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3rd International Conference on Information Technology and Quantitative management (ITQM)

作者： Liu, Ying Cui, Hongyuan Univ Chinese Acad Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Antenna arrays are used in many digital signal processing applications due to their ability to locate signal sources. Direction of Arrival (DOA) estimation is a key task of array signal processing. Although various algorithms have been developed for DOA estimation, their high complexity prevents their use in real-time applications. In this paper, we design and develop an efficient parallel implementation of DOA on DSP which is the most widely used processor in embedded system. Due to the potential parallelism in MUSIC algorithm, it is selected for 2-D DOA estimation. Two computational cores in MUSIC are identified and parallelized. Vectorization of multiple single precision floating point operations is proposed to make good use of the 128-bit vectors on DSP C6678. Then, the parallel DOA estimation algorithm is implemented on one core of DSP C6678 which is the latest version up to now. Experiments are conducted on both 1-D and 2-D antenna array signals. Considerable performance improvement is obtained. (C) 2015 Published by Elsevier B.V.

关键词： parallel computing signal processing DSP DOA estimation

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Ranking the research productivity of business and management institutions in Asia-Pacific region: empirical research in leading ABS journals

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SCIENTOMETRICS 2015年第2期105卷 1253-1272页

作者： Wu, Dengsheng Li, Minglu Zhu, Xiaoqian Song, Hongfang Li, Jianping Chinese Acad Sci Inst Policy & Management Beijing 100190 Peoples R China Natl Nat Sci Fdn China Bur Planning Beijing 100085 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Ranking research productivity of institutions periodically is of a great necessity nowadays, for that can not only help understand the latest development level of related fields but also contribute to finding gaps and quickly improve. In previous studies, the number of publications and further impact factors of journals is two widely used indexes to measure research productivity. However, impact factors do not always tally with a quality of the journal, which will lead to a bias of research productivity. Given this, a new journal rating adjusted publications (JRAP) index is constructed in this paper, which is based on the journal rating of the ABS journal guide. It takes the quantity of publications and the quality of the publications into account at the same time. Compared with impact factors, academic journal guide can provide more authoritative and accurate measurement to the quality of journals. Experiments are conducted to rank Asia-Pacific institutions in business and management area based on JRAP, and it is the first time to rank Asia-Pacific institutions systematically. Ranking of institutions measured by three methods is also given. Compared the results obtained by three different rank methods, although institutions ranked in the top places keep the same, the specific rank differs. The results indicate that JRAP do prefer the institutions perform well in paper quantity and quality of journals.

关键词： Research productivity Institute ranking Business and management Journal rating adjusted publications ABS journals

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f-Fractional Bit Minwise Hashing for Large-Scale Learning

f-Fractional Bit Minwise Hashing for Large-Scale Learning

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Tang, Jingjing Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China

ISBN: (纸本)9781467396189

Recently, document similarity detection technology captures a host of researchers' attention. In this paper, we propose to integrate linear SVM with f-fractional bit minwise hashing to make a wide range of choices for accuracy and storage space requirements. According to the derived properties of f-fractional bit minwise hashing, we obtained the optimal combination of fractional bit with the minimum estimator of variances and ultimately applied it to the process of integration. The innovation of this algorithm is the continuous selectivity of bit instead of the discrete integer value, which not only improves the theoretical system of b-bit minwise hashing SVM algorithm, but also satisfies the various needs of accuracy and storage space in the practical system. Due to the nonlinear of the resemblance matrix considered as kernel matrix, it can not be used in linear SVM training directly. However, in the theoretical analysis, we provide the proof of positive definiteness for resemblance matrix generated by f-fractional bit minwise hashing scheme, which is a logical and feasible basis for the integration. Meanwhile, experimental results on publicly available large-scale datasets validate the effectiveness of this algorithm.

关键词： minwise hashing f-fractional bit similarity estimation large-scale linear SVM

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Antenna Array Signal Direction of Arrival Estimation on Digital Signal Processor (DSP)

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Procedia Computer Science 2015年 55卷 782-791页

作者： Ying Liu Hongyuan Cui University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

关键词： parallel computing signal processing DSP DOA estimation

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Pedestrian Detection Using Privileged Information

Pedestrian Detection Using Privileged Information

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IEEE International Conference on data mining Workshops (ICDM Workshops)

作者： Zhiquan Qi Yingjie Tian Lingfeng Niu Fan Meng Limeng Cui Yong Shi Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467384940

How to balance the speed and the quality is always a challenging issue in pedestrian detection. In this paper, we introduce the Learning model Using Privileged Information (LUPI), which can accelerate the convergence rate of learning and effectively improve the quality without sacrificing the speed. In more detail, we give the clear definition of the privileged information, which is only available at the training stage but is never available for the testing set, for the pedestrian detection problem and show how much the privileged information helps the detector to improve the quality. All experimental results show the robustness and effectiveness of the proposed method, at the same time show that the privileged information offers a significant improvement.

