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Topic discovery of clusters from documents with geographical location

Topic discovery of clusters from documents with geographical...

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作者： Zhang, Li Sun, Xiaoping Zhuge, Hai Knowledge Grid Lab Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Nanjing University of Posts and Telecommunications Nanjing Jiangsu China State Key Laboratory of Software Engineering Wuhan University Wuhan Hubei China University of Chinese Academy of Sciences Beijing China

As smart phones with GPS become popular, more and more textual documents with geographical locations are published on the Web. keyword-based location services like vehicle navigation, tour planning, nearby object querying, and location pattern discovering of spatial objects are becoming popular and important. However, processing both text and geographical locations brings more challenges to information-retrieval techniques. In this paper, we focus on the problem of finding textual topics of clusters containing spatial objects with text descriptions. The key is how to combine clustering techniques with topic-retrieval models to integrate both geo-location information and text information. We investigated methods that combine clustering methods with the Latent Dirichlet Allocation model to discover topics of clusters of documents with geo-locations. Six different methods of combination are investigated, each having outputs with different meanings, which can be further leveraged to answer different types of queries over spatial documents. Experiments are conducted on both synthetic and real data. The results show that the combination of the probabilistic topic model with clustering algorithms is an efficient and effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information. © 2015 John Wiley & Sons, Ltd.

关键词： Cluster analysis

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An efficient two-stage level set segmentation framework for overlapping plant leaf image

An efficient two-stage level set segmentation framework for ...

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8th International Conference on intelligent computing Technology, ICIC 2012

作者： Wang, Xiao-Feng Min, Hai Key Lab. of Network and Intelligent Information Processing Department of Computer Science and Technology Hefei University Hefei Anhui 230022 China Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230031 China Department of Automation University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9783642315879

In this paper, an efficient two-stage segmentation framework was proposed to address the plant leaf image with overlapping phenomenon, which is built based on the leaf approximate symmetry and level set evolution theory. In the pre-segmentation stage, a straight line was manually set on the target leaf to approximate the principal leaf vein and the Local Chan-Vese (LCV) model was used on the global image region to help searching the so-called un-overlapping contour in target leaf. In the formal segmentation stage, the symmetry detection was performed based on the pre-defined approximated principal vein to obtain the narrow-band evolution region and the second initial contour. Next, the LCV model was once again used to find the complete target leaf contour in the narrow-band evolution region. Finally, experiments on some real leaf images with overlapping phenomenon have demonstrated the efficiency and robustness of the proposed segmentation framework. © 2012 Springer-Verlag.

关键词： Image segmentation

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Representative local features mining for large-scale near-duplicates retrieval

Representative local features mining for large-scale near-du...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoguang Gu Yongdong Zhang Dongming Zhang Jintao Li Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Local features have been widely used in many computer vision related researches, such as near-duplicate image and video retrieval. However, the storage and query cost of local features become prohibitive on large-scale database. In this paper, we propose a representative local features mining method to generate a compact but more effective feature subset. First, we do an unsupervised annotation for all similar images(or frames in video) in the database. Second, we compute a comprehensive score for every local feature. The score function combines the robustness and discrimination. Finally, we sort all the local features in an image by their scores and the low-score local features can be removed. The selected local features are robust and discriminative, which can guarantee the better retrieval quality than using full of the original feature set. By our method, the number of local features can be significantly reduced and a large amount of storage and computational cost can be saved. The experimental results show that we can use 30% of the features to get a better query performance than that of full feature set.

关键词： Databases Robustness Vectors Visualization Feature extraction Binary codes Complexity theory

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Pattern Synthesis of MIMO Radar Based on Immune Differential Evolution Algorithm

Pattern Synthesis of MIMO Radar Based on Immune Differential...

