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Optimal Decisions of Countries with Carbon Tax and Carbon Tariff

JOURNAL OF INDUSTRIAL ENGINEERING AND management-JIEM 2015年第3期8卷 981-1001页

作者： Hou, Yumei Jia, Meng Tian, Xin Wei, Fangfang Wei, Kun Yanshan Univ Coll Econ & Management Qinhuangdao Hebei Peoples R China First Hosp Qinhuangdao Postdoctoral Innovat Practice Base Qinhuangdao Hebei Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100864 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100864 Peoples R China

Purpose: Reducing carbon emission has been the core problem of controlling global warming and climate deterioration recently. This paper focuses on the optimal carbon taxation policy levied by countries and the impact on firms' optimal production decisions. Design/methodology/approach: This paper uses a two-stage game theory model to analyze the impact of carbon tariff and tax. Numerical simulation is used to supplement the theoretical analysis. Findings: Results derived from the paper indicate that the demand in an unstable market is significantly affected by environmental damage level. Carbon tariff is a policy-oriented tax while the carbon tax is a market-oriented one. Comprehensive carbon taxation policy benefit developed countries and basic policy is more suitable for developing countries. Research limitations/implications: In this research, we do not consider random demand and asymmetric information, which may not well suited the reality. Originality/value: This work provides a different perspective in analyzing the impact of carbon tax and tariff. It is the first study to consider two consuming market and the strategic game between two countries. Different international status of countries considered in the paper is also a unique point.

关键词： carbon tax carbon tariff leader-follower game social welfare

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Improving Ranking-based Recommendation by Social Information and Negative Similarity

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Procedia Computer Science 2015年 55卷 732-740页

作者： Ying Liu Jiajun Yang School of Computer and Control University of Chinese Academy of Sciences Beijing 100049 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100049 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.

关键词： Recommendation system Ranking-based recommendation Collaborative filtering

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Ramp Loss Linear Programming Nonparallel Support Vector Machine

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Procedia Computer Science 2016年 80卷 1745-1754页

作者： Dalian Liu Dandan Chen Yong Shi Yingjie Tian School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Department of Basic Course Teaching Beijing Union University Beijing 100101 China College of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Motivated by the fact that the l 1 -penalty is piecewise linear, we proposed a ramp loss linear programming nonparallel support vector machine (ramp-LPNPSVM), in which the l 1 -penalty is applied for the RNPSVM, for binary classification. Since the ramp loss has the piecewise linearity as well, ramp- LPNPSVM is a piecewise linear minimization problem and a local minimum can be effectively found by the Concave Convex Procedure and experimental results on benchmark datasets confirm the effectiveness of the proposed algorithm. Moreover, the l 1 -penalty can enhance the sparsity.

关键词： support vector machine nonparallel CCCP linear programming ramp loss

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

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Procedia Computer Science 2015年 55卷 876-885页

作者： Ying Liu Pengshan Ma Hongyuan Cui School of Computer and Control University of Chinese Academy of Sciences Beijing 101408 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 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.

关键词： data mining clustering signal processing FPGA parallel computing

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Linear Twin SVM for Learning from label Proportions

Linear Twin SVM for Learning from Label Proportions

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

作者： Wang, Bo Chen, Zhensong Qi, Zhiquan Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Res Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Management Beijing 100190 Peoples R China

ISBN: (纸本)9781467396189

In this paper, we study the problem of learning from label proportions in which label information of data is provided in bag level. In this kind of problem, training data is grouped into various bags and only the proportions of positive instances is known. Inspired by proportion-SVM, we propose a new classification model based on twin SVM, which is also in a large-margin framework and only needs to solve two smaller problems. Avoiding making restrictive assumptions of the data, our model can learn the labels of every single instance based on group proportions information. In order to solve the non-convex problem in our new model, we propose an alternative algorithm to obtain the optimal solution efficiently. Also, we prove the effectiveness of our method in theoretical and experimental way.

关键词： Learning from label proportions Twin SVM Alternative algorithm Large-margin

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Search Engine Marketing, Financing Ability and Firm Performance in E-commerce 3

Search Engine Marketing, Financing Ability and Firm Performa...

