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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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Random analysis of statistical rough set

Random analysis of statistical rough set

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International Conference on Machine Learning and Cybernetics (ICMLC)

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

ISBN: (纸本)9781509003914

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.

关键词： Rough sets Standards Cybernetics Fluctuations Stability analysis Big data Algorithm design and analysis

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Genre-based Link Prediction in Bipartite Graph for Music Recommendation

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Procedia Computer Science 2016年 91卷 959-965页

作者： Daozhen Zhao Lingling Zhang Weiqi Zhao 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 National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences BeiJing 100190 China

The bipartite graph method of link prediction can apply in many fields of recommendations, with the nodes (users and items) and links (interactions between users and items). However, that links cannot represent the users’ dual preferences (like and dislike). Some researchers improved that limits by complex number representations, but still not consider the influence of users’ similarity recommendation performance. Here, we proposed an improved method to cope with this deficiency, build the relational dualities by complex number representations and computing the users’ similarity by genres weight relations. In experiments with the *** music dataset, the proposed music genre weight-based music recommendation model (MGW) performances better than the CORLP method.

关键词： Music recommendation Link prediction Music genres

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Image reconstruction algorithm of compressed sensing based on neural dynamics

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Journal of Information Hiding and Multimedia Signal Processing 2017年第3期8卷 623-631页

作者： Gan, Zhenhua Xiong, Baoping Zou, Fumin Liao, Lyuchao Du, Min College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province China College of Electrical Engineering and Automation Fuzhou University No.2 Xueyuan Road University Town Minhou Fuzhou CityFujian Province China Fujian Provincial Key Laboratory of Big Data Mining and Applications No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province China Key Lab of Automotive Electronics and Electric Drive Technology of Fujian Province No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province China

To improve the recovery performance of compressed sensing (CS) recon- struction, this paper proposed a CS reconstruction model based on neural dynamics optimization algorithm (NDOA) with l1-norm minimization, and applied it to the two- dimensional (2D) image signals to verify the reconstruction effectiveness. Experiments proved the NDOA had performed better in reconstruction than the orthogonal matching pursuit (OMP) and its improved algorithms considering its perfect recovery. When the compressed sampling rate is 0:875 for 64×64 Lena image, the application of NDOA would raise the peak signal to noise ratio (PSNR) by 3:6466dB while decrease the relative error (REOR) by 2:21% in comparison with OMP and SWOMP. It showed that the effective- ness of CS reconstruction of NDOA was evident. At the same time, the adoption of NDOA could realize real-time processing by parallel computing, which would effectively solve real-time problems such as image processing in the realm of high dimensional sparse signals. © 2017.

关键词： Compressed sensing

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Determination and(Re)Parametrization of Rational Developable Surfaces

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Journal of Systems Science & Complexity 2015年第6期28卷 1426-1439页

作者： SHEN Liyong PREZ-DíAZ Sonia School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge management Chinese Academy of Sciences Dpto de Física y Matemáticas Universidad de Alcalá

The developable surface is an important surface in computer aided design, geometric modeling and industrial manufactory. It is often given in the standard parametric form, but it can also be in the implicit form which is commonly used in algebraic geometry. Not all algebraic developable surfaces have rational parametrizations. In this paper, the authors focus on the rational developable surfaces. For a given algebraic surface, the authors first determine whether it is developable by geometric inspection, and then give a rational proper parametrization in the affrmative case. For a rational parametric surface, the authors also determine the developability and give a proper reparametrization for the developable surface.

关键词： Parametrization proper rational developable surface reparametrization

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Research on the Classification of Commercial Banks’ Fund Clients Based on Learning with Label Proportions

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Procedia Computer Science 2016年 91卷 988-994页

作者： Fuhai Ma Yong Shi Bo Wang Zhensong Chen Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China School of Management Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing100190 China University of Nebraska Omaha Omaha NE 68106 USA

Due to the problem of privacy protection and other issues in reality, we cannot get the specific label of commercial banks’ fund client, which indicates whether each individual is an important client or not, but merely knows the proportion of certain type of clients. In this paper, a novel method, called learning with label proportions (LLP), is applied, by using Inverse Calibration algorithm to identify important commercial banks’ fund clients. The model shows promising results with high accuracy and good stability. The research is practical in solving privacy-preserving data analysis problems that cannot be processed with traditional method.