关键词： Feature extraction Image color analysis Detectors Training Support vector machines Standards Histograms

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China's New Third Board Market: Opportunities and Challenges

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Procedia Computer Science 2015年 55卷 1050-1059页

作者： Yumeng Li Xiangying Meng Xianhua Wei School of Management University of Chinese Academy Sciences Beijing 100010 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences

The New Third Board Market is China's OTC market, established in 2006. Compared to China's Main Board Market and the Second Board Market, it attracts a lot of start-up companies needing financing with lower listing requirements. Meanwhile, it is full of opportunities and challenges that appeal to numerous securities traders and investors with the rapid development momentum. This paper is intended to build a comprehensive and systematic knowledge framework of China's New Third Board Market for those enterprises and individuals interested in it, and to provide a research base for future researchers.

关键词： China's New Third Board Market SME financing market makers opportunities challanges

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Design and Development of FPGA-based High-Performance Radar data Stream mining System 3

Design and Development of FPGA-based High-Performance Radar ...

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3rd International Conference on Information Technology and Quantitative management (ITQM)

作者： Liu, Ying Ma, Pengshan Cui, Hongyuan Univ Chinese Acad Sci Sch Comp & Control Beijing 101408 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Radar signal sorting is a key technique in electronic reconnaissance systems and is currently an important research direction in radar signal processing. Clustering, one of data mining techniques, has been adopted to solve radar sorting problem. However, none of the existing clustering-based methods is able to perform real-time analysis on high speed radar stream. Therefore, in this paper, we propose, design and implement a high performance FPGA-based data stream mining system to perform real-time radar signal sorting on continuous data stream. Firstly, a density-based clustering algorithm is proposed for radar signal sorting;secondly, FPGA-based program of the clustering algorithm is designed and implemented;thirdly, the FPGA board is designed and implemented. Experiments are performed on the board we designed. The results show that the proposed system can achieve real-time radar signal sorting on FPGA, and the resource consumption on FPGA is very low. The clustering algorithm is efficient in terms of accuracy. (C) 2015 Published by Elsevier B.V.

关键词： data mining clustering signal processing FPGA parallel computing

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Improving Ranking-based Recommendation by Social Information and Negative Similarity 3

Improving Ranking-based Recommendation by Social Information...

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3rd International Conference on Information Technology and Quantitative management (ITQM)

作者： Liu, Ying Yang, Jiajun Univ Chinese Acad Sci Sch Comp & Control Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China

Recommender system is able to suggest items that are likely to be preferred by the user. Traditional recommendation algorithms use the predicted rating scores to represent the degree of user preference, called rating-based recommendation methods. Recently, ranking-based algorithms have been proposed and widely used, which use ranking to present the user preference rather than rating scores. In this paper, we propose two novel methods to overcome the weaknesses in VSRank, a state-of-the-art ranking-based algorithm. Firstly, a novel similarity measure is proposed to make better use of negative similarity;secondly, social network information is integrated into the model to smooth ranking. Experimental results on a publicly available dataset demonstrate that the proposed methods outperform the existing widely used ranking-based algorithms and rating-based algorithms considerably. (C) 2015 Published by Elsevier B.V.

关键词： Recommendation system Ranking-based recommendation Collaborative filtering

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Activity Auto-Completion: Predicting Human Activities from Partial Videos 15

Activity Auto-Completion: Predicting Human Activities from P...

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IEEE International Conference on Computer Vision

作者： Xu, Zhen Qing, Laiyun Miao, Jun Chinese Acad Sci Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China

ISBN: (纸本)9781467383905

In this paper, we propose an activity auto-completion (AAC) model for human activity prediction by formulating activity prediction as a query auto-completion (QAC) problem in information retrieval. First, we extract discriminative patches in frames of videos. A video is represented based on these patches and divided into a collection of segments, each of which is regarded as a character typed in the search box. Then a partially observed video is considered as an activity prefix, consisting of one or more characters. Finally, the missing observation of an activity is predicted as the activity candidates provided by the auto-completion model. The candidates are matched against the activity prefix on-the-fly and ranked by a learning-to-rank algorithm. We validate our method on UT-Interaction Set #1 and Set #2 [19]. The experimental results show that the proposed activity auto-completion model achieves promising performance.

关键词： Forecasting

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Challenges to Engineering management in the big data Era

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Frontiers of Engineering management 2015年第3期2卷 293-303,322-323页

作者： Yong Shi Research Center of Fictitious Economy & Data Science University of the Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences

This paper presents a review of the challenges to engineering management in the big data Era as well as the big data applications. First, it outlines the definitions of big data, data science and intelligent knowledge and the history of big data. Second, the paper reviews the academic activities about big data in China. Then, it elaborates a number of challenging big data problems, including transforming semi-structured and non-structured data into"structured format" and explores the relationship of data heterogeneity, knowledge heterogeneity and decision heterogeneity. Furthermore, the paper reports various real-life applications of big data, such as financial and petroleum engineering and internet business.

关键词： big data data science intelligent knowledge engineering management real-life applications

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