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Annals of data Science 2017年第4期4卷 433-440页

作者： Shi, Yong Shan, Zhiguang Li, Jianping Fang, Yufei School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Informatization Research Department State Information Center Beijing100045 China Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China School of Computer and Communication Engineering University of Science and Technology Beijing100083 China Informatization Research Department State Information Center Beijing100045 China

On September 5, 2015, the State Council of Chinese Government, China’s cabinet formally announced its Action Framework for Promoting big data (***, 2015). This is the milestone for China to catch up the global wave of big data. Since 2012 big data became a hot issue for scientific communities as well as the governments of many countries (Lazer et al. in Science 343:1203–1205, 2014;Einav et al. in Science 345:715, 2014;Cate in Science 346:818, 2014;Khoury and Ioannidis in Science 346:1054–1055, 2014). At the 2013 G8 Summit, the leaders of Canada, France, Germany, Italy, Japan, Russia, U.S.A. and United Kingdom agreed on an "open government plan" (***/government/publications/open-data-charter/g8-open-data-charter-and-technical-annex, 2013). China’s framework, however, mainly emphasizes the integration of all trans-departmental data and establishes a number of government-driven national big data platforms so as to provide big data services to research, public and enterprises. The framework not only demonstrates a strong commitment of the Chinese government on big data, but also covers a wide range of governmental branches, enterprises and institutions far more than that of other countries. In addition, the framework shows an interpretation of big data that differs from other countries. If its objective is achieved, China would become a strong "big data country". © 2017, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： big data

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Improved New Word Detection Method Used in Tourism Field

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Procedia Computer Science 2017年 108卷 1251-1260页

作者： Wei Li Kun Guo Yong Shi Luyao Zhu Yuanchun Zheng Fictitious Economy & Data Science Research Center Chinese Academy of Science Beijing China School of Economics and Management UCAS Beijing China Key Laboratory of Big Data Mining and knowledge Management Chinese Academy of Sciences

Chinese segmentation has attracted amounts of attention in natural language processing in recent years and is the basis of web text mining. The article improved statistics-based method EMI, then we use improved approach to detect new words in tourism field. The result demonstrates that our method can detect new words significantly, especially in detecting proper nouns and sentiment words which will be helpful in subsequent tasks such as sentiment analysis and word embedding. In additional, this paper analyze parameters which are influential on the effects of new words detection. At last, the article discussed possible application of new word detection in sentiment analysis.

关键词： EMI PMI online travel new word detection

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The impact of the change of trade pattern on China’s energy industry——Based on the measurement of the upstreamness of production of the value chains

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Procedia Computer Science 2018年 139卷 248-255页

作者： Xinran Wang Xiaoxian Guo Yan Song Shasha Cao Tian Wu Xin Tian Zhenping Li Shanghai Pudong Development Bank Shnaghai 200000 China China Petroleum Technology and Development Corporation Beijing 100028 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Information Beijing Wuzi University Beijing 101149 China

The natural monopoly of China’s upstream industry and the vertical structure of industrial chain’s upstream and downstream competition is an important feature of China’s industrial economic structure, and the impact of the change of trade pattern on the energy industry, which located at the upstream of the China’s value chain, has become an important issue in the industrial economy. Since China has entered into the WTO in 2001, we use it for event analysis based on 1987-2007 China input-output data. The study calculates the upstreamness of production of the value chain in various industrial sectors of China’s energy industry every five years, and make correlation analysis of upstreamness of production and other economic indicators to check the characteristics of periodic inspection of China’s industrial structure with the development of industrial economy and the change of trade pattern.

关键词： Energy industry industrial economic structure upstreamness of production trade pattern

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Research and Realization of Ten-print data Quality Control Techniques for Imperial Scale Automated Fingerprint Identification System

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Journal of Forensic Science and Medicine 2017年第2期3卷 90-96页

作者： Qian Wang Wei Wang Wei Zhang Tong Zhao Guangnv Jin Division of Forensic Science Liaoning Provincial PoliceShenyang Beiji ng Oriental Golden Fing er Technology Company Limited School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijingPeople's Republic of China

As the first individualization-information processing equipment put into practical service worldwide,Automated Fingerprint Identification System(AFIS)has always been regarded as the first choice in individualization of criminal suspects or those who died in mass *** integrating data within the existing regional large-scale AFIS database,many countries are constructing an ultra large state-of-the-art AFIS(or Imperial Scale AFIS)***,it is very important to develop a series of ten-print data quality controlling process for this system of this type,which would insure a substantial matching efficiency,as the pouring data come into this imperial scale *** the image quality of ten-print data is closely relevant to AFIS matching proficiency,a lot of police departments have allocated huge amount of human and financial resources over this issue by carrying out manual verification works for ***,quality control method above is always proved to be inadequate because it is an astronomical task involved,in which it has always been problematic and less affiant for potential ***,we will implement quality control in the above procedure with supplementary-acquisition effect caused by the delay of feedback instructions sent from the human verification *** this article,a series of fingerprint image quality supervising techniques has been put forward,which makes it possible for computer programs to supervise the ten-print image quality in real-time and more accurate manner as substitute for traditional manual *** its prominent advantages in the human and financial expenditures,it has also been proved to obviously improve the image quality of the AFIS ten-print database,which leads up to a dramatic improvement in the AFIS-matching accuracy as well.

