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Asia-Pacific Conference on Microwave

作者： Wei Sha Xianliang Wu Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong China Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China

Using the locally conformal technique and the high-order symplectic integrators, the high- order conformal symplectic FDTD scheme is accurate and efficient for modeling the scattering from three-dimensional curved perfectly conducting objects. In addition, the decreased time step caused by the conformal model can be offset by using coarse grids.

关键词： Finite difference methods Time domain analysis Equations Computational modeling Interpolation Magnetic fields Laboratories signal processing Anisotropic magnetoresistance Computational efficiency

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AN INCREMENTAL UPDATING TECHNIQUE FOR MINING INDIRECT ASSOCIATION RULES

AN INCREMENTAL UPDATING TECHNIQUE FOR MINING INDIRECT ASSOCI...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： CHENG ZHENG Educational Department Key Laboratory of Intelligent Computing & Signal Processing Anhui University School of Computer Science and Technology Anhui University Hefei230039 China

While direct association rules are dedicated to describe the direct correlations among the items in a frequent itemset, indirect association rules are dedicated to describe the indirect correlations between the two items in a infrequent itemset. All the existing research works about indirect association rules are focused on improving the efficiency of mining algorithm for indirect association rules. Like incremental updating algorithm is important for mining association rules, incremental updating algorithm is also important for mining indirect association rules. In this paper, we put forward an incremental updating algorithm for mining indirect association rules to deal with the maintenance of discovered indirect association rules resulted from the change of the minimum support. The main idea is to re-utilize the results acquired in process with the old minimum support.

关键词： Indirect Association Rules Incremental Updating

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Transfer learning from multiple source domains via consensus regularization

Transfer learning from multiple source domains via consensus...

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17th ACM Conference on Information and Knowledge Management, CIKM'08

作者： Luo, Ping Zhuang, Fuzhen Xiong, Hui Xiong, Yuhong He, Qing HP Labs China China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China MSIS Department Rutgers University United States

ISBN: (纸本)9781595939913

Recent years have witnessed an increased interest in transfer learning. Despite the vast amount of research performed in this field, there are remaining challenges in applying the knowledge learnt from multiple source domains to a target domain. First, data from multiple source domains can be semantically related, but have different distributions. It is not clear how to exploit the distribution differences among multiple source domains to boost the learning performance in a target domain. Second, many real-world applications demand this transfer learning to be performed in a distributed manner. To meet these challenges, we propose a consensus regularization framework for transfer learning from multiple source domains to a target domain. In this framework, a local classifier is trained by considering both local data available in a source domain and the prediction consensus with the classifiers from other source domains. In addition, the training algorithm can be implemented in a distributed manner, in which all the source-domains are treated as slave nodes and the target domain is used as the master node. To combine the training results from multiple source domains, it only needs share some statistical data rather than the full contents of their labeled data. This can modestly relieve the privacy concerns and avoid the need to upload all data to a central location. Finally, our experimental results show the effectiveness of our consensus regularization learning. Copyright 2008 ACM.

关键词： Classification Consensus regularization Transfer learning

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High-order parabolic equation method for electromagnetic computation

High-order parabolic equation method for electromagnetic com...

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2008 Asia Pacific Microwave Conference, APMC 2008

作者： Huang, Zhi-Xiang Bo, Wu Wei, Sha Chen, Ming-Sheng Wu, Xian-Liang Hong, Dai Key Lab of Intelligent Computing and Signal Processing Anhui University China School of physical science and technology Yunnan University China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong Department of Physics and Electronic Engineering Hefei Teachers College China

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An Adaptive Travel Itinerary Selection Assistant under Fuzzy User Preferences

An Adaptive Travel Itinerary Selection Assistant under Fuzzy...

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2008 Conference on Human System Interactions (HSI 2008), vol.1

作者： Ruihong Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

This paper presents a candidate-evaluation model (CEM) which interactively elicits user preferences and assists decision makers in decision making in applications such as travel itinerary planning. The CEM contrasts with traditional decision analytic and planning frameworks in which a complete user model is elicited beforehand or is constructed by a human expert. We used the CEM model to implement an Itinerary Selection Assistant (ISA) system, which helps tourists identify satisfactory travel itineraries. The ISA starts with fuzzy user preferences and gradually approximate the optimal solution through carefully choosing candidate solutions to present to the user and inferring user's actual preferences by analyzing user evaluations over the candidates.