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

作者： Yang, Zhuofan Shi, Yong Wang, Bo Univ Chinese Acad Sci Management Sch Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Res Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

This paper explores the relationship of search engine marketing, financing ability and e-commerce firm performance by the empirical research on China's B2C e-commerce firms. Results show that search engine marketing and business model has a strong positive relation with firm performance while financing ability has a negative effect on firm performance. It verifies the low returns to inputs in e-commerce and enlightens the managers should concentrate on business model innovation and consumer relation management. (C) 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Search Engine Marketing E-commerce Financing ability Firm performance

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Cross-media Topic Detection with Refined CNN based Image-Dominant Topic Model 15

Cross-media Topic Detection with Refined CNN based Image-Dom...

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ACM International Conference on Multimedia (ACM Multimedia)

作者： Wang, Zhiyi Li, Liang Huang, Qingming Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9781450334594

Online heterogenous data is springing up while the data has the rich auxiliary information (e.g. pictures and videos) around the text. However, traditional topic models are suffering from the limitations to discover the topics effectively from the cross-media data. Incorporating with the convolutional neural network (CNN) feature, we propose a novel image dominant topic model, which projects both the text modality and the visual modality into a semantic simplex. Further, an improved CNN feature is introduced to capture more visual details by fusing the convolutional layer and fully-connected layer. Experimental comparisons with state-of-the-art methods in the cross-media topic detection task show the effectiveness of our model.

关键词： Convolutional Neural Networks Topic Model Cross-media topic Detection

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Online learning affinity measure with CovBoost for multi-target tracking

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NEUROCOMPUTING 2015年 168卷 327-335页

作者： Li, Guorong Huang, Qingming Jiang, Shuqiang Xu, Yingkun Zhang, Weigang Univ Chinese Acad Sci CAS Beijing 100190 Peoples R China Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing 100080 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Harbin Inst Tech Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China

In this paper, we propose a new online learning method for measuring affinity between tracklets in multi-target tracking. As targets and background usually keep changing in the video, fixed affinity measurement could not adapt to their variations. Most existing affinity learning methods construct labeled samples based on the obtained tracklets, and then minimize a predefined loss function to get an optimal affinity measurement. However, those methods simply assume that the training error equals to testing error which is not true in many of real time tracking scenarios. Differently, we propose to learn affinity measurement through CovBoosting, which considers the evolution of the tracklets and could obtain affinity measurement with more discriminative ability. To deal with targets' disappearance and new targets' appearance, we combine tracklet affinity with contextual information to do an optimal inference. Moreover, an online updating algorithm is developed to guarantee that the learned tracklet affinity is always optimal for tracking targets in current sliding window. Experimental results on benchmark datasets demonstrate that tracklet affinity learned with our method is more discriminative and could greatly improve the performance of the multi-target tracker. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-target tracking Tracklet affinity CovBoost Online learning

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Large-Scale Linear Support Vector Ordinal Regression Solver 15

Large-Scale Linear Support Vector Ordinal Regression Solver

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IEEE 15th International Conference on data mining Workshops (ICDMW)

作者： Shi, Yong Wang, Huadong Niu, Lingfeng Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781467384933

In multiple classification, there is a type of common problems where each instance is associated with an ordinal label, which arises in various settings such as text mining, visual recognition and other information retrieval tasks. The support vector ordinal regression (SVOR) is a good model widely used for ordinal regression. In some applications such as document classification, data usually appears in a high dimensional feature space and linear SVOR becomes a good choice. In this work, we develop an efficient solver for training large-scale linear SVOR based on alternating direction method of multipliers(ADMM). When compared empirically on benchmark data sets, the proposed solver enjoys advantages in terms of both training speed and generalization performance over the method based on SMO, which invalidate the effectiveness and efficiency of our algorithm.

关键词： Classification (of information)

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Text clustering based on a divide and merge strategy 3

Text clustering based on a divide and merge strategy

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

作者： Yuan, Man Shi, Yong China Huarong Asset Management CO LTD Beijing Peoples R China Univ Chinese Acad Sci Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

A text clustering algorithm is proposed to overcome the drawback of division based clustering method on sensitivity of estimated class number. Complex features including synonym and co-occurring words are extracted to make a feature space containing more semantic information. Then the divide and merge strategy helps the iteration converge to a reasonable cluster number. Experimental results showed that the dynamically updated center number prevent the deterioration of clustering result when k deviates from the real class numbers. When k is too small or large, the difference of clustering results between FC-DM and k-means is more obvious and FC-DM also outperformed other benchmark algorithms. (C) 2015 Published by Elsevier B.V.

关键词： Text clustering feature extension k-means

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