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30th ACM Conference on Human Factors in Computing Systems, CHI 2012

作者： Obrist, Marianna Roto, Virpi Vermeeren, Arnold Väänänen-Vainio-Mattila, Kaisa Law, Effie Lai-Chong Kuutti, Kari Culture Lab School of Computing Science Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Aalto University School of Art Design and Architecture P.O.Box 31000 Aalto00076 Finland Delft University of Technology Fac. of Ind. Design Engineering Landbergstraat 15 NL CE Delft2628 Netherlands Tampere University of Technology Unit of Human-Centered Tech P.O.Box 589 TampereFI-33101 Finland University of Leicester Department of Computer Science LeicesterLE1 7RH United Kingdom University of Oulu Department of Information Processing Science Oulu90014 Finland

In this paper we point out the relevance of and the need for a theoretical discussion around UX research and practice. Although there is a good coverage of methodological and design related topics in the HCI literature, there is still a lack of theoretical focus in the rapidly increasing work on user experience (UX). We analyzed 122 individual items on theories collected in a CHI'11 special interest group session on UX theories and theoretical frameworks. The data set was filtered and categorized in several iterations, resulting in 56 items distributed over 7 major theory categories and related to 9 relevant disciplines. The categories are an initial mapping of the field and point towards the directions for further conceptual and theoretical clarification. Our results help to explore the multi-disciplinary nature of UX and to build a more solid foundation for UX research and practice. © 2012 Authors.

关键词： Group theory

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An approach to content-independent feature extraction for Chinese-Korean spoken language identification

An approach to content-independent feature extraction for Ch...

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2011 IEEE International Conference on computer science and Automation Engineering, CSAE 2011

作者： Lu, Shi-Dan Cui, Rong-Yi Intelligent Information Processing Lab. Department of Computer Science and Technology Yanbian University Yanji Yanji China

ISBN: (纸本)9781424487257

A new classification feature extraction method for Chinese-Korean spoken language identification was proposed in this paper. Firstly, speech signal was divided into frame serial and the number of frames was counted. Furthermore, the ratio between short-time zero-crossing rate and short-time energy, i.e. short-time-frequency-energy-ratio (STFER), was computed, and the mean STFER per frame was treated as the classification feature to implement Chinese-Korean spoken language identification. Finally, the classification threshold was determined using information gain. Experimental results show that the proposed method is simpler than MFCC feature parameters and has better ability to identify spoken language with lower complexity, can be adopted in preprocessing procedure of language recognition. © 2011 IEEE.

关键词： Feature extraction

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The criterion of Korean written language, policy regulations of Korean language and the 21st century information technology standards

The criterion of Korean written language, policy regulations...

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2011 4th International Conference on on Intelligent Networks and Intelligent Systems, ICINIS 2011

作者： Xuan, Long-Yun Cui, Rong-Yi Lu, Shi-Dan Yanbian North-Asia Information Technology Institute Yanji China Intelligent Information Processing Lab. Department of Computer Science and Technology Yanbian University Yanji China

Criterion, standard and policy of Korean language have played a very important role as Autonomy regulations in many aspects, especially input standards of computer using Korean language. So it is very necessary to inv... 详细信息

ISBN: (纸本)9780769545431

关键词： Laws and legislation

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Speech signal representation via dictionary learning in STFT transformed domain

Speech signal representation via dictionary learning in STFT...

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International Conference on Multimedia and Signal processing

作者： Xu, Jing Liu, Benyong Liao, Xiang College of Computer Science and Information Institute of Intelligent Information Processing Guizhou University Guiyang China Provincial Key Lab of Audio-Visual Material Examination Guizhou Public Security Department Guiyang China

ISBN: (纸本)9780769543567

Speech signal representation is basic step for speech signal processing. We discuss an overcomplete dictionary learning algorithm to sparsely represent speech signal in short-time Fourier transform (STFT) transformed domain, wherein a speech signal is transformed into time-frequency domain by STFT, followed by estimating the presentation coefficients using exiting matching pursuit algorithm such as order recursive matching pursuit (ORMP), and dictionary learning is completed using the currently proposed algorithm called recursive least squares dictionary learning algorithm (RLS-DLA), in the complex domain of STFT. To illustrate the application feasibility, the presented method are experimentally compared with ORMP and RLS-DLA algorithms directly applied in the time domain, the experimental results show good performance of discussed algorithm in speech signal representation with respect to mean representation error.© 2011 IEEE.

关键词： Speech communication

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Improving stock market prediction by integrating both market news and stock prices

Improving stock market prediction by integrating both market...

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22nd International Conference on Database and Expert Systems Applications, DEXA 2011

作者： Li, Xiaodong Wang, Chao Dong, Jiawei Wang, Feng Deng, Xiaotie Zhu, Shanfeng Department of Computer Science City University Hong Kong Hong Kong Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai 200433 China School of Computer State Key Lab of Software Engineering Wuhan University Wuhan 430072 China Department of Computer Science University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9783642230905

Stock market is an important and active part of nowadays financial markets. Addressing the question as to how to model financial information from two sources, we focus on improving the accuracy of a computer aided prediction by combining information hidden in market news and stock prices in this study. Using the multi-kernel learning technique, a system is presented that makes predictions for the Hong Kong stock market by incorporating those two information sources. Experiments were conducted and the results have shown that in both cross validation and independent testing, our system has achieved better directional accuracy than those by the baseline system that is based on single one information source, as well as by the system that integrates information sources in a simple way. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Forecasting

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Projection kernel regression/or image registration and fusion in video-based criminal investigation

Projection kernel regression/or image registration and fusio...

