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1st IAPR TC3 Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2012

作者： Wang, Shangfei Shen, Peijia Liu, Zhilei Key Lab. of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China HeFei AnHui 230027 China

ISBN: (纸本)9783642370809

By using the knowledge of facial structure and temperature distribution, this paper proposes an automatic eye localization method from infrared thermal images. A facial structure consisting of 15 sub-regions is proposed to extract Haar-like features. Eight classifiers are learned from the features selected by Adaboost algorithm for left and right eye, respectively. A vote strategy is used to find the most likely eyes. Experimental results on the NVIE and Equinox databases show the effectiveness of our approach. © 2013 Springer-Verlag.

关键词： Adaptive boosting

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Real-Valued Negative Databases 12

Real-Valued Negative Databases

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12th European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL 2013

作者： Zhao, Dongdong Luo, Wenjian School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230027 China Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei Anhui230027 China

ISBN: (纸本)9780262317092

The negative database (NDB) is the negative representation of original data. Existing work has demonstrated that NDB can be used to preserve privacy and hide information. However, most work about NDB is based on binary representation. In some applications which are naturally descripted in real-valued space, the binary negative database is hard to be applied appropriately. Therefore, the real-valued negative database is proposed in this paper, and reversing the real-valued negative database is proved to be an NP-hard problem. Moreover, an effective algorithm for generating real-valued negative databases is given. Finally, an example of applying the realvalued negative database to the privacy-preserving data publication is descripted, and it shows that the real-valued negative database is valuable in practice. © 2013 Proceedings of the 12th European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL 2013. All rights reserved.

关键词： Computational complexity

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A qualitative modeling and simulation method based on cloud model

A qualitative modeling and simulation method based on cloud ...

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2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013

作者： Shao, Chenxi Sun, Kelu Zhenzhong, Shao Computer Science and Technology Dept. University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China Jianghuai Automobile Co. Ltd. Hefei 230022 China

ISBN: (纸本)9781467358149

Qualitative simulation is effective methods processing incomplete knowledge, how to represent qualitative knowledge and reduce spurious behaviors is its research emphasis. Contraposing the limitations of existing methods representing qualitative knowledge and filtering spurious behaviors, an approach is proposed to qualitatively model and simulate on the basis of normal cloud model. In this research, the cloud numeric characteristics and cloud measurement are introduced into this approach;consequently, it can more accurately assess the qualitative knowledge, and expand the variables expression and quantity space;meanwhile, it is constructed the means of modeling and simulation by adopting more amply portraying the system status, more intuitively transforming status and more accurately filtering spurious behaviors. Furthermore, in simulation algorithm, cloud measurement indices are employed to significantly select the successor status which can not be determined by existing constraint means. The results of simulation example demonstrate that our approach effectively improves the ability to filter spurious behaviors, and it is a feasible method. © 2013 IEEE.

关键词： Cloud computing

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Accurate classification of time series based on a new robust similarity measure

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Journal of Computational Information Systems 2013年第9期9卷 3521-3528页

作者： He, Xiaoxu Shao, Chenxi Xiong, Yan School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

Among different time series data mining, time series classification is one of the most important aspects. How to design a suitable similarity measure of similarity is a burning issue for accurate time series classification. In this paper, we propose a new similarity measure based on feature exaction of the original time series. The new similarity measure is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted three sets of experiments, testing effectiveness on different time series datasets from a wide variety of application domains. Experimental evaluations show that the proposed similarity measure can tolerate the most distortions than other three typical similarity;besides, the new classifier is not only superior to other conventional classifier, but also more excellent in 1nn classifier than other similarity measure. © 2013 by Binary Information Press.

关键词： Data mining

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Package of the vector math library based on the sunway processor

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Ruan Jian Xue Bao/Journal of software 2014年 25卷 70-79页

作者： Xie, Qing-Chun Zhang, Yun-Quan Li, Yan Pang, Ren-Bo Wu, Zai-Long Lu, Yong-Quan Gao, Peng-Dong High Performance Computing Center Communication University of China Beijing100024 China Laboratory of Parallel Computing Institute of Software The Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology The Chinese Academy of Sciences Beijing100190 China Department of Computer and Network National Marine Environmental Forecasting Center Beijing100081 China School of Information Sceience and Technology The Ocean University of China Qingdao266100 China

This paper first introduces the SIMD (single instruction multiple data) extension technology and presents three ways to use SIMD instructions. It is considered that calling the third party library, which is optimized for the target platform by using those instructions, is the best way to benefit the developers. Next, it introduces the China-developed SW-1600 CPU, and a software package called SW-VML, which consists of many mathematical functions, by using the SIMD extension technology. In order to solve the additional overhead caused by unaligned address access and transformation between vector and scalar array, the paper provides some performance optimized methods, such as aligned address access, simplifying vector condition branch and loop unrolling. An upgrade to SW-VML is also offered to support multi-thread with OpenMP. Finally, functions in the package are tested using arrays of different sizes on SW-1600, and the test results show that high performance is achieved with the technology of the SIMD vectorization. Compared with the traditional methods of the scalar calculation, the average speedup is up to 2.06. The performance speedup of package using 4 threads is up to 2.26 compared to using a single thread. SW-VML is a common vector function package for domestic Sunway processor series, and it can be used as a basic toolkit which is beneficial to high performance computing on Sunway platform. ©2014 ISCAS.

