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Construction and correlation analysis of combiner functions with 1 bit memory

Journal of Computational Information Systems 2013年第2期9卷 521-528页

作者： Che, Xiaoliang Yang, Xiaoyuan Institute of Network and Information Security under the Chinese Armed Police Force Department of Electronic Technology Engineering University of Armed Police Force Xi'an 710086 China Key Laboratory of Computer Network and Information Security of the Ministry of Education Xidian University Xi'an 710086 China

In this paper, we construct a kind of combiner functions with high nonlinearity and correlation-immunity. This construction is based on the relationship between properties of the combiners with 1bit memory and the cryptographic criteria of Boolean function. It is shown that the nonlinearity of the output sequence can reach 2n-2n=2 and the correlation-immune can arrive at (n-1)th-order. In addition, through correlation analysis of combiner functions, the conditional transition probability matrix is uniformly distributed and the correlation coefficient does not change even if we have known all prior output bits. Copyright © 2013 Binary Information Press.

关键词： Boolean functions

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Improvements on bottleneck feature for large vocabulary continuous speech recognition

Improvements on bottleneck feature for large vocabulary cont...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Maimaitiaili Tuerxun Shiliang Zhang Yebo Bao Lirong Dai The Key Laboratory for Network Information Security and Public Opinion Analysis Xinjiang Normal University Urumqi Xinjiang P. R. China National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei Anhui P. R. China

In this paper, we have proposed three methods to improve the performance of the bottleneck(BN) feature based GMM-HMM system. Firstly, we recommend a new bottleneck feature architecture, namely LBN, which places the bottleneck layer at the last hidden layer instead of the middle, in order to take advantage of the more discriminative and invariant higher layer features. Secondly, we employ the rectified linear units (ReLUs) based DNN as bottleneck feature extractor. Finally, we investigate the sequence discriminative training of bottleneck neural network to achieve more powerful bottleneck feature. We have evaluated our methods in 309-hour Switchboard (SWB) task. Compared with the traditional hybrid DNN-HMM system, our proposed ReLUs based LBN-GMM-HMM system can achieve about 9.7% recognition error rate reduction relatively.

关键词： Training Neural networks Speech recognition Hidden Markov models Feature extraction Acoustics Computer architecture

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A mesh editing algorithm based on Gaussian curvature

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Journal of Information and Computational Science 2013年第10期10卷 2881-2890页

作者： Wang, Feng Huang, Zhangjin Guo, Licai School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Laboratory of Software in Computing and Communication Hefei 230027 China USTC-SICT Joint Laboratory on Network and Communication Hefei 230027 China

Mesh editing plays an important role in the design of mesh models. The As-Rigid-As-Possible (ARAP) surface editing algorithm is likely to produce degenerated results in sharp regions. This degeneration affects the quality of results. This paper proposes a new mesh editing algorithm based on the ARAP surface modeling algorithm. By setting constraints on triangle transformations, this algorithm has better detail-preserving properties. With the constraints on the ratios among scaling factors, we can reduce the skinny triangles produced by the ARAP algorithm. The experimental results showed that this algorithm improved robustness and detail-preservation over the As-Rigid-As-Possible algorithm. Copyright © 2013 Binary Information Press.

关键词： Mesh generation

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Towards accountable authority attribute-based encryption

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High technology Letters 2013年第1期19卷 82-87页

作者：王永涛 Chen Kefei Long Yu Department of Computer Science and Engineering Shanghai Jiao Tong University China Information Technology Security Evaluation Center Shanghai Key Laboratory of Scalable Computing and System Shanghai JiaoTong University

An accountable authority attribute-based encryption （A-ABE） scheme is presented in this paper. The notion of accountable authority identity-based encryption （A-IBE） was first introduced by Goyal at Crypto＇07. It is a novel approach to mitigate the （inherent） key escrow problem in identity-based cryptosystems. In this work, the concept of accountable authority to attribute-based encryption （ABE） setting is generalized for the first time, and then a construction is given. The scheme non-trivially integrates an A-IBE scheme proposed by Libert et al. with an ABE scheme. In our construction, a user will be identified by a pair （ id, o~）, where id denotes the user＇ s identity and ω denotes the set of attributes associated to the user. In addition, our construction is shown to be secure under some reasonable assumptions.

关键词： cryptography attribute-based encryption accountable authority key escrow traceability

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Flickr group recommendation based on quaternary semantic analysis

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Journal of Computational Information Systems 2013年第6期9卷 2235-2242页

作者： Wang, Xiaofang Ma, Jun Cui, Chaoran Gao, Shuai School of Computer Science and Technology Shandong University Jinan 250101 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan 250022 China

Flickr groups are self-organized user communities revolving around a common interest and of massive popularity. In recent years, the explosive growth in the number of the group makes it difficult for Flickr users to find relevant groups they are really interested in. In this paper, we focus on automatically recommending groups to users. Many existing works have utilized only ternary relationship such as users-tags-groups to generate their recommendations. In our work, we show that ternary relationship is insufficient to provide accurate recommendations. Instead, we represent the quaternary relationship among users, tags, image clusters and groups as a 4-order tensor and further employ the Higher-Order Singular Value Decomposition to reduce the dimensionality of the 4-order tensor;accordingly the group recommendation problem is casted as a latent semantic analysis problem between users and groups. Experiments on the dataset crawled from Flickr and comparisons with the ternary relationship model demonstrate the effectiveness of the proposed approach. Copyright © 2013 Binary Information Press.

