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International Conference on Document Analysis and Recognition

作者： Liu Wenyin Guanglin Huang Liu Xiaoyue Xiaotie Deng Zhang Min Department of Computer Science City University of Hong Kong Hong Kong China State Key Laboratory of Intelligent Technology & System Tsinghua University Beijing China

An approach to detection of phishing Web pages based on visual similarity is proposed, which can be utilized as a part of an enterprise solution to antiphishing. A legitimate Web page owner can use this approach to search the Web for suspicious Web pages which are visually similar to the true Web page. The approach first decomposes the Web pages into salient (visually distinguishable) block regions. The visual similarity between two Web pages is then evaluated in three metrics: block level similarity, layout similarity, and overall style similarity. A Web page is reported as a phishing suspect if any of them (with regards to the true one) is higher than its corresponding preset threshold. Preliminary experiments show that the approach can successfully detect those phishing Web pages with few false alarms at a speed adequate for online application.

关键词： Uniform resource locators Testing computer science Text analysis Information analysis Information filtering Credit cards Internet Electronic mail Size measurement

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Application of string kernels in protein sequence classification

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Applied Bioinformatics 2005年第1期4卷 45-52页

作者： Zaki, Nazar M. Deris, Safaai Illias, Rosli College of Information Technology United Arab Emirates University Al Ain United Arab Emirates Artificial Intelligence and Bioinformatics Laboratory Faculty of Computer Science and Information System Universiti Teknologi Skudai Johor Malaysia Department of Bioprocess Engineering Faculty of Chemical and Natural Resource Engineering Universiti Teknologi Skudai Johor Malaysia College of Information Technology United Arab Emirate University Al Ain PO Box 17555 United Arab Emirates

Introduction: The production of biological information has become much greater than its consumption. The key issue now is how to organise and manage the huge amount of novel information to facilitate access to this useful and important biological information. One core problem in classifying biological information is the annotation of new protein sequences with structural and functional features. Method: This article introduces the application of string kernels in classifying protein sequences into homogeneous families. A string kernel approach used in conjunction with support vector machines has been shown to achieve good performance in text categorisation tasks. We evaluated and analysed the performance of this approach, and we present experimental results on three selected families from the SCOP (Structural Classification of Proteins) database. We then compared the overall performance of this method with the existing protein classification methods on benchmark SCOP datasets. Results: According to the F1 performance measure and the rate of false positive (RFP) measure, the string kernel method performs well in classifying protein sequences. The method outperformed all the generative-based methods and is comparable with the SVM-Fisher method. Discussion: Although the string kernel approach makes no use of prior biological knowledge, it still captures sufficient biological information to enable it to outperform some of the state-of-the-art methods. © 2005 Adis Data Information BV. All rights reserved.

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Research on the link traffic based edge measurement

Research on the link traffic based edge measurement

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International Conference on Communications, Circuits and systems (ICCCAS)

作者： Feng-jun Shang Key Laboratory of Opto-Electronic Technology and System of Ministry of Education College of Opto-Electronic Engineering Chongqing University Chongqing China College of Computer Science and Technology University of Posts and Telecommunications Chongqing China

In order to manage the whole network, a novel traffic model is proposed based the active probe method. The core of the model has three parts: first, we measure the routing probability matrix by injecting a probe packet train into the network based on the Internet protocol measurement protocol (IPMP) by the Poisson law. Second, we use a passive measurement method to acquire sd traffic. Last, we can compute link traffic by means of network tomography. We prove the validity of the edge measurement method. Our computing results show the competing link traffic error within about 30% by the edge measurement model.

关键词： Traffic control Telecommunication traffic Communication system traffic control computer networks Educational institutions computer network management Probes Routing protocols Tomography Time measurement

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Research on the link traffic based on passive measurement

Research on the link traffic based on passive measurement

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Feng-Jun Shang Ying-Jun Pan Key Laboratory of Opto-electronic Technology and System of Ministry of Education College of Opto-electronic Engineering Chongqing University Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

In order to manage the whole network, a novel traffic model is proposed based on the active probe method. The core of the model has three parts: first, we measure routing probability matrix by injecting probe packet train into the network based on the Internet protocol measurement protocol (IPMP) by Poisson law. Second, we use passive measurement method to acquire sd traffic. Last, we can compute link traffic by means of network tomography. We prove the validity of edge measurement method. Our computing results show the competing link traffic error within about 30% by edge measurement model.

