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Multifractal analysis of weighted networks by a modified sandbox algorithm

Scientific reports 2015年第1期5卷 17628页

作者： Yu-Qin Song Jin-Long Liu Zu-Guo Yu Bao-Gen Li Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China. College of Science Hunan University of technology Zhuzhou Hunan 412007 China. School of Mathematical Sciences Queensland University of Technology Brisbane Q4001 Australia.

Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks. First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): "Sierpinski" WFNs and "Cantor dust" WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply the SBw algorithm to study multifractal properties of some real weighted networks - collaboration networks. It is found that the multifractality exists in these weighted networks, and is affected by their edge-weights.

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Support vector machine with mixture of kernels for automatic image annotation

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High technology Letters 2013年第3期19卷 295-300页

作者：田东平 Zhao Xiaofei Shi Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic *** this paper,a novel support vector machine with mixture of kernels(SVM-MK)for automatic image annotation is *** one hand,the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as *** the other hand,SVM-MK is constructed to shoot for better annotating *** results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier,SVM-MK,can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation.

关键词： automatic image annotation （ AIA） support vector machine （ SVM） kernel func-tion principal component analysis （PCA）

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Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization

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Computational biology and chemistry 2015年 57卷 21-8页

作者： Zhi-Qin Zhao Guo-Sheng Han Zu-Guo Yu Jinyan Li Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China. Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. Electronic address: yuzg1970@***. Advanced Analytics Institute & Centre for Health Technologies University of Technology Sydney Broadway NSW 2007 Australia. Electronic address: jinyan.li@uts.edu.au.

Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request.

关键词： Disease genes and phenotypes Heterogeneous network Laplacian normalization Leave-one-out cross-validation Random walk with restart Shannon information entropy

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Learning hierarchical translation spans

Learning hierarchical translation spans

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2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014

作者： Zhang, Jingyi Utiyama, Masao Sumita, Eiichro Zhao, Hai Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong Unviersity Shanghai200240 China Key Laboratory Shanghai Education Commission Intelligent Interaction Cognitive Engineering Shanghai Jiao Tong Unviersity Shanghai200240 China National Institute of Information and Communications Technology 3-5Hikaridai Keihanna Science City Kyoto619-0289 Japan

ISBN: (纸本)9781937284961

We propose a simple and effective approach to learn translation spans for the hierarchical phrase-based translation model. Our model evaluates if a source span should be covered by translation rules during decoding, which is integrated into the translation system as soft constraints. Compared to syntactic constraints, our model is directly acquired from an aligned parallel corpus and does not require parsers. Rich source side contextual features and advanced machine learning methods were utilized for this learning task. The proposed approach was evaluated on NTCIR-9 Chinese-English and Japanese-English translation tasks and showed significant improvement over the baseline system. © 2014 Association for Computational Linguistics.

关键词： Syntactics

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Action recognition of motion capture data

Action recognition of motion capture data

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Congress on Image and Signal Processing, CISP

作者： Na Lv Zhiquan Feng Lingqiang Ran Xiuyang Zhao Shandong Provincial Key Laboratory of Network based Intelligent Computing Jinan P.R. China Department of Information Science and Technology University of Jinan Jinan China School of Computer Science and Technology Shandong University Jinan China

ISBN: (纸本)9781479958368

With the advancement of motion capture technology, 3D skeleton data is easier to be obtained. 3D skeleton data has the advantage over traditional video data for the reason that it is less affected by illumination, complex background, self-occlusion and noise. 3D skeleton data brings new opportunities and challenges to the action recognition research. In this paper, we propose a new method for action recognition of motion capture data. We use relative velocity of all the joint pairs to encode the kinematic characteristics and the primary vector decomposed from Motion Sequence Volume(MSV) to represent the distribution of joint positions in the motion sequence. The extracted features are fed into a Spectral Regression Kernel Discriminant Analysis(SRKDA) classifier to identify motion types. In the experiment, our method obtains higher recognition accuracy than the state-of-art methods.

关键词： Feature extraction Joints Three-dimensional displays Conferences Pattern recognition Computer vision

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Topological properties and fractal analysis of a recurrence network constructed from fractional Brownian motions

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Physical Review E 2014年第3期89卷 032814-032814页

作者： Jin-Long Liu Zu-Guo Yu Vo Anh Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia

