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2012 International Conference on information Technology and Software Engineering, ITSE 2012

作者： Shen, Zhuowei Cao, Min Key Lab. of Computer Network and Information Integration School of Computer and Engineering Southeast University Nanjing China

ISBN: (纸本)9783642345210

In mobile data collector (MDC)-based wireless sensor networks, we need to design paths for MDCs for better data collection. In this paper, we use predictable mobility pattern and apply a heuristic algorithm to design paths of MDCs. The algorithm aims to make the time cost by MDCs covering the whole network as short as possible. The algorithm is taken by two steps. Firstly, choose the stop points, and then, construct the paths. Both steps are computed using genetic algorithm (GA). In choosing stop points algorithm, improved stochastic tournament selection is adopted to deal with restraints and special fitness is used to enhance differentiation of solutions. An asexual reproduction based GA with 2-opt optimization mechanism is used to construct MDCs' paths. As experiments show, the algorithm proposed in this paper performs better than greedy algorithm. © 2013 Springer-Verlag.

关键词： Wireless sensor networks

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Tag-based social image search with hyperedges correlation

Tag-based social image search with hyperedges correlation

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IEEE Visual Communications and Image Processing (VCIP)

作者： Leiquan Wang Zhicheng Zhao Fei Su School of Information and Communication Engineering School of Computer and Communication Engineering China University of Petroleum (Huadong) QingDao China Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781479961405

In social image search, most existing hypergraph methods use the visual and textual features in isolation by treating each feature term as a hyperedge. Nevertheless, they neglect the correlations of visual and textual hyperedges, which are more robust to represent the high-order relationship among vertices. In this paper, we propose a hypergraph with correlated hyperedges (CHH), which introduces high-order relationship of hyperedges into hypergraph learning. Based on CHH, a pairwise visual-textual correlation hypergraph (VTCH) model is used for tag-based social image search. To overcome the large number of newly generated hybrid hyperedges, a bagging-based method is adopted to balance the accuracy and speed. Finally, adaptive hyperedges learning method is used to obtain the relevance score for social image search. The experiments conducted on MIR Flickr show the effectiveness of our proposed method.

关键词： Visualization Correlation Bagging Accuracy Animals Adaptation models Learning systems

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Improving deep neural networks with multilayer maxout networks

Improving deep neural networks with multilayer maxout networ...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Weichen Sun Fei Su Leiquan Wang School of Information and Communication Engineering Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing China School of Computer and Communication Engineering China University of Petroleum (Huadong) Qingdao China

ISBN: (纸本)9781479961405

For the purpose of enhancing discriminability of convolutional neural networks (CNNs) and facilitating optimization, a multilayer structured variant of the maxout unit (named Multilayer Maxout network, MMN) is proposed in this paper. CNNs with maxout units employ linear convolution filters followed by maxout units to abstract representations from less abstract ones. Our model instead applies MMNs as activation functions of CNNs to abstract representations, which inherits advantages of both maxout units and deep neural networks, and is a more general nonlinear function approximator as well. Experimental results show that our proposed model yields better performance on three image classification benchmark datasets (CIFAR-10, CIFAR-100 and MNIST) than some state-of-the-art methods. Furthermore, the influence of MMN in different hidden layers is analyzed, and a trade-off scheme between the accuracy and computing resources is given.

关键词： Neural networks Training Nonhomogeneous media Feature extraction Error analysis Educational institutions Convolution

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Detecting genuine communities from large-scale social networks: A pattern-based method

Detecting genuine communities from large-scale social networ...

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作者： Wu, Zhiang Cao, Jie Wu, Junjie Wang, Youquan Liu, Chunyang Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing210003 China College of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Information Systems School of Economics and Management Beihang 100191 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

Community detection is a long-standing yet very difficult task in social network analysis. It becomes more challenging as many online social networking sites are evolving into super-large scales. Numerous methods have been proposed for community detection from massive networks, but how to reconcile the partitioning efficiency and the community quality remains an open problem. In this paper, we attempt to address this challenge by introducing a COSine-pattern-based COMmunity extraction framework: COSCOM. The COSCOM adopts an extracting view of community detection. It first extracts the so-called asymptotically equivalent structures (AESs) from networks, from which the nodes are further partitioned into crisp communities using any of the existing methods. Specifically, we prove that an AES is a very tight group of nodes, and is actually a cosine pattern defined by the extended cosine similarity. A novel cosine-pattern mining algorithm based on the ordered anti-monotone of cosine similarity is thus proposed for the efficient extraction of AESs. Experiments on various real-world social networks demonstrate the advantage of the extracting view of community detection. In particular, COSCOM shows merits in detecting genuine communities by either internal or external validity. © The British computer Society 2013. All rights reserved.

关键词： Extraction

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A New Sybil Attack Detection for Wireless Body Sensor network

A New Sybil Attack Detection for Wireless Body Sensor Networ...

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International Conference on Computational Intelligence and Security

作者： Ruixia Liu Yinglong Wang Shandong Provincial Key Laboratory of Computer Network National Supercomputing Center in Jinan Jinan China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781479974351

The body sensor network (BSN) possess enormous potential for changing people's daily lives. The data of BSN's is associated with the physiological health of the user information and privacy, therefore require higher security protection. Sybil attack is particularly easy to perform in BSN where the communication medium is broadcast with multiple node identifiers (ID).In contrast to existing solutions which are based on sharing encryption keys, we propose a new received signal strength indicator (RSSI) based technique to identify Sybil nodes when they are regulating their transmission power. This mechanism not only does not adopt symmetric key encryption technology, but also does not require each node maintains its own identity certificate. Both analysis and simulation results show that our solutions are effective and efficient, providing high detection rate, while incurring limited overhead.