关键词： Traffic control Telecommunication traffic Communication system traffic control computer networks Time measurement Tomography Routing protocols Educational institutions Educational technology computer network management

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Spectral feature analysis

Spectral feature analysis

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International Joint Conference on Neural Networks (IJCNN)

作者： Fei Wang Jingdong Wang Changshui Zhang State Key Laboratory of Intelligent Technology and System Department of Automation Tsinghua University Beijing China Department of Computer Science Hong Kong University of Science and Technology Clear Water Bay Hong Kong China

We have seen a surge of interest in spectral-based methods and kernel-based methods for machine learning and data mining. Despite the significant research, these methods remain only loosely related. In this paper, we give theoretically an explicit relation between spectral clustering and weighted kernel principal component analysis (WKPCA). We show that spectral clustering is not only a method for data clustering, but also for feature extraction. We are then able to reinterpret the spectral clustering algorithm in terms of WKPCA and propose our spectral feature analysis (SFA) method. The spectral features extracted by SFA can capture the distinguishing information of data from different classes effectively. Finally some experimental results are presented to show the effectiveness of our method.

关键词： Spectral analysis Kernel Data mining Feature extraction Electronic mail Principal component analysis Laboratories Intelligent systems Automation Machine learning

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Editorial

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International Journal of Web Engineering and technology 2005年第2-3期2卷 145-147页

作者： Frasincar, Flavius Houben, Geert-Jan Van Ossenbruggen, Jacco Eindhoven University of Technology PO Box 513 NL-5600 MB Eindhoven Netherlands Centrum voor Wiskunde en Informatica PO Box 94079 NL-1090 GB Amsterdam Netherlands Department of Information Systems Erasmus University Rotterdam Netherlands Department of Information Systems Vrije Universiteit Brussel Belgium WISE Laboratory for Web and Information System Engineering Netherlands Technische Universiteit Eindhoven Netherlands CWI Netherlands Multimedia and Human-computer Interaction Group Netherlands

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Rapid synchronization and accurate phase-locking of rhythmic motor primitives

Rapid synchronization and accurate phase-locking of rhythmic...

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2005 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： D. Pongas A. Billard S. Schaal Autonomous System Laboratory Swiss Federal Institute of Technology Switzerland Computer Science & Neuroscience University of Southern California Los Angeles CA USA ATR Computational Neuroscience Laboratories Kyoto Japan

Rhythmic movement is ubiquitous in human and animal behavior, e.g., as in locomotion, dancing, swimming, chewing, scratching, music playing, etc. A particular feature of rhythmic movement in biology is the rapid synchronization and phase locking with other periodic events in the environment, for instance music or visual stimuli as in ball juggling. In traditional oscillator theories to rhythmic movement generation, synchronization with another signal is relatively slow, and it is not easy to achieve accurate phase locking with a particular feature of the driving stimulus. Using a recently developed framework of dynamic motor primitives, we demonstrate a novel algorithm for very rapid synchronization of a rhythmic movement pattern, which can phase lock any feature of the movement to any particular event in the driving stimulus. As an example application, we demonstrate how an anthropomorphic robot can use imitation learning to acquire a complex drumming pattern and keep it synchronized with an external rhythm generator that changes its frequency over time.

关键词： Frequency synchronization Nonlinear dynamical systems Robot kinematics Laboratories Humans Oscillators Music Legged locomotion Tuning computer science

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Collaborative Filtering by Mining Association Rules from User Access Sequences

Collaborative Filtering by Mining Association Rules from Use...

