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The 2013 3rd International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2013)

作者： Qiang Zhang Mingli Zhang Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of EducationDalian University

ISBN: (纸本)9780819495587

To resist on general attacks, such as compression, noising and geometric distortion attacks is a common requirement for digital image watermarking or data hiding techniques comparing with rotation, scaling and translation(RST) attacks which are considered to be more challenging. Based on invariant centroid and LPM which is also used to analyze the angle of rotation and ratio of scaling influence after attacks of watermarked image combining with chaotic system and a novel adaptive coefficient α, this paper presents a robust and invisible digital image watermarking technology. The experiment results show that this method has high invisibility and robust, especially withstand RST attacks.

关键词： watermarking LPM α mix-chaotic system invariant centroid

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Facial animation based on feature points

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Telkomnika - Indonesian Journal of Electrical Engineering 2013年第3期11卷 1697-1706页

作者： Li, Beibei Zhang, Qiang Zhou, Dongsheng Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Liaoning Dalian China

This paper presents a hybrid method for synthesizing natural animation of facial expression with data from motion capture. The captured expression was transferred from the space of source performance to that of a 3D target face using an accurate mapping process in order to realize the reuse of motion data. The transferred animation was then applied to synthesize the expression of the target model through a framework of two-stage deformation. A local deformation technique preliminarily considered a set of neighbor feature points for every vertex and their impact on the vertex. Furthermore, the global deformation was exploited to ensure the smoothness of the whole facial mesh. The experimental results show our hybrid mesh deformation strategy was effective, which could animate different target face without complicated manual efforts required by most of facial animation approaches. © 2013 Universitas Ahmad Dahlan.

关键词： Animation

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Mining Semantic Orientation of Multiword Expression from Chinese Microblogging with Discriminative Latent Model

Mining Semantic Orientation of Multiword Expression from Chi...

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International Conference on Asian Language Processing (IALP)

作者： Xiao Sun Chengcheng Li Chenyi Tang Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China

Extracting semantic orientation of Multiword Expression, especially some newly generated Multiword Expression from internet, is an important task for sentiment analysis of web texts or other real word text as some Multiword Expressions can express more integrative sentiments than words units. This paper proposes a method contains a novel latent discriminative algorithm, which attempts to attack this problem by integrating discriminative model and latent value model. Although Chinese Multiword Expressions consist of multiple words, the semantic orientation of the Multiword Expression is not just simple integration of orientations of the component words, as some words can invert the affective orientation so the Multiword Expressions can have totally opposite semantic orientation. In order to capture the property of such Multiword Expressions, hidden semi-CRF which includes a latent valuable layer, which can be used to address dual-sequence labeling tasks synchronously, is adopted. The method is tested experimentally by adopting a manually labeled set of positive and negative Multiword Expressions from microblog or other internet resources, and the experiments have shown very promising results, which is comparable to the best value ever reported.

关键词： Semantics Dictionaries Feature extraction Computational modeling Labeling Tagging Educational institutions

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9th International Conference on intelligent computing, ICIC 2013

作者： Wang, Bing Ji, Zhiwei Advanced Research Institute of Intelligent Sensing Network Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China School of Electronics and Information Engineering Tongji University Shanghai 201804 China

ISBN: (纸本)9783642394812

The development of novel high-throughput experimental techniques makes it possible to comprehensively analyze biological data in health and disease. However, a large amount of data generated results in dramatic data-analytic challenges in discovery of 'signature' molecules, which are specific to different biological conditions (e.g. normal vs. disease, treated vs. untreated). Current statistical methods are effective only in the case their hypothesis can be matched. In this paper, we apply an ensemble statistical method to infer significant molecules. In our approach, four well-done and well-understanding statistical techniques had been used for the analysis to the experimental data, and then the results will be collected into an ensemble framework to find the high confident "significant" molecules which can distinguish the different experimental conditions. We evaluate the performance of our approach on a test dataset which deposited on GEO database with an access number of GSE45114. © 2013 Springer-Verlag.

关键词： Molecules

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Forward non-rigid motion tracking for facial MoCap

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The Visual Computer 2013年第2期30卷 139-157页

作者： Xiaoyong Fang Xiaopeng Wei Qiang Zhang Dongsheng Zhou Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education Dalian China School of Computer and Information Science Hunan Institute of Technology Hengyang China

For the existing motion capture (MoCap) data processing methods, manual interventions are always inevitable, most of which are derived from the data tracking process. This paper addresses the problem of tracking non-rigid 3D facial motions from sequences of raw MoCap data in the presence of noise, outliers and long time missing. We present a novel dynamic spatiotemporal framework to automatically solve the problem. First, based on a 3D facial topological structure, a sophisticated non-rigid motion interpreter (SNRMI) is put forward; together with a dynamic searching scheme, it cannot only track the non-missing data to the maximum extent but recover missing data (it can accurately recover more than five adjacent markers under long time (about 5 seconds) missing) accurately. To rule out wrong tracks of the markers labeled in open structures (such as mouth, eyes), a semantic-based heuristic checking method was raised. Second, since the existing methods have not taken the noise propagation problem into account, a forward processing framework is presented to solve the problem. Another contribution is the proposed method could track facial non-rigid motions automatically and forward, and is believed to greatly reduce even eliminate the requirements of human interventions during the facial MoCap data processing. Experimental results proved the effectiveness, robustness and accuracy of our system.

