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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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QUATRE Algorithm with Sort Strategy for Global Optimization in Comparison with DE and PSO Variants 4th

QUATRE Algorithm with Sort Strategy for Global Optimization ...

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4th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2017

作者： Pan, Jeng-Shyang Meng, Zhenyu Chu, Shu-Chuan Roddick, John F. Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia

ISBN: (纸本)9783319685267

Optimization algorithm in swarm intelligence is getting more and more prevalent both in theoretical field and in real-world applications. Many nature-inspired algorithms in this domain have been proposed and employed in different applications. In this paper, a new QUATRE algorithm with sort strategy is proposed for global optimization. QUATRE algorithm is a simple but powerful stochastic optimization algorithm proposed in 2016 and it tackles the representational/positional bias existing in DE structure. Here a sort strategy is used for the enhancement of the canonical QUATRE algorithm. This advancement is verified on CEC2013 test suite for real-parameter optimization and also is contrasted with several state-of-the-art algorithms including Particle Swarm Optimization (PSO) variants, Differential Evolution (DE) variants on COCO framework under BBOB2009 benchmarks. Experiment results show that the proposed QUATRE algorithm with sort strategy is competitive with the contrasted algorithms. © 2018, Springer International Publishing AG.

关键词： Swarm intelligence

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Advanced Information technology and Global Business Competition: Preface for ITQM 2018

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Procedia computer science 2018年 139卷 1-8页

作者： Shi, Yong Wolcott, Peter Kwak, Wikil Chen, Zhengxin Tian, Yingjie Lee, Heeseok College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Economics and Management University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Business Administration University of Nebraska at Omaha OmahaNE68182 United States College of Business Korea Advanced Institute Science and Technology Dongdaemum-gu Seoul130-722 Korea Republic of

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Credit and guarantee insurance: A consistent pricing approach in supply chain finance 9th

Credit and guarantee insurance: A consistent pricing approac...

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

作者： Zhang, Linzi Xue, Jiayu Shang, Mengyu Qi, Zhiquan School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 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

Credit insurance is a new loan guarantee contract which can effectively stimulate domestic demand and promote consumption. According to different modes of payment, individual credit loan guarantees mainly include credit insurance and guarantee insurance. In this paper, we apply the Complete Information Game to analyze the strategies of the insured subject and the risk subject under different scenarios of personal loan guarantee, and obtain the influencing factors of each decision-making subject in supply chain finance. The conclusion shows that the credit status of individual loan applicants greatly affects the decision-making of supply chain financial entities such as banks. Based on this, this paper puts forward corresponding suggestions for the credit insurance business management. © 2022 The Authors. Published by Elsevier B.V.

关键词： Costs

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Global Green Digital Economy: Challenges and Development: Preface for ITQM 2022

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Procedia computer science 2022年第C期214卷 1-3页

作者： Shi, Yong Li, Jianping Kou, Gang Berg, Daniel Tien, James College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Business Administration Southwestern University of Finance and Economics Chengdu611130 China College of Engineering University of Miami Coral GablesFL33124 United States

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Frontiers of Artificial Intelligent and Quantitative management: Preface for ITQM 2024

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Procedia computer science 2024年 242卷 1-8页

作者： Filip, Florin Gheorghe Shi, Yong Pocatilu, Paul Ciurea, Cristian-Eugen Li, Jianping Tien, James M. Berg, Daniel Romanian Academy Information Science and Technology Section Bucharest Romania Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Bucharest University of Economic Studies Bucharest Romania College of Engineering University of Miami Coral GablesFL33124 United States

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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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An Orthogonal QUasi-Affine TRansformation Evolution (O-QUATRE) Algorithm for Global Optimization 15th

An Orthogonal QUasi-Affine TRansformation Evolution (O-QUATR...

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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2019

作者： Liu, Nengxian Pan, Jeng-Shyang Xue, Jason Yang College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Business School of Qingdao University Qingdao China

ISBN: (纸本)9789811397097

In this paper, a new Orthogonal QUasi-Affine TRansformation Evolution (O-QUATRE) algorithm was proposed for global optimization. The O-QUATRE algorithm is actually implemented as a combination of both the QUATRE algorithm and the orthogonal array, both of which together secured an overall better performance on complex optimization problems. The proposed algorithm is verified under CEC2013 test suite for real-parameter optimization. The experimental results indicated that the proposed O-QUATRE algorithm obtained better mean and standard deviation of fitness error than QUATRE algorithm, which means that the O-QUATRE algorithm was of more robustness and better stability. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Global optimization

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Learning Unified Embeddings for Recommendation via Meta-path Semantics 21

Learning Unified Embeddings for Recommendation via Meta-path...

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29th ACM International Conference on Multimedia, MM 2021

作者： Hao, Qianxiu Xu, Qianqian Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781450386517

Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years. As a typical instantiation of HINs, meta-path is introduced in search of higher-level representations of user-item interactions. Though remarkable success has been achieved along this direction, existing meta-path-based recommendation methods face at least one of the following issues: 1) existing methods merely adopt simple meta-path fusion rules, which might be insufficient to exclude inconsistent information of different meta-paths that may hurt model performance;2) the representative power is limited by shallow/stage-wise formulations. To solve these issues, we propose an end-to-end and unified embedding-based recommendation framework with graph-based learning. To address 1), we propose a flexible fusion module to integrate meta-path-based similarities into relative similarities between users and items. To address 2), we take advantage of the powerful representative ability of deep neural networks to learn more complicated and flexible latent embeddings. Finally, empirical studies on real-world datasets demonstrate the effectiveness of our proposed method. © 2021 ACM.

关键词： Embeddings

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Exploring Freight Loading management by Deep Learning: a Case Study in Home Furnishing Industry

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Annals of data science 2022年第2期9卷 213-228页

作者： Deng, Wei Patil, Rajvardhan Liu, Fangyao Daji, Ergu Shi, Yong College of Big Data Statistics Guizhou University of Finance and Economics Guizhou550025 China College of Computer and Information Science Arkansas Tech University RussellvilleAR68182 United States College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Research Center on Fictitious Economy and Data Science The Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Nebraska Furniture Mart OmahaNE68124 United States Division of Science Research and Technology Southwest Minzu University Chengdu610041 China

Long short-term memory (LSTM) networks as state-of-the-art Deep Learning models, have achieved remarkable results in time series forecasting. However, they are less commonly applied to the industry of logistics. This paper presents two novel LSTM networks to predict the freight loading of routing areas, and the design of a smart loading management system is introduced. While most existing works on LSTM utilize the power of its prediction very well, our study shows less accurate and instable results if only LSTM network is applied, that arise from the big variation in the dataset. Instead, two constraints are inspired in this paper. The first constraint adds a time factor node to strengthen the correlation between predicted final loading units and booked loading units as close to delivery date. And the second proposal extends to parallel training by groupwise constraint. Experiments with across 3-year records in a national wide home furnishing retail company show that constraint LSTM networks significantly improve both the accuracy and stability of the prediction. Besides, the design of a smart loading management system will be shown, in which LSTM model plays the core rule to predict loading capacity, meanwhile the rule-based system triggers different events based on loading prediction and truck size. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Long short-term memory

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