关键词： big data fingerprint data quality control Imperial Scale Automated Fingerprint Identification System rank sum ratio

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Hybrid Learning Network: A Novel Architecture for Fast Learning

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Procedia Computer Science 2017年 122卷 622-628页

作者： Ying Liu Chao Xiang School of Computer and Engineering University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

There’re many effective architectures of the artificial neural network(ANN). For which the training is a hard work. The cost for training an ANN increases exponentially when the ANN gets deeper or wider. We therefore propose a novel architecture, the Hybrid Learning Network(HLN), to achieve a fast learning with good stablity. The HLN can learn from both labeled data and unlabeled data at the same time in a hybrid learning manner. It uses a Self Organizing Map unified by the specially designed nonlinear function as the sparsity mask for a hidden layer to improve the training speed. We experiment our architecture on a synthetic dataset to test its regression capability against the traditional architecture, the result is promising.

关键词： hybrid learning neural network sparsity mask self organizing map fast learning

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A generalized concept-cognitive learning: A machine learning viewpoint

arXiv

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arXiv 2018年

作者： Mi, Yunlong Shi, Yong Li, Jinhai School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha NE68182 United States Faculty of Science Kunming University of Science and Technology Kunming650500 China

Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described. To this end, we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a generalized concept-cognitive learning (GCCL) from the point of view of machine learning. Finally, experiments on some data sets are conducted to verify the feasibility of concept formation and concept-cognitive process of GCCL. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Saliency detection with two-level fully convolutional networks

Saliency detection with two-level fully convolutional networ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yang Yi Li Su Qingming Huang Zhe Wu Chunfeng Wang University of Chinese Academy of Sciences Beijing China Key Lab on Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509060689

This paper proposes a deep architecture for saliency detection by fusing pixel-level and superpixel-level predictions. Different from the previous methods that either make dense pixellevel prediction with complex networks or region-level prediction for each region with fully-connected layers, this paper investigates an elegant route to make two-level predictions based on a same simple fully convolutional network via seamless transformation. In the transformation module, we integrate the low level features to model the similarities between pixels and superpixels as well as superpixels and superpixels. The pixel-level saliency map detects and highlights the salient object well and the superpixel-level saliency map preserves sharp boundary in a complementary way. A shallow fusion net is applied to learn to fuse the two saliency maps, followed by a CRF post-refinement module. Experiments on four benchmark data sets demonstrate that our method performs favorably against the state-of-art methods.

关键词： Feature extraction Image color analysis Semantics Predictive models Fuses Image segmentation Computational modeling

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Metric learning for multi-instance classification with collapsed bags

Metric learning for multi-instance classification with colla...

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International Joint Conference on Neural Networks (IJCNN)

作者： Dewei Li Dongkuan Xu Jingjing Tang Yingjie Tian Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509061839

As a kind of popular problem in machine learning, multi-instance task has been researched by means of many classical methods, such as kNN, SVM, etc. For kNN classification, its performance on traditional task can be boosted by metric learning, which seeks for a data-dependent metric to make similar examples closer and separate dissimilar examples by a margin. It is a challenge to define distance between bags in multi-instance problem, let alone learning appropriate metric for the problem. In this paper, we propose a new approach for multi-instance classification, with the idea of metric learning embedded. A new kind of distance is used to measure the similarity between bags. To weaken redundant information from bags and reduce computation complexity, k-means method is implemented to get collapsed bags by replacing each instance with its corresponding cluster centroid. The aim of metric learning is to expand inter-class bag distance and shrink intra-class bag distance, leading to the construction of an optimization problem with maximal relative distance. Kernel function can be introduced into the model to extract nonlinear information from the inputs. Gradient descent is utilized to solve the problem effectively. Numerical experiments on both artificial datasets and benchmark datasets demonstrated that the method can obtain competitive performance comparative to kNN and the state-of-the-art method in multi-instance classification.

关键词： Measurement Feature extraction data mining Optimization Kernel Training Drugs

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Creating knowledge and Wisdom via big data Analytics: Preface for ITQM 2017

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Procedia Computer Science 2017年 122卷 1-9页

作者： Ahuja, Vandana Shi, Yong Khazanchi, Deepak Abidi, Naseem Tian, Yingjie Berg, Daniel Tien, James M. Jaypee Business School A-10 Sector-62 Noida Uttar Pradesh201 307 India School of Economics and Management University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha Chinese Academy of Sciences OmahaNE68182 United States College of Engineering University of Miami Coral GablesFL33124 United States

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The Tourism-Specific Sentiment Vector Construction Based on Kernel Optimization Function

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Procedia Computer Science 2017年 122卷 1162-1167页

作者： Luyao Zhu Wei Li Kun Guo Yong Shi Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing China Fictitious Economy & Data Science Research Center Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

Sentiment analysis in tourism domain has drawn much attention in past few years, which calls for more precise sentiment word embedding method. The article proposes a kernel optimization function for sentiment word embedding. And the method aims at integrating the semantic information, statistics information and sentiment information and maintains the similarity between sentiment words in terms of sentiment orientation. The experiment result shows that the optimal sentiment vectors successfully extract the features in terms of sentiment information and the difference between concretization and abstraction of a sentiment words.

关键词： kernel function sentiment vector word embedding sentiment analysis

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