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2011 International Conference on Multimedia and Signal processing, CMSP 2011

作者： Liu, Benyong Zhang, Ling Liao, Xiang Department of Computer Science Institute ofintelligent Information Processing Guizhou University Guiyang 550025 China Provincial Key Lab of Audio-Visual Material Examination Guizhou Public Security Department Guiyang 550001 China

ISBN: (纸本)9780769543567

A projection kernel regression framework is set and applied for image registration and fusion in video-based criminal investigation. In image registration, a dominant point is defined to capture the local variation property of an image when the resolution is very low and thus most algorithms proposed till now for control point extraction fail. The order relationship between the sorted dominant point sequences, extracted respectively from the reference image and the target image, matches the two images, while the location relationship between the two sequences determines the input-output pairs in projection kernel regression, for approximation of the coordinate mapping function of the reference image and the target image. In image reconstruction, an analogue image is firstly estimated by the projection kernel regression using all of the registered images, and then re-sampled to obtain an enlarged image with arbitrary resolution. Experimental results on a section of surveillance video for criminal investigation show that the presented method is effective in solving the image registration and fusion problems in the aforementioned case.© 2011 IEEE.

关键词： Image reconstruction

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Mining dynamical frequent itemsets based on ant colony algorithm

Mining dynamical frequent itemsets based on ant colony algor...

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IEEE International Conference on computer science and Automation Engineering (CSAE)

作者： Chen, Shengbing Wang, Xiaofeng Wang, Xiaofang Key Lab of Network and Intelligent Information Processing Hefei University Hefei 230601 China Department of Computer Science and Technology Hefei University Hefei 230601 China Intelligent Computing Lab Institute of Intelligent Machines Chinese Academy of Sciences Hefei 230031 China Department of Computer Artillery Academy Hefei 230031 China

ISBN: (纸本)9781424487257

Mining frequent itemsets is a core problem in many data mining tasks, most existing works on mining frequent itemsets can only capture the long-term and static frequency itemsets, they do not suit the task whose frequent itemsets often change. Using the theory of ant colony algorithm, we proposed a new method for mining dynamical frequent itemset(called AC-MFI). The method considers the item of transaction as a node in the path, takes the itemset as a path, and takes each transaction as a foraging behavior. According to the pheromone updating policy of ant colony algorithm, AC-MFI mines dynamical frequent itemsets from transaction data stream. Experiment results show that the method is valid and practicable. © 2011 IEEE.

关键词： Data mining

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An approach to content-independent feature extraction for Chinese-Korean spoken language identification

An approach to content-independent feature extraction for Ch...

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IEEE International Conference on computer science and Automation Engineering (CSAE)

作者： Shi-Dan Lu Rong-Yi Cui Intelligent Information Processing Lab. Department of Computer Science & Technology Yanbian University Yanji China

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Mean first-passage time for random walks on undirected networks

Mean first-passage time for random walks on undirected netwo...

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作者： Zhang, Zhongzhi Julaiti, Alafate Hou, Baoyu Zhang, Hongjuan Chen, Guanrong School of Computer Science Fudan University Shanghai 200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai 200433 China Department of Mathematics College of Science Shanghai University Shanghai 200444 China Department of Electronic Engineering City University of Hong Kong Hong Kong Hong Kong

In this paper, by using two different techniques we derive an explicit formula for the mean first-passage time (MFPT) between any pair of nodes on a general undirected network, which is expressed in terms of eigenvalues and eigenvectors of an associated matrix similar to the transition matrix. We then apply the formula to derive a lower bound for the MFPT to arrive at a given node with the starting point chosen from the stationary distribution over the set of nodes. We show that for a correlated scale-free network of size N with a degree distribution P(d) ∼ d-γ, the scaling of the lower bound is N1-1/γ. Also, we provide a simple derivation for an eigentime identity. Our work leads to a comprehensive understanding of recent results about random walks on complex networks, especially on scale-free networks. © 2011 EDP sciences, SIF, Springer-Verlag Berlin Heidelberg.

关键词： Complex networks

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Evolutionary method for finding communities in bipartite networks

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Physical Review E 2011年第6期83卷 066120-066120页

作者： Weihua Zhan Zhongzhi Zhang Jihong Guan Shuigeng Zhou Department of Computer Science and Technology Tongji University 4800 Cao’an Road Shanghai 201804 China School of Computer Science Fudan University Shanghai 200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai 200433 China

An important step in unveiling the relation between network structure and dynamics defined on networks is to detect communities, and numerous methods have been developed separately to identify community structure in different classes of networks, such as unipartite networks, bipartite networks, and directed networks. Here, we show that the finding of communities in such networks can be unified in a general framework—detection of community structure in bipartite networks. Moreover, we propose an evolutionary method for efficiently identifying communities in bipartite networks. To this end, we show that both unipartite and directed networks can be represented as bipartite networks, and their modularity is completely consistent with that for bipartite networks, the detection of modular structure on which can be reformulated as modularity maximization. To optimize the bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA), which is shown to be especially efficient for community structure detection. The high efficiency of the MAGA is based on the following three improvements we make. First, we introduce a different measure for the informativeness of a locus instead of the standard deviation, which can exactly determine which loci mutate. This measure is the bias between the distribution of a locus over the current population and the uniform distribution of the locus, i.e., the Kullback-Leibler divergence between them. Second, we develop a reassignment technique for differentiating the informative state a locus has attained from the random state in the initial phase. Third, we present a modified mutation rule which by incorporating related operations can guarantee the convergence of the MAGA to the global optimum and can speed up the convergence process. Experimental results show that the MAGA outperforms existing methods in terms of modularity for both bipartite and unipartite networks.

关键词： Community structure Modularity Azanza Network TWO-COMPONENT

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