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检索条件"机构=Key Lab. of Scientific Computing and Intelligent Information Processing"

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Automatic sports genre categorization and view-type classification over large-scale dataset 09

Automatic sports genre categorization and view-type classifi...

引用

17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Li, Lingfang Zhang, Ning Duan, Ling-Yu Huang, Qingming Du, Jun Guan, Ling Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Ryerson Multimedia Research Laboratory Ryerson University Toronto ON Canada Institute of Digital Media Peking University Beijing 100871 China Graduate University of Chinese Academy of Sciences Beijing 100190 China NEC Research Labs. China Beijing 100084 China

ISBN: (纸本)9781605586083

This paper presents a framework with two automatic tasks targeting large-scale and low quality sports video archives collected from online video streams. The framework is based on the bag of visual-words model using speeded-up robust features (SURF). The first task is sports genre categorization based on hierarchical structure. Following on the second task which is based on automatically obtained genre, views are classified using support vector machines (SVMs). As a consequence, the views classification result can be used in video parsing and highlight extraction. As compared with state-of-the-art methods, our approach is fully automatic as well as domain knowledge free and thus provides a better extensibility. Furthermore, our dataset consists of 14 sport genres with 6850 minutes in total. Both sport genre categorization and view type classification have more than 80% accuracy rates, which validate this framework's robustness and potential in web-based applications. Copyright 2009 ACM.

关键词： Support vector machines

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An Approach for Diagnosing Unexpected Faults in Web Service Flows

An Approach for Diagnosing Unexpected Faults in Web Service ...

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International Conference on Grid and Cooperative computing (GCC)

作者： Xu Han Zhongzhi Shi Wenjia Niu Fen Lin Donglei Zhang Key Lab. of Intell. Inf. Process. Chinese Acad. of Sci. Beijing Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Potential faults have greatly reduced the dependability of business processes, so fault diagnosis is becoming an important issue which aims at supporting self-healing service flow execution. The existing fault handling mechanism provided by BPEL can only identify the faults which have been pre-defined in standards or by users. However, unexpected faults are also the main cause of failures in service flow execution. Therefore an effective diagnosis approach is needed to solve this problem. In this paper, we propose a logic-based approach for diagnosing unexpected faults in Web service flows. This approach uses dynamic description logic (DDL) to model business processes, and diagnoses faults based on DDL reasoning. We provide the DDL-based diagnosing algorithm, which takes process description and runtime information as inputs, and returns the related information of possible faults as the result. Moreover, to improve the efficiency of online diagnosis, the incremental DDL-based diagnosing algorithm is presented. Experimental results on a demo system show the effectiveness of this approach.

关键词： Web services Fault diagnosis Logic Grid computing lab.ratories information processing Computers Testing Runtime Engines

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Sgde-gep: A novel algorithm of gep

Sgde-gep: A novel algorithm of gep

引用

International Conference on Computer Science and Software Engineering, CSSE 2008

作者： Peng, Yu-Zhong Yuan, Chang-An Wang, Yan Qin, Xiao Key Lab of Scientific Computing Intelligent Information processing in Universities of Guangxi Guangxi Teachers Education University Nanning China

ISBN: (纸本)9780769533360

Traditional GEP algorithm takes up many system resources in decoding and evaluating due to the operation of the tree construction and corresponding traversing. This paper aims to introduce a novel GEP algorithm to alleviate the drawback mentioned above. The main contributions include:(1) presenting a new method for decoding and evaluating chromosome (SGDE), and proposing the corresponding ETs construction schema;(2) proving the relative natures of SGDE-GEP;(3)The experiments showed that the average efficiency of SGDE-GEP can be raised from 18.94% to 23.11% compared with the traditional GEP. © 2008 IEEE.

关键词： Trees (mathematics)

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Focused crawling with heterogeneous semantic information

Focused crawling with heterogeneous semantic information

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2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008

作者： Huang, Rui Lin, Fen Shi, Zhongzhi Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China Graduate University Chinese Academy of Sciences 100049 Beijing China

ISBN: (纸本)9780769534961

Focused crawlers selectively retrieve Web documents that are relevant to a predefined set of topics. To intelligently make predictions and decisions about relevant URLs and web pages, different topic models have been introduced to represent topic-specific knowledge. Yet it is difficult to support semantic interoperability among different models. Moreover, some manually specified additional semantic information, such as semantic markups and social annotations, could not be effectively used to improve crawling. This paper proposes to boost focused crawling with four kinds of semantic models and semantic information, including thesauruses, categories, ontologies, and folksonomies. A statistical semantic association model is proposed to integrate different semantic models, represent heterogeneous semantic information, and support semantic relevance computation. A focused crawling framework is developed which adopts both keyword based contents and different kinds of additional information for relevance prediction and ranking. Experiments show that the proposed model and framework effectively integrates heterogeneous semantic information for focused crawling. © 2008 IEEE.

关键词： Websites

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Word lattice reranking for Chinese word segmentation and part-of-speech tagging

Word lattice reranking for Chinese word segmentation and par...

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22nd International Conference on Computational Linguistics, Coling 2008

作者： Jiang, Wenbin Mi, Haitao Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781905593446

In this paper, we describe a new reranking strategy named word lattice reranking, for the task of joint Chinese word segmentation and part-of-speech (POS) tagging. As a derivation of the forest reranking for parsing (Huang, 2008), this strategy reranks on the pruned word lattice, which potentially contains much more candidates while using less storage, compared with the traditional n-best list reranking. With a perceptron classifier trained with local features as the baseline, word lattice reranking performs reranking with non-local features that can't be easily incorporated into the perceptron baseline. Experimental results show that, this strategy achieves improvement on both segmentation and POS tagging, above the perceptron baseline and the n-best list reranking. © 2008. Licensed to the Coling.

