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Parallel Crime Scene Analysis Based on ACP Approach

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2018年第1期5卷 244-255页

作者： Wang, Shuai Wang, Xiao Ye, Peijun Yuan, Yong Liu, Shuo Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Minist Publ Secur Inst Forens Sci Beijing 100038 Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha 410073 Hunan Peoples R China

Crime scene analysis is a retrospective process from traces to psychology and physiology. It is not only the starting point and foundation of criminal investigation, but also the key part for solving criminal cases. As a typical open complex social system, it has three features, namely, uncertainty, diversity, and complexity, thus making the system modeling a huge challenge. In this paper, we propose the parallel crime scene analysis system based on the artificial societies, computational experiments and parallel execution (ACP) approach, which uses artificial (A) crime scene to describe the basic elements, functions and states of the criminals, computational (C) experiments to compute and predict the different forms of crime scene, and parallel (P) execution to guide or control the evolution of the physical crime process in accordance with the results from the artificial crime scene. First, we propose the concept of parallel crime scene from the perspective of complex system theory and give an overview of its architecture, then we present the construction method of artificial crime scene and the blackboard-based multiagent artificial crime scene analysis system. On this basis, the temporal and spatial interaction models of the criminal subjects are proposed and verified. After that, we introduce the software-defined crime scene analyzing model systematically. The ACP approach sheds light on the intelligent management and control for complex crime scene analysis.

关键词： Artificial societies computational experiments and parallel execution (ACP) approach computational experiments crime scene analysis parallel systems

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Blockchain and Cryptocurrencies: Mode Techniques, and Applications

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2018年第9期48卷 1421-1428页

作者： Yuan, Yong Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Innovat Ctr Parallel Blockchain Qingdao 266109 Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha 410073 Hunan Peoples R China Univ Chinese Acad Sci Ctr China Econ & Social Secur Beijing 100049 Peoples R China

As an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies, blockchain has attracted intensive attention in both research and applications in recent years. The key advantage of this technology lies in the fact that it enables the establishment of secured, trusted, and decentralized autonomous ecosystems for various scenarios, especially for better usage of the legacy devices, infrastructure, and resources. In this paper, we presented a systematic investigation of blockchain and cryptocurrencies. Related fundamental rationales, technical advantages, existing and potential ecosystems of Bitcoin and other cryptocurrencies are discussed, and a six-layer reference model of the blockchain framework is proposed with detailed description for each of its six layers. Potential applications of blockchain and cryptocurrencies are also addressed. Our aim here is to provide guidance and reference for future research along this promising and important direction.

关键词： Bitcoin blockchain consensus algorithm cryptocurrency smart contract

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Research on Social Marketing Strategies with An Agent-based Propagation Model

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IFAC-PapersOnLine 2017年第1期50卷 13581-13586页

作者： Zeng, Shuai Li, Juanjuan Ni, Xiaochun Yuan, Yong Wang, Fei-Yue The State Key Lab of Management Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Qingdao Academy of Intelligent Industries BeijingShandong China Research Center of Military Computational Experiments and Parallel Systems National University of Defense Technology Changsha China

Considering the increasing complexity of social networks and user behaviors, it is very challenging for advertisers to formulate their strategies of selecting proper initial seed users in their social marketing efforts. In this paper, we tackle this challenge by proposing an agent-based propagation model and injecting it into typical social networks with three types of structures, i.e., Erds-Rényi random graph, Watt-Strogatz small world graph, and Barabási-Albert scale-free graph. We instantiate this agent-based model with demographic characteristics extracted from real-world census data collected in China. By investigating the diffusion process of advertising information in these social networks, we can analyze and compare the performance of advertisers’ targeting and influencer strategies. Our experimental results indicate that advertisers adopting influencer strategies should manipulate the initial well-connected seeds to deliver information only to the potential customers instead of a wide range of generic users. © 2017

关键词： Marketing Autonomous agents Behavioral research Commerce Computational methods Population statistics Agent based model Demographic characteristics Information diffusion Potential customers Propagation modeling Seeding strategies Small world graphs Social marketings

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Primitives Generation Policy Learning without Catastrophic Forgetting for Robotic Manipulation

Primitives Generation Policy Learning without Catastrophic F...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Fangzhou Xiong Zhiyong Liu Kaizhu Huang Xu Yang Amir Hussain State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science China School of Computer and Control University of Chinese Academy of Sciences (UCAS) China CAS Centre for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China Department of EEE Xi’an Jiaotong-Liverpool University China School of Computing Edinburgh Napier University U.K.

