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CSMCCVA:Framework of cross-modal semantic mapping based on cognitive computing of visual and auditory sensations

High Technology Letters 2016年第1期22卷 90-98页

作者：刘扬 Zheng Fengbin Zuo Xianyu Laboratory of Spatial Information Processing Henan University College of Computer Science and Information Engineering Henan University College of Environment and Planning Henan University Institute of Data and Knowledge Engineering Henan University

Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search *** study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory *** mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the ***,the algorithms of cross-modal semantic mapping were *** results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.

关键词： multimedia neural cognitive computing （MNCC） brain-like computing cross-modal semantic mapping （CSM） selective attention limbic system multisensory integration memory-enhancing mechanism

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How China Deals with Big data

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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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Utility-based anonymisation for dataset with multiple sensitive attributes

Utility-based anonymisation for dataset with multiple sensit...

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作者： Wang, Lixia Zhu, Qing Key Laboratory for Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing100872 China

Privacy-preserving data publication problem has attracted more and more attentions in recent years. A lot of related research works have been done towards dataset with single sensitive attribute. However, usually, original dataset contains more than one sensitive attribute. In this paper, we apply k-anonymity principle to solve the data publication problem for dataset with multiple sensitive attributes. We first cluster sensitive values based on a utility matrix. Then, we use a greedy strategy to partition tuples into equivalence classes. Our method can guarantee that the size of equivalence class is k except the last one, which reduces information loss. Also, we can guarantee the diversity of sensitive value in an equivalence class, which can protect privacy against the homogeneity attack. Experiments on a real dataset show that our method performs well on information loss, which indicates that we can guarantee data utility while protecting personal privacy. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Equivalence classes

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Linked data Crowdsourcing Quality Assessment based on Domain Professionalism

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Lu Yang Li Huang Zhenzhen Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

With the rapid development of Internet technology, crowdsourcing, as a flexible, effective and low-cost problem-solving method, has begun to receive more and more attention. The use of crowdsourcing to evaluate the quality of linked data has also become a research hotspot. This paper proposes the concept of Domain Specialization Test (DST), which uses domain professional testing tasks DSTs to evaluate the professionalism of workers, and combines the idea of Mini-batch Gradient Descent (MBGD) to improve the EM algorithm, and the MBEM algorithm is proposed to achieve efficient and accurate evaluation of task results. The experimental results show that the proposed method can screen out the appropriate workers for the linked data crowdsourcing task and improve the accuracy and iteration efficiency of the results.

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Accuracy estimation of link-based similarity measures and its application

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Frontiers of Computer Science 2016年第1期10卷 113-123页

作者： Yinglong ZHANG Cuiping LI Chengwang XIE Hong CHEN School of Software East China Jiaotong University Nanchang 330045 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and Department of Computer Science Renmin University of China Beijing 100872 China Intelligent Optimization and Information Processing Laboratory East China Jiaotong University Nanchang 330013 China

Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank （PPR） and Sim- Rank （SR） have emerged as the most popular and influen- tial link-based similarity measures. Recently, a novel link- based similarity measure, penetrating rank （P-Rank）, which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calcula- tion. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guaran- tee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition： the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similar- ity scores becomes. Furthermore, we demonstrate the effec- tiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of exper- iments on real world data sets to verify the effectiveness and efficiency of our upper bounds.

关键词： personalized PageRank SimRank P-Rank up-per bound

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Emergency Event Matching using Hierarchical Blocking Method

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Chang Wen Yu Liu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

With the extensive application of the knowledge base (KB), how to complete it is a hot topic on Semantic Web. However, many problems go with the big data, and the event matching is one of these problems, which is finding out the entities referring to the same things in the real world and also the key point in the extending process. To enrich the emergency knowledge base (E-SKB) we constructed before, we need to filter out the news from several web pages and find the same news to avoid data redundancy. In this paper, we proposed a hierarchy blocking method to reduce the times of comparisons and narrow down the scope by extracting the news properties as the blocking keys. The method transforms the event matching problem into a clustering problem. Experimental results show that the proposed method is superior to the existing text clustering algorithm with high precision and less comparison times.

