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Hyponymy Graph Model for Word Semantic Similarity Measurement

Chinese Journal of Electronics 2015年第1期24卷 96-101页

作者： WANG Junhua ZUO Wanli PENG Tao College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering attached to the Ministry of Education

Measuring word semantic similarity is a generic problem with a broad range of applications such as ontology mapping, computational linguistics and artificial intelligence. Previous approaches to computing word semantic similarity did not consider concept occurrence frequency and word’s sense number. This paper introduced Hyponymy graph, and based on which proposed a novel word semantic similarity model. For two words to be compared, we first retrieve their related concepts; then produce lowest common ancestor matrix and distance matrix between concepts; finally calculate distance-based similarity and information-based similarity, which are integrated to get final semantic similarity. The main contribution of our method is that both concept occurrence frequency and word’s sense number are taken into account. This similarity measurement more closely fits with human rating and effectively simulates human thinking process. Our experimental results on benchmark dataset M&C and R&G with Word Net2.1 as platform demonstrate roughly 0.9%–1.2%improvements over existing best approaches.

关键词： Word semantic similarity Concept semantic similarity Semantic similarity Hyponymy graph model Word Net.

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Dynamics Analysis of Bionic Parallel Joint Mechanism for the Snake Robot

Dynamics Analysis of Bionic Parallel Joint Mechanism for the...

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第35届中国控制会议

作者： Chuncheng Wu Zhengcai Cao Qing Xiao Yili Fu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University State Key Laboratory of Robotics and Systems Harbin Institute of Technology College of Information Science and Technology Beijing University of Chemical Technology

ISBN: (纸本)9781509009107

Joint mechanism is a key factor for a snake robot adjusting its postures adapted to clutter environments in search and rescue *** joint mechanisms in prior research simply consist of serially connected revolute joints,which are lack of great load carrying *** nature snake structure,a modular bionic parallel joint mechanism(BPJM) is proposed for the rescue snake *** analysis of the BPJM is necessary for its optimal design and control,providing the force and constraint that must be resisted by joints,links and *** reduce the dynamics computation load,Newton equation and Euler equation are combined by synchronizing the inertial force and inertial moment with the aid of screw ***,the dynamics equations for moving platform and links are formulated in a simplified *** friction at the joints and external force acting on BPJM,which actually affect the motion,are both considered in the ***,the virtual prototype is provided in order to visualize the joint mechanism and the numerical results from the dynamics analysis are given.

关键词： Rescue snake robot Dynamics analysis Bionic parallel joint mechanism Screw theory Newton method

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Research on Trust Prediction from a Sociological Perspective

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Journal of Computer Science & Technology 2015年第4期30卷 843-858页

作者：王英王鑫左万利 College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun 130012 China School of Computer Technology and Engineering Changehun Institute of Technology Changchun 130012 China

Trust, as a major part of human interactions, plays an important role in helping users collect reliable infor-mation and make decisions. However, in reality, user-specified trust relations are often very sparse and follow a power law distribution; hence inferring unknown trust relations attracts increasing attention in recent years. Social theories are frameworks of empirical evidence used to study and interpret social phenomena from a sociological perspective, while social networks reflect the correlations of users in real world; hence, making the principle, rules, ideas and methods of social theories into the analysis of social networks brings new opportunities for trust prediction. In this paper, we investigate how to exploit homophily and social status in trust prediction by modeling social theories. We first give several methods to compute homophily coe？cient and status coe？cient, then provide a principled way to model trust prediction mathe-matically, and propose a novel framework, hsTrust, which incorporates homophily theory and status theory. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of homophily theory and status theory in trust prediction.

关键词： trust prediction homophily coefficient status coefficient social theory matrix factorization

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Detecting communities from signed network based on local search

Detecting communities from signed network based on local sea...

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2016 2nd International Conference on Electronics,Network and Computer engineering(ICENCE 2016)

作者： Xueyan Liu Bo Yang Xuehua Zhao Yi Yang School of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Digital Media Shenzhen Institute of Information Technology China UnionPay

Many researchers have begun to study signed networks which are widely existed in real world. In the signed network, the links are labeled the positive or negative sign to represent the active or passive relation between individuals, such as trusted or distrusted relation in social networks. Communities mining is still a great challenge to the domain of signed networks because of negative links. Unlike communities of unsigned networks, positive links mainly occur in the communities and negative links tend to occur between the communities in the signed networks. Nowadays, many methods which are based on global search for signed network community have been raised, and most of these methods require the global information at each iteration. Besides, determining the number of communities is an important problem for current algorithm for the lack of priori knowledge. To address above problems, a novel community detection method based on local information, is proposed for signed networks in this paper. The proposed method mainly includes two steps. In the first step, the number of communities is determined in terms of the centrality of nodes. In the second step, the local objective function is optimized by the local information of nodes, so the global objective function can also be optimized indirectly. Finally, the communities in signed networks are efficiently found. To validate the proposed method, the comparisons are made with other methods in the synthetic and real signed networks. The experimental results indicate that communities in signed networks can be efficiently found by the proposed method.