关键词： Computational linguistics

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A cascaded linear model for joint Chinese word segmentation and part-of-speech tagging

A cascaded linear model for joint Chinese word segmentation ...

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46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT

作者： Jiang, Wenbin Huang, Liang Liu, Qun Lü, Yajuan Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Department of Computer and Information Science Levine Hall University of Pennsylvania 3330 Walnut Street Philadelphia PA 19104 United States

ISBN: (纸本)9781932432046

We propose a cascaded linear model for joint Chinese word segmentation and partof- speech tagging. With a character-based perceptron as the core, combined with realvalued features such as language models, the cascaded model is able to efficiently utilize knowledge sources that are inconvenient to incorporate into the perceptron directly. Experiments show that the cascaded model achieves improved accuracies on both segmentation only and joint segmentation and part-of-speech tagging. On the Penn Chinese Treebank 5.0, we obtain an error reduction of 18.5% on segmentation and 12% on joint segmentation and part-of-speech tagging over the perceptron-only baseline. © 2008 Association for Computational Linguistics.

关键词： Computational linguistics

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Forest-based translation

Forest-based translation

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46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT

作者： Mi, Haitao Huang, Liang Liu, Qun Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Department of Computer and Information Science Levine Hall University of Pennsylvania 3330 Walnut Street Philadelphia PA 19104 United States

ISBN: (纸本)9781932432046

Among syntax-based translation models, the tree-based approach, which takes as input a parse tree of the source sentence, is a promising direction being faster and simpler than its string-based counterpart. However, current tree-based systems suffer from a major drawback: they only use the 1-best parse to direct the translation, which potentially introduces translation mistakes due to parsing errors. We propose a forest-based approach that translates a packed forest of exponentially many parses, which encodes many more alternatives than standard n-best lists. Large-scale experiments show an absolute improvement of 1.7 BLEU points over the 1-best baseline. This result is also 0.8 points higher than decoding with 30-best parses, and takes even less time. © 2008 Association for Computational Linguistics.

关键词： Syntactics

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Forest-based translation rule extraction

Forest-based translation rule extraction

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2008 Conference on Empirical Methods in Natural Language processing, EMNLP 2008, Co-located with AMTA 2008 and the International Workshop on Spoken Language Translation

作者： Mi, Haitao Huang, Liang Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China Dept. of Computer and Information Science University of Pennsylvania Levine Hall 3330 Walnut St. Philadelphia PA 19104 United States

Translation rule extraction is a fundamental problem in machine translation, especially for linguistically syntax-based systems that need parse trees from either or both sides of the bitext. The current dominant practice only uses 1-best trees, which adversely affects the rule set quality due to parsing errors. So we propose a novel approach which extracts rules from a packed forest that compactly encodes exponentially many parses. Experiments show that this method improves translation quality by over 1 BLEU point on a state-of-the-art tree-to-string system, and is 0.5 points better than (and twice as fast as) extracting on 30- best parses. When combined with our previous work on forest-based decoding, it achieves a 2.5 BLEU points improvement over the baseline, and even outperforms the hierarchical system of Hiero by 0.7 points. © 2008 Association for Computational Linguistics.

关键词： Hierarchical systems

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PKU at ImageCLEF 2008: Experiments with query extension techniques for text-based and content-based image retrieval

PKU at ImageCLEF 2008: Experiments with query extension tech...

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2008 Working Notes for CLEF Workshop, CLEF 2008 - Co-located with the 12th European Conference on Digital Libraries, ECDL 2008

作者： Zhou, Zhi Tian, Yonghong Li, Yuanning Liu, Ting Huang, Tiejun Gao, Wen Institute of Digital Media School of EE and CS Peking University Beijing100871 China Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing100190 China Graduate University Chinese Academy of Sciences Beijing100080 China

In this paper, we present our solutions for the WikipediaMM task at ImageCLEF 2008. The aim of this task is to investigate effective retrieval approaches in the context of a large-scale and heterogeneous collection of Wikipedia images that are searched by textual queries (and/or sample images and/or concepts) describing a user's information need. We first experimented with a text-based image retrieval approach with query extension, where the expansion terms are automatically selected from a knowledge base that is (semi-)automatically constructed from Wikipedia. We show how this open, constantly evolving encyclopedia can yield inexpensive knowledge structures that are specifically tailored to effectively enhance the semantics of queries. Encouragingly, the experimental results rank in the first place among all submitted runs. The second approach we experimented with is content-based image retrieval (CBIR), in which we first train 1-vs-all classifiers for all query concepts by using the training images obtained by Yahoo! search, and then treat the retrieval task as visual concept detection in the given Wikipedia image set. By comparison, this approach performs better than other submitted CBIR runs. Finally, we experimented with a cross-media image retrieval approach by combining and re-ranking text-based and content-based retrieval results. Despite the final experimental results were not formally submitted before the deadline, this approach performs remarkably better than the text-based retrieval or CBIR approaches.

关键词： Content based retrieval

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SGDE-GEP: A Novel Algorithm of GEP

SGDE-GEP: A Novel Algorithm of GEP

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International Conference on Computer Science and Software Engineering

作者： Yu-zhong Peng Chang-an Yuan Yan Wang Xiao Qin Graduate Student of Key Lab of Scientific Computing &Intelligent Information processing in Universities of Guangxi Guangxi Teachers Education University Nanning China Information processing in Universities of Guangxi Guangxi Teachers Education University Nanning China

关键词： Educational institutions Biological cells Decoding scientific computing Competitive intelligence information processing Systems engineering education Genetic programming Tail Software algorithms

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