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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Bu, Hua-Long Li, Guo-Zheng Zeng, Xue-Qiang Yang, Jack Y. Yang, Mary Qu School of Computer Engineering and Science Shanghai University Shanghai 200072 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Harvard Medical School Harvard University Cambridge MA 02140 United States U.S. Department of Health and Human Services Bethesda MD 20852 United States

ISBN: (纸本)1424415098

Partial Least Squares (PLS) is one of the widely used dimension reduction methods for analysis of gene expression microarray data, it represents the data in a low dimensional space through linear transformation, the size of the reduced space by PLS is critical to generalization performance of classifiers. The previous works always determined the top fixed number of components or the top several components by cross-validation. Here we demonstrate the usage of feature selection for PLS based dimension reduction. As a case study, PLS is combined with two feature selection methods (Genetic Algorithm and Sequential Backward Floating Selection) to get more robust and efficient dimensional space, and then the constructed data from the selected components is used as input for the Support Vector Machine (SVM) classifier. We use the method for tumor classification on gene microarray data, experimental results illustrate that our proposed framework is effective both to reduce classification error rates and get compact dimensional space. ©2007 IEEE.

关键词： Linear transformations

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Support vector regression with feature selection for the multivariate calibration of spectrofluorimetric determination of aromatic amino acids

Support vector regression with feature selection for the mul...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Li, Guo-Zheng Meng, Hao-Hua Yang, Mary Qu Yang, Jack Y. State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China School of Computer Science and Engineering Shanghai University Shanghai 200072 China U.S. Department of Health and Human Services Bethesda MD 20852 United States Harvard Medical School Harvard University Cambridge MA 02140 United States

ISBN: (纸本)1424415098

Several artificial intelligent methods, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Partial Least Square (PLS) are used for the multivariate calibration in the determination of the three aromatic amino acids (phenylalanine, tyrosine and tryptophan) in their mixtures by fluorescence spectroscopy. The results of the leave-one-out method show that SVR perform better than other methods, and appear to be good methods for this task. Furthermore, feature selection is performed for SVR to remove redundant features and a novel algorithm named PRIFER (Prediction RIsk based FEature selection for support vector Regression) is proposed. Results on the above multivariate calibration data set show that PRIFER is a powerful tool for solving the multivariate calibration problems. ©2007 IEEE.

关键词： Amino acids

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Tree formation algorithm for data aggregation based on estimate cost in sensor networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2007年第5期35卷 806-810页

作者： Ye, Ning Wang, Ru-Chuan College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China Department of Information Science Nanjing College for Population Programme Management Nanjing 210042 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China

Wireless sensor network is a novel technology, and can be applied to both abominable and military environments. During gathering the data, wireless sensor network should decrease the power costs of redundancy information and delay time. The technology of data aggregation can be adopted. A tree formation algorithm for data aggregation based on estimate cost is presented. The paper applies directed transmission model and message route mechanism. The idea of this algorithm is optimization of data aggregation route by estimate cost about energy, distant and aggregation gain, then realizes data aggregation trade-off in energy and delay.

关键词： Wireless sensor networks

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Feature selection for co-training: A QSAR study

Feature selection for co-training: A QSAR study

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2007 International Conference on Artificial Intelligence, ICAI 2007

作者： Li, Guo-Zheng Li, Dan Lu, Wen-Cong Yang, Mary Q. Yang, Jack Y. School of Computer Science and Engineering Shanghai University Shanghai 200072 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Department of Chemistry School of Science Shanghai University Shanghai 200444 China U.S. Department of Health and Human Services Bethesda MD 20852 United States Harvard Medical School Harvard University Cambridge MA 02140 United States

ISBN: (纸本)9781601320254

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with known activity is less than that of unknown, it is important to predict molecular activities utilizing unlabeled instances. Here, semi-supervised learning (SSL) is introduced and a SSL method, Co-Training is investigated on predicting drug activities utilizing unlabeled instances. At the same time, the numerous features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named FESCOT is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for Co-Training. Numerical experimental results on a data set of molecular activities show that Co-Training improves prediction accuracy of molecular activities with unlabeled instances, and feature selection furthermore helps to improve the prediction ability of Co-Training.

关键词： Feature Selection

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Pseudo Trust: Zero-Knowledge Based Authentication in Anonymous Peer-to-Peer Protocols

Pseudo Trust: Zero-Knowledge Based Authentication in Anonymo...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Li Lu Jinsong Han Lei Hu Jinpeng Huai Yunhao Liu Lionel M. Ni State Key Laboratory of Information Security Graduate School of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Software Developing Environment Beihang University Beijing China

Most of the current trust models in peer-to-peer (P2P) systems are identity based, which means that in order for one peer to trust another, it needs to know the other peer's identity. Hence, there exists an inherent tradeoff between trust and anonymity. To the best of our knowledge, there is currently no P2P protocol that provides complete mutual anonymity as well as authentication and trust management. We propose a zero-knowledge authentication scheme called pseudo trust (PT), where each peer, instead of using its real identity, generates an unforgeable and verifiable pseudonym using a one-way hash function. A novel authentication scheme based on zero-knowledge proof is designed so peers can be authenticated without leaking any sensitive information. With the help of PT, most existing identity-based trust management schemes become applicable in mutual anonymous P2P systems. We analyze the levels of security and anonymity in PT, and evaluate its performance using trace-driven simulations and a prototype implementation. The strengths of pseudo trust include the lack of need for a centralized trusted party or CA, high scalability and security, low traffic and cryptography processing overheads, and man-in-middle attack resistance. We aim for the pseudo trust design to be included in the P2P trust and anonymity context.

