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IEEE International Conference on Robotics and Biomimetics

作者： Changqin Quan Yao Qian Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China P.R. The Japan Federation of Engineering Societies University of Tokushima Shinkura-cho Tokushima Japan

ISBN: (纸本)9781479973989

With the development of artificial intelligence and pattern recognition, facial expression recognition plays a more and more important role in intelligent human-computer interaction. In this paper, we present a model named K-order emotional intensity model (K-EIM) which is based on K-Means clustering. Different from other related works, the proposed approach can quantify emotional intensity in an unsupervised way. And then the output from K-EIM is encoded. The coding results are used for the dynamic facial expression recognition. The experiment is conducted on Cohn-Kanade facial expression database and the support vector machine classifier is used for facial expression classification. This method achieved a dynamic facial expression recognition accuracy of 88.32% which suggest that the proposed method shows better performance and proves its validity. Moreover, effect of different segments of emotional intensity is also discussed in the paper.

关键词： Face recognition Encoding Accuracy Emotion recognition Principal component analysis Training Feature extraction

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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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A Connected Component-Based Distributed method for overlapping community detection

A Connected Component-Based Distributed method for overlappi...

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International Conference on computing and Networking Technology (ICCNT)

作者： Tian Chen Kai Liu Xin Yi Dandan Shen Wei Wang Fuji Ren Jun Liu Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei AnHui Agricultural University Hefei School of Computer and Information Hefei University of Technology Hefei The University of Tokushima Japan

ISBN: (纸本)9781479926978

In order to find the overlapping community structure more quickly and accurately in complex network, this paper proposes a Connected Component-Based Distributed method (CCBD) for overlapping community detection. CCBD first divides edges set in the network into smaller one. Next, it seeks out connected component using distributed platforms and gives a serials number to them according to certain rules. Then, it classifies these connected components according to the serials number. Task nodes determine whether any two connected components and non-propagating edges can be connected to become a larger community. Finally, CCBD negotiates a new serials name for the new community. Through repeated iterations, we get the community structure in the network. Nodes belong to multiple communities are overlapping nodes. Experiment results show that CCBD has higher time efficiency benefiting from distributed computing. Moreover, the quality of communities detected by CCBD surpasses those found by other algorithms.

关键词： Communities Algorithm design and analysis Educational institutions Complex networks Clustering algorithms Distributed computing Complexity theory

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Plant growth simulation algorithm for solving bilevel symbiosis optimization between manufacturing enterprise and logistics enterprise

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ICIC Express Letters 2014年第2期8卷 629-636页

作者： Li, Tong Jiang, Yufei Chen, Chouyong Ji, Tongcai Management College Hangzhou Dianzi University No. 2 St Hangzhou 310018 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University No. 10 Xuefu St. Jinzhou New District Dalian 116622 China

An intelligence optimization algorithm for solving nonlinear bilevel programming is proposed herein. In this algorithm, the upper solution space and lower reaction set of bilevel programming are looked as two growth environments of plants. Then the plant system evolution style based on growth regulation and the probability growth model based on plant phototropism theory are established. The optimization model combined with above two has realized the evolution of artificial plant from initial status to whole final status (that means no new branch growing), thus the optimal solutions of bilevel programming can be found. The algorithm herein shows its high accuracy and strong astringency. This article solves the problem of bilevel symbiosis optimization between manufacturing enterprise and logistics enterprise by using this algorithm, and the result shows that Plant Growth Simulation Algorithm (PGSA) has good effectiveness and feasibility. © 2014 ISSN 1881-803X.

关键词： Optimization

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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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Finding Emotional Focus for Emotion Recognition at Sentence Level

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电子学报(英文版) 2013年第1期22卷 99-103页

作者： QUAN Changqin REN Fuji Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei 230009 China Faculty of Engineering The University of Tokushima Tokushima Japan

Emotion recognition at sentence level is one of the fundamental problems of textual emotion un- derstanding. Based on the observation that sentence emo- tional focus can be expressed by some clauses in this sen- tence, this paper proposes to find the emotional focus for sentence emotion recognition. For the sake of breaking through the problems brought about by depending on emo- tion lexicons, we first recognize word emotions in a sen- tence based on Maximum entropy model. And then homo- geneous Markov model is built for clause emotion recogni- tion; After that, a strategy based on emotion selection is proposed for a sentence with multiple clauses, and genetic algorithm is used for clause selection by textual feature weighting. The experimental results show that, comparing with the baseline, there are 9.1% and 3.6% improvement respectively for two different evaluations. It is demon- strated that finding emotional focus by clause selection is able to improve the performance of sentence emotion recognition significantly.

关键词：情感识别句子层面聚焦马尔可夫模型最大熵模型特征加权遗传算法

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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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Face recognition based on independent component analysis image reconstruction and linear discriminant analysis

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ICIC Express Letters 2014年第9期8卷 2457-2462页

作者： Zhou, Changjun Wang, Lan Lv, Ling Zheng, Xuedong Wei, Xiaopeng Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University No. 10 Xuefu Street Jinzhou Xinqu Dalian 116622 China Department of Mechanical Engineering Dalian Institute of Science and Technology No. 999-26 Bingang Road Lvshun District Dalian 116052 China

Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA) are two conventional methods in feature extraction. In this paper, we propose a novel face recognition method which is based on ICA image reconstruction, LDA and SVM. Firstly, we use the inner-classes covariance matrix as generating matrix, and then we can obtain eigenvectors from each person and the reconstructed images through ICA method. Secondly, the residual images are computed by subtracting reconstructed images from original face images. Furthermore, the residual images are applied by LDA to obtain the coefficient matrices. Finally, the features are utilized to train and test SVMs for face recognition. The experimental results on the ORL face database are presented to demonstrate the efficacy of the proposed method. © 2014 ISSN 1881-803X.

关键词： Image reconstruction

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Improved hybrid optimization algorithm for 3D protein structure prediction

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Journal of molecular modeling 2014年第7期20卷 2289页

作者： Changjun Zhou Caixia Hou Xiaopeng Wei Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian China

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

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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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