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Genome-wide identification of cancer-related polyadenylated and non-polyadenylated RNAs in human breast and lung cell lines

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Science China(Life Sciences) 2013年第5期56卷 503-512,1-4页

作者： ZHAO GuoGuang JIAO Fei LIAO Qi LUO HaiTao LI Hui SUN Liang BU DeChao YU KunTao ZHAO Yi CHEN RunSheng Bioinformatic Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences Department of Biochemistry and Molecular Biology Binzhou Medical College Department of Preventive Medicine School of MedicineNingbo University College of Computer Science and Technology Jilin University Bioinformatics Laboratory and National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China

Eukaryotic mRNAs consist of two forms of transcripts:poly(A)+ and poly(A),based on the presence or absence of poly(A) tails at the 3 ***(A)+ mRNAs are mainly protein coding mRNAs,whereas the functions of poly(A) mRNA are largely *** studies have shown that a significant proportion of gene transcripts are poly(A) or bimorphic(containing both poly(A)+ and poly(A) transcripts).We compared the expression levels of poly(A) and poly(A)+ RNA mRNAs in normal and cancer cell *** also investigated the potential functions of these RNA transcripts using an integrative workflow to explore poly(A)+ and poly(A) transcriptome sequences between a normal human mammary gland cell line(HMEC) and a breast cancer cell line(MCF-7),as well as between a normal human lung cell line(NHLF) and a lung cancer cell line(A549).The data showed that normal and cancer cell lines differentially express these two forms of *** ontology(GO) annotation analyses hinted at the functions of these two groups of transcripts and grouped the differentially expressed genes according to the form of their *** data showed that cell cycle-,apoptosis-,and cell death-related functions corresponded to most of the differentially expressed genes in these two forms of transcripts,which were also associated with the ***,translational elongation and translation functions were also found for the poly(A) protein-coding genes in cancer cell *** demonstrate that poly(A) transcripts play an important role in cancer development.

关键词： polyadenylation non-polyadenylation function annotation high throughput sequencing cancer bioinformatics

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Speech emotion recognition using a novel feature set

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Journal of Computational Information Systems 2013年第15期9卷 6097-6104页

作者： Wang, Kunxia Li, Lian Yang, Jing Ren, Fuji Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei 230000 China School of Electricity and Information Engineering Anhui University of Architecture Hefei 230000 China

Speech emotion recognition has attracted attentions from increased number of researchers in Psychology, Computer science, Phonetics and related disciplines. This paper discusses combining multiple features for speech emotion recognition to yield improved performance. It proposes using combination of multiple features to improve recognition performance. Using ChineseLDC emotional database, the experiment results show that a novel feature set of MFCC, fundamental frequency, Δ MFCC, Δ Δ MFCC, energy, zero-crossing ratio and amplitude can get the best performance as 95% and 84% for emotional speech recognition of four emotional state and six emotional state. © 2013 Binary Information Press.

关键词： Speech recognition

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Application of Hopfield Neural Networks Approach in Solar Energy Product Conceptual Design

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Computer Aided Drafting,Design and Manufacturing 2013年第2期23卷 48-52页

作者： XIA Zhi-qiu WANG Ling REN Na WEI Xiao-peng ZHANG Qiang ZHAO Ting-ting Department of Computer Science Collage of EngineeringUniversity of Leicester The archives Dalian University Department of Information Engineering Shenyang Institute of Engineering Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education

A new product conceptual design approach is put forward based on Hopfield neural networks models. By research on the mechanisms of Hopfield neural networks, the associative simulation approaches are proposed. The approach is given by Hebb learn- ing law, Hopfield neural networks and crossover and mutation. The calculating models and the calculating formulas for the concep- tual design are put forward. Finally, an example for the conceptual design of a solar energy lamp is given. The better results are ob- tained in the conceptual design.

关键词： Hopfield neural networks conceptual design solar energy

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Combine sentiment lexicon and dependency parsing for sentiment classification

Combine sentiment lexicon and dependency parsing for sentime...

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IEEE/SICE International Symposium on System Integration

作者： Changqin Quan Xiquan Wei Fuji Ren Anhui Province Key laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China School of Computer and Information Hefei University of Technology Hefei China

With the rapid development of internet technology and e-commerce sites, there are more and more products review in the network. People are willing to make a survey on the internet before purchasing the products. The automatic identification of the sentiment of comments is necessary. We propose a method, which combines sentiment lexicon and dependency parsing to determine the sentiment orientation and the positive or negative attitudes of the topic. The dependency parsing is used to get the objects and sentiment words. Then the allocation of weights is done, and finally the positive and negative results of the products evaluation are concluded. Experiments show that the validity and efficiency of the proposed method.

关键词： Semantics Syntactics Benchmark testing Educational institutions Dictionaries Internet Computational modeling

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Reduce the dimensions of emotional features by principal component analysis for speech emotion recognition

Reduce the dimensions of emotional features by principal com...

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IEEE/SICE International Symposium on System Integration

作者： Changqin Quan Dongyu Wan Bin Zhang Fuji Ren AnHui Province Key laboratory of Affective Computing and Advanced Intelligent Machine HeFei University of Technology HeFei China The School of Computer and Information HeFei University of Technology HeFei China

In this paper, the principal component analysis (PCA) is applied to speech emotion recognition for improving the accuracy of the system. The traditional prosodic features like pitch-related features and formant-related features are extracted from the Berlin speech database [7] and a Chinese database. These collected feature data is processed by PCA to remove the irrelevant information. After that, three kinds of features including the processed features by PCA, unprocessed features and other speech-related features are used to train a SVM classifier. And six emotions are tested in the experiment. The classification accuracy of the processed features by PCA is about 3.1% higher than the unprocessed features and about 17.6% higher than the MFCC features when using 240 utterances. The recognition accuracies among different emotions in both databases are also presented in the study.

关键词： Speech Speech recognition Principal component analysis Emotion recognition Feature extraction Accuracy Databases

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