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15th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2015

作者： Liu, Chao Zeng, Deze Yao, Hong Hu, Chengyu Yan, Xuesong Fan, Yuanyuan Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430074 China China Services Computing Technology and System Lab and Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783319271392

Hadoop/MapReduce has emerged as a de facto programming framework to explore cloud-computing resources. Hadoop has many configuration parameters, some of which are crucial to the performance of MapReduce jobs. In practice, these parameters are usually set to default or inappropriate values. This severely limits system performance (e.g., execution time). Therefore, it is essential but also challenging to investigate how to automatically tune these parameters to optimize MapReduce job performance. In this paper, we propose an automatic MapReduce configuration optimization framework named as MR-COF. By monitoring and analyzing the runtime behavior, the framework adopts a cost-based performance prediction model that predicts the MapReduce job performance. In addition, we design a genetic search algorithm which iteratively tunes parameters in order to find out the best one. Testbed-based experimental results show that the average MapReduce job performance is increased by 35% with MR-COF compared to the default configuration. © Springer International Publishing Switzerland 2015.

关键词： MapReduce

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A nonlocal interferometric phase filtering strategy for practical InSAR system

A nonlocal interferometric phase filtering strategy for prac...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xue Lin Dongsheng Fang Fangfang Li Ke Wang Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China School of Information Science and Engineering University of Jinan Jinan China Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Information Science and Engineering Henan University of Technology Zhengzhou China

Nonlocal interferometric phase filtering methods achieve excellent performance in both noise reduction and texture preservation, even in the case of complicated topography and low coherence. The main limitation of the nonlocal methods is the computational burden. This paper proposed a nonlocal phase filtering strategy for the practical InSAR system, which combine the nonlocal algorithm with the traditional method to improve the efficiency.

关键词： Filtering Coherence Noise reduction Frequency estimation Estimation Algorithm design and analysis Surfaces

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Aimed information quantity in text

Aimed information quantity in text

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作者： Chen, Jingqiang Zhuge, Hai Nanjing University of Posts and Telecommunications No.9 Wenyuan RoadYadong New District Nanjing210023 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

People often read with aims and reading process significantly influences understanding. This paper defines a new measure of information in text named Aimed information Quantity in Text by simulating human reading process with reading aims. Aimed information Quantity in Text reflects not only human memory of words but also relevancy between words emerging in aimed reading process. We demonstrate its application in recommendation. © 2015 John Wiley & Sons, Ltd.

关键词： Behavioral research

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Understanding taxi drivers＇ routing choices from spatial and social traces

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Frontiers of Computer Science 2015年第2期9卷 200-209页

作者： Siyuan LIU Shuhui WANG Ce LIU Ramayya KRISHNAN Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China University of Chinese Academy of Sciences Beijing 100049 China Heinz CoUege Carnegie Mellon University Pittsburgh PA 15213 USA Key Lab of Intellectual Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Information Sciences University of Pittsburgh Pittsburgh PA 15260 USA

Most of our learning comes from other people or from our own experience. For instance, when a taxi driver is seeking passengers on an unknown road in a large city, what should the driver do？ Alternatives include cruising around the road or waiting for a time period at the roadside in the hopes of finding a passenger or just leaving for another road enroute to a destination he knows （e.g., hotel taxi rank）？ This is an interesting problem that arises everyday in cities all over the world. There could be different answers to the question poised above, but one fundamental problem is how the driver learns about the likelihood of finding passengers on a road that is new to him （as he has not picked up or dropped off passengers there before）. Our observation from large scale taxi driver trace data is that a driver not only learns from his own experience but through interactions with other drivers. In this paper, we first formally define this problem as socialized information learning （SIL）, second we propose a framework including a series of models to study how a taxi driver gathers and learns information in an uncertain environment through the use of his social network. Finally, the large scale real life data and empirical experiments confirm that our models are much more effective, efficient and scalable that prior work on this problem.

关键词： routing choices socialized information learn-ing social network

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LENSELET IMAGE COMPRESSION SCHEME BASED ON SUBAPERTURE IMAGES STREAMING

LENSELET IMAGE COMPRESSION SCHEME BASED ON SUBAPERTURE IMAGE...