关键词： Tensors

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Real-time detection and recognition of landmarks for mobile robot in indoor scenes

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous Engineering 2013年第4期8卷 329-341页

作者： Xue, Yanbing Zhang, Hua Ju, Yucui Wang, Jin Key Laboratory of Computer Vision and System Tianjin University of Technology Tianjin 300191 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China

This paper proposes a robust real-time artificial landmarks detection and recognition system for indoor mobile robot. Landmarks detection and recognition for indoor robots faces two major difficulties, one is the illumination changes and the other is processing speed. In this paper, first, histograms of oriented gradient (HOG) features are extracted to resolve the problem of illumination changes. Second, AdaBoost based algorithm is used in detection phase to increase the processing speed. Finally, RBF-SVM classifier is used for recognition. Experimental results show a high detection and recognition accuracy and the processing speed is about 10 frames per second.

关键词： Adaptive boosting

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Feedforward and feedback optimal vibration rejection for active suspension discrete-time systems under in-vehicle networks

Feedforward and feedback optimal vibration rejection for act...

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World Congress on Nature & Biologically Inspired computing (NaBIC)

作者： Shi-Yuan Han Yue-Hui Chen Kun Ma Dong Wang Ajith Abraham Zhong-Guang Liu Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China IT4Innovations VSB-Technical University of Ostrava Ostrava Czech Republic Machine Intelligence Research Labs (MIR Labs) Auburn Washington USA Vehicle Management Agency of Jinan Public Security Bureau Jinan China

This paper studies the vibration rejection problem of active suspension discrete-time systems under in-vehicle networks and designs a controller of feedforward and feedback optimal vibration rejection. Based on the ground displacement power spectral density, an discrete-time exosystem is employed to estimate the random road disturbances. A two degree of freedom discrete-time system is introduced to describe the active suspension under in-vehicle networks. Then, the original vibration control is formulated as the optimal control for a linear discrete-time system affected by external disturbances. The feedforward and feedback optimal vibration rejection law (FFOVRL) is designed by solving the Riccate and Stein equations, in which the feedforward term incorporates the information of the random road disturbances and the feedback loop includes the status of suspension system. The feasibility and effectiveness of the proposed approaches are validated by an active suspension structure.

关键词： Vibrations Ear Wheels Tires Equations Biological system modeling

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A systematic approach to capturing and analysing botnets

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Information technology Journal 2013年第23期12卷 7152-7159页

作者： Wei, Jiang Tian, Zhihong College of Computer Science Beijing University of Technology Beijing 100124 China School of Computer National University of Defense Technology Changsha 410073 China Key Laboratory of Information and Network Security Ministry of Public Security Shanghai 201204 China School of Computer Science and Technology Harbin Institute of Technology Haerbin 150001 China

In this study, a systematic approach to Capturing and Analysing botnets is presented. Our framework is a scalable and robust infrastructure and consists of four modules. Honeynet-based capture system is proposed that is the first step towards our framework, which can automatically and dynamically collect and analyze malware traffic over the Internet without supervision. Furthermore, a multidimensional analysis system is designed to analyze binaries captured in the capture system. In addition, we discuss our preliminary results and lessons learned from this work. © 2013 Asian network for Scientific Information/.

关键词： network security

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Optimal Feature Selection for Robust Classification via l2,1-Norms Regularization

Optimal Feature Selection for Robust Classification via l2,1...

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International Conference on Pattern Recognition

作者： Jiajun Wen Zhihui Lai Wai Keung Wong Jinrong Cui Minghua Wan Bio-Computing Research Center Harbin Institute of Technology Shenzhen Graduate School 518055 China ITC The Hong Kong Polytechnic University Hong Kong Key Laboratory of Network Oriented Intelligent Computation Shenzhen China SIE Nanchang Hangkong University Nanchang China

This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l 2,1 -norms regularization. To solve the proposed model, an alternately iterative algorithm is proposed to optimize both the jointly sparse projection matrix and representation matrix. Experimental results on three public face datasets and one action dataset validate the quick convergence of our algorithm and show that the proposed method is more competitive than the state-of-the-art methods.

关键词： Training Face recognition Robustness Face Convergence Accuracy Vectors

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A Steganalysis based on OC-SVM and two Clustering Methods

A Steganalysis based on OC-SVM and two Clustering Methods

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International Conference on Information technology and Management Innovation (ICITMI2012)

作者： Yang, Haibin Guo, Duntao Luo, Peng Key Laboratory of Network and Information Security Chinese Armed Police Force Department of Electronic Technology Engineering University of Armed Police Force China

ISBN: (纸本)9783037855744

In this paper, a novel and efficient steganalysis based on improved K-means clustering using One-Class Support Vector Machine (OC-SVM) was proposed to blindly determine the existence of hidden messages in an image. The performance of sample clustering is concerned in the OC-SVM with multi-sphere. In previous work, the K-means was mainly used to create such multi-sphere by clustering. But the traditional K-means depends on initial clustering centers and ended local minimum value. So, to improve the clustering performance of K-means, the SA is employed into the scheme, which can create more reasonable multi-sphere by finding global optimum solutions in the clustering process. Simulation results with the chosen feature set and well-known steganographic techniques indicated that our approach was able to afford reasonable accuracy to distinguish between covers and stego images.

关键词： Steganalysis One-class Support Vector Machine K-means Stimulated Annealing

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