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Construction of the recognition model for trigger words of Chinese acupuncture and moxibustion events

Journal of Information Hiding and Multimedia Signal Processing 2017年第6期8卷 1283-1293页

作者： Sun, Shuihua Huang, Degen Zhang, Jing School of Computer Science and Technology Dalian University of Technology Dalian116024 China College of Information Science and Engineering Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

The recognization of trigger words for Chinese acupuncture and moxibustion events is a key step in the extraction of Chinese acupuncture and moxibustion events. It plays an important role in knowledge mining in the field of Chinese acupuncture and moxibustion. This paper extracts the manually annotated trigger words from the training corpus, constructs the table of trigger words for Chinese acupuncture and moxibustion events, then expands this table with the Tongyici Cilin (Chinese thesaurus), and identifies the candidate trigger words for Chinese acupuncture and moxibustion events based on the extended trigger word table;and then according to characteristics of the expressions in the field of Chinese acupuncture and moxibustion, this paper prepares the filtering rules for candidate trigger words for Chinese acupuncture and moxibustion events. This paper uses the above techniques to construct the recognition model of trigger words for Chinese acupuncture and moxibustion events that integrates dictionary matching and rule-based filtering. The results of the experiment show that this model has a good trigger word recognition function, and F-measure of trigger words recognition for the cure and health events reaches 88.28% and 34.15% respectively. © 2017, Ubiquitous International. All rights reserved.

关键词： Vocabulary control

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Analysis of the Relation between Artificial Intelligence and the Internet from the Perspective of Brain Science

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Procedia Computer Science 2017年 122卷 377-383页

作者： Feng Liu Yong Shi Peijia Li Research Center on Fictitious Economy and Data Science the Chinese Academy of Sciences Beijing 100190 China The Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China

Artificial intelligence (AI) like deep learning, cloud AI computation has been advancing at a rapid pace since 2014. There is no doubt that the prosperity of AI is inseparable with the development of the Internet. However, there has been little attention to the link between AI and the internet. This paper explores them with brain insights mainly from four views:1) How is the general relation between artificial intelligence and Internet of Things, cloud computing, big data and Industrial Internet from the perspective of brain science. 2) Construction of a new AI system model with the Internet and brain science.

关键词： Internet’s virtual brain Big SNS AI model of the Internet-like brain

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Research on entity relation extraction in TCM acupuncture and moxibustion field

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Journal of Information Hiding and Multimedia Signal Processing 2017年第2期8卷 358-367页

For the context of entity relation instance in TCM acupuncture and moxibustion field, effective words, syntax and semantics features are chosen to combine into feature template, and the entity relation instances are vectorized. The classification models of entity relations in TCM acupuncture and moxibustion area are trained by the machine learning method which is based on support vector machine. The experimental results show that the feature template of entity relations in this thesis have excellent effects on the entity relation extraction in TCM acupuncture and moxibustion area. The F-measure of entity relation classification model of DM, HM, AM and DRM reached 93.25%, 87.19%, 86.57%and84.57% respectively. © 2017 ISSN.

关键词： Support vector machines

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Inverse extreme learning machine for learning with label proportions

Inverse extreme learning machine for learning with label pro...

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IEEE International Conference on Big data

作者： Limeng Cui Jiawei Zhang Zhensong Chen Yong Shi Philip S. Yu School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Computer Science Florida State University FL USA Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China Department of Computer Science University of Illinois at Chicago IL USA

In large-scale learning problem, the scalability of learning algorithms is usually the key factor affecting the algorithm practical performance, which is determined by both the time complexity of the learning algorithms and the amount of supervision information (i.e., labeled data). Learning with label proportions (LLP) is a new kind of machine learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, LLP can estimate a classifier from groups of weakly labeled data, where only the positive/negative class proportions of each group are known. Due to its weak requirements for the input data, LLP presents a variety of real-world applications in almost all the fields involving anonymous data, like computer vision, fraud detection and spam filtering. However, even through the required labeled data is of a very small amount, LLP still suffers from the long execution time a lot due to the high time complexity of the learning algorithm itself. In this paper, we propose a very fast learning method based on inversing output scaling process and extreme learning machine, namely Inverse Extreme Learning Machine (IELM), to address the above issues. IELM can speed up the training process by order of magnitudes for large datasets, while achieving highly competitive classification accuracy with the existing methods at the same time. Extensive experiments demonstrate the significant speedup of the proposed method. We also demonstrate the feasibility of IELM with a case study in real-world setting: modeling image attributes based on ImageNet Object Attributes dataset.

关键词： Training Calibration Support vector machines Supervised learning Visualization Optimization

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Comments on a privacy preserving public auditing mechanism for shared cloud data 17

Comments on a privacy preserving public auditing mechanism f...

