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IEEE International Conference on Data Mining (ICDM)

作者： Zhiwei Jin Juan Cao Yu-Gang Jiang Yongdong Zhang University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing CAS China School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9781479943012

Benefiting from its openness, collaboration and real-time features, Micro blog has become one of the most important news communication media in modern society. However, it is also filled with fake news. Without verification, such information could spread promptly through social network and result in serious consequences. To evaluate news credibility on Micro blog, we propose a hierarchical propagation model. We detect sub-events within a news event to describe its detailed aspects. Thus, for a news event, a three-layer credibility network consisting of event, sub-events and messages can represent it from different scale and reveal vital information for credibility evaluation. After linking these entities with their semantic and social associations, the credibility value of each entity is propagated on this network to achieve the final evaluation result. By formulating this propagation process as a graph optimization problem, we provide a globally optimal solution with an iterative algorithm. Experiments conducted on two real-world datasets show that the proposed model boosts the accuracy by more than 6% and the F-score by more than 16% over a baseline method.

关键词： Optimization Clustering algorithms Vectors Semantics Feature extraction Symmetric matrices Media

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Chinese Microblog Sentiment Classification Based on Deep Belief Nets with Extended Multi-Modality Features

Chinese Microblog Sentiment Classification Based on Deep Bel...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Xiao Sun Chengcheng Li Wanyi Xu Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China School of Information Science and Engineering Southeast University Shanghai China Faculty of Engineering University of Tokushima Tokushima Japan

ISBN: (纸本)9781479942732

This paper presents a DBN (deep belief nets) model and a multi-modality feature extraction method to extend features' dimensionalities of short text for Chinese micro blogging sentiment classification. Besides traditional features sets for document classification, comments for certain posts are also extracted as part of the micro blogging features according to the relationship between commenters and posters though constructing micro blogging social network as input information. Then, the integration of the above modality features is combined and represented as input vector for DBN. In this paper, a DBN model, which is stacked with several layers of RBM (Restricted Boltzmann Machine), is implemented to initialize the structure of neural network. The RBM layers can take probability distribution samples of original data to learn hidden structures for better feature representation. A Class RBM (Classification RBM) layer, which is stacked on top of several RBM layers, is adapted to achieve the final sentiment classification. The results demonstrate that, with proper structure and parameter, the performance of the proposed deep learning method on sentiment classification is better than state of the art surface learning models such as SVM or NB, which proves that DBN is suitable for short-length document classification with the proposed feature dimensionality extension method.

关键词： Feature extraction Semantics Visualization Training Social network services Blogs Educational institutions

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Twin support vector machines based on Quantum particle swarm optimization

Journal of Software

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Journal of Software 2013年第7期8卷 1743-1750页

作者： Ding, Shifei Wu, Fulin Nie, Ru Yu, Junzhao Huang, Huajuan School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Twin Support Vector Machines (TWSVM) are developed on the basis of Proximal Support Vector Machines (PSVM) and Proximal Support Vector Machine based on the generalized eigenvalues(GEPSVM). The solving of binary classification problem is converted to the solving of two smaller quadratic programming problems by TWSVM. And then it gets two non-parallel hyperplanes. Its efficiency of dealing with the problems and performance are better than the traditional support vector machines. However, it also has some problems. Its own parameters are difficult to be appointed. In order to solve this problem, on the basis of in-depth study of TWSVM, this paper proposes an algorithm that is the Twin Support Vector Machines based on Quantum Particle Swarm Optimization (QPSO-TWSVM).By the use of the global searching ability of the Quantum Particle Swarm Optimization (QPSO), QPSO-TWSVM can search the optimal parameters in the global scope and avoid itself falling into the local optimum prematurely to find the values of the parameters which are the closest to the optimal parameters. QPSO-TWSVM avoids using the empirical values to appoint the parameters successfully. Compared with the traditional TWSVM, QPSO-TWSVM can appoint the parameters more accurately and avoid selecting the parameters blindly. Because of the better parameter selections, QPSO-TWSVM improves the classification accuracy of TWSVM. © 2013 ACADEMY PUBLISHER.

