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

共 143 条记录，以下是121-130 订阅

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Forest-based translation rule extraction

Forest-based translation rule extraction

引用

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...

引用

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

引用

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

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.

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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Algebra and calculus of the resource space model

Algebra and calculus of the resource space model

引用

3rd International Conference on Semantics, Knowledge, and Grid, SKG 2007

作者： Xing, Yunpeng Zhuge, Hai Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)0769530079

Resource Space Model (RSM) is a semantic model to manage resources in the future interconnection environment. The query capability is an important aspect of RSM as a semantic resource management model. This paper reports the research result on the query capability of RSM from two perspectives: resource space algebra and resource space calculus. The equivalence of the resource space algebra and the resource space calculus has been discussed. © 2007 IEEE.

关键词： Algebra

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An authorization mechanism for resource space model

An authorization mechanism for resource space model

引用

3rd International Conference on Semantics, Knowledge, and Grid, SKG 2007

作者： Zhu, Yuan Xing, Yunpeng Hunan Knowledge Grid Lab. Hunan University of Science and Technology Hunan China China Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100080 Beijing China

ISBN: (纸本)0769530079

Authorization mechanism is an effective technique of access control. In this paper, we construct a multidimensional authorization space for RSM with the guidance of the methodology of RSM design. This authorization space permits both positive and negative authorizations and supports exceptions and strong enforcement as well as the temporal constraints on the authorizations. For the sake of efficiency and convenience we extend the representation of object in GRANT command. © 2007 IEEE.

关键词： Authorization

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Urban road network extraction from SAR image

Urban road network extraction from SAR image

引用

MIPPR 2007: Remote Sensing and GIS Data processing and Applications;and Innovative Multispectral Technology and Applications

作者： Cheng, Pengxue Hu, Maolin Lab. of Intelligent Computing and Signal Processing Anhui University Hefei Anhui 230039 China Key Laboratory of Wireless Sensor Network and Communication Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

ISBN: (纸本)9780819469540

In this paper, a technique for the extraction of roads in a high resolution synthetic aperture radar (SAR) image is presented. And a three-step method is developed for the extraction of road network from space borne SAR image: the process of the feature points, road candidate detection and connection. Roads in a high resolution SAR image can be modeled as a homogeneous dark area bounded by two parallel boundaries. Dark areas, which represent the candidate positions for roads, are extracted from the image by a Gaussian probability iteration segmentation. Possible road candidates are further processed using the morphological operators. And the roads are accurately detected by Hough Transform, and the extraction of lines is achieved by searching the peak values in Hough Space. In this process, to detect roads more accurately, post-processing, including noisy dark regions removal and false roads removal is performed. At last, Road candidate connection is carried out hierarchically according to road established models. Finally, the main road network is established from the SAR image successfully. As an example, using the ERS-2SAR image data, automatic detection of main road network in Shanghai Pudong area is presented.

关键词： Feature extraction

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Point matching of two images under large different resolution

Point matching of two images under large different resolutio...

引用

MIPPR 2007: Automatic Target Recognition and Image Analysis;and Multispectral Image Acquisition

作者： Ruan, Lei Hu, Maolin Lab. of Intelligent Computing and Signal Processing AnHui University Hefei AnHui 230039 China Key Laboratory of Wireless Sensor Network and Communication Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

ISBN: (纸本)9780819469502

Image matching is the first step in almost any 3D computer vision task, and hence has received extensive attention. In this paper, the problem is addressed from a novel perspective, which is different from the classic stereo matching paradigm. Two images with different resolutions, that is high resolution versus low resolution are matched. Since the high resolution image only corresponds to a small region of the low resolution one, the matching task therefore consists in finding a small region in the low resolution image that can be assigned to the whole high resolution image under the plane similarity transformation, which can be defined by the local area correlation coefficient to match the interest points and rectified by similarity transform. Experiment shows that our matching algorithm can be used for scale changing up to a factor of 6. And it is successful to deal with the point matching between two images under large scale.

关键词： Image matching

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A flexible stem-based local search algorithm for predicting RNA secondary structures including pseudoknots

A flexible stem-based local search algorithm for predicting ...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Chen, Xiang He, Si-Min Bu, Dong-Bo Chen, Run-Sheng Gao, Wen Key Lab. of Intelligent Information Processing Chinese Academy of Sciences Beijing 100080 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Insitute of Biophysics Chinese Academy of Sciences Beijing 100101 China Graduate University Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)1424415098

We demonstrate a new algorithm named FlexStem to predict RNA secondary structures with pseudoknots. Our approach is based on the free energy minimization criterion, and utilizes a sophisticated energy model that is more accurate and supports more types of pseudoknots. By using a "maximal stem" and "stem merging" strategy, the search space for RNA structure prediction is significantly reduced. We have also developed a greedy search algorithm with perturbation on stems. The FlexStem algorithm is applied to a large number of sequences taken from Sprinzl, Pseudobase and other reliable resource. We find that FlexStem outperforms the well-known optimal and heuristic algorithms such as Mfold, PKNOTS, HotKnots and ILM in overall sensitivity and has comparable results to those algorithms in overall specificity. Performance evaluation demonstrate that FlexStem requires significantly less time than the optimal algorithm PKNOTS. In addition, our algorithm has better prediction results on pseudoknot-free sequences compared to competing algorithms. ©2007 IEEE.

关键词： Heuristic algorithms

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Semantic-driven management and search for resources in research community

Semantic-driven management and search for resources in resea...

引用

2006 2nd International Conference on Semantics Knowledge and Grid, SKG

作者： Junsheng, Zhang Zhixian, Yan Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)0769532055

In recent years, semantic search has become one hot motivation of the semantic web. In this paper, we propose a semantic-based resource management and search architecture and its implementation in research community, including most of the research relevant resources. We have built two authoring tools respectively for concepts and instances. With the authoring tools, semantic search portal is provided, which has three different interfaces, respectively for retrieval of concept, instance and semantic relationship. Compared with the traditional search methods, semantic search methodology not only gives us more interesting and satisfying search results, whose precision is higher than those of traditional ones only based on keywords, but also displays the results in a more structural and semantic way. © 2006 IEEE.

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Semantic-driven management and search for resources in research community

Semantic-driven management and search for resources in resea...

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2nd International Conference on Semantics, Knowledge, and Grid, SKG 2006

作者： Zhang, Junsheng Yan, Zhixian Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)076952673X

In recent years, semantic search has become one hot motivation of the semantic web. In this paper, we propose a semantic-based resource management and search architecture and its implementation in research community, including most of the research relevant resources. We have built two authoring tools respectively for concepts and instances. With the authoring tools, semantic search portal is provided, which has three different interfaces, respectively for retrieval of concept , instance and semantic relationship. Compared with the traditional search methods, semantic search methodology not only gives us more interesting and satisfying search results, whose precision is higher than those of traditional ones only based on keywords, but also displays the results in a more structural and semantic way. © 2006 IEEE.

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