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15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013

作者： Ruan, Wenjing Wang, Chaokun Han, Lu Peng, Zhuo Bai, Yiyuan School of Software Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University China Department of Computer Science and Technology Tsinghua University China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9783642374005

In some real-world applications, data cannot be measured accurately. Uncertain graphs emerge when this kind of data is modeled by graph data structures. When the graph database is uncertain, our query is highly possibly uncertain too. All of the prior works do not consider the uncertainty of the query. In this paper, we propose a new algorithm called Mutual-Match to solve the problem of uncertain subgraph query under the situation where both the graph dataset and the query are uncertain. Considering that the subgraph isomorphism verification is an NP-hard problem, and it will be more complex on uncertain graph data, the Mutual-Match algorithm uses MapReduce processes to find out all the subgraph matches and can adapt to dynamic graphs well. Experimental results on both real-world and synthetic datasets show the correctness and effectiveness of our proposed methods. © 2013 Springer-Verlag.

关键词： MapReduce

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A quasi-static clustering algorithm in mobile heterogeneous sensor network

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Journal of Computational information Systems 2013年第9期9卷 3741-3749页

作者： Wang, Bingting Lin, Qibin Hu, Yanjun Liang, Hui Wang, Xianbing Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China School of Mechanical and Electronic Engineering Chuzhou University Chuzhou 239000 China School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China

In this paper, we constructed the architecture of mobile heterogeneous sensor network by introducing user equipments (UEs) in static wireless sensor network. We analyzed the failure of traditional static clustering algorithm, gave a set of parameters that affected the performance of cluster heads in the mobile environment and proposed a Quasi-Static Clustering Algorithm (QSCA). Through theoretical analysis and simulation, this algorithm can reduce average delay and prolong network lifetime. We verified the adaptability of QSCA algorithm to energy consumption and dynamic topology by comparing with the LEACH algorithm. © 2013 by Binary information Press.

关键词： Clustering algorithms

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Semantic separator learning and its applications in unsupervised Chinese text parsing

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Frontiers of Computer Science 2013年第1期7卷 55-68页

作者： Yuming WU Xiaodong LUO Zhen YANG Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of the Chinese Academy of Sciences Beijing 100049 China China Telecom Corporation Limited Shanghai Branch Shanghai 200120 China Shanghai Research Institute of China Telecom Corporation Limited Shanghai 200120 China

Grammar learning has been a bottleneck problem for a long time. In this paper, we propose a method of seman- tic separator learning, a special case of grammar learning. The method is based on the hypothesis that some classes of words, called semantic separators, split a sentence into sev- eral constituents. The semantic separators are represented by words together with their part-of-speech tags and other infor- mation so that rich semantic information can be involved. In the method, we first identify the semantic separators with the help of noun phrase boundaries, called subseparators. Next, the argument classes of the separators are learned from cor- pus by generalizing argument instances in a hypernym space. Finally, in order to evaluate the learned semantic separators, we use them in unsupervised Chinese text parsing. The exper- iments on a manually labeled test set show that the proposed method outperforms previous methods of unsupervised text parsing.

关键词： semantic separator separator learning unsuper vised text parsing

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Refining image annotation by integrating PLSA with random walk model

Refining image annotation by integrating PLSA with random wa...

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19th International Conference on Advances in Multimedia Modeling, MMM 2013

作者： Tian, Dongping Zhao, Xiaofei Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of the Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9783642357244

In this paper, we present a new method for refining image annotation by integrating probabilistic latent semantic analysis (PLSA) with random walk (RW) model. First, we construct a PLSA model with asymmetric modalities to estimate the posterior probabilities of each annotating keywords for an image, and then a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity. Followed by a random walk process over the label graph is employed to further mine the correlation of the keywords so as to capture the refining annotation, which plays a crucial role in semantic based image retrieval. The novelty of our method mainly lies in two aspects: exploiting PLSA to accomplish the initial semantic annotation task and implementing random walk process over the constructed label similarity graph to refine the candidate annotations generated by the PLSA. Compared with several state-of-the-art approaches on Corel5k and Mirflickr25k datasets, the experimental results show that our approach performs more efficiently and accurately. © Springer-Verlag 2013.

