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International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA)

作者： Xiaomei Yu Yongqin Li Hong Wang School of Information Science and Engineering Shandong Normal University Jinan Shandong China Shandong Provincial Key Laboratory for Distributed Computer software Novel Technology School of Teacher's Education Shandong Normal University Jinan Shandong China

ISBN: (纸本)9781467383165

As an important branch in the field of frequent pattern mining, approximate frequent pattern (AFP) mining attracts much attention recently. Various algorithms have been proposed to discover long true AFPs in presence of random noise. This paper considers the key issues of AFP mining in noisy databases, and categorizes the previous approaches according to the ways they cope with missing items in the transactions. And then a study of different data models on AFP is presented, in which the merits and defects are analyzed. Finally, we draw a conclusion and propose some solutions to deal with the problems in the field of AFP mining.

关键词： Itemsets Algorithm design and analysis Approximation algorithms Data mining Noise measurement Data models

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Research and development of advanced computing technologies

Scientific World Journal

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Scientific World Journal 2015年第1期2015卷 239723页

作者： Ding, Shifei Shi, Zhongzhi Azar, Ahmad Taher School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Faculty of Computers and Information Benha University Benha 13511 Egypt

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A new category-based weighting scheme for automated text categorization

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Journal of Computational and Theoretical Nanoscience 2015年第12期12卷 5198-5205页

作者： Jia, Longjia Sun, Tieli Yang, Fengqin Sun, Hongguang Zhang, Bangzuo Hung, Chih-Cheng School of Computer Science and Information Technology Key Laboratory of Intelligent Information Processing of Jilin Universities Northeast Normal University Changchun130117 China College of Humanities and Sciences Northeast Normal University Changchun130117 China Center for Machine Vision and Security Research Kennesaw State University Marietta30144 United States

Term weighting is a strategy that assigns weights to terms in order to improve the performance of text categorization. In this paper, we propose a new category-based term weighting scheme named the probability of relevance frequency (prf), which uses available labeling information to assign appropriate weights to terms. The main idea of prf is that the more concentrated a highfrequency term is in the positive category than in the negative category, the more contribution it makes in separating the positive samples from the negative samples. By replacing word features with category-based features, the dimensionality of the document feature space can be reduced from tens of thousands to a small number of categories. In the experiments, we investigate the effects of prf on the 20 Newsgroups, Reuters-21578, and Yahoo! Answers datasets using the SVM and κ-NN as classifiers. The results show that the prf scheme outperforms other term weighting schemes, such as term frequency (tf), term frequency and inverse document frequency (tf ***;idf), term frequency and relevance frequency (tf ***;rf), term frequency and inverse question frequency and question frequency and inverse category frequency (iqf ***;qf ***;icf). Copyright © 2015 American Scientific Publishers All rights reserved.

关键词： Classification (of information)

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Graph Pattern Matching through Model Checking

Graph Pattern Matching through Model Checking

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International Conference on Database Theory and Application (DTA)

作者： Rui Qiao Xiaolei Zhong Ling Zhang Heng He College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China School of Software Huazhong University of Science Wuhan China College of Management Wuhan University of Science and Technology Wuhan China

Graph pattern matching is a hot spot in the big data era, which is to find answer graphs matching a given query graph in a data graph of graph databases. “Matching” means two graphs satisfy some relation, such as isomorphism, simulation, bisimulation, etc. Since there are seldom algorithms for the subgraph bisimulation, our work commits to solve the graph pattern matching problem involving bisimulation relations through the model checking technology. We characterize query graphs by modal formulas. By model checking the formulas in the data graphs, the answer graphs bisimilar to the query graphs can be discovered. We add * to basic modal logic language resulting in ML + * language, and add r* to form ML + * formulas. Then a theorem which states that ML + * formulas characterize finite directed graphs modulo bisimulation is put forward. Furthermore, we list steps to find answer graphs bisimilar to a query graph.