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International Workshop on Challenges in Web Information Retrieval and Integration (WIRI)

作者： Mei-Ling Shyu C. Haruechaiyasak Shu-Ching Chen Na Zhao Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Information Research and Development Division National Electronics and Computer Technology Center Pathum Thani Thailand Distributed Multimedia Information System Laboratory School of Computer Science Florida International University Miami FL USA

Recent research in mining user access patterns for predicting Web page requests focuses only on consecutive sequential Web page accesses, i.e., pages which are accessed by following the hyperlinks. In this paper, we propose a new method for mining user access patterns that allows the prediction of multiple non-consecutive Web pages, i.e., any pages within the Web site. Our approach consists of two major steps. First, the shortest path algorithm in graph theory is applied to find the distances between Web pages. In order to capture user access behavior on the Web, the distances are derived from user access sequences, as opposed to static structural hyperlinks. We refer to these distances as minimum reaching distance (MRD) information. The association rule mining (ARM) technique is then applied to form a set of predictive rules which are further refined and pruned by using the MRD information. The proposed approach is applied as a collaborative filtering technique to recommend Web pages within a Web site. Experimental results demonstrate that our approach improves performance over the existing Markov model approach in terms of precision and recall, and also has a better potential of reducing the user access time on the Web

关键词： Collaboration Data mining Association rules Web pages Predictive models Information filtering Information filters Web server Research and development Distributed computing

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An improved investment priority decision method for the electrical facilities considering reliability of distribution networks

An improved investment priority decision method for the elec...

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2005 IEEE Power Engineering Society General Meeting

作者： Choi, J.H. Park, C.H. Chae, W.K. Jang, S.I. Kim, K.H. Park, K. IEEE Korea Republic of Power System Laboratory Korea Electric Power Research Institute Korea Electric Power Corporation Taejeon 103-16 Korea Republic of BK21 Research Division for Information Technology Seoul National University Seoul 151-742 Korea Republic of Department of Electrical and Computer Engineering Kangwon National University Chuncheon 200-701 Korea Republic of Next-Generation Power Technology Center Korea Republic of School of Electrical Engineering Seoul National University Seoul 151-742 Korea Republic of Power System Laboratory Korea Electric Power Research Institute Korea Electric Power Corporation Korea Republic of KIEF United States KIIEE Korea Republic of BK21 Research Division for Information Technology Seoul National University Korea Republic of Department of Electrical and Computer Engineering Kangwon National University Korea Republic of School of Electrical Engineering Seoul National University Korea Republic of

ISBN: (纸本)078039156X

This paper proposes an improved investment priority decision method of the facilities considering the reliability of distribution networks. The proposed method decides an investment order of the facilities combining, by fuzzy rules, the investment priority decision by KEPCO and it by reliability evaluation indices. Where reliability evaluation indices are SAIFI(system Average Interruption Frequency Index) and SAIDI(system Average Interruption Duration Index). The reliability analysis method of distribution networks applied in this paper utilizes analytic method, where the used reliability data is historical data of KEPCO. Particularly, we assumed that the failure rate increased as the equipment ages. To verify the performance of the proposed method, we applied it with the planned projects to reinforce the weak facility electrical facilities in KEPCO in 2004. The evaluation result showed that, under a limited budget, the reliability of the KEPCO in the Busan region using the proposed method could be enhanced than using the conventional KEPCO's method. Therefore, the results verify the proposed method can be efficiently used in the actual priorities method for investing the electrical facilities. © 2005 IEEE.

关键词： Electric utilities

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Unknown Input Detection Using Receding Horizon Approach

Unknown Input Detection Using Receding Horizon Approach

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IEEE International Symposium on Intelligent Control (ISIC)

作者： B. Kulcsar J. Bokor Department of Transport Automation Faculty of Transportation Engineering Budapest University슠of슠Technology슠and슠Economics Budapest Hungary System and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The paper offers the possibility of the design of unknown input detection for dynamic systems under external noise effect. The presented geometric based fundamental problem in residual generation (FPRG) method uses on the one hand the Kalman filtering and on the other hand the moving horizon estimation (MHE) when stochastic noise on the input and on the output, with additive failure directions, are presents. The paper combines the optimal Kalman and MHE method with geometric based unknown input observer strategy. The MHE solution makes to treat constraints during the estimation process possible. A numerical example supports the necessity of constrained unknown input estimation

关键词： Fault detection State estimation Control systems Kalman filters Space technology Additive noise Automation Stability Predictive models Predictive control

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