Catastrophic forgetting is a tough challenge when agent attempts to address different tasks sequentially without storing previous information, which gradually hinders the development of continual learning. Except for image classification tasks in continual learning, however, there are little reviews related to robotic manipulation. In this paper, we present a novel hierarchical architecture called Primitives Generation Policy Learning to enable continual learning. More specifically, a generative method by Variational Autoencoder is employed to generate state primitives from task space, then separate policy learning component is designed to learn torque control commands for different tasks sequentially. Furthermore, different task policies could be identified automatically by comparing reconstruction loss in the autoencoder. Experiment on robotic manipulation task shows that the proposed method exhibits substantially improved performance over some other continual learning methods.

关键词： Task analysis Robots Neural networks Learning systems Trajectory Decoding Aerospace electronics

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The process of 3D printed skull models for anatomy education

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COMPUTER ASSISTED SURGERY 2019年第Supp 1期24卷 121-130页

作者： Shen, Zhen Yao, Yong Xie, Yi Guo, Chao Shang, Xiuqin Dong, Xisong Li, Yuqing Pan, Zhouxian Chen, Shi Xiong, Gang Wang, Fei-Yue Pan, Hui Chinese Acad Sci CASIA Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Shandong Peoples R China Chinese Acad Med Sci Peking Union Med Coll Hosp PUMCH Dept Neurosurg Beijing Peoples R China Peking Union Med Coll Beijing Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester Lancs England Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China CAMS PUMCH Beijing Peoples R China PUMC Eight Year Program Clin Med Beijing Peoples R China CAMS PUMCH Minist Hlth Dept EndocrinolEndocrine Key Lab Beijing Peoples R China PUMC Beijing Peoples R China CAMS PUMCH Natl Virtual Simulat Lab Educ Ctr Med Sci Beijing Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan Peoples R China CAMS PUMCH Dept Educ Beijing Peoples R China

In general, the 3 D printed medical models are made based on virtual digital models obtained from machines such as the computed tomography scanner. However, due to the limited accuracy of CT scanning technology, which is usually 1 millimeter, there are differences between scanned results and the real structure. Besides, the collected data can hardly be printed directly because of some errors in the model. In this paper, we present a general and efficient procedure to process the digital skull data to make the printed structures meet the requirements of anatomy education, which combines the use of five 3 D manipulation tools and the procedure can be finished within 6 hours. Then the model is printed and compared with the cadaveric skull from frontal, left, right and anterior views respectively. The printed model can describe the correct structure and details of the skull clearly, which can be considered as a good alternative to the cadaveric skull. The manipulation procedure presented in this study is an easily availab.e and cost-effective way to obtain a printed skull model from the original CT data, which has a considerable economic and social benefit for the medical education. The steps of the data processing can be performed easily. The cost for the 3 D printed model is also low. Outcomes of this study can be applied widely in processing skull data.

关键词： Skull model 3D printing Anatomy education

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Research on the Selection Strategies of Blockchain Mining Pools

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2018年第3期5卷 748-757页

作者： Qin, Rui Yuan, Yong Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha 410073 Hunan Peoples R China Univ Chinese Acad Sci Ctr China Econ & Social Secur Beijing 101408 Peoples R China

Since the increasing popularization of the emerging blockchain technology, blockchain mining has attracted more and more attention. Due to the difficulty of solo mining, typically miners choose to join a mining pool. As there are many mining pools and different mining pools may adopt different reward mechanisms, how to choose the appropriate mining pool has become one of the most important issues faced by miners, since miners can get different rewards in different pools. In practice, there are three commonly used reward mechanisms for the mining pools to distribute the reward among their miners, namely, the proportional mechanism, the pay-per-share mechanism, and the pay-per-last-N-share mechanism. In this paper, we study the pool selection problem faced by the miners, and model it as a risk decision problem since different reward mechanisms have different risks. We establish a pool selection model based on the maximum-likelihood criterion and also study the effect of N on the miners' optimal pool selection decisions. By utilizing the computational experiments approach, we validate our proposed pool selection strategies. Our results can provide important managerial insights for miners when making their pool selection decisions.

