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

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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Examination of the energy trading status of China and India and the prospect for cooperation

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Procedia Computer Science 2019年 162卷 819-826页

作者： Shuying He Kun Guo Department of Economics Queen’s University Kingston Ontario Canada School of Economics and Management University of Chinese Academy of Science Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Science Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science Beijing China

China and India are two major contributors to the growth of global energy demand. As energy is one central part of the geopolitics, the energy security for both countries get complicated by the political environment. The paper investigates the positions of China and India in the trading relationships with different regions of the world. The paper also examines recent events that affect the energy security of China and India. The conclusion is that during the past decade, China and India have diversified sources of energy, especially from the Middle East, though China demonstrates a higher level of diversification and occupies a less passive position in its trading relationships. Shale revolution, the US energy sanctions on Iran and Venezuela and the US-Chinese trade conflicts, can bring China, India and Russia closer to each other due to the shrinking market influence of Russia in Europe and the needs of India and China to further diversify import sources so that energy supplies will not be disrupted by political chaos.

关键词： China India energy cooperation interdependence relations shale revolution US sanction

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Impact of China Railway Express on Urban Exports: Evidence from Prefectural-Level City Economic data 9th

Impact of China Railway Express on Urban Exports: Evidence f...

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Yan, Bochao Tian, Xin Deng, Shiyong Wu, Junfeng Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China 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 Beijing100190 China China Railway Fifth Survey and Design Institute Group Co. Ltd. Beijing102600 China Shanghai WinJoin Information Technology Co. Ltd. Shanghai200126 China

As a key project of infrastructure connectivity in the "Five-Pronged Approach"of strategic construction "The Belt and Road", the China Railway Express, has significantly improved the level of trade level in countries along the railway. This study uses an empirical model to correlate the container export data of China Railway Express from June 2020 to June 2021 with the export value and finds that the trade volume of China Railway Express is positively correlated with the export value of cities. The relationship between the operation of the China Railway Express and the export trade of cities that enjoy the rail service is rigorously explained through the actual data of the operation of the China Railway Express provided by the enterprises. The empirical results further prove the positive impact of the China Railway Express on cities that enjoy the rail service. © 2022 The Authors. Published by Elsevier B.V.

关键词： Commerce

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Penetration Effect of Informatization on Technological Progress in Logistics Industry: Empirical Evidence from Inter-provincial Panel data in China 9th

Penetration Effect of Informatization on Technological Progr...

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Gong, Yaling Tian, Xin Jiang, Zuoliang Jiao, Yamin School of Economics and Management Yuzhang Normal University Nanchang330103 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Shanghai Heading Information Engineering Co. Ltd. Shanghai201112 China Shanghai Winjoin Information Technology Co. Ltd. Shanghai200126 China

This paper investigates pervasiveness effect of information technology on the technological progress of logistics in China. Using panel data of 30-regions in China during 2008 to 2020, we propose both static and dynamic panel data models, and find both resource level and spreading rate of information technology have negative influences on technological progress of logistics. The resource level of information technology has a short-term effect on technological progress of logistics, whereas the spreading rate of information technology has a long-term effect. Moreover, the result reveals that the discordant problem of information construction of logistics between resource level and spreading rate cause the negative influence of information technology. © 2022 The Authors. Published by Elsevier B.V.

关键词： Industry 4.0

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Nonparallel Hyperplanes Support Vector Machine for Multi-class Classification

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Procedia Computer Science 2015年 51卷 1574-1582页

作者： Xuchan Ju Yingjie Tian Dalian Liu Zhiquan Qi School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy & Data Science CAS Beijing China Key Laboratory of Big Data Mining and Knowledge management CAS Beijing China Department of Basic Course Teaching Beijing Union University Beijing China

In this paper, we proposed a nonparallel hyperplanes classifier for multi-class classification, termed as NHCMC. This method inherits the idea of multiple birth support vector machine(MBSVM), that is the “max” decision criterion instead of the “min” one, but it has the incomparable advantages than MBSVM. First, the optimization problems in NHCMC can be solved efficiently by sequential minimization optimization (SMO) without needing to compute the large inverses matrices before training as SVMs usually do; Second, kernel trick can be applied directly to NHCMC, which is superior to existing MBSVM. Experimental results on lots of data sets show the efficiency of our method in multi-class classification accuracy.

关键词： Multi-class classification Support vector machine Nonparallel Quadratic programming Kernel function

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Large-Scale Linear Support Vector Ordinal Regression Solver

Large-Scale Linear Support Vector Ordinal Regression Solver

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IEEE International Conference on data mining Workshops (ICDM Workshops)

作者： Yong Shi Huadong Wang Lingfeng Niu Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing China University Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467384940

In multiple classification, there is a type of commonproblems 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 vectorordinal regression (SVOR) is a good model widely used for ordinalregression. In some applications such as document classification, data usually appears in a high dimensional feature space andlinear SVOR becomes a good choice. In this work, we developan efficient solver for training large-scale linear SVOR basedon alternating direction method of multipliers(ADMM). Whencompared empirically on benchmark data sets, the proposedsolver enjoys advantages in terms of both training speed andgeneralization performance over the method based on SMO, which invalidate the effectiveness and efficiency of our algorithm.

