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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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Mining top-K Frequent and Flexible Pattern from sequences

Mining top-K Frequent and Flexible Pattern from sequences

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2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Junyan, Zhang Fan, Min Information Science and Technology College Chengdu University China University Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781479916603

Pattern Mining is a popular issue in biological sequence analysis. With the introduction of wildcard gaps, more interesting patterns can be mined. In this paper, we propose a new definition related to pattern frequency, under which the Apriori property holds. We define a pattern mining problem called Ming top-K Frequent Patterns (MFP), where gaps are mined instead of specified. Compared with existing problems, MFP does not require any domain knowledge of the user. However, theoretical analysis and experimental results show that MFP favors inflexible patterns. We then define another problem where the flexibility threshold of each gap is specified by the user. The problem is called Mining top-K Frequent and Flexible Patterns (MF2P). We develop algorithm with polynomial complexities for both problems. Patterns can grow from both sides. Some interesting biological patterns mined by our algorithms are discussed. © 2013 IEEE.

关键词： Bioinformatics

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Feature selection method for 3d terrain model based on profile recognition and polygon-breaking algorithm

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ICIC Express Letters 2013年第3 A期7卷 741-746页

作者： Zhang, Huijie Ma, Zhiqiang Ren, Guangxu Wu, Di Yang, Fengqin School of Computer Science and Information Technology Jilin Universities Northeast Normal University No. 2555 Jingyue Street Changchun 130117 China Key Laboratory of Intelligent Information Processing Jilin Universities Northeast Normal University No. 2555 Jingyue Street Changchun 130117 China Aviation University of Air Force Changchun 130022 China

This paper presents a framework to support feature visualization in 3D terrain roaming. In this framework, feature points are redefined based on the target points of profile recognition and polygon-breaking algorithm, combined with connotative hierarchical structure. The diamond neighborhood space is proposed to decrease the computation complexity of feature selection. Additionally, feature connection and polygon breaking have been completed in order to build the whole framework. Finally, experiments have been conducted on the GTOPO30 terrain models. As a result, our framework can meet real time feature visualization and the quality of skeleton feature selection is good as a whole. By further test, the frame rate of terrain roaming with feature selection is satisfied. © 2013 ICIC International.

关键词： Visualization

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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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Optimized PCA based face recognition for mobile devices

Optimized PCA based face recognition for mobile devices

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2nd International Conference on Mechanical Engineering, Industrial Electronics and Informatization, MEIEI 2013

作者： Zou, Dai Kun Hu, Wei Xia, Hong Wei Li, Wen Fei Yao, Zhuo Min Guo, Hong College of Computer Science Wuhan University of Computer Science and Technology Wuhan Hubei 430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9783037858905

With the rapid development of embedded technology, mobile devices have been widely used than before. Face recognition has also been taken as a key application with PCA as the basic algorithm. Though PCA can provide basic information processing, it still has some problems to be used for mobile devices. The movement of the faces increases the difficulty of the recognition and the limited resources of mobile devices propose more constraints to traditional PCA algorithm. A novel approach is presented to optimize PCA based face recognition for better performance and faster recognition speed. The experiments show that the new approach can achieve its target though the optimization. © (2013) Trans Tech Publications, Switzerland.

关键词： Face recognition

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Multifractal analyses of daily rainfall time series in Pearl River basin of China

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Physica A: Statistical Mechanics and its Applications 2014年 405卷 193-202页

作者： Zu-Guo Yu Yee Leung Yongqin David Chen Qiang Zhang Vo Anh Yu Zhou Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia Department of Geography and Resource Management The Chinese University of Hong Kong Hong Kong China Institute of Environment Energy and Sustainability The Chinese University of Hong Kong Hong Kong China Department of Water Resources and Environment Sun Yat-sen University Guangzhou 510275 China Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute Sun Yat-sen University Guangzhou 510275 China

The multifractal properties of daily rainfall time series at the stations in Pearl River basin of China over periods of up to 45 years are examined using the universal multifractal approach based on the multiplicative cascade model and the multifractal detrended fluctuation analysis (MF-DFA). The results from these two kinds of multifractal analyses show that the daily rainfall time series in this basin have multifractal behavior in two different time scale ranges. It is found that the empirical multifractal moment function K ( q ) of the daily rainfall time series can be fitted very well by the universal multifractal model (UMM). The estimated values of the conservation parameter H from UMM for these daily rainfall data are close to zero indicating that they correspond to conserved fields. After removing the seasonal trend in the rainfall data, the estimated values of the exponent h ( 2 ) from MF-DFA indicate that the daily rainfall time series in Pearl River basin exhibit no long-term correlations. It is also found that K ( 2 ) and elevation series are negatively correlated. It shows a relationship between topography and rainfall variability.

关键词： Daily rainfall time series Multifractal property Universal multifractal model Multifractal detrended fluctuation analysis

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Bilingual lexical cohesion trigger model for document-level machine translation

Bilingual lexical cohesion trigger model for document-level ...

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

作者： Ben, Guosheng Xiong, Deyi Teng, Zhiyang Lü, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology Soochow University China Centre for Next Generation Localisation Dublin City University Ireland

ISBN: (纸本)9781937284510

In this paper, we propose a bilingual lexical cohesion trigger model to capture lexical cohesion for document-level machine translation. We integrate the model into hierarchical phrase-based machine translation and achieve an absolute improvement of 0.85 BLEU points on average over the baseline on NIST Chinese-English test sets. © 2013 Association for Computational Linguistics.

关键词： Machine translation

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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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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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A model of three points for location estimation in wireless sensor networks

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2013 2nd International Symposium on Materials Science and Engineering technology, ISMSET 2013

作者： Xie, Shao Guo Hu, Yan Jun Wang, Yi Jiang, Fang Liu, Jing Jing Wang, Ying Guan Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education AnHui University Hefei China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai 200050 China

ISBN: (纸本)9783037858028

In this paper, we propose a novel model of three points named TP for location estimation in wireless sensor networks (WSNs) with random deployment of anchor nodes. In this model, we select three anchor nodes which have the strongest received signal strength (RSS) for location estimation, the centroid algorithm and the method of intersection of judgment are used to estimate the location of unknown nodes. To further exploit three nearest intersection points in TP, the enhanced TP (ETP) is proposed. The simulation results show that the proposed models outperform MMSE and BML in terms of the localization accuracy for WSNs. Moreover, the localization accuracy of the proposed models in scenario 2 with random deployment of anchor nodes are better than in scenario 1 with planned deployment of anchor nodes. Additionally, compared with MMSE and BML, ETP and TP can reduce the environmental impact on location estimation. © (2013) Trans Tech Publications, Switzerland.

关键词： Location

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