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World Congress on Intelligent Control and Automation

作者： Shanaz Tiwari Yuan Wang Zhong-Ping Jiang Dept. of Math. Sciences Florida Atlantic University Dept. of Elec & Computer Engineering Polytechnic Institute of NYU

ISBN: (纸本)9781467313971

The notion of integral input-to-state stability (integral-ISS) is now recognized as a central concept in nonlinear systems analysis. In this work, we explore the Razumikhin approach to the integral-ISS property for systems with delays. The main idea is to treat the delayed state variable as a disturbance input. The results in this work extend the previous results on the Razumikhin method in the contexts of asymptotic stability and ISS to the context of integral-ISS.

关键词： Nonlinear systems with delays Robust stability Integral-ISS Lyapunov-Razumikhin method

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HINT-KB: The human interactome knowledge base

HINT-KB: The human interactome knowledge base

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8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB

作者： Theofilatos, Konstantinos Dimitrakopoulos, Christos Kleftogiannis, Dimitrios Moschopoulos, Charalampos Papadimitriou, Stergios Likothanassis, Spiros Mavroudi, Seferina Department of Computer Engineering and Informatics University of Patras Greece Math. and Computer Sciences and Engineering King Abdullah Univ. of Science and Technology Saudi Arabia Department of Electrical Engineering-ESAT SCD-SISTA Katholieke Universiteit Leuven Kasteelpark Arenberg 10 box 2446 300 Leuven Belgium IBBT Future Health Department Katholieke Universiteit Leuven Kasteelpark Arenberg 10 box 2446 300 Leuven Belgium Department of Information Management Technological Institute of Kavala Greece Department of Social Work School of Sciences of Health and Care Technological Educational Institute of Patras Greece

ISBN: (纸本)9783642334115

Proteins and their interactions are considered to play a significant role in many cellular processes. The identification of Protein-Protein interactions (PPIs) in human is an open research area. Many Databases, which contain information about experimentally and computationally detected human PPIs as well as their corresponding annotation data, have been developed. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://150.140.142.24:84/***) which is a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, estimates a set of features of interest and computes a confidence score for every candidate protein interaction using a modern computational hybrid methodology. © 2012 IFIP International Federation for Information Processing.

关键词： Genetic algorithms

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Characterizing distribution rules for cost sharing games

Characterizing distribution rules for cost sharing games

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International Conference on NETwork Games, Control and Optimization, NetGCooP 2011

作者： Gopalakrishnan, Ragavendran Marden, Jason R. Wierman, Adam Computing and Math. Sciences California Institute of Tech. United States Electrical Computer and Energy Engineering University of Colorado Boulder CO United States

ISBN: (纸本)9781467303842

We consider the problem of designing the distribution rule used to share "welfare" (cost or revenue) among individually strategic agents. There are many distribution rules known to guarantee the existence of a (pure Nash) equilibrium in this setting, e.g., the Shapley value and its weighted variants;however a characterization of the space of distribution rules that yield the existence of a Nash equilibrium is unknown. Our work provides a step towards such a characterization. We prove that when the welfare function is strictly submodular, a budget-balanced distribution rule guarantees equilibrium existence for all games (i.e., all possible sets of resources, agent action sets, etc.) if and only if it is a weighted Shapley value. © 2011 INRIA.

关键词： Game theory

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Naturally occurring data as research instrument: Analyzing examination responses to study the novice programmer

SIGCSE Bulletin Inroads

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SIGCSE Bulletin Inroads 2009年第4期41卷

作者： Clear, Tony Bouvier, Dennis J. Jacková, Jana Robbins, Phil Lister, Raymond Carter, Paul Lopez, Mike Seppälä, Otto Simon Eckerdal, Anna McCartney, Robert Thompson, Errol School of Comp. and Math. Sciences AUT University New Zealand Department of Computer Science Southern Illinois Univ. Edwardsville United States Faculty of Manag. Sci. and Informatics University of Zilina Slovakia Department of Computer Science University of British Columbia Canada Manukau Institute of Technology New Zealand Dept. of Computer Science and Engineering Helsinki University of Technology Finland Faculty of Science and IT University of Newcastle Australia Dept of Information Technology Uppsala University Sweden Dept Comp. Sci. and Engineering University of Connecticut United States United Kingdom