关键词： Authentication Peer to peer computing Protocols Knowledge management Identity management systems Performance analysis Information security Analytical models Virtual prototyping Scalability

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A kind of fuzzy constructive neural network and its application

A kind of fuzzy constructive neural network and its applicat...

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26th Chinese Control Conference, CCC 2007

作者： Tao, Wu Liwei, Chen Junjun, Mao Ling, Zhang State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210093 China Key Lab. IC and SP Anhui University Hefei 230039 China School of Mathematics and Computational Science Anhui University Hefei 230039 China

ISBN: (纸本)7900719229

Support vector machine (SVM) has been studied and applied extensively for its high accuracy, but it must construct SVM decision tree to classify sample sets with multiclass for it just be applicable for binary classification and solve a quadratic programming problem to gain optimal hyperplane either in sample spaces or in feature spaces;Alternative covering algorithm which designs neutral networks with spherical domains has the advantages of fast performance, some hard solving problems have been solved using this algorithm. In this paper, a new kind of structural learning algorithm which combining covering design algorithm, fuzzy set and SVM is put forward, instances show that this kind of networks has the virtue of both covering design algorithm and SVM.

关键词： Fuzzy neural networks

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An Energy-Aware Protocol for Data Gathering Applications in Wireless Sensor Networks

An Energy-Aware Protocol for Data Gathering Applications in ...

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IEEE International Conference on Communications (ICC)

作者： M. Liu Y. Zheng J. Cao G. Chen L. Chen H. Gong School of Computer Science and Engineering University of Electronic Science and Technology China Internet and Mobile Computing Lab Department of Computing Hong Kong Polytechnic University Hong Kong China State Key Laboratory for Novel Software Technology Nanjing University China

Data gathering is a major function of many applications in wireless sensor networks (WSNs). The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirement of applications/users such as sensing area coverage. In this paper, we propose a novel hierarchical clustering protocol for long-lived sensor network. EAP achieves a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load among all nodes. EAP introduces a new clustering parameter for cluster head election, which can better handle the heterogeneous energy capacities. Furthermore, it also introduces a simple but efficient approach, namely intra-cluster coverage to cope with the area coverage problem. We evaluate the performance of the proposed protocol using a simple temperature sensing application. Simulation results show that our protocol significantly outperforms LEACH and HEED in terms of network lifetime and the amount of data gathered.

关键词： Wireless application protocol Wireless sensor networks Energy consumption Application software Temperature sensors computer networks Clustering algorithms Peer to peer computing Scalability Paper technology

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Uncalibrated eye-in-hand visual servoing using recursive least squares

Uncalibrated eye-in-hand visual servoing using recursive lea...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Miao Hao Zengqi Sun Masakazu Fujii Wei Song Candidate of State Key Laboratory of Intelligent Technology and Systems Department Computer Science & Technology Tsinghua University Beijing China Department Computer Science & Technology Tsinghua University Beijing China Corporate Research and Development Ishikawajima-Harima Heavy Industries Company Limited Yokohama Japan Software engineer of BCM Department Infosys Technologies Limited Shanghai China

In this paper, an uncalibrated dynamic visual servoing algorithm is proposed and analyzed. No calibration or robot model is needed. After a brief introduction of the development of uncalibrated visual servoing, the theoretical backgrounds and mathematical requirements of recursive least square (RLS) are stated respectively. Then the core uncalibrated visual servoing algorithm, in RLS form, or more technically, VS-RLS as well as its performance analysis, is investigated. After that, the experimental 6DOF Puma560 simulation of static and moving target tracking is demonstrated. Finally the weak and strength of the algorithm as well as the potential and promising improvements are discussed.

关键词： Visual servoing Least squares methods Resonance light scattering Heuristic algorithms Algorithm design and analysis Calibration Robots Performance analysis Target tracking Potential well

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Surface Remeshing on Triangular Domain for CAD Applications

Surface Remeshing on Triangular Domain for CAD Applications

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10th IEEE International Conference on computer Aided Design and computer Graphics(第十届CAO/Graphics国际会议)

作者： Qingwei Guo Weining Yue Guoping Wang Department of Computer Science and Technology Peking UniversityBeijing 100871China Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationChina

In this paper a systematic remeshing method of triangle mesh surface is proposed for CAD applications. A densitysensitive edge marking function is proposed for surface resampling. Surface energy minimization is used to redistribute vertices for face geometry regularization. A simulated annealing method is proposed for vertex connectivity optimization. During each stage,the deviations from original mesh are prevented,and the boundaries and features are well preserved. While all modifications are performed locally,the error-prone global parameterization is entirely avoided. The advantage and robusmess of this technique are verified by many examples from real-world product design.

关键词： Geometry Application software Mesh generation Surface fitting Educational technology Optimization methods computer graphics computer science Laboratories computer science education

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An efficient Method for Protein Secondary Structure Prediction

An efficient Method for Protein Secondary Structure Predicti...

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The 1st International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2007)(首届IEEE生物信息与生物医学工程国际会议)

作者： Tao WU Junjun MAO Ling ZHANG State Key Laboratory for Novel Software Technology Department of Computer Science & TechnologyNanj School of Mathematics and Computational Science Anhui University Hefei 230039 R.P.China Key Lab IC&SP Anhui University Hefei 230039 R.P.China

The secondary structure prediction of protein plays an important role to obtain its tertiary structure and function. In the past thirty years, a huge amount of algorithms have been employed to this task. The better predicators are based on machine learning techniques, especially based on neural networks. But the architecture of neural network is hard to define, and the training process is time-consuming. In this paper,a constructive machine learning approach is used to predict protein secondary structure with five different encoding schemes,the results show that the constructive algorithm can achieve high predicting accuracies and the encoding schemes have influence on predicting result.

关键词： protein secondary structure prediction constructive machine learning spherical neighborhood encoding scheme

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