关键词： Model checking Pattern matching Databases Big data Topology Arrays computer science

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Discriminative feature combination selection for enhancing multiclass classification

Discriminative feature combination selection for enhancing m...

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International Conference on Behavior, Economic and Social Computing (BESC)

作者： Aibo Song Wei Qian Zhiang Wu Jinghua Zhao School of Computer Science and Engineering Southeast University Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China College of Information Science and Engineering Shandong University of Science and Technology Shandong China

Frequent pattern mining is commonly utilized to generate combined-feature candidates, yet many are non-discriminative and thus might be useless for predictive models. In this paper, we propose to use feature combinations derived from frequent patterns to obtain more accurate multiclass classification models. Specifically, we present a novel mathematics inference to show what are discriminative feature combinations. Hence, an efficient algorithm is proposed for mining and selecting discriminative patterns. Experimental results on twenty UCI datasets demonstrate that the proposed method can help to improve the classification performance remarkably, compared with other baseline methods. Moreover, an internal evaluation is employed to validate the strong discriminative power of our feature combinations.

关键词： Breast Diabetes Vehicles Classification algorithms Niobium Size measurement

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Network Forensics Scenario Reconstruction Method Based on Hidden Markov Models

Network Forensics Scenario Reconstruction Method Based on Hi...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Weiping Gu Liancheng Xu Min Ren Xiaoyan Han School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China School of Mathematics and Quantitative Economics Shandong University of Finance and Economics Jinan China

Reconstruction Method of Network Forensics Scenario has grown into a mature and rich technology that provides advanced skills to get the chain of evidence. Using statistical methods to analyze intrusion logs in order to present evidentiary values in court are often refuted as baseless and inadmissible evidences which is not considering the input spent. These spendings is to generate the reports no matter they are well-grounded evidences or not. Thus, this paper presents the Scenario Reconstruction Method combines the Viterbi algorithm, the most likely sequence of Meta evidence which replaces the Meta evidence was acquired. With suspected evidence, thus obtaining the chain of evidence. However, the Viterbi algorithm parameters is derived from the Baum-Welch (B-W) algorithm, and the B-W algorithm is easy to fall into local optima solution. While an Adaptive Genetic Algorithm (AGA) is used to estimate parameters of the Hidden Markov model (HMM), where Chromosome coding method and genetic operation mode are designed. The experimental results show that, this method can accurately reproduce the crime scene of network intrusion, compared with the network forensic evidence fusion method which is based on the HMM. The method has been applied to forensics system, and has obtained good result.

关键词： Hidden Markov models Sociology Statistics Maximum likelihood decoding Biological cells Genetics Genetic algorithms

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Color PNG Image Authentication Scheme Based on Rehashing and Secret Sharing Method

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Journal of information Hiding and Multimedia Signal processing 2015年第3期6卷 523-533页

作者： Lyu, Wan-Li Chang, Chin-Chen Wang, Feng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui University Hefei230039 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Rd Seatwen Taichung40724 Taiwan Department of Mathematics and Physics Fujian University of Technology Fuzhou Fujian350108 China

An authentication scheme that uses rehashing and secret sharing methods to verify the reality of a color image’s content is proposed in this paper. First, the proposed scheme uses Shamir’s secret sharing method to share image features. Second, the proposed scheme innovatively utilizes the rehashing method to overcome easy collisions of the random numbers to enhance the effectiveness against attacks. Third, the proposed method utilizes Portable Network Graphics images to hide authentication messages. Using the rehashing method, the collisions take up little in address space, making the process suitable for processing authentication messages. The experimental results indicated that the proposed scheme had very low false acceptance and rejection ratios and verified that the rehashing method can be used in image authentication applications. © 2009 ISSN 2073-4212.