关键词： Vectors

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Sparse learning for face recognition with social context

Sparse learning for face recognition with social context

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4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013

作者： Gui, Jie Mi, Jian-Xun Lei, Ying-Ke Wang, Hong-Qiang State Key Laboratory of Software Engineering Wuhan University 430072 China Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei Anhui China State Key Laboratory for Novel Software Technology Nanjing University China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen Guangdong Province China Electronic Engineering Institute Hefei Anhui 230037 China

ISBN: (纸本)9783642420566

Face recognition in uncontrolled environments, such as large pose variations, and extreme ambient illumination and expressions is a challenging task for traditional face recognition methods. Some recent works show that context information such as clothes and social relationships is very important for solving the problem. Furthermore, sparse representation-based method is very robust for face occlusion and pixel contamination. In this paper, the authors propose a sparse learning framework for face recognition with social context. First, sparse representation based dimensionality reduction method is used to find the low dimension representation of the face images. Second, sparse representation based classification is utilized to classify test face images as known label and unknown label. Third, for test images classified as unknown label, social context descriptor is constructed according to co-existence information. Finally, based on social context descriptor and the low dimension representation of the test images classified as unknown label, sparse representation based clustering is adopted to perform face recognition. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Face recognition

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Estimating properties of chaotic system based on S-NURBS resampling method

Estimating properties of chaotic system based on S-NURBS res...

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International Conference on Measurement, Information and Control (MIC)

作者： Chenxi Shao Qingqing Liu Tingting Wang Binghong Wang Anhui Province Key Laboratory of Software in Computing and Communication University of Science and Technology of China Hefei China Computer Science and Technology Department University of Science and Technology of China Hefei China

ISBN: (纸本)9781479913916

Physical properties of the chaotic system play important roles on studying the innate character and deciding the practical predictability of the dynamics. For a short piece of undersampled chaotic signals, it is very hard to abstract the physical properties of the signal source from the sequence. In this paper, we model this type of data with global S-NURBS method to reconstruct a smooth trajectory and resample the trajectory to get enough series. In this way, the problem of estimating the physical properties from small undersampled data is turned to the work of calculating the properties of the resampled time series. The new interpolation method is named as S-NURBS resampling method, and the simulation experiment demonstrates that the new method has a good performance in studying physical systems from the observed time series.

关键词： Time series analysis Interpolation Trajectory Chaos Splines (mathematics) Surface topography Surface reconstruction

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A qualitative modeling and simulation method based on cloud model

A qualitative modeling and simulation method based on cloud ...

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International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)

作者： Chenxi Shao Kelü Sun Zhenzhong Shao Computer Science and Technology Department University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Software in Computing and Communication Hefei China Jianghuai Automobile Company Limited Hefei China

ISBN: (纸本)9781467358125

Qualitative simulation is effective methods processing incomplete knowledge, how to represent qualitative knowledge and reduce spurious behaviors is its research emphasis. Contraposing the limitations of existing methods representing qualitative knowledge and filtering spurious behaviors, an approach is proposed to qualitatively model and simulate on the basis of normal cloud model. In this research, the cloud numeric characteristics and cloud measurement are introduced into this approach; consequently, it can more accurately assess the qualitative knowledge, and expand the variables expression and quantity space; meanwhile, it is constructed the means of modeling and simulation by adopting more amply portraying the system status, more intuitively transforming status and more accurately filtering spurious behaviors. Furthermore, in simulation algorithm, cloud measurement indices are employed to significantly select the successor status which can not be determined by existing constraint means. The results of simulation example demonstrate that our approach effectively improves the ability to filter spurious behaviors, and it is a feasible method.

关键词： Uncertainty Coordinate measuring machines Computational modeling Entropy Measurement uncertainty Numerical models Size measurement

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A mesh editing algorithm based on Gaussian curvature

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Journal of Information and Computational Science 2013年第10期10卷 2881-2890页

作者： Wang, Feng Huang, Zhangjin Guo, Licai School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Laboratory of Software in Computing and Communication Hefei 230027 China USTC-SICT Joint Laboratory on Network and Communication Hefei 230027 China

Mesh editing plays an important role in the design of mesh models. The As-Rigid-As-Possible (ARAP) surface editing algorithm is likely to produce degenerated results in sharp regions. This degeneration affects the quality of results. This paper proposes a new mesh editing algorithm based on the ARAP surface modeling algorithm. By setting constraints on triangle transformations, this algorithm has better detail-preserving properties. With the constraints on the ratios among scaling factors, we can reduce the skinny triangles produced by the ARAP algorithm. The experimental results showed that this algorithm improved robustness and detail-preservation over the As-Rigid-As-Possible algorithm. Copyright © 2013 Binary Information Press.

关键词： Mesh generation

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