 12 exhaustive iris recognition tests undertaken for University of Bath Iris Image database while using three different iris code dimensions (256x16, 128x8 and 64x4), two different iris texture encoders (Log Gabor and Haar Hilbert) and two different types of safety models. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Biometrics

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An innovative approach for automatically grading spelling in essays using rubric-based scoring

An innovative approach for automatically grading spelling in...

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作者： Fazal, Anhar Hussain, Farookh Khadeer Dillon, Tharam S. School of Information Systems Curtin University of Technology Perth WA Australia Decision Support and E-Service Intelligence Lab. School of Software University of Technology Sydney NSW Australia Department of Computer Science and Computer Engineering La Trobe University Melbourne VIC Australia

Automated Essay Grading (AEG) is defined as a computer technology that evaluates and scores written prose. A number of AEG systems have been developed since the 1960s and in most of them, an ad-hoc or generalized approach is used to grade spelling even though it is an important element of an essay-scoring rubric. Existing approaches do not therefore give an accurate representation or measure of spelling in essays. According to the rubric-based scoring method used in the National Assessment Program "Literacy and Numeracy (NAPLAN) in Australia, spelling is marked in three steps" first, by identifying the correct and incorrect words in the essay;second, by categorizing each word based on the difficulty level into one of four classes: Simple, Common, Difficult or Challenging, and counting the number of correct and incorrect words in each category;finally, by using the pre-defined NAPLAN rubric scale to assign the mark. Only a small number of existing AEG systems can be used for rubric-based scoring, and none can be used to grade spelling according to the NAPLAN rubric. In this paper, we address this shortcoming in the existing literature and present an innovative approach to automatically mark spelling using rubric based scoring. We develop two algorithms based on the rules and heuristics of the English language to formulize the rubric for spelling and then implement these algorithms in Java language and perform a series of evaluations of our system using an essay dataset. Our results are very promising, even though it is the first system of this kind.

关键词： Grading

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Leveraging Performance Counters and Execution Logs to Diagnose Memory-Related Performance Issues

Leveraging Performance Counters and Execution Logs to Diagno...

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International Conference on software Maintenance (ICSM)

作者： Mark D. Syer Zhen Ming Jiang Meiyappan Nagappan Ahmed E. Hassan Mohamed Nasser Parminder Flora Software Analysis and Intelligence Lab Queen's University Canada Department of Electrical Engineering & Computer Science York University Canada Performance Engineering School of Computing BlackBerry Canada

Load tests ensure that software systems are able to perform under the expected workloads. The current state of load test analysis requires significant manual review of performance counters and execution logs, and a high degree of system-specific expertise. In particular, memory-related issues (e.g., memory leaks or spikes), which may degrade performance and cause crashes, are difficult to diagnose. Performance analysts must correlate hundreds of megabytes or gigabytes of performance counters (to understand resource usage) with execution logs (to understand system behaviour). However, little work has been done to combine these two types of information to assist performance analysts in their diagnosis. We propose an automated approach that combines performance counters and execution logs to diagnose memory-related issues in load tests. We perform three case studies on two systems: one open-source system and one large-scale enterprise system. Our approach flags ≤ 0.1% of the execution logs with a precision ≥ 80%.

关键词： Radiation detectors Standards Transient analysis Lifting equipment Memory management Couplings Visualization

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