Many studies have shown that we can gain additional information on time series by investigating their accompanying complex networks. In this work, we investigate the fundamental topological and fractal properties of recurrence networks constructed from fractional Brownian motions (FBMs). First, our results indicate that the constructed recurrence networks have exponential degree distributions; the average degree exponent 〈λ〉 increases first and then decreases with the increase of Hurst index H of the associated FBMs; the relationship between H and 〈λ〉 can be represented by a cubic polynomial function. We next focus on the motif rank distribution of recurrence networks, so that we can better understand networks at the local structure level. We find the interesting superfamily phenomenon, i.e., the recurrence networks with the same motif rank pattern being grouped into two superfamilies. Last, we numerically analyze the fractal and multifractal properties of recurrence networks. We find that the average fractal dimension 〈dB〉 of recurrence networks decreases with the Hurst index H of the associated FBMs, and their dependence approximately satisfies the linear formula 〈dB〉≈2−H, which means that the fractal dimension of the associated recurrence network is close to that of the graph of the FBM. Moreover, our numerical results of multifractal analysis show that the multifractality exists in these recurrence networks, and the multifractality of these networks becomes stronger at first and then weaker when the Hurst index of the associated time series becomes larger from 0.4 to 0.95. In particular, the recurrence network with the Hurst index H=0.5 possesses the strongest multifractality. In addition, the dependence relationships of the average information dimension 〈D(1)〉 and the average correlation dimension 〈D(2)〉 on the Hurst index H can also be fitted well with linear functions. Our results strongly suggest that the recurrence network inherits the basic characteristic

关键词： Fractal dimension

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On the α-universal multiple I restriction method for general fuzzy reasoning

On the α-universal multiple I restriction method for genera...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Yiming Tang Xiaomei Li Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei P. R. China School of Computer and Information Hefei University of Technology Hefei P. R. China

Based on the differently implicational idea, the a-universal multiple I restriction method is put forward for general fuzzy reasoning, which contains the a-multiple I restriction method as its specific case. First of all, we give the a-universal multiple I restriction principle, which improves the previous restriction principle, and then provide the existing condition of the solutions of the new method. Furthermore, we obtain the optimal solution of the new method for the fuzzy implications with residual pair, the R-implications, as well as some particular fuzzy implications. Finally, it is found that the new method is more reasonable by contrast with the a-multiple I restriction method.

关键词： Fuzzy reasoning Fuzzy systems Educational institutions Bismuth Conferences Abstracts Laboratories

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NEW WORD DETECTION AND EMOTIONAL TENDENCY JUDGMENT BASED ON MIXED MODEL

NEW WORD DETECTION AND EMOTIONAL TENDENCY JUDGMENT BASED ON ...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiao Sun Chongyuan Sun Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and InformationHefei University of Technology Faculty of Engineering University of Tokushima2-1 Minami-JosanjimaTokushima 770-8506Japan

ISBN: (纸本)9781467369541

The paper studies a new method for Chinese new word detection and emotional tendency judgment based on mixed model and proposes a new word generation *** we construct conditional random fields(CRFs) to recognize the new words,lead-in features based on character combined with the crowd sourcing network *** then express word as a word vector based on neural network language model(NNLM) to judge the new word emotional *** experimental results show that the method can improve the precision and recall of the new word detection with a good system performance,and it also provides a new way for forecasting the public mood.

关键词： New Word Detection Conditional Random Fields Emotional Tendency Judgment Neural Network Language Model

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MODELING BLOG USER FOR CHINESE MICRO-BLOG SENTIMENT CLASSIFICATION WITH FUZZY SVM

MODELING BLOG USER FOR CHINESE MICRO-BLOG SENTIMENT CLASSIFI...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiao Sun Chengcheng Li Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and InformationHefei University of Technology Faculty of Engineering University of Tokushima2-1 Minami-JosanjimaTokushima 770-8506Japan

ISBN: (纸本)9781467369541

Micro-blog is easily posted and communicated because of its short text ***,its brevity limits the feature *** paper presents a blogger modeling method for Chinese micro-blog sentiment *** paper would adopt a multidimensional strategy to accomplish the feature extraction *** with traditional feature extraction methods and combined with the interactive property of blog,the blog comment will be considered as the performance characteristic of micro-blog emotion *** comes to comments content and the relationship between commentators and micro-bloggers while it extracts the micro blogging text *** last,the paper adopts fuzzy SVM model to reduce the sensation of noise points and outliers on classification *** results demonstrate the effectiveness of the multidimensional feature extraction method on sentiment classification and the classify effect of fuzzy SVM is better than SVM.

关键词： sentiment classification multidimensional fuzzy SVM

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MINING THE IMPACT OF SOCIAL NEWS ON THE EMOTIONS OF USERS BASED ON DEEP MODEL

MINING THE IMPACT OF SOCIAL NEWS ON THE EMOTIONS OF USERS BA...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiao Sun Fei Gao Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and InformationHefei University of Technology Faculty of Engineering University of Tokushima2-1 Minami-JosanjimaTokushima 770-8506Japan

ISBN: (纸本)9781467369541

In this paper,Deep Belief Nets(DBN) model and Support Vector Machine(SVM) are used to mine the features hidden in social news,which can influence the emotions of *** feature selection methods for text modeling are used to build the input vectors of DBN,with the purpose of keeping the text information to the greatest *** take advantage of the deep features abstracted by DBN to build social news text *** the same time,three optimal models are used as inputs of SVM to train and classify the social *** get a conclusion that DBN not only reduces the dimension of original features,but also makes the abstracted features with more text information and shows better performance in determining the influence on people' s emotions by social news.

关键词： Deep Belief Network(DBN) Restricted Boltzmann Machine(RBM) Impacts on Emotion Social News

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