In New Zealand and Australia, the BRACElet project has been investigating students' acquisition of programming skills in introductory programming courses. The project has explored students' skills in basic syntax, tracing code, understanding code, and writing code, seeking to establish the relationships between these skills. This ITiCSE working group report presents the most recent step in the BRACElet project, which includes replication of earlier analysis using a far broader pool of naturally occurring data, refinement of the SOLO taxonomy in code-explaining questions, extension of the taxonomy to code-writing questions, extension of some earlier studies on students' 'doodling' while answering exam questions, and exploration of a further theoretical basis for work that until now has been primarily empirical.

关键词： Students

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Research on rough set theory and applications in China

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Lecture Notes in computer Science

作者： Wang, Guoyin Zhang, Qinghua Huang, Houkuan Ye, Dongyi Hu, Qinghua Hu, Xuegang Shi, Zhongzhi Li, Yongli Shang, Lin An, Liping Sai, Ying Chen, Shanben Liang, Jiye Qin, Keyun Zeng, Huanglin Xie, Keming Miao, Duoqian Min, Fan Wu, Zhaocong Wu, Weizhi Dai, Jianhua Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China College of Math. and Computer Fuzhou University Fuzhou 350002 China Harbin Institute of Technology PO Box 458 Harbin 150006 China School of Computer and Information Hefei University of Technology Hefei 230009 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Lanzhou University No.222 Tianshui South Rd. Lanzhou 730000 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Management Science and Engineering Department Business School Nankai University Tianjin 300071 China Shandong University of Finance No.40 Shun-geng Road Jinan 250014 China Welding Engineering Institute Shanghai Jiaotong University Shanghai 200030 China School of Computer and Information Technology Shanxi University Taiyuan 030006 China Intelligent Control Development Center Southwest Jiaotong University Chengdu 610031 China Artificial Intelligence of Key Laboratory Sichuan University of Science and Engineering Zigong 643000 China Department of Automation College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China 4800 CaoAn Highway Shanghai 201804 China School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu 610054 China School of Remote Sensing Information Engineering Wuhan University Wuhan 430079 China Institute for Information and System Sciences Faculty of Science Xi'an Jiaotong University Xi'an 710049 China Information College Zhejiang Ocean University Zhejiang 316004 China Institute of Artificial Intelligence Zhejiang University Hangzhou 310027 China

ISBN: (纸本)3540850635

This article gives a capsule view of research on rough set theory and applications ongoing at universities and laboratories in China. Included in this capsule view of rough set research is a brief description of the following things: Chinese research groups on rough set with their URLs for web pages, names of principal researchers (supervisors), numbers of graduate students, and topics being investigated. Statistical summaries showing the growth in the research on rough set theory and application in China are included. In addition, an introduction summarizing the research interests of Chinese researchers is included in this article. The contribution of this article is a complete overview of the principal research directions in rough set theory and its applications in China. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Rough set theory

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Virtual sensor networks - A resource efficient approach for concurrent applications

Virtual sensor networks - A resource efficient approach for ...

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4th International Conference on Information Technology-New Generations, ITNG 2007

作者： Jayasumana, Anura P. Qi, Han Illangasekare, Tissa H. Dept. of Electrical and Computer Engineering Colorado State University Dept. of Math. and Computer Sciences Colorado School of Mines Division of Environmental Science and Engineering Colorado School of Mines

ISBN: (纸本)0769527760

Current focus of sensor networks is on systems dedicated for a specific application. Wide variety of circumstances point to the need for sensor network deployments capable of sharing the physical network resources. We introduce the concept of Virtual Sensor Networks (VSN) to provide protocol support for the formation, usage, adaptation and maintenance of subsets of sensors collaborating on specific tasks. VSN concept also promises to enhance applications that involve dynamically varying subsets of sensor nodes collaborating tightly to achieve the desired outcomes, while relying on the remaining nodes to achieve connectivity and overcome the deployment and resource constraints. This article explores the concept, mechanisms, and benefits of using VSN. Examples of VSN support functionality include support for nodes to join and leave VSNs, broadcast within a VSN, and merging of VSNs. VSNs will simplify application deployment, enhance performance and scalability, facilitate resource sharing, and provide a degree of physical topology independence in wireless sensor networks. Deployment of overlapping sensor networks aimed at different tasks, especially in harsh environments, will also significantly benefit when organized as VSNs over a shared infrastructure © 2007 IEEE.

