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12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013

作者： Yu, Han Shen, Zhiqi Miao, Chunyan An, Bo School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781627487665

A crowdsourcing system is a useful platform for utilizing the intelligence and skills of the mass. Nevertheless, like any open system that involves the exchange of things of value, selfish and malicious behaviors exist in crowdsourcing systems and need to be mitigated. Trust management has been proven to be a viable solution in many systems. However, a major difference between crowdsourcing systems and existing trust models designed for multi-agent systems is that human trustees have limited task processing capacity per unit time compared to an intelligent agent program. This paper recognizes a problem in current trust-aware decision-making methods for task assignment in crowdsourcing platforms. On the one hand, trust-based methods over-assign tasks to trusted workers, while on the other hand, workload-based solutions do not give sufficient guarantees on the quality of work. The proposed solution, the social welfare optimizing reputation-aware decision-making (SWORD) approach, strikes a balance between the two and is shown through extensive simulations to significantly improve social welfare of crowdsourcing platforms compared to related work. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Decision making

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Dual-loop Control Strategy for Grid-connected Inverter with LCL Filter

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Energy and Power Engineering 2013年第4期5卷 97-101页

作者： Qiubo Peng Hongbin Pan Yong Liu Lidan Xiang Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan China

As to the concrete topology of three-phase LCL type grid-connected inverter with damping resistance, mathematical model was deduced in detail, using method of equivalent transformation to the structure diagram, damping resistance was virtualized, mathematical model under the DQ frame that can realize decoupling control was established, a dual-loop control strategy for grid-connected inverter with LCL filter was proposed, the system stability was analyzed and the design method of controller was given. The proposed method overcame the flaws of loss increase, efficiency reduce and cost increase which were caused by damping resistance in LCL type grid-connected inverter, the system efficiency and power supply quality of the output were improved. Feasibility and effectiveness of the new method were validated by simulation and experimental results.

关键词： Virtual Damping LCL Filter Three-phase Grid-connected Inverter Decoupling Control

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Iterative transformation of annotation guidelines for constituency parsing

Iterative transformation of annotation guidelines for consti...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Li, Xiang Jiang, Wenbin Lü, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Centre for Next Generation Localisation Faculty of Engineering and Computing Dublin City University Ireland

ISBN: (纸本)9781937284510

This paper presents an effective algorithm of annotation adaptation for constituency treebanks, which transforms a treebank from one annotation guideline to another with an iterative optimization procedure, thus to build a much larger treebank to train an enhanced parser without increasing model complexity. Experiments show that the transformed Tsinghua Chinese Treebank as additional training data brings significant improvement over the baseline trained on Penn Chinese Tree-bank only. © 2013 Association for Computational Linguistics.

关键词： Iterative methods

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A novel method for geographical social event detection in social media 13

A novel method for geographical social event detection in so...

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5th International Conference on Internet Multimedia computing and Service, ICIMCS 2013

作者： Gao, Xingyu Cao, Juan He, Qin Li, Jintao Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450322522

Popular microblogging service has attracted much attention around the world recently. With tremendous amount of tweets published each day, social event detection is becoming one of the most challenging research topics, especially for geographical social event. This paper proposes a novel geographical social event detection approach by mining geographical temporal pattern and analyzing the content of tweets. For the tweets published by users in the geographical area at each time unit, we first estimate its geographical temporal pattern based on the alternation regularity of tweets. Furthermore, we discovery the unusual geographical area by more frequent alternation of tweet count, and adopt adaptive K-means clustering algorithm for the tweets published in the geographical area. Finally, the geographical social event is detected by the number of the tweets in the cluster. We implement and validate our approach on realistic data collected from real-world social media websites. Experimental results show that our method can detect geographical social event with better performance than traditional methods. In addition, vivid demonstration of geographical social event can be effectively performed by our method. © 2013 ACM.

关键词： Clustering algorithms

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Granular computing in visual haze-free task

Granular computing in visual haze-free task

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IEEE International Conference on Granular computing (GRC)

作者： Hong Hu Liang Pang Dongping Tian Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9781479912803

In the past decade, granular computing (GrC) has been an active topic of research in machine learning and computer vision. However, the granularity division is itself an open and complex problem. Deep learning, at the same time, has been proposed by Geoffrey Hinton, which simulates the hierarchical structure of human brain, processes data from lower level to higher level and gradually composes more and more semantic concepts. The information similarity, proximity and functionality constitute the key points in the original insight of granular computing proposed by Zadeh. Many GrC researches are based on the equivalence relation or the more general tolerance relation, either of which can be described by some distance functions. The information similarity and proximity depended on the samples distribution can be easily described by the fuzzy logic. From this point of view, GrC can be considered as a set of fuzzy logical formulas, which is geometrically defined as a layered framework in a multi-scale granular system. The necessity of such kind multi-scale layered granular system can be supported by the columnar organization of the neocortex. So the granular system proposed in this paper can be viewed as a new explanation of deep learning that simulates the hierarchical structure of human brain. In view of this, a novel learning approach, which combines fuzzy logical designing with machine learning, is proposed in this paper to construct a GrC system to explore a novel direction for deep learning. Unlike those previous works on the theoretical framework of GrC, our granular system is abstracted from brain science and information science, so it can be used to guide the research of image processing and pattern recognition. Finally, we take the task of haze-free as an example to demonstrate that our multi-scale GrC has high ability to increase the texture information entropy and improve the effect of haze-removing.