关键词： Peer-to-peer computing Security Body area networks Body sensor networks Simulation Computational intelligence

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A Particle Swarm Algorithm Based Routing Recovery Method for Mobile Sink Wireless Sensor networks

A Particle Swarm Algorithm Based Routing Recovery Method for...

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第26届中国控制与决策会议

作者： Yi-Fan Hu Xiao-Ming Wu Fu-Qiang Wang Hua Han Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Network College of Information Science and Engineering Shandong University of Science and Technology College of Electronic and Electrical Engineering Shanghai University of Engineering Science

This paper presents the routing recovery problem of mobile sink wireless sensor networks(mWSNs), which is caused by the sink mobility. We propose an immune orthogonal learning particle swarm optimization algorithm(IOLPSOA) based routing recovery method to build and optimize the alternative path, in order to repair the broken path and maintain the available route from the source nodes to the mobile sink through a multi-hop network. The IOLPSOA can improve the method with faster global convergence and higher solution quality, which can provide more efficient routing recovery capability to mWSNs. We have evaluated the performance of routing recovery method through both mathematical analysis and simulation experiments. The results show that our method effectively supports sink mobility with low energy consumption, communication overhead, and the improvement of routing recovery problem.

关键词： Wireless Sensor networks,Mobile Sink,Routing Recovery,Particle Swarm Optimization,Immune Al ith

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A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator

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China Communications 2014年第9期11卷 112-123页

作者： YI Huawei ZHANG Fuzhi LAN Jie School of Information Science and Engineering Yanshan University Qinhuangdao 066004 Hebei Province E R.China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 P. R. China Liaoning University of Technology Jinzhou 121001 Liaoning Province P. R. China

The existing collaborative recommendation algorithms have lower robustness against shilling *** this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey ***,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor *** influence of attack profiles on the recommendation results is reduced through adjusting similarities among ***,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature ***,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization *** results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

关键词： shilling attacks robust collaborative recommendation matrix factori-zation k-distance Tukey M-estimator

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Asymptotic tracking control of 4WS vehicles based on virtual points

Asymptotic tracking control of 4WS vehicles based on virtual...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Changfang Chen Minglei Shu Ruixia Liu Shandong Provincial Key Laboratory of Computer Network National Supercomputing Center in Jinan Jinan China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China

This paper considers the lateral and longitudinal path tracking control of four-wheel steering vehicles. By introducing the virtual points, a robust adaptive path tracking control strategy is proposed to simultaneously counteract modeling uncertainties, unexpected disturbances, and coupling effects. An adaptive model-based feedforward adaptive term and the robust integral of the sign of the error (RISE) feedback term are used to yield an asymptotic tracking result, which improve the tracking performance and reduce the control effort. The stability of closed-loop system is analyzed using a Lyapunov method. Simulation results are included to illustrate the proposed control scheme.

关键词： Vehicles Adaptation models Mathematical model Vehicle dynamics Uncertainty Couplings Tires

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Public verifiable key encapsulation mechanism over the group of signed energy quadratic residues

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Energy Education Science and Technology Part A: Energy Science and Research 2013年第1期31卷 525-528页

作者： Liu, Zhen Yang, Xiaoyuan Wang, Xu'an Xiao, Haiyan Key Laboratory of computer Network and Information Security of Engineering Institute of the Armed Police Xi'an 710086 China

The most of existing encryption algorithm with public verifiability of the cipher-text are based on pairings. To our knowledge, it cost much time on the computing of the pairings. So it is always an interested studying aspect to construct cryptographic algorithm without pairings in the field of cryptographic. Using the Gap quality that the Decisional Diffie-Hellman problem is easy but the Computational Dife-Hellman problem is difficult as factoring over the group of signed energy quadratic residues, under the higher residuosity assumption;we design a public verifiable key encapsulation mechanism. Furthermore, using the hash proof system, we prove our scheme is indistinguishable against chosen-cipher-text attacks under the standard model. To compare with the similar schemes, our scheme has a higher efficiency due to the nonuse of the cost computing of pairings. It will be useful to construct a threshold key encapsulation mechanism with indistinguishability against chosen-cipher-text attacks. © Sila Science.

关键词： Public key cryptography

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A metadata extraction approach from papers based on meta-learning

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Journal of information and Computational Science 2013年第4期10卷 1121-1129页

作者： Zhang, Fuzhi Zhao, Zihao School of Information Science and Engineering The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

To improve the accuracy of paper metadata extraction, a paper metadata extraction approach based on meta-learning is presented. Firstly, we propose a construction method of base-classifiers, which combines the Support Vector Machine (SVM) with the created diverse base-level training sets to construct some base-classifiers with larger diversity. The training sets are created according to the Open Access (OA) journal categories. Secondly, we present a paper metadata extraction algorithm based on meta-learning, which uses the meta-classifier to integrate the classification results of the base-classifiers and generates the final extraction results. The proposed approach guarantees extraction accuracy and independence of the base-classifiers, increases the diversities of them, and improves the classification performance of the meta-classifier, as well as reduces the degree of correlation of misclassifications. The experimental results show that our approach is superior to other single machine learning algorithm and the accuracy of paper metadata extraction is improved. © 2013 by Binary information Press.

关键词： Support vector machines

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