关键词： Augmented weighted Tchebycheff programming Fuzzy user preferences Multi-attribute decision making Travel itinerary User modeling

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COUNT PASSENGERS BASED ON HAAR-LIKE FEATURE IN ELEVATOR APPLICATION

COUNT PASSENGERS BASED ON HAAR-LIKE FEATURE IN ELEVATOR APPL...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： HONG LIU YUE-LIANG QIAN QUN LIU JIN-TAO LI Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080

Present elevator control use button sensors to determine when and where to dispatch an elevator car, which don't use the number of passengers. In this paper, we analyze images from camera to detect how many persons waiting for the elevator or in an elevator. A novel framework is proposed for optimized elevator schedule. Extended Haar-like features and Adaboost are used to train a head-shoulder classifier. Some images are selected from video according to elevator button callings to detect head-shoulder. To reduce false alarms a post process is added after detecting. Experimental results show the proposed method with post process has higher performance than existed methods. The information of passenger number can be send to elevator control system for effective schedule, which can reduce passengers waiting time and elevator's unnecessary stop, finally save energy and reduce maintain fee.

关键词： Elevator control system Haar-like features Adaboost Post process

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Novel DCT-based real-valued discrete Gabor transform

Novel DCT-based real-valued discrete Gabor transform

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Liang Tao Hon Keung Kwan MOE Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei Anhui China Department of Electrical and Computer Engineering University of Windsor Windsor ONT Canada

The traditional DCT-based real-valued discrete Gabor transform (RGDT) was limited to the critical sampling case. The biorthogonality relationship between the analysis window and the synthesis window for the transform has not been unveiled. To overcome those drawbacks, this paper proposes a novel DCT-based real-valued discrete Gabor transform, which can be applied under both the critical sampling condition and the over-sampling condition. And the biorthogonality relationship between the analysis window and the synthesis window for the transform is also proved in this paper. Because it only involves real operations and can utilize fast DCT and DDCT algorithms for fast computation, it facilitates computation and implementation by hardware and/or software compared to the traditional complex-valued discrete Gabor transform.

关键词： Discrete transforms Sampling methods Hardware Discrete cosine transforms Software algorithms Biomedical signal processing signal sampling signal synthesis signal processing algorithms Parallel algorithms

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The Maximum Entropy based Rule Selection Model for Statistical Machine Translation (Invited Paper)

The Maximum Entropy based Rule Selection Model for Statistic...

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International Symposium on Universal Communication, ISUC

作者： Qun Liu Zhongjun He Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

This paper presents a novel rule selection model for statistical machine translation (SMT) that uses the maximum entropy approach to predict target-side for an ambiguous source-side. The maximum entropy based rule selection (MERS) model combines rich contextual information as features, thus can help SMT systems perform context-dependent rule selection. We incorporate the MERS model into two kinds of the state-of-the-art syntax-based SMT models: the hierarchical phrase-based model and the tree-to-string alignment template model. Experiments show that our approach achieves significant improvements over both the baseline systems.

关键词： Entropy Surface-mount technology Helium Decoding Predictive models Context modeling Power generation economics Laboratories Machine intelligence Information processing

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Focused Crawling with Heterogeneous Semantic Information

Focused Crawling with Heterogeneous Semantic Information

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Rui Huang Fen Lin Zhongzhi Shi Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

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.

关键词： Crawlers Web pages Uniform resource locators intelligent agent Computers Ontologies Information processing Semantic Web Information retrieval Predictive models

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Object-based image retrieval with attention analysis and spatial re-ranking

IFIP Advances in Information and Communication Technology

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IFIP Advances in Information and Communication Technology 2008年 288卷 118-128页

作者： Gao, Ke Lin, Shouxun Zhang, Yongdong Tang, Sheng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of the Chinese Academy of Sciences Beijing100080 China Key Laboratory of Intelligent Information Processing nstitute of Computing Technology Chinese Academy of Sciences Beijing100080 China

ISBN: (纸本)9780387876849

In this paper, a new method is proposed for object-based image retrieval. The user supplies a query object by selecting a region from a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large image database. The main outcomes of this research are as follows: (1) An novel object-based image retrieval framework that integrates effective pre-treatment and re-ranking is presented, (2) a new feature filtration method based on attention analysis is proposed for pre-treatment, (3) to further improve object retrieval precision, we add an efficient spatial configuration model to rerank the primary retrieval result using Bag of Word method. Experimental results demonstrate the effectiveness of our method. © 2008, Springer New York LLC . All rights rserved.

关键词： Query processing

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