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Efficient incremental decoding for tree-to-string translation

Efficient incremental decoding for tree-to-string translatio...

引用

Conference on Empirical Methods in Natural Language processing, EMNLP 2010

作者： Huang, Liang Mi, Haitao Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey CA 90292 United States Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)1932432868

Syntax-based translation models should in principle be efficient with polynomially-sized search space, but in practice they are often embarassingly slow, partly due to the cost of language model integration. In this paper we borrow from phrase-based decoding the idea to generate a translation incrementally left-to-right, and show that for tree-to-string models, with a clever encoding of derivation history, this method runs in average-case polynomial-time in theory, and linear-time with beam search in practice (whereas phrase-based decoding is exponential-time in theory and quadratic-time in practice). Experiments show that, with comparable translation quality, our tree-to-string system (in Python) can run more than 30 times faster than the phrase-based system Moses (in C++). © 2010 Association for Computational Linguistics.

关键词： Decoding

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The Interactive computing Model for Cyber Physical Society

The Interactive Computing Model for Cyber Physical Society

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Xiaoping Sun Hai Zhuge Xiaorui Jiang Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China

With the permeation of information technology intothe physical world and human society, we are on the way to a Cyber Physical Society (CPSocio). A CPSocio inter-connects the nature, the cyber space, and the society, so as to provide human beings with a better living and working environment. Researches on CPSocio will lead to a new revolution of information science and technology. In realizing CPSocio, semantics is the most fundamental problem. It is the foundation of all the intelligent activities such as communication, interaction, and cooperation in CPSocio. We are facing various scientific challenges to understand and clarify principles and models of the computing in CPSocio. In this paper we focus on the interactive computing model of Cyber Physical Society, discussing a possible architecture and computing model that enable future intelligent distributed services in CPSocio. The proposed model focuses on the interactive computation emerged from self-interpretation, self-organization and interactions of individual objects. Selfinterpretation enables objects to actively express their own semantic information to others. Interaction among objects will help achieving computing solutions in open, dynamic, limited and heterogeneous environments with partial available information and services. The model aims at providing a feasible framework for systematically tackling key problems and building intelligent services in CPSocio.

关键词： Semantics Computational modeling Data models Artificial intelligence Humans Object recognition Servers

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Improved particle swarm algorithm for portfolio optimization problem

Improved particle swarm algorithm for portfolio optimization...

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International Conference on Industrial Mechatronics and Automation, ICIMA

作者： Jianguo Cao Liang Tao Department of Computer Science Anhui Vocational and Technical College of Industry and Trade Huainan China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781424476534

Particle swarm optimization (PSO) is a recently proposed population-based random search algorithm, which performs well in some optimization problems. In this paper, we proposed an improved PSO algorithm to solve portfolio selection problems. The proposed approach IPSO employs an opposite mutation operator to enhance the performance of the standard PSO. In order to verify the performance of IPSO, we test it on five well-known benchmark function optimization problems. At last, we use IPSO to solve a classical portfolio selection problem. The results show that the proposed approach is effective and achieves better results than standard PSO.

关键词： Particle swarm optimization Portfolios signal processing algorithms Benchmark testing Artificial neural networks Computer industry Educational institutions Genetic mutations Optimization methods Support vector machines

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C*-tree: A Multi-Dimensional Index Structure for Resource Space Model

C*-tree: A Multi-Dimensional Index Structure for Resource Sp...

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Qiang Zeng Hai Zhuge Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Resource Space Model (RSM) is a semantic data model for specifying, organizing, and retrieving resources based on classification of *** efficient index mechanism is critical for its implementation. However, traditional index mechanisms are not well suitable to resource spaces because the spaces are discrete and hierarchical. This paper proposes a new multidimensional index structure C*-tree. It employs boxes to represent resources so that the containment of boxes can reflect the hierarchical structure of resources. The metric function defined in the C*-tree is able to gather close resources in the index structure. To avoid overlap between bounding boxes, a new restricted bounding box is further introduced. Moreover, the insertion algorithms based on the data partition approach can fully utilize characteristics of RSM. The C*-tree is scalable and efficient on both the number of dimension and the amount of data for different distributions.

关键词： Indexes Semantics Encoding Measurement Data models Approximation methods Partitioning algorithms

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A HSC-based sample selection method for Support Vector Machine

A HSC-based sample selection method for Support Vector Machi...