关键词： Image annotation

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15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013

作者： Shang, Tianfeng He, Qing Zhuang, Fuzhen Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate School Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9783642374005

Collaborative filtering is one of the most popular techniques in recommender systems, and the key point is to find similar users and items. There are already some similarity measures, such as vector cosine similarity and Pearson's correlation coefficient, and so on. However, in some cases, what recommender systems get are not the ratings, but preference sequences of users on a series of items. For this type of data, those traditional similarity measures may fail to meet the practical application requirements. In this paper, a similarity measure based on inversion is proposed for preference sequences naturally. Based on the Inversion similarity measure, some structural information of user preference sequences is analyzed. By merging average precision and weighted inversion into similarity computation, a new similarity measure based on preference sequences is proposed for collaborative filtering. Experimental results show that the proposed similarity measure based on preference sequences outperforms the common similarity measures on the datasets with continuous real numbers. © 2013 Springer-Verlag.

关键词： Collaborative filtering

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Translation with source constituency and dependency trees

Translation with source constituency and dependency trees

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2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013

作者： Meng, Fandong Xie, Jun Song, Linfeng 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)9781937284978

We present a novel translation model, which simultaneously exploits the constituency and dependency trees on the source side, to combine the advantages of two types of trees. We take head-dependents relations of dependency trees as backbone and incorporate phrasal nodes of constituency trees as the source side of our translation rules, and the target side as strings. Our rules hold the property of long distance reorderings and the compatibility with phrases. Large-scale experimental results show that our model achieves significantly improvements over the constituency-to-string (+2.45 BLEU on average) and dependency-to-string (+0.91 BLEU on average) models, which only employ single type of trees, and significantly outperforms the state-of-the-art hierarchical phrase-based model (+1.12 BLEU on average), on three Chinese-English NIST test sets. © 2013 Association for Computational Linguistics.

关键词： Forestry

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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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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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Hybrid Designing of a Neural System by Combining Fuzzy Logical Framework and PSVM for Visual Haze-Free Task

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International Journal of Intelligence Science 2013年第4期3卷 145-161页

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

Brain-like computer research and development have been growing rapidly in recent years. It is necessary to design large scale dynamical neural networks (more than 106 neurons) to simulate complex process of our brain. But such kind of task is not easy to achieve only based on the analysis of partial differential equations, especially for those complex neural models, e.g. Rose-Hindmarsh (RH) model. So in this paper, we develop a novel approach by combining fuzzy logical designing with Proximal Support Vector Machine Classifiers (PSVM) learning in the designing of large scale neural networks. Particularly, our approach can effectively simplify the designing process, which is crucial for both cognition science and neural science. At last, we conduct our approach on an artificial neural system with more than 108 neurons for haze-free task, and the experimental results show that texture features extracted by fuzzy logic can effectively increase the texture information entropy and improve the effect of haze-removing in some degree.

关键词： Artificial Brain Research Brain-Like Computer Fuzzy Logic Neural Network Machine Learning Hopfield Neural Network Bounded Fuzzy Operator

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Mining approximate keys based on reasoning from XML data

Mining approximate keys based on reasoning from XML data

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17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013

作者： Yijun, Liu Feiyue, Ye Sheng, He School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu 213001 China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Changzhou Jiangsu 213001 China

ISBN: (纸本)9783642403187

Keys are very important for data management. Due to the hierarchical and flexible structure of XML, mining keys from XML data is a more complex and difficult task than from relational databases. In this paper, we study mining approximate keys from XML data, and define the support and confidence of a key expression based on the number of null values on key paths. In the mining process, inference rules are used to derive new keys. Through the two-phase reasoning, a target set of approximate keys and its reduced set are obtained. Our research conducted experiments over ten benchmark XML datasets from XMark and four files in the UW XML Repository. The results show that the approach is feasible and efficient, with which effective keys in various XML data can be discovered. © Springer-Verlag 2013.

关键词： Data mining

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