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4th Multidisciplinary International Social Networks Conference, MISNC 2017

作者： Wu, Tsu-Yang Lin, Yueshan Wang, King-Hang Chen, Chien-Ming Pan, Jeng-Shyang Wu, Mu-En Fujian Provincial Key Laboratory of Big Data Mining and Applications National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China Harbin Institute of Technology Shenzhen Graduate School Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Mathematic Soochow University Taipei Taiwan

ISBN: (纸本)9781450348812

Auditing mechanisms are received much attentions from researchers due to the developing of cloud storage. Recently, Wang et al. proposed a privacy preserving public auditing mechanism for shared cloud data with supporting group dynamic. However, we find some security flaws in their mechanism. In this paper, a forgery attack and a data corruption attack are proposed to demonstrate the insecurity. Then, we propose a modified scheme based on their mechanism. The security analysis is demonstrated that our improvement can resist the mentioned two attacks. © 2017 ACM.

关键词： Cloud storage

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Tri-level combination for image representation 17th

Tri-level combination for image representation

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Ruiying Zhang, Chunjie Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319488899

The context of objects can provide auxiliary discrimination beyond objects. However, this effective information has not been fully explored. In this paper, we propose Tri-level Combination for Image Representation (TriCoIR) to solve the problem at three different levels: object intrinsic, strongly-related context and weakly-related context. Object intrinsic excludes external disturbances and more focuses on the objects themselves. Strongly-related context is cropped from the input image with a more loose bound to contain surrounding context. Weaklyrelated one is recovered from the image other than object for global context. First, strongly and weakly-related context are constructed from input images. Second, we make cascade transformations for more intrinsical object information, which depends on the consistency between generated global context and input images in the regions other than object. Finally, a joint representation is acquired based on these three level features. The experiments on two benchmark datasets prove the effectiveness of TriCoIR. © Springer International Publishing AG 2016.

关键词： Image representation

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Efficient ontology meta-matching based on metamodel-assisted compact MOEA/D

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Journal of Information Hiding and Multimedia Signal Processing 2017年第5期8卷 1021-1028页

作者： Xue, Xingsi Tsai, Pei-Wei Feng, Guirong College of Information Science and Engineering Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications National Demostration Center for Experimental Electronic Information and Electronical Technology Education Fujian University of Technology No.3 Xueyuan Road University Town Minhou FuzhouFujian350118 China School of Electrical Engineering Xidian University No.2 South Taibai Road XianShaanxi710071 China

In this paper we propose an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), i.e. metamodel-assisted compact MOEA/D, to efficiently solve the ontology meta-matching problem. Particularly, we dedicate to determine the optimal ontology alignment by tuning four different basic similarity measures, i.e. Syntactic Measure, Linguistic Measure, Taxonomy-based Measure and Instance-based Measure. The experimental results show that our proposal can significantly improve the efficiency of MOEA/D based approach without sacrificing the quality of the ontology alignment, and the results obtained by our approach are better than state-of-the-art ontology matching systems. © 2017, Ubiquitous International. All rights reserved.

关键词： Ontology

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AN EXTENDED MODEL FOR PROJECT PORTFOLIO SELECTION WITH PROJECT DIVISIBILITY AND INTERDEPENDENCY

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Journal of Systems Science and Systems engineering 2016年第1期25卷 119-138页

作者： Xingmei Li Shu-Cherng Fang Xiaoling Guo Zhibin Deng Jianxun Qi School of Economics and Management North China Electric Power University Beijing 102206 China Industrial and Systems Engineering Department North Carolina State University Raleigh NC 27695 USA College of Engineering andlnformation Technology University of Chinese Academy of Sciences Beijing100049 China School of 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

In this paper, we develop an extended model for the project portfolio selection problem over a planning horizon with multiple time periods. The model incorporates the factors of project divisibility and interdependency at the same time for real-life applications. The project divisibility is considered as a strategy, not an unfortunate event as in the literature, in choosing the best execution schedule for the projects, and the classical concept of＂project interdependencies＂ among fully executed projects is then extended to the portions of executed projects. Additional constraints of reinvestment consideration, setup cost, cardinality restriction, precedence relationship and scheduling are also included in the model. For efficient computations, an equivalent mixed integer linear programming representation of the proposed model is derived. Numerical examples under four scenarios are presented to highlight the characteristics of the proposed model. In particular, the positive effects of project divisibility are shown for the first time.

关键词： Project management project portfolio selection problem project divisibility project interdependency

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Biogeography-based optimization for cluster analysis

Biogeography-based optimization for cluster analysis

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International Conference on Computer, Communication and Computational Sciences, ICCCCS 2016

作者： Wu, Xueyan Wang, Hainan Chen, Zhimin Lu, Zhihai Phillips, Preetha Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Nanjing Normal University NanjingJiangsu210023 China Key Laboratory of Statistical Information Technology & Data Mining State Statistics Bureau ChengduSichuan610225 China School of Computer and Information Engineering Henan Normal University XinxiangHenan453000 China School of Electronic Information Shanghai Dianji University Shanghai200240 China Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology GuilinGuangxi541004 China School of Natural Sciences and Mathematics Shepherd University ShepherdstownWV25443 United States West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Department of Electrical Engineering The City College of New York CUNY New YorkNY10031 United States State Key Lab of CAD & CG Zhejiang University HangzhouZhejiang310027 China Manchester Metropolitan University ManchesterM156BH United Kingdom

ISBN: (纸本)9789811037696

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Cluster analysis

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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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