关键词： signed networks community detection local search

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Video face recognition based on the convex hull model of kernel subspace sample selection

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Journal of computational and Theoretical Nanoscience 2016年第2期13卷 1436-1441页

作者： Wang, Yu Shen, Xuan-Jing Chen, Hai-Peng College of Computer Science and Technology Jilin University Changchun130012 China Applied Technology College Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun13001 China

This paper proposes a novel video face recognition method based on the convex hull model of kernel subspace sample selection. This method treats each video as an image set. Each image is represented as a point in the feature vector space, then each image set is characterized by a convex hull spanning its feature points. The similarity between image sets can be measured using the distance of closest approach between their convex hulls. Before using the convex hull model, the kernel subspace sample selection was used to discard points that were far from the fitted model. The sample selection on the kernel subspace reduces the influence of confounding factors such as motion and gestures. Through experiments using the YouTube Face Database, the algorithm achieved high recognition accuracy, which verified the validity of the algorithm. © 2016 American Scientific Publishers All rights reserved.

关键词： Face recognition

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The BBC News Hunter: A Novel Crawler for BBC News

The BBC News Hunter: A Novel Crawler for BBC News

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The Second International Conference of Young Computer Scientists, Engineers and Educators,ICYCSEE 2016

作者： Mingxin Wang Ning Wang Boran Wang Can Tian Yanchun Liang Guozhong Zhao Xiaosong Han College of Software Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Daqing Oilfield Personnel Development Institute CNPC

In order to distinguish and extract the topic information from other interferential information on the BBC news website for the study in social computing, the BBC News Hunter was proposed in this paper. The whole system consists of 6 subsystems, respectively named: UI, Control, Download, Analysis,Storage and Log. Numerical experiments show that satisfactory results can be obtained from the BBC news website, whose average accuracy as well as efficiency are acceptable.

关键词： BBC Crawler News HTML parser Multithread

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Link prediction in social network based on local information and attributes of nodes

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Journal of Physics: Conference Series 2017年第1期887卷

作者： Yingying Liang Lan Huang Zhe Wang College of Computer Science and Technology Jilin University 130012 Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University) Ministry of Education 130012 Changchun China Zhuhai College of Jilin University 519041 Zhuhai China

Link prediction is essential to both research areas and practical applications. In order to make full use of information of the network, we proposed a new method to predict links in the social network. Firstly, we extracted topological information and attributes of nodes in the social network. Secondly, we integrated them into feature vectors. Finally, we used XGB classifier to predict links using feature vectors. Through expanding information source, experiments on a co-authorship network suggest that our method can improve the accuracy of link prediction significantly.

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Otsu multi-thresholding based on DCG3A for medical image

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ICIC Express Letters, Part B: Applications 2016年第8期7卷 1651-1656页

作者： Fang, Zheng Shen, Xuanjing Chen, Haipeng Feng, Yuncong College of Computer Science and Technology Jilin University No. 2699 Qianjin Ave. Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Ave. Changchun130012 China

In order to solve the problem that multi-thresholding segmentation spends too much time finding the optimal solution in medical image segmentation, Otsu multi-thresholding based on dynamic combination of genetic algorithm and ant algorithm (abbreviated as DCG3A-OM) was proposed in this paper. Firstly, the algorithm uses genetic algorithm to generate preliminary partition results, and then converts them into the initial information of ant colony. Finally ant algorithm will approach to optimal values efficiently. While running genetic algorithm, the conditions of combination will be analyzed dynamically to avoid stopping genetic algorithm early or late. Besides, it increases the probability of variation and decreases the numbers of individuals which are calculated for the evolution rate of colony. The experimental results demonstrate that DCG3A-OM gets the optimal solution more quickly and efficiently than the others, and it can always get the optimal values mostly. © 2016 ICIC International.

关键词： Genetic algorithms

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Heterogeneous data mining for planning active surveillance of malaria 15

Heterogeneous data mining for planning active surveillance o...

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ASE BigData and SocialInformatics, ASE BD and SI 2015

作者： Gu, Xiao Chen, Hechang Yang, Bo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China

ISBN: (纸本)9781450337359

Malaria is one of the most serious diseases in the world, which is densely distributed in poverty and remote areas. In the prevention and control of malaria, active surveillance is more efficient than passive surveillance to discover the incidences timely and accurately. However, it is always faced with the challenge of how to allocate the limited sources, such as medical staff and medicine, appropriately so as to achieve a maximum infect. In this paper, we propose a novel method to characterize the spatiotemporal patterns of infection risk for active surveillance planning. Specifically, we propose a temporal heterogeneous diffusion network model to discover high risk areas timely, and a mixture optimization method to find high risk areas accurately. The validation on existing real-world data shows that our method outperforms the existing state-of-the-art both in terms of infection risk prediction and planning of active surveillance under different thresholds. © 2015 ACM.

关键词： Mixtures

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A Chinese Topic Crawler Focused on Customer Development

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Procedia CIRP 2016年 56卷 476-480页

作者： Tong Wu Yanchun Liang Chunguo Wu Shifeng Piao Deyin Ma Guozhong Zhao Xiaosong Han College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry Jilin University Changchun 130012 China Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041 China School of Business Jilin University Changchun 130012 China Daqing Oilfield Personnel Development Institute CNPC Daqing 163000 China

This paper presents a Chinese topic crawler focused on customer development, in order to meet the needs of users for more accurate and particular Internet information. The concept of meta-search engine is introduced, and the keywords are expanded by the ontology of HowNet. Through the web crawler, preprocessing and classification, the information on customer relations can be divided into three categories: company, platform and meaningless. Numerical experiments show that satisfactory results can be obtained in some particular information-seeking areas. The average accuracy for classification is more than 80%, which can meet customer needs in most cases.

关键词： Topic crawler Customer development Meta-search engine Semantic similarity

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