关键词： Wireless sensor networks

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Activity identification utilizing data mining techniques

Activity identification utilizing data mining techniques

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2007 IEEE Workshop on Motion and Video Computing, WMVC 2007

作者： Lee, Jae Young Hoff, William Dept. of Math. and Computer Sciences Colorado School of Mines Golden CO United States Engineering Division Colorado School of Mines Golden CO United States

ISBN: (纸本)0769527930

We propose a novel method that, given an unknown moving object trajectory, determines which known activity type the trajectory would belong to. The proposed method utilizes various data mining techniques, including clustering, classification, and Markov model. We collect trajectories of moving objects of known activity types and build one Markov model for each activity type. Given an unknown trajectory, we compute the likelihood of this trajectory belonging to each activity type using the Markov model and the trajectory is determined to belong to the activity type that results in the highest likelihood. We use only location information of moving objects. We do not use any other information such as color, size, or shape of objects, or contextual information. We demonstrate the effectiveness of this method using trajectories of students playing two sports activities - Ultimate Frisbee and volleyball. We show that the accuracy of this method is as high as 94%. © 2007 IEEE.

关键词： Data mining

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Activity Identification Utilizing Data Mining Techniques

Activity Identification Utilizing Data Mining Techniques

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Workshop on Motion and Video Computing

作者： Jae Young Lee William Hoff Department of Math. and Computer Sciences Colorado Schml of Mines Golden CO USA Engineering Division Colorado Schml of Mines Golden CO USA

ISBN: (纸本)9781424431366;9780769527932

We propose a novel method that, given an unknown moving object trajectory, determines which known activity type the trajectory would belong to. The proposed method utilizes various data mining techniques, including clustering, classification, and Markov model. We collect trajectories of moving objects of known activity types and build one Markov model for each activity type. Given an unknown trajectory, we compute the likelihood of this trajectory belonging to each activity type using the Markov model and the trajectory is determined to belong to the activity type that results in the highest likelihood. We use only location information of moving objects. We do not use any other information such as color, size, or shape of objects, or contextual information. We demonstrate the effectiveness of this method using trajectories of students playing two sports activities Ultimate Frisbee and volleyball. We show that the accuracy of this method is as high as 94%.

关键词： Data mining Shape Surveillance Image segmentation Histograms Pattern recognition Computational complexity Monitoring Humans Vehicles

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YCSc: A modified clustering technique based on LCS

Journal of Digital Information Management

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Journal of Digital Information Management 2007年第3期5卷 160-166页

作者： Tamee, Kreangsak Bull, Larry Pinngern, Ouen Faculty of Computing Engineering and Math. Sciences University of the West of England Bristol BS16 1QY United Kingdom Department of Computer Engineering Faculty of Engineering Research Center for Communication Bangkok 10520 Thailand

This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System with a modification to the original YCS fitness function has been found to improve the identification of less-separated data sets. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of datasets.

关键词： Evolutionary algorithms

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Using a Learning Classifier System for Clustering

Using a Learning Classifier System for Clustering

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Kreangsak Tamee Larry Bull Ouen Pinngern Pornthep Rojanavasu Phaitoon Srinil Department of Computer Engineering Faculty of EngineeringResearch Center for Communication and Information Technology (ReCCIT) King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand Faculty of Computing Engineering & Math. Sciences University of West of England Bristol UK

This paper presents a novel approach to clustering using a simple accuracy-based learning classifier system. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of datasets

关键词： Clustering algorithms Information technology Genetic algorithms Neural networks Production systems Unsupervised learning Particle measurements Euclidean distance Winches Guidelines

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