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Where Does AlphaGo Go: From Church-Turing Thesis to AlphaGo Thesis and Beyond

IEEE/CAA Journal of Automatica Sinica 2016年第2期3卷 113-120页

作者： Fei-Yue Wang Jun Jason Zhang Xinhu Zheng Xiao Wang Yong Yuan Xiaoxiao Dai Jie Zhang Liuqing Yang IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences (SKL-MCCS CASIA) Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology the Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer Science University of Denver the Department of Computer Science and Engineering University of Minnesota the Qingdao Academy of Intelligent Industries (QAII) Qingdao Shandong China and SKL-MCCS CASIA the Department of Electrical and Computer Engineering Colorado State University

An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation. © 2014 Chinese Association of Automation.

关键词： Religious buildings

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Scene-specific pedestrian detection based on parallel vision

arXiv

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arXiv 2017年

作者： Zhang, Wenwen Wang, Kunfeng Qu, Hua Zhao, Jihong Wang, Fei-Yue School of Software Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Communication and Information Engineering Xi’an Jiaotong University Xi’an710049 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

As a special type of object detection, pedestrian detection in generic scenes has made a significant progress trained with large amounts of labeled training data manually. While the models trained with generic dataset work bad when they are directly used in specific scenes. With special viewpoints, flow light and backgrounds, datasets from specific scenes are much different from the datasets from generic scenes. In order to make the generic scene pedestrian detectors work well in specific scenes, the labeled data from specific scenes are needed to adapt the models to the specific scenes. While labeling the data manually spends much time and money, especially for specific scenes, each time with a new specific scene, large amounts of images must be labeled. What’s more, the labeling information is not so accurate in the pixels manually and different people make different labeling information. In this paper, we propose an ACP-based method, with augmented reality’s help, we build the virtual world of specific scenes, and make people walking in the virtual scenes where it is possible for them to appear to solve this problem of lacking labeled data and the results show that data from virtual world is helpful to adapt generic pedestrian detectors to specific scenes. Copyright © 2017, The Authors. All rights reserved.

关键词： Virtual reality

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Training and testing object detectors with virtual images

arXiv

引用

arXiv 2017年

作者： Tian, Yonglin Li, Xuan Wang, Kunfeng Wang, Fei-Yue Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named parallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining parallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors. Copyright © 2017, The Authors. All rights reserved.

关键词： Object detection

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Exploring Optimal Frequency Caps in Real Time Bidding Advertising

Exploring Optimal Frequency Caps in Real Time Bidding Advert...

引用

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Rui Qin Yong Yuan Fei-Yue Wang Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Research Center of Military Computational Experiments and Parallel System National University of Defense Technology Changsha China

ISBN: (纸本)9781509039371

Benefited from the high-speed development of Internet and the popularization of big data technology, Real Time Bidding (RTB) emerged and developed quickly, and has become one of the most important and popular model for online computational advertising. In the pay-per-impression based RTB advertising, frequency capping is undoubtedly one of the most crucial issues faced by advertisers and Demand Side Platforms (DSPs), since there are generally vast amount of ad requests in RTB markets, among which many may be triggered by the same target audience. As such, choosing the optimal frequency cap considering all the advertisers becomes a critical issue faced by DSPs. Considering that displaying one advertisement multiple number of times to the same user will diminish the advertising effect, we introduce the concept of discount factor and establish an optimization model of frequency capping to seek for the optimal frequency cap for each advertiser. We also design some experiments to validate our proposed model by utilizing the computational experiment approach, and the experimental results show that the optimal frequency cap can be influenced by the values of the discount factors, and higher value of the discount factor can deduce the advertising effect of larger frequency cap. Moreover, when the discount factors of the advertisers take different values, there also exists an optimal frequency cap, at which the advertisers can get the expected maximum revenue in the long run.

关键词： Advertising Digital signal processing computational modeling Big data Time-frequency analysis Radio spectrum management Real-time systems

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Optimizing Market Segmentation Granularity in RTB Advertising: A computational Experimental Study

Optimizing Market Segmentation Granularity in RTB Advertisin...

引用

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

ISBN: (纸本)9781509039371

Real Time Bidding (RTB) is a novel business model of online computational advertising with the integration of Internet economy and big data analysis. It can help advertisers achieve the precision marketing through the market segmentation strategies of Demand Side Platforms (DSPs). Based on a mathematical programming approach, this paper studied DSPs' strategies for market segmentation, and established a selection model of the granularity for segmenting RTB advertising markets. We proposed to validate our model using the computational experiment approach, and the experimental results show that: 1) with the increasing refinement of the market segmentation granularity, the total revenue has a tendency of a rise first followed by a decline, 2) the optimal granularity of market segmentation will be significantly influenced by the number of advertisers on the DSP, but less influenced by the number of ad requests. Our findings show the crucial role of market segmentation on the RTB advertising effect, and indicate that the DSPs should adjust their market segmentation strategies according to their total number of advertisers. Our findings also highlight the importance of advertisers as well as the characteristics of the target audiences in DSPs' market segmentation decisions.

关键词： Digital signal processing computational modeling Advertising Numerical models Big data Complex systems Automation

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Scanning the Issue and Beyond: Transportation and Mobility Transformation for Smart Cities

Scanning the Issue and Beyond: Transportation and Mobility T...