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Examining scientific writing styles from the perspective of linguistic complexity

arXiv

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arXiv 2018年

作者： Lu, Chao Bu, Yi Wang, Jie Ding, Ying Torvik, Vetle Schnaars, Matthew Zhang, Chengzhi School of Economics and Management Nanjing University of Science and Technology Nanjing Jiangsu China School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Information Management Nanjing University Nanjing Jiangsu China School of Information Management Wuhan University Wuhan Hubei China School of Information Sciences University of Illinois UrbanaIL United States Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing Jiangsu China

Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. In order to uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (1) syntactic complexity, including measurements of sentence length and sentence complexity;and (2) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity. Copyright © 2018, The Authors. All rights reserved.

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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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Towards an Integrated Approach to Simultaneously Estimating the Frequency and Amplitude Modulations of SSVEP Signals from Consumer-grade EEG

arXiv

引用

arXiv 2018年

作者： Autthasan, Phairot Du, Xiangqian Perera, Maneesha Lamyai, Sirakorn Itthipuripat, Sirawaj Yagi, Tohru Manoonpong, Poramate Wilaiprasitporn, Theerawit Rayong Thailand Department of Psychology Vanderbilt University Tennessee and Neurosciences Graduate Program University of California California USA and Learning Institute King Mongkuts University of Technology Thonburi Bangkok Thailand BRAIN Lab IST VISTEC Rayong Thailand Embodied AI & Neurorobotics Centre for BioRobotics Msk McKinney Mller Institute University of Southern Denmark Odense MDK-5230 Denmark Yagi Lab Department of Mechanical Engineering Tokyo Institute of Technology Tokyo Japan Data Analysis and Knowledge Discovery Laboratory Department of Computer Engineering Kasetsart University Bangkok Thailand department of Computer Engineering Sirindhorn International Institute of Technology Pathum Thani Thailand

For several decades, researchers have sought several solutions for helping patients who suffer from neuromuscular disorders. One of a few promising approaches is to invent brain-computer interfaces (BCIs) in which brain signals could be used to control computers and prosthetics. Due to their high signal-to-noise ratio, steady-state visually evoked potentials (SSVEPs) or electrical brain signals that oscillate at the same frequency as the frequency of a flickering visual stimulus on the computer screen has been widely used to build BCIs. While researchers have focused on developing frequency recognition algorithms for SSVEP-based BCIs, these algorithms do not precisely predict the modulation of SSVEP amplitude, known to change as a function of stimulus luminance contrast. Thus, in the present study we aimed to develop an integrated approach to simultaneously estimating the frequency and contrast-related amplitude modulations of the SSVEP signal. To do so, we developed a behavioral task in which human participants looked at a visual target flicking at a 7.5 Hz. Critically, across different experimental conditions, the luminance contrast of the visual target could change across time in several ways (i.e., the luminance contrast remained constant, increased gradually, decreased gradually, or increased and then decreased). SSVEPs were measured via an open-source and low-cost consumer-grade EEG device, namely OpenBCI, placed at the central occipital site. Our results displayed that the filter bank canonical correlation analysis (FBCCA) performed generally well in SSVEP frequency recognition, while support vector regression (SVR) outperformed the other supervised machine learning algorithms in predicting the contrast-dependent amplitude modulations of the SSVEP signal. These results demonstrated the applicability and efficiency of our integrated method at simultaneously predicting both frequency and amplitude of visually evoked signals in the human brain, proven useful for

关键词： Brain computer interface

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Random analysis of statistical rough set

Random analysis of statistical rough set

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Tsang, Eric C.C. Zhao, Su-Yun Faculty of Information Technology Macau University of Science and Technology C-Macau China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

ISBN: (纸本)9781509003891

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling. © 2016 IEEE.

关键词： Sampling

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