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IEEE International Conference on Image processing

作者： Feng Dai Jun Zhang Yike Ma Yongdong Zhang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (纸本)9781479983407

Plenoptic cameras capture the light field in a scene with a single shot and produce lenselet images. From a lenselet image, light field can be reconstructed, with which we can render images with different viewpoints and focal length. Because of large volume data, high efficient image compression scheme for storage and transmission is urgent. Containing 4D light field information, lenselet images have much more redundant information than traditional 2D images. In this paper, we propose a subaperture images streaming scheme to compress lenselet images, in which rotation scan mapping is adopted to further improve compression efficiency. The experiment results show our approach can efficient compress the redundancy in lenselet images and outperform traditional image compression method.

关键词： Plenoptic camera Lenselet image Light field Image compression Subaperture images streaming Light fields Image compression Data compression images image compression method single-shot two dimentional images FOCAL LENGTH redundancy

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A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

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2016 IEEE International Conference on Progress in Informatics and computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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A kind of binary image complete level authentication method based on hamming code and digital watermark

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Journal of information and Computational Science 2015年第1期12卷 181-190页

作者： Li, Min Deng, Shaobo Scholar of Information Engineering Nanchang Institute of Technology Nanchang China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Each pixel of the binary image can be viewed as a binary number. Base on this characteristic, we design a supervisory relationship. A detection image is gotten from the supervisory relationship and binary image. Based on this, a kind of pixel level digital watermark complete authentication method is put forward. This method is able to conduct binary image pixel authentication. When authenticating, only from the detection image, people can accurately identify whether a pixel point is falsified or not, furthermore, it has falsification localization and repair capacity.

关键词： Authentication

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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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No reference video quality assessment based on 3D convolutional neural network

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Ruan Jian Xue Bao/Journal of Software 2016年 27卷 103-112页

作者： Wang, Chun-Feng Su, Li Zhang, Wei-Gang Huang, Qing-Ming Key Laboratory on Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology The Chinese Academy of Sciences Beijing100190 China Weihai264209 China

No reference video quality assessment (NR-VQA) measures distorted videos quantitatively without the reference of original high quality videos. Conventional NR-VQA methods are generally designed for specific types of distortions, or not consistent with human's perception. This paper innovatively introduces 3D deep convolutional neural network (3D-CNN) into VQA and proposes a 3D-CNN based NR-VQA method, which is universal for non-specific types of distortions. First, the proposed method utilizes 3D patches to learn spatio-temporal features that represent video content effectively. Second, the original 3D-CNN model is modified which is used to classify videos to make it adapt to VQA task. Experiments demonstrate that the proposed method is highly consistent with human's perception across numerous distortions and metrics. Compared with other state-of-the-art no-reference VQA methods, the proposed method runs much faster while keeping the similar performance. As a no-reference VQA method, it is even comparable with many of the state-of-the-art full-reference VQA methods, which provides the proposed method with better application prospects. © Copyright 2016, institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Convolution

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SEMANTICS CONSTRAINED DICTIONARY LEARNING FOR SIGNER-INDEPENDENT SIGN LANGUAGE RECOGNITION

SEMANTICS CONSTRAINED DICTIONARY LEARNING FOR SIGNER-INDEPEN...

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IEEE International Conference on Image processing

作者： Fang Yin Xiujuan Chai Yu Zhou Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Institute of Information Engineering Chinese Academy of Sciences

ISBN: (纸本)9781479983407

In this paper, a sparse coding based framework is proposed for sign language recognition (SLR), especially for the signer-independent case. To deal with the inter-signer variation, a dictionary capturing the common features among different signers is learnt by considering the semantic constraint. Thus for a given sign from an unknown signer, the sparse representation, which maintains more information of this specific sign class while neglecting the identity information as much as possible, can be generated. In our implementation, each sign is partitioned into a fixed number of fragments and the features fusing hand shape and moving trajectory are extracted from the fragments. The dictionary learnt from the training fragments can be taken as the basic subunits of signs and each fragment of sign video can be coded by these basis vectors. Finally, the recognition result is achieved through SVM with the concatenated sparse coding features of the fragments. The experiments and comparisons show that our method is more effective for the signer-independent recognition problem than other baseline methods. At the same time, it also performs well for the signer-dependent case.

关键词： Sparse coding Dictionary learning Sign language recognition Signer-independent Semantic constraint

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