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作者： Wang, Fie-Yue State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Qingdao Academy of Intelligent Industries Qingdao266109 China

The first issue of IEEE Intelligent Transportation systems Society (ITSS) starts with survey papers on technology and security for intelligent vehicles. The first paper titled 'Intra-Vehicle Networks: A Review' by S. Tuohy, M. Glavin, C. Hughes, E. Jones, M. Trivedi, and L. Kilmartin presents a comprehensive overview of current research on advanced intra-vehicle networks and identifies outstanding research questions for the future. J. Petit and S. E. Shladover's paper, 'Potential Cyberattacks on Automated Vehicles' analyzes the threats on autonomous automated vehicle and cooperative automated vehicle. 'A Video-Analysis-Based Railway-Road System for Detecting Hazard Situations at Level Crossings' by H. Salmane, L. Khoudour, and Y. Ruichek explores the possibility of implementing a smart video surveillance security system that is tuned toward detecting and evaluating abnormal situations induced by users in level crossing. 'Traffic Flow Prediction for Road Transportation Networks with Limited Traffic Data' by A. Abadi, T. Rajabioun, and P. A. Ioannou, uses a dynamic traffic simulator to generate flows in all links using available traffic information, estimated demand, and historical traffic data available from links equipped with sensors. The paper titled 'GNSS Multipath and Jamming Mitigation Using High-Mask-Angle Antennas and Multiple Constellations' studies the optimal antenna mask angle that maximizes the suppression of interference but still maintains the performance of a single constellation with a low-mask-angle antenna.

关键词： Antennas

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An extend SIR opinion dynamic model base on empirical signed network mining

An extend SIR opinion dynamic model base on empirical signed...

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2014 International Conference on Advances in Materials Science and Information Technologies in Industry, AMSITI 2014

作者： Li, Wei Li, Pei Wang, Hui Research Center of Computational Experiments and Parallel System Technology College of Information Systems and Management National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9783038350125

Relations between users on online social media sites often reflect a mixture of positive and negative interactions. The network composed by those positive and negative relations is called signed social network. We design a web crawler to collect the data base on a special web event of battle between "Fang Zhouzi" and "Han Han". And we construct a signed social network with sentiment weighted relationships base on this empirical data. Under this empirical spread web structure, we construct an extended SIR spread model in such a signed social network with sentiment weighted relationships, and we study influence with the network factors of signed, directed and weighted on opinion spreading. Under this model, we could know the proportion of signed edges is most important factor to the spread result. © (2014) Trans Tech Publications, Switzerland.

关键词： Web crawler

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The Chinese ‘‘HumanFlesh’’ Web:the first decade and beyond

引用

Chinese Science Bulletin 2014年第26期59卷 3352-3361页

作者： Fei-Yue Wang Daniel Zeng Qingpeng Zhang James A. Hendler Jianping Cao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Management Information Systems The University of Arizona Tucson Department of Systems Engineering and Engineering Management City University of Hong Kong Tetherless World Constellation Department of Computer Science Rensselaer Polytechnic Institute Troy The Research Center for Computational Experiments and Parallel Systems National University of Defense Technology

Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities, the patterns of HFS crowd collaboration process, and the characteristics of HFS participant networks. A survey of HFS participants was conducted to provide an in-depth understanding of the HFS community and various factors that motivate these participants to contribute. This article also advocates a new stream of Web science and social computing research that will be important in predicting the future growth and use of the World Wide Web.

关键词：中国人网址肉 Web功能实证分析参与者共同体万维网

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On mobilizing processes of Cyber Movement Organizations 19

On mobilizing processes of Cyber Movement Organizations

引用

19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Wang, Tao Liu, Zhong Cui, Kainan Zhang, Jie Wang, Fei-Yue Philip Chen, C.L. College of Information System and Management National University of Defense Technology Changsha410073 China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Department of Computer and Information Science Science and Technology University of Macau China

ISBN: (纸本)9783902823625

Cyber Movement Organization (CMO) is a special kind of social movement organization on the Web. In this paper, we propose a model to simulate the mobilizing process of CMO, which consists of the individual unit, organization unit, and the mobilizing mechanisms. The mobilizing mechanisms has three sub-mechanisms: the participation mechanism, the choice mechanism, and the inviting mechanism. A dataset of more than two million "human flesh search" related microblogs is used to validate the model. Empirical results show that our model can capture the key features of the real-world mobilizing process. © IFAC.

关键词： Automation

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Opinion dynamics in social networks with heterogeneous relationships

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Journal of Information and computational Science 2014年第6期11卷 1831-1842页

作者： Fan, Pengyi Wang, Hui Yu, Wenguang Li, Pei Hu, Yanli Research Center of Computational Experiments and Parallel System Technology College of Information Systems and Management National University of Defense Technology Changsha 410073 China Beijing Institute of Tracking and Telecommunication Technology Beijing 100094 China

Recently, significant attention has been devoted to the models of opinion dynamics, which are on the hypothesis of homogeneous relationships. However, many realistic social networks display a large heterogeneity in the relationships of individuals, which have signed and weighted properties. In this paper, we propose a model of opinion dynamics in a network with heterogeneous relationships, where the influence of individuals depends on their heterogeneous relationships. By conducting a series of simulations in different complex social networks, we find that the distribution of opinions at steady state depends on the ratio of number of positive relationships and average connectivity of a network. The final opinions undergo a transition from the bipolarization phase to the consensus phase gradually as increasing the ratio of positive relationships. We also observe that the topological properties of complex networks such as short path length, high clustering, and heterogeneous degrees have few effects on the opinion dynamics at steady state. As to the effects of initial opinion distribution on opinion dynamics, it only makes sense when the social system is full of positive relationships. © 2014 Binary Information Press.

关键词： Complex networks

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