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2020 International Conference on Modeling, Big Data Analytics and Simulation, MBDAS 2020

作者： Jiang, Ling Jiang, Yi Chen, Guangfu Shi, Guimin Cheng, Qi School of Mathematics and Computer Wuyi University Wuyishan354300 China Key Lab. of Cogn. Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan354300 China Big Data Development Administration of Anji County Huzhou313000 China

In order to optimize the pattern synthesis of multiple input and multiple output (MIMO) radar, immune mechanism is adopted to overcome the premature risk of differential evolution (DE) algorithm, namely immune differential evolution (IDE). Firstly, the modeling of MIMO radar is introduced by encoding the position of array in the binary. Secondly, the immune mechanism is employed to improve the DE. In IDE, two parameters are self-adapted for the mutation operator by immune mechanism to enhance the convergence ability of DE, including scaling factor and crossover rate. Several experiments are conducted to analysis the performance of IDE. The simulation results show that the IDE with variable parameters can get optimal results in MIMO radar with lower the peak side-lobe level (PSLL) and maintain the diversity with stronger convergence ability and shorter calculation speed. © Published under licence by IOP Publishing Ltd.

关键词： Optimization

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Towards Occlusion Robust Facial Landmark Detector

Towards Occlusion Robust Facial Landmark Detector

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International Conference on Automatic Face and Gesture Recognition

作者： Yuyang Sha Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9781665431774

Facial landmark detection plays an essential role in many face analysis tasks, such as face recognition, facial action unit detection, etc. However, when occlusion occurs, the existing methods might not perform well. This paper studies a new framework for facial landmark detection referred to as the Occlusion Robust Facial Landmark Detector (OR-FLD), which attempts to solve the occlusion problem. Specifically, the proposed OR-FLD consists of three main modules: Feature Random Dropping Module (FRD), Feature Activation Diversity Module (FAD), and Landmark Refinement Attention Module (LRA). The FRD forces the model to focus on the geometric relationships between different facial components by randomly dropping some appearance information and then automatically recovering them. The FAD tries to enhance the model representation and make the extracted features discriminative and robust to occlusions. Besides, the recovered features contribute differently to the final results. Therefore, we propose a simple and effective attention mechanism to refine the landmark location and make the model more robust to occlusions. We extensively evaluate OR-FLD on four facial landmark detection benchmarks (300W, WFLW, COFW, and AFLW). The results show that our method outperforms the state-of-the-art by a significant margin, especially on the datasets with large occlusion.

关键词： Location awareness Sensitivity Face recognition Pose estimation Detectors Gesture recognition Benchmark testing

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An intelligent load balancing algorithm towards efficient cloud computing

An intelligent load balancing algorithm towards efficient cl...

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2011 AAAI Workshop

作者： Xu, Yang Wu, Lei Guo, Liying Chen, Zheng Yang, Lai Shi, Zhongzhi Agent-based Cooperative System Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu SC 611731 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781577355243

MapReduce provided a novel computing model for complex job decomposition and sub-tasks management to support cloud computing with large distributed data sets. However, its performance is significantly influenced by the working data distributions over those data sets. In this paper, we put forward a novel model to balance data distribution to improve cloud computing performance in data-intensive applications, such as distributed data mining. By extending the classic MapReduce model with an agent-aid layer and abstracting working load requests for data blocks as tokens, the agents can reason from previously received tokens about where to send other tokens in order to balance the working tasks and improve system performance. Our key contribution lies in building an efficient token routing algorithm in spite of agents' unknowing to the global state of data distribution in cloud. We also built a prototype of our system, and the experimental results show that our approach can significantly improve the efficiency of cloud computing. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： MapReduce

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A Resource Space Model for Dataspace

A Resource Space Model for Dataspace

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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： Xiaorui Jiang Xiaoping Sun Hai Zhuge Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China

Dataspace is a semi-structural data model for the management of large-scale heterogeneous data *** a dataspace,each data object consists of a set of attribute-value pairs to describe the internal properties of the object and their relationships to other objects. The dataspace model provides a flexible query language that supports attribute-value pair-based predicate queries and semantic link queries. In this paper we introduce a Resource Space Model that extends the concepts of dataspace to make it more flexible by incorporating classification semantics. We model a dataspace as a set of Resource Spaces (RS) with each RS representing a kind of resources that share common attributes and can be put into the same category, either according to a user’s knowledge of the world or his query preferences. Our model is suitable for designing a user-tailored semantic view of a dataspace while at the same time without losing the schema-later data-centric nature. A practical query language is introduced to implement flexible query operations on the resource spaces of the dataspace. Examples show how the proposed model and language support both the schema query and the data query.