关键词： Blockchain mining maximum-likelihood criterion pool selection reward mechanism risk decision

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Efficient Deployment of Patrols to Catch Arsonists

Efficient Deployment of Patrols to Catch Arsonists

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Chinese Automation Congress (CAC)

作者： Jinpeng Han Fei-Yue Wang Yisheng Lv Fenghua Zhu Yilun Lin Xingyuan Dai Li Li School of Software Engineering Xian Jiaotong University Xian China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing Qingdao Academy of Intelligent Industries Qingdao Shandong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China Department of Automation Tsinghua University Beijing China

Forest fires are serious disasters that endanger the safety and property of residents. To deal with the arson-caused forest fires, we use security game theory to generate optimal patrol strategy under limited patrolling resources. The existing security game algorithms focus on a single behavior model to prevent attacks, while our work is to incorporate the secondary damage caused by the attack behavior into the optimization object. For forest fires, the security game algorithm only expects to catch arsonists in the forest, which is unreasonable in our opinion. Our algorithm incorporates the spread of fires into patrolling path planning so that defenders avoid being swallowed by fire via capturing the spread of it.

关键词： Games Forestry Security Game theory Mathematical model Automation control systems

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Efficient Deployment of Patrols to Catch Arsonists

Efficient Deployment of Patrols to Catch Arsonists

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Chinese Automation Congress (CAC)

作者： Han, Jinpeng Lin, Yilun Dai, Xingyuan Wang, Fei-Yue Lv, Yisheng Zhu, Fenghua Li, Li Xi An Jiao Tong Univ Sch Software Engn Xian Shaanxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao Shandong Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781728113128

关键词： Security game Forest fires model Stackelberg equilibrium

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A survey of generative adversarial networks

A survey of generative adversarial networks

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Chinese Automation Congress

作者： Kongtao Zhu Xiwei Liu Hongxue Yang Beijing Polytechnic College of Telecom. Eng. Beijing China The State Key Laboratory of Management and Control for Complex Systems Qingdao Academy of Intelligent Industries Qingdao Beijing China

Generative adversarial networks (GANs) coming from the game theory allow machines to learn deep representations without extra training data. By training two adversarial networks, including a generator and a discriminator, GANs could get the distribution of the real samples. This capability makes it a prospect learning method in image synthesis, image recognition, image translation etc. In this paper, we survey the state of the art of GANs by categorizing the GANs into four classifications on the basis of GANs' functions and list two application domains: vision computing & natural language processing (NLP) regarding to GANs' applications.

关键词： Generative adversarial networks Vision computing Natural language processing (NLP)

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Overcoming catastrophic forgetting with self-adaptive identifiers 25th

Overcoming catastrophic forgetting with self-adaptive identi...

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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Xiong, Fangzhou Liu, Zhiyong Yang, Xu The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science Beijing100190 China Beijing100049 China Centre for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China Cloud Computing Center Chinese Academy of Sciences DongGuanGuangDong523808 China

ISBN: (纸本)9783030041816

Catastrophic forgetting is a tough issue when the agent faces the sequential multi-task learning scenario without storing previous task information. It gradually becomes an obstacle to achieve artificial general intelligence which is generally believed to behave like a human with continuous learning capability. In this paper, we propose to utilize the variational Bayesian inference method to overcome catastrophic forgetting. By pruning the neural network according to the mean and variance of weights, parameters are vastly reduced, which mitigates the storage problem of double parameters required in variational Bayesian inference. Based on this lightweight version, autoencoders trained on different tasks are employed to self-adaptively match the corresponding task parameters to tackle sequential multi-task learning problem. We show experimentally on several fundamental datasets that the proposed method can perform substantial improvements without catastrophic forgetting over other classic methods especially in the setting where the probability distributions between tasks present more different. © Springer Nature Switzerland AG 2018.

关键词： Probability distributions

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