关键词： Support vector machines

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A feature weighted spectral clustering algorithm based on knowledge entropy

Journal of Software

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Journal of Software 2013年第5期8卷 1101-1108页

作者： Jia, Hongjie Ding, Shifei Zhu, Hong Wu, Fulin Bao, Lina School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Spectral clustering has aroused extensive attention in recent years. It performs well for the data with arbitrary shape and can converge to global optimum. But traditional spectral clustering algorithms set the importance of all attributes to 1 as default, when measuring the similarity of data points. In fact, each attribute contains different information and their contributions to the clustering are also different. In order to make full use of the information contained in each attribute and weaken the interference of noise data or redundant attributes, this paper proposes a feature weighted spectral clustering algorithm based on knowledge entropy (FWKE-SC). This algorithm uses the concept of knowledge entropy in rough set to evaluate the importance of each attribute, which can be used as the attribute weights, and then applies spectral clustering method to cluster the data points. Experiments show that FWKE-SC algorithm deals with high-dimensional data very well and has better robustness and generalization ability. © 2013 ACADEMY PUBLISHER.

关键词： Entropy

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Multi-Modality Emotion Analysis of Chinese Microblogging

Multi-Modality Emotion Analysis of Chinese Microblogging

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2014 International Conference on Artificial,Intelligence and Software Engineering(AISE 2014)

作者： Xiao SUN Cheng-cheng LI Yong-an YU Xiao-li NAN Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Dalian Informedia Electronic Co. Ltd. Dalian University of Technology AAVIC Jiangxi Hongdu Aviation Industry Group Corporation Limited

This paper presents a multi-modality feature extraction method based sentiment classification for Chinese microblogging. Human sentiment in microblogging is divided into three categories(positive, neutral, negative). Compared with traditional Chinese microblogging sentiment classification approaches, the method adopts the characteristics of microblogging social environment modality(including microblogging comments and relationships between commentators and micro-bloggers), while it extracts the microblogging text feature. The paper integrates the above two modality features and improve the performance of sentiment classification. At last, we model the feature sets by adopting fuzzy SVM model. The experimental results demonstrate the performance of the method on sentiment classification.

关键词： Emotion analysis Sentiment classification Multi-modality Sentiment fuzzy SVM

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Recognition algorithm for plant leaves based on adaptive supervised locally linear embedding

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International Journal of Agricultural and Biological Engineering 2013年第3期6卷 52-57页

作者： Yan Qing Liang Dong Zhang Dongyan Wang Xiu Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei 230039China Beijing Research Center for Information Technology in Agriculture Beijing 100097China

Locally linear embedding(LLE)algorithm has a distinct deficiency in practical *** requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is presented based on supervised LLE in this article.A similarity measure is formed by utilizing the Fisher projection distance,and then it is used as a threshold to select *** samples will produce different k adaptively according to the density of the data *** method is applied to classify plant *** experimental results show that the average classification rate of this new method is up to 92.4%,which is much better than the results from the traditional LLE and supervised LLE.

关键词： supervised locally linear embedding manifold learning Fisher projection adaptive neighbors leaf recognition Precision Agriculture

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2-D shape completion with shape priors

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Chinese Science Bulletin 2013年第27期58卷 3430-3436页

作者： GUO Ge JIANG TingTing WANG YiZhou GAO Wen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences National Engineering Laboratory for Video Technology Key Laboratory of Machine Perception School of EECS Peking University

We propose new techniques for 2-D shape/contour completion, which is one of the important research topics related to shape analysis and computer vision, e.g. the detection of incomplete objects due to occlusion and noises. The purpose of shape completion is to find the optimal curve segments that fill the missing contour parts, so as to acquire the best estimation of the original complete object shapes. Unlike the previous work using local smoothness or minimum curvature priors, we solve the problem under a Bayesian formulation taking advantage of global shape prior knowledge. With the priors, our methods are expert in recovering significant shape structures and dealing with large occlusion cases. There are two different priors adopted in this paper: (i) A generic prior model that prefers minimal global shape transformation (including non-rigid deformation and affine transformation with respect to a reference object shape) of the recovered complete shape; and (ii) a class-specific shape prior model learned from training examples of an object category, which prefers the reconstructed shape to follow the learned shape variation models of the category. Efficient contour completion algorithms are suggested corresponding to the two types of priors. Our experimental results demonstrate the advantage of the proposed shape completion approaches compared to the existing techniques, especially for objects with complex structure under severe occlusion.