关键词： Semantics Data models Argon Portals Organizations Database languages Resource description framework

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Noise Robust Hard Example Mining for Human Detection with Efficient Depth-Thermal Fusion

Noise Robust Hard Example Mining for Human Detection with Ef...

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International Conference on Automatic Face and Gesture Recognition

作者： Zijian Zhao Jie Zhang Shiguang Shan Key Lab of Intelligent Information Processing Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

Identity-preserving human detection is important for the privacy-protecting applications. IPHD [1] is a newly collected identity-preserving dataset that only contains depth and thermal images, which have much less information than RGB images. While less information and weakly labeled ground-truth boxes make it difficult to locate the objects correctly. In this paper, we adopt an efficient depth-thermal fusion approach to combine these two different inputs and enhance the representation. Moreover, a noise robust hard example mining algorithm is proposed to deal with weakly labeled data. The experiments show that our single model with single scale testing can get the AP=88.1 at IoU=0.5, which is a significant improvement compared with other competition results.

关键词： Noise robustness Feature extraction Training Head Thermal noise Kernel Detectors

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Summarization of Related Work through Citations 12

Summarization of Related Work through Citations

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12th International Conference on Semantics, Knowledge and Grids, SKG 2016

作者： Chen, Jingqiang Zhuge, Hai Nanjing University of Posts and Telecommunications 210023 Nanjing China. Key Lab of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences 100190 Beijing China. Systems Analytics Research Institute Aston University Birmingham UK

ISBN: (纸本)9781509047956

Automatically generating the related work section for a writing paper is useful for researchers as it can save a lot of time and avoid missing related works. The related work section of scientific paper usually introduces other researchers' work published before and makes comparisons with the current author's work. This paper proposes an approach to automatically generating related work by comparing the main text of the writing paper with the citations to the references from other papers. Our approach firstly collects the papers that cite the reference papers of the writing paper and extracts the corresponding citation sentences to form a citation document. It then makes a summarization that makes a comparison between the citation document and the writing paper's abstract, introduction and conclusion. It extracts the representative keywords from the citation document and the writing paper, and constructs a graph of the keywords. The discriminated nodes in the graph are figured out, and then the minimum Steiner tree that covers the nodes is extracted. A summary is generated by extracting the sentences covering the Steiner tree. The experiments show that our approach outperforms three baselines MEAD, ReWoS and ARWG according to ROUGE evaluation results. © 2016 IEEE.

关键词： Trees (mathematics)

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Bilingually-constrained (monolingual) shift-reduce parsing

Bilingually-constrained (monolingual) shift-reduce parsing

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2009 Conference on Empirical Methods in Natural Language processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Huang, Liang Jiang, Wenbin Liu, Qun Google Research 1350 Charleston Rd. Mountain View CA 94043 United States Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

Jointly parsing two languages has been shown to improve accuracies on either or both sides. However, its search space is much bigger than the monolingual case, forcing existing approaches to employ complicated modeling and crude approximations. Here we propose a much simpler alternative, bilingually-constrained monolingual parsing, where a source-language parser learns to exploit reorderings as additional observation, but not bothering to build the target-side tree as well. We show specifically how to enhance a shift-reduce dependency parser with alignment features to resolve shift-reduce conflicts. Experiments on the bilingual portion of Chinese Treebank show that, with just 3 bilingual features, we can improve parsing accuracies by 0.6% (absolute) for both English and Chinese over a state-of-the-art baseline, with negligible (∼6%) efficiency overhead, thus much faster than biparsing. © 2009 ACL and AFNLP.

关键词： Forestry

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