关键词： Support vector machines Training Prediction algorithms Machine learning algorithms big data Measurement Optimization

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GOMES: A group-aware multi-view fusion approach towards real-world image clustering

GOMES: A group-aware multi-view fusion approach towards real...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhe Xue Guorong Li Shuhui Wang Chunjie Zhang Weigang Zhang Qingming Huang Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Key Lab. of Intell. Info. Process Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology at Weihai

Different features describe different views of visual appearance, multi-view based methods can integrate the information contained in each view and improve the image clustering performance. Most of the existing methods assume that the importance of one type of feature is the same to all the data. However, the visual appearance of images are different, so the description abilities of different features vary with different images. To solve this problem, we propose a group-aware multi-view fusion approach. Images are partitioned into groups which consist of several images sharing similar visual appearance. We assign different weights to evaluate the pairwise similarity between different groups. Then the clustering results and the fusion weights are learned by an iterative optimization procedure. Experimental results indicate that our approach achieves promising clustering performance compared with the existing methods.

关键词： Visualization Clustering methods Kernel Feature extraction Fuses Cost function

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Parameters Optimization for Nonparallel Support Vector Machine by Particle Swarm Optimization

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Procedia Computer Science 2016年 91卷 482-491页

作者： Seyed Mojtaba Hosseini Bamakan Huadong Wang Ahad Zare Ravasan Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research center on fictitious economy and data science Chinese Academy of Sciences Beijing 100190 China Department of Industrial Management Allameh Tabataba’i University Tehran Iran

Support vector machine is a well-known and computationally powerful machine learning technique for pattern classification and regression problems, which has been successfully applied to solve many practical problems in a wide variety of fields. Nonparallel Support Vector Machine (NPSVM) which is an extension of Twin-SVMs, is proved to be theoretically and practically more flexible and superior than TWSVMs and also it overcomes several drawbacks of the existing typical SVMs in order to be applicable in large-scale data sets. However, one of the difficulties in successful implementation of NPSVM is its different parameters, which should be well adjusted during the training process. In fact, the generalization power, robustness and sparsity of NPSVM are extremely depended on well setting of its parameters. In this paper, we propose a hybrid approach for parameter determination of the NPSVM by Particle Swarm Optimization techniques. Furthermore, in order to increase the sparsity of NPSVM and to reduce the training time, we take into account the number of support vectors (SVs) along with classification accuracy as a weighted objective function. Our experiments on several public datasets show that the proposed method can achieve better classification accuracy compare to that of TWSVM and NPSVM with less computational time.

关键词： Nonparallel Support Vector Machine Particle Swarm Optimization Twin SVM Parameter setting

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Multi-view Feature Augmentation with Adaptive Class Activation Mapping

arXiv

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arXiv 2022年

作者： Gao, Xiang Tian, Yingjie Qi, Zhiquan School of Computer Science and Technology University of Chinese Academy of Sciences China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences China

We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance. Different from using global average pooling (GAP) to extract vectorized features from only the global view, we propose to sample and ensemble diverse multi-view local features to improve model robustness. To sample class-representative local features, we incorporate a simple auxiliary classifier head (comprising only one 1×1 convolutional layer) which efficiently and adaptively attends to class-discriminative local regions of feature maps via our proposed AdaCAM (Adaptive Class Activation Mapping). Extensive experiments demonstrate consistent and noticeable performance gains achieved by our multi-view feature augmentation module. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Impact of Consumer Characteristics on IP Product Purchase: Empirical Evidence from a Chinese Chain Retailer 10

Impact of Consumer Characteristics on IP Product Purchase: E...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Tang, Jiayu Wang, Tingyan Tian, Xin Cai, Qiang Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China 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 Beijing100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing100190 China Nuffield Department of Medicine University of Oxford South Parks Road OxfordOX1 3SY United Kingdom NIHR Oxford Biomedical Research Centre Big Data Institute University of Oxford Old Road Campus OxfordOX3 7LF United Kingdom School of Economics and Management Ningbo University of Technology Ningbo315211 China

Developing IP product has become a common differentiation strategy for retailers to attract customers and remain competence strength. This study uses empirical model to relate consumer characteristics with IP product purchase. The results show that consumers shopping on weekends are more likely to buy IP products, and consumers who have stocking behavior are less likely to purchase IP products. Consumers who are members of the retailer also have lower probability to buy IP products. These findings provide suggestions for the retailers who want to apply differentiation strategy by introducing co-branded IP products. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Consumer behavior

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