关键词：形状结构计算机视觉贝叶斯公式电子相关最佳估计最小曲率先验知识仿射变换

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Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks

Energy-Efficient Flow Scheduling and Routing with Hard Deadl...

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International Conference on Distributed computing Systems

作者： Lin Wang Fa Zhang Kai Zheng Athanasios V. Vasilakos Shaolei Ren Zhiyong Liu University of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences IBM China Research Lab University of Western Macedonia Greece Florida International University USA Institute of Computing Technology Chinese Academy of Sciences

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.

关键词： Schedules Routing Power demand Switches Optimal scheduling Data models Approximation algorithms

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Fractal and complex network analyses of protein molecular dynamics

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Physica A: Statistical Mechanics and its Applications 2014年 416卷 21-32页

作者： Yuan-Wu Zhou Jin-Long Liu Zu-Guo Yu Zhi-Qin Zhao Vo Anh Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China Civil Construction Engineering Department Guangxi University of Science and Technology Liuzhou 545006 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia

Based on protein molecular dynamics, we investigate the fractal properties of energy, pressure and volume time series using the multifractal detrended fluctuation analysis (MF-DFA) and the topological and fractal properties of their converted horizontal visibility graphs (HVGs). The energy parameters of protein dynamics we considered are bonded potential, angle potential, dihedral potential, improper potential, kinetic energy, Van der Waals potential, electrostatic potential, total energy and potential energy. The shape of the h ( q ) curves from MF-DFA indicates that these time series are multifractal. The numerical values of the exponent h ( 2 ) of MF-DFA show that the series of total energy and potential energy are non-stationary and anti-persistent; the other time series are stationary and persistent apart from series of pressure (with H ≈ 0.5 indicating the absence of long-range correlation). The degree distributions of their converted HVGs show that these networks are exponential. The results of fractal analysis show that fractality exists in these converted HVGs. For each energy, pressure or volume parameter, it is found that the values of h ( 2 ) of MF-DFA on the time series, exponent λ of the exponential degree distribution and fractal dimension d B of their converted HVGs do not change much for different proteins (indicating some universality). We also found that after taking average over all proteins, there is a linear relationship between 〈 h ( 2 ) 〉 (from MF-DFA on time series) and 〈 d B 〉 of the converted HVGs for different energy, pressure and volume.

关键词： Protein molecular dynamics Multifractal detrended fluctuation analysis Horizontal visibility graph Fractal analysis Degree distribution

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Attribute granulation based on attribute discernibility and AP algorithm

Journal of Software

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Journal of Software 2013年第4期8卷 834-841页

作者： Zhu, Hong Ding, Shifei Zhao, Han Bao, Lina School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China School of Medical Information Xuzhou Medical College Xuzhou 221000 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

For high dimensional data, the redundant attributes of samplers will not only increase the complexity of the calculation, but also affect the accuracy of final result. The existing attribute reduction methods are encountering bottleneck problem of timeliness and spatiality. In order to looking for a relatively coarse attributes granularity of problem solving, this paper proposes an efficient attribute granulation method to remove redundancy attribute. The method calculates the similarity of attributes according attribute discernibility first, and then clusters attributes into several group through affinity propagation clustering algorithm. At last, representative attributes are produced through some algorithms to form a coarser attribute granularity. Experimental results show that the attribute granulation method based on affinity propagation clustering algorithm(AGAP) method is a more efficient algorithm than traditional attribute reduction algorithm(AR). © 2013 ACADEMY PUBLISHER.

关键词： Granulation

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