关键词： Visualization Organizations Fuzzy logic information entropy Pattern recognition Vectors Entropy

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A directed continuous time random walk model with jump length depending on waiting time

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The Scientific World Journal 2014年第1期2014卷 182508-182508页

作者： Shi, Long Yu, Zuguo Mao, Zhi Xiao, Aiguo Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China Institute of Mathematics and Physics Central South University of Forest and Technology Changsha Hunan 410004 China School of Mathematical Sciences Queensland University of Technology Brisbane QLD 4001 G.P.O. Box 2434 Australia

In continuum one-dimensional space, a coupled directed continuous time random walk model is proposed, where the random walker jumps toward one direction and the waiting time between jumps affects the subsequent jump. In the proposed model, the Laplace-Laplace transform of the probability density function P (x, t) of finding the walker at position x at time t is completely determined by the Laplace transform of the probability density function (t) of the waiting time. In terms of the probability density function of the waiting time in the Laplace domain, the limit distribution of the random process and the corresponding evolving equations are derived. © 2014 Long Shi et al.

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Stem translation with affix-based rule selection for agglutinative languages

Stem translation with affix-based rule selection for aggluti...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Wangt, Zhiyang Lü, Yajuan Sun, Meng Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Centre for Next Generation Localisation Faculty of Engineering and Computing Dublin City University Ireland

ISBN: (纸本)9781937284510

Current translation models are mainly designed for languages with limited morphology, which are not readily applicable to agglutinative languages as the difference in the way lexical forms are generated. In this paper, we propose a novel approach for translating agglutinative languages by treating stems and affixes differently. We employ stem as the atomic translation unit to alleviate data spare-ness. In addition, we associate each stem-granularity translation rule with a distribution of related affixes, and select desirable rules according to the similarity of their affix distributions with given spans to be translated. Experimental results show that our approach significantly improves the translation performance on tasks of translating from three Turkic languages to Chinese. © 2013 Association for Computational Linguistics.

关键词： Translation (languages)

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Triplex transfer learning: Exploiting both shared and distinct concepts for text classification 13

Triplex transfer learning: Exploiting both shared and distin...

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6th ACM International Conference on Web Search and Data Mining, WSDM 2013

作者： Zhuang, Fuzhen Luo, Ping Du, Changying He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Hewlett-Packard Labs. Beijing China Graduate University Chinese Academy of Sciences China

ISBN: (纸本)9781450318693

Transfer learning focuses on the learning scenarios when the test data from target domains and the training data from source domains are drawn from similar but different data distribution with respect to the raw features. Some recent studies argued that the high-level concepts (e.g. word clusters) can help model the data distribution difference, and thus are more appropriate for classification. Specifically, these methods assume that all the data domains have the same set of shared concepts, which are used as the bridge for knowledge transfer. However, besides these shared concepts each domain may have its own distinct concepts. To address this point, we propose a general transfer learning framework based on non-negative matrix tri-factorization which allows to explore both shared and distinct concepts among all the domains simultaneously. Since this model provides more flexibility in fitting the data it may lead to better classification accuracy. To solve the proposed optimization problem we develop an iterative algorithm and also theoretically analyze its convergence. Finally, extensive experiments show the significant superiority of our model over the baseline methods. In particular, we show that our method works much better in the more challenging tasks when distinct concepts may exist. © 2013 ACM.

关键词： Iterative methods

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Adaptive Particle Filter with Estimation Windows

Adaptive Particle Filter with Estimation Windows

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2013年中国智能自动化学术会议

作者： Peng Li Jian Tang College of Information and Technology Key Laboratory of Intelligent Computing and Information ProcessingMinistry of EducationXiangtan University

Particle filter is well suited to estimate the state of non-linear non-Gaussian dynamic systems,which comes at the cost of higher computational *** in many real time applications,it must deal with constraints imposed by limited computational *** deal with this question,we distribute the samples among the different observations arriving during a filter update, the novel algorithm represents densities over the state space by mixtures of sample *** contribution of this paper is to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation *** to the relative entropy theory and particle number controller idea,we choose the number of samples,decrease computation overhead.A simulation of the classic HARD bearing only tracking problem is presented,the results show that the novel algorithm performs better than generic particle filter.

关键词： Particle filter Estimation window Relative entropy Particle number controller

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Discriminative learning with natural annotations: Word segmentation as a case study

Discriminative learning with natural annotations: Word segme...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Jiang, Wenbin Sun, Meng Lü, Yajuan Yang, Yating Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Multilingual Information Technology Research Center Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences China Centre for Next Generation Localisation Faculty of Engineering and Computing Dublin City University Ireland

ISBN: (纸本)9781937284503

Structural information in web text provides natural annotations for NLP problems such as word segmentation and parsing. In this paper we propose a discriminative learning algorithm to take advantage of the linguistic knowledge in large amounts of natural annotations on the Internet. It utilizes the Internet as an external corpus with massive (although slight and sparse) natural annotations, and enables a classifier to evolve on the large-scaled and real-time updated web text. With Chinese word segmentation as a case study, experiments show that the segmenter enhanced with the Chinese wikipedia achieves significant improvement on a series of testing sets from different domains, even with a single classifier and local features. © 2013 Association for Computational Linguistics.

关键词： Learning algorithms

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