引用

International Conference on Machine Learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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Cyber Physical Society

Cyber Physical Society

引用

Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Hai Zhuge Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China

Natural physical space provides material basis for the birth and evolution of human beings and *** progress of human society has created the cyber space. With the rapid development of information technology, the cyber space is connecting physical space, social space and mental space to form a new world - Cyber Physical Society. The way to explore the cyber physical society is different from the way to explore the natural physical space and society. This paper describes the ideal of the Cyber Physical Society, and presents its distinguished characteristics and scientific issues. Research on the Cyber Physical Society could lead to the revolution of society, science and technology.

关键词： Semantics Space technology Aerospace electronics Humans Materials Communities Sensors

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From Use Case Model to Service Model An Environment Ontology Based Approach

From Use Case Model to Service Model An Environment Ontology...

引用

The Ninth International Conference on Grid and Cloud computing(第九届网格与云计算国际学术会议 GCC 2010)

作者： Guangjun Cai Bin Zhao Zhi Jin Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Graduate Unive Software Engineering Institute Peking University Beijing China

One fundamental problem in services computing is how to bridge the gap between business requirements and various heterogeneous IT services. This involves eliciting business requirements and building a solution accordingly by reusing available services. While the business requirements are commonly elicited through use cases and scenarios, it is not straightforward to transform the use case model into a service model, and the existing manual approach is cumbersome and error-prone. In this paper, the environment ontology, which is used to model the problem space, is utilized to facilitate the model transformation process. The environment ontology provides a common understanding between business analysts and software engineers. The required software functionalities as well as the available services’ capabilities are described using this ontology. By semi-automatically matching the required capability of each use case to the available capabilities provides by services, a use case is realized by that set of services. At the end of this paper, a fictitious case study was used to illustrate how this approach works.

关键词： services computing requirements engineering use case environment ontology service model

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Tunnel morph model for time frequency bio-signal waveform processing

Tunnel morph model for time frequency bio-signal waveform pr...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Gang Zheng Shiliu Lian Shanling Mou Jia Qi Laboratory of Biological Signal and Intelligent Processing Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Tianjin University of Technology Tianjin CN China

In this paper, we proposed a tunnel morph model for bio-signal waveform in measuring their similarity. Firstly, the formal specifications of bio-signal waveforms are given. And then, a series of model establishing related definitions are presented. These definitions contain waveform segmentation; waveforms distance measurement, and tunnel width computation. Moreover, on the base of the model, a similarity measuring strategy which takes the curve feature of bio-signal into account was presented. In the end, the strategy was compared with other similarity measurement methods by AECG (Ambulatory Electrocardiogram) waveform data. The data are adopted from MIT/BIH arrhythmia database. Experiment results show that the sensitivity and the positive predictivity of the strategy based on tunnel morph model are prior to other strategies.

关键词： Electrocardiography Equations Biological system modeling Correlation Sensitivity Mathematical model

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Subspace selection using semi-supervised harmonic mean of Kullback-Leibler divergences

Subspace selection using semi-supervised harmonic mean of Ku...

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Si-Bao Chen Hai-Xian Wang Bin Luo Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Lab of Child Development and Learning Science of Ministry of Education Research Center of Learning Sciences South-East University Nanjing Jiangsu China

In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), can significantly reduce the class separation problem. Furthermore, maximizing the harmonic mean of Kullback-Leibler (KL) divergences of class pairs (MHMD) emphasizes smaller divergences more than MGMD, and deals with the class separation problem more effectively. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MHMD (SSMHMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMHMD.

关键词： Harmonic analysis Training Laplace equations Manifolds Covariance matrix Machine learning Symmetric matrices

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Automatic video genre categorization and event detection techniques on large-scale sports data 10

Automatic video genre categorization and event detection tec...

引用

Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research

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

This paper presents an efficient and robust automatic process for large-scale sports video analysis. The proposed system firstly identifies the genre of the query video, and then accomplishes the interesting event detection task. The significance of this framework is its automatic characteristic in testing with minimum human involvement in training, as well as the scalability and expansibility in dealing with a large-scale dataset. Domain-knowledge independent local features are extracted from an input video sequence and a histogram based distribution representation is created using the bag-of-visual-words (BoW) model. In genre categorization, k-nearest neighbor (k-NN) classifiers with various dissimilarity measures are assessed and evaluated analytically. For the event detection, a hidden conditional random field (HCRF) structured prediction model is utilized. Overall, this framework demonstrates the efficiency and accuracy in processing voluminous data from sports collection and achieves various tasks in video analysis. It also demonstrates a potential technology transformation from the "laboratory bench" to commercial applications. © 2010 Ning Zhang, Ling-Yu Duan, Qing-ming Huang, Lingfang Li, Wen Gao and Ling Guan.

关键词： Sports

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