关键词： Learning with label proportions Inverse Calibration Important client identification

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mining opinion word from customer review

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2016年第2期9卷 129-136页

作者： Tengjiao, Jiang Minjuan, Zhong Shumei, Liao Siwen, Luo School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

Online customer review is considered as a significant informative resource which is useful for both potential customer and product manufacturers. As a result, it is one of the most challenging tasks to mine customer reviews automatically and to provide users with opinion summary. Product features and opinion word play the most important roles in the customers' opinions mining. In this paper, we dedicate our work to opinion word mining. We proposed an approach for opinion word identification based on the association rule mining algorithm. The method makes full use of co-occurrence syntactic characteristic between product features and opinion word. Firstly, the product feature is identified by two-stage filtering scheme, and secondly the opinion word is extracted through association rule mining. The final experiment results show that the proposed method could not only obtain the product features related to domain characteristics, but identify the opinion word effectively. Meanwhile, our approach possesses much higher precision and recall than Hu's work.

关键词： Association rules

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Developing Evaluation Module for Battery Lifetime

Developing Evaluation Module for Battery Lifetime

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2016 International Conference on Artificial Intelligence: Techniques and Applications(AITA 2016)

作者： Jing ZHANG Kenji TANAKA Shen-ming GU Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Department of System Innovation Graduate School of EngineeringThe University of Tokyo

Lithium-ion batteries(Li Bs) have seen increasing use in automobiles and buildings over the past decade. Usable lifetimes of several years are sufficient for Li Bs used in electronic devices,but infrastructure applications require lifetimes measured in decades. Users of Li Bs are faced with uncertainty about Li B lifetimes,which hinders the spread of Li Bs. Predicting Li B performance degradation and evaluating useful lifetimes by examining present performance is therefore important for planning. Our research group has developed an evaluation model for Li Bs that comprises an empirically obtained degradation speed database and a method for recognizing Li B usage patterns.

关键词： Battery performance Evaluation Model Simulation

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Time-Series Analysis on the Impact Level upon Taiwan Stock Market with Events of Signing International Agreements 3

Time-Series Analysis on the Impact Level upon Taiwan Stock M...

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3rd International Conference on Computing Measurement Control and Sensor Network, CMCSN 2016

作者： Tsai, Pei-Wei Lin, You-Xin Zhang, Jing Chang, Jui-Fang Chen, Jhih-Sheng Zhang, Jilin College of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of International Business National Kaohsiung University of Applied Sciences Kaohsiung Taiwan Department of Mathematics and Physics Fujian University of Technology Fuzhou China

ISBN: (纸本)9781509010936

Stock investment is one of the available conventional financial commodities in the investment market. The individual price of stocks including stocks in the field of industrial, medical, financial, overall, etc. Is subject to many different aspects. The relationship between aspects, the international economy, and the stock price is complex and is not easy to be categorized. Moreover, the trend of the world increases the cooperation and links between countries and different nations. Many agreements in tourism, trade, etc. Are signed between countries. In this paper, we focus on the effect on the stock prices caused by the agreements signed between Taiwan and other countries. The time series method is utilized to analyze the effect on the stock price forecasting caused by 5 of the incidents including WTO, Taiwan-Nicaragua FTA, Taiwan-Honduras FTA, and Taiwan-Japan BIA. Both the pre-event and the post-event periods are included for observing the fluctuation behavior of the stock market price. The MAPE value is used as the criterion to verify the correctness of the forecasting result. The MAPE values obtained in all experiments are all less than 10%. It implies that the time series method presents good forecasting ability in the analyzed cases. © 2016 IEEE.

关键词： Forecasting

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Exploring outliers in crowdsourced ranking for QoE

arXiv

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arXiv 2017年

作者： Xu, Qianqian Yan, Ming Huang, Chendi Xiong, Jiechao Huang, Qingming Yao, Yuan Institute of Information Engineering Cas Beijing100093 Department of Computational Mathematics Michigan State University East LansingMI48824 United States BICMR-LMAM-LMEQF-LMP School of Mathematical Sciences Peking University Beijing100871 Tencent Ai Lab Shenzhen518057 University of Chinese Academy of Sciences Beijing100049 China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology 100871 Hong Kong

Outlier detection is a crucial part of robust evaluation for crowd-sourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast algorithms for outlier detection and robust QoE evaluation based on the nonconvex optimization principle. Several iterative procedures are designed with or without knowing the number of outliers in samples. Theoretical analysis is given to show that such procedures can reach statistically good estimates under mild conditions. Finally, experimental results with simulated and real-world crowdsourcing datasets show that the proposed algorithms could produce similar performance to Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up, without or with a prior knowledge on the sparsity size of outliers, respectively. Therefore the proposed methodology provides us a set of helpful tools for robust QoE evaluation with crowdsourcing data. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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