关键词： Authentication

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Whole-proteome based phylogenetic tree construction with inter-amino-acid distances and the conditional geometric distribution profiles

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Molecular phylogenetics and evolution 2015年 89卷 37-45页

作者： Xian-Hua Xie Zu-Guo Yu Guo-Sheng Han Wei-Feng Yang Vo Anh Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China School of Mathematics and Computer Science Gannan Normal University Jiangxi 341000 PR China. Electronic address: xxianhua@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: yuzuguo@***. Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China. Electronic address: korea10282003@***. Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China. Electronic address: yangweifeng09@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: v.anh@qut.edu.au.

There has been a growing interest in alignment-free methods for whole genome comparison and phylogenomic studies. In this study, we propose an alignment-free method for phylogenetic tree construction using whole-proteome sequences. Based on the inter-amino-acid distances, we first convert the whole-proteome sequences into inter-amino-acid distance vectors, which are called observed inter-amino-acid distance profiles. Then, we propose to use conditional geometric distribution profiles (the distributions of sequences where the amino acids are placed randomly and independently) as the reference distribution profiles. Last the relative deviation between the observed and reference distribution profiles is used to define a simple metric that reflects the phylogenetic relationships between whole-proteome sequences of different organisms. We name our method inter-amino-acid distances and conditional geometric distribution profiles (IAGDP). We evaluate our method on two data sets: the benchmark dataset including 29 genomes used in previous published papers, and another one including 67 mammal genomes. Our results demonstrate that the new method is useful and efficient.

关键词： Alignment-free whole-proteome comparison Conditional geometric distribution profile Inter-amino-acid distances Phylogenetic tree

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Sparse belief propagation algorithm based on latent dirichlet allocation

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Diangong Jishu Xuebao/Transactions of China Electrotechnical Society 2015年 30卷 506-511页

作者： Tu, Xionggang Chen, Jun Yang, Lu Yan, Jianfeng School of Design and Arts Zhejiang Industry Polytechnic College ShaoxingZhejiang312000 China School of Computer Science and Technology Soochow University Suzhou215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

Latent Dirichlet allocation(LDA) is a popular and unsupervised tool for reducing dimension, has been applied in text mining and information retrieval. Belief propagation is competitive in both speed and accuracy compared with other inference algorithms of LDA. In this paper, we propose belief propagation algorithm with sparseness constraints and use L1-norm, L2-norm to measure the sparseness of vector. In iteration, we project the topic-word distribution to a sparse space for getting better semantic representation. Experimental results show that sparse belief propagation algorithm improves the accuracy of text clustering and classification than belief propagation, Gibbs sampling and nonnegative matrix factorization. ©, 2015, The editorial office of Transaction of China Electrotechnical Society. All right reserved.

关键词： Belief propagation

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Detecting Optic Disk Based on Structured Learning

Detecting Optic Disk Based on Structured Learning

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IEEE International Conference on Robotics and Biomimetics

作者： Zhun Fan Yibiao Rong Xinye Cai Wenji Li Huibiao Lin Zefeng Yu Jiewei Lu Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial College of Engineering Shantou University. 515063 Shantou China College of Engineering Shantou University. 515063 Shantou China school of computer science and technology Nanjing University of Aeronautics and Astronautics Jiangsu China

ISBN: (纸本)9781467396769

Optic Disk (OD) detection plays an important role for fundus image analysis. In this paper, we propose an algorithm for detecting OD mainly based on a classifier model trained by structured learning. Then we use the model to achieve the edge map of OD. Thresholding is performed on the edge map to obtain a binary image. Finally, circle Hough transform is carried out to approximate the boundary of OD by a circle. The proposed algorithm has been evaluated on the public database and obtained promising results. The results (an area overlap and Dices coefficients of 0.8636 and 0.9196, respectively, an accuracy of 0.9770, and a true positive and false positive fraction of 0.9212 and 0.0106) show that the proposed method is a robust tool for the segmentation of OD and is very competitive with the stage-of-the-art methods.

关键词： algorithm detecting method

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