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14th International Conference on Computational Intelligence and Security, CIS 2018

作者： Li, Xunzhang Wang, Yong Ke, Wenlong Feng, Hao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi541004 China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin Guangxi541004 China

ISBN: (纸本)9781728101699

In recent years, with the rapid development of the Internet, the data scale of application behavior and application traffic have exploded. How to classify the real-time traffic of network becomes a big challenge. How to balance the accuracy and real-time of traffic classification is a difficult problem in technology. Therefore, this paper proposes a pattern matching real-time traffic classification method named PM, which first uses jpcap to accept network traffic data in real time, and then uses pattern matching to perform real-time matching traffic characteristics to achieve traffic classification. Among them, the use of the distributed message system kafka and the parallel computing framework Spark significantly improve the execution efficiency of the program. The experimental results show that PM has good performance in terms of accuracy. © 2018 IEEE.

关键词： Pattern matching

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A distributed higher-order pi calculus: Dho-pi-beam

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ICIC Express Letters 2015年第9期9卷 2615-2621页

作者： Li, Desheng College of Mathematics Physics and Information Engineering Anhui Science and Technology University No. 9 Donghua Road Chuzhou China Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology No. 1 Jinji Road Guilin China

Pi Calculus, a kind of process algebra has been widely used to describe distributed computation system currently, such as Web service and cloud platform. A variant of Pi Calculus, Pi-beam, is proposed with some imported operators, such as logic- parallels and beam channel. Based on these calculi, in this paper, we introduce a distributed higher-order Pi Calculus with the combination of higher-order and distribution extensions. To describe the mobility of distributed system, we import the distributed ex- tensions, such as domain and migration application;moreover, this calculus is designed into high-order to fit function abstraction and application. These extensions show usability and soundness by the semantic rules and example illustration. © ICIC International 2015.

关键词： Calculations

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A structural VAR and VECM modeling method for open-high-low-close data contained in candlestick chart

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Financial Innovation 2024年第1期10卷 2017-2045页

作者： Wenyang Huang Huiwen Wang Shanshan Wang College of Economics and Management China Agricultural UniversityBeijingChina School of Economics and Management Beihang UniversityBeijingChina Key Laboratory of Complex System Analysis Management and Decision(Beihang University)Ministry of EducationBeijingChina Beijing Advanced Innovation Center for Big Data and Brain Computing BeijingChina

The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial ***,the inherent constraints in OHLC data pose immense challenges to its structural *** that fail to process these constraints may yield results deviating from those of the original OHLC data *** address this issue,a novel unconstrained transformation method,along with its explicit inverse transformation,is proposed to properly handle the inherent constraints of OHLC data.A flexible and effective framework for structurally modeling OHLC data is designed,and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series *** simulations and three authentic financial datasets from the Kweichow Moutai,CSI 100 index,and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling *** modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.

关键词： OHLC data Structural modeling Unconstrained transformation Candlestick chart VAR VECM

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A multi-scale model integrating multiple features for vehicle detection

A multi-scale model integrating multiple features for vehicl...

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International Conference on Intelligent Transportation

作者： Ye Li Fei-Yue Wang Bo Li Bin Tian Fenghua Zhu Gang Xiong Kunfeng Wang Institute of Automation State Key Laboratory of Management and Control for Complex Systems Beijing China Chinese Academy of Sciences Cloud Computing Center Dongguan China

In traffic video surveillance systems, vehicles with various distances from the camera have different sizes, resolutions, and angles in traffic images. The common multi-scale method, which scales one vehicle template or the input image for detecting vehicles with different sizes, may fail to detect vehicles with various distances from the camera due to the change of the resolution and angle. To deal with this problem, we have proposed a multi-scale model including multiple templates with different scales and features. Our method includes two steps: constructing the multi-scale model and its probability model, and detecting vehicles from traffic images. In the first step, the multi-scale model is constructed by using three templates T 1 , T 2 , T 3 which represent vehicles with the short, medium, and long distance from the camera respectively. Each template contains one or some combination of sketch, texture, flatness, and color. In the second step, the three templates are applied for vehicle detection by using the template matching with local maximization operations. The main innovation of this paper is that the combination of multi-template and multi-scale method is applied to detect vehicles with various distances from the camera. To test our method, we have done several experiments on various traffic conditions. The experimental results show that our method effectively copes with vehicles with various distances from the camera and provides the detailed vehicle information after vehicle detection. Moreover, our method adapts to various weather conditions, slight pose variance, and slight occlusion.

关键词： Vehicles Vehicle detection Image color analysis Cameras Image edge detection Training Image resolution

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An empirical study of information diffusion in micro-blogging systems during emergency events

An empirical study of information diffusion in micro-bloggin...

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14th International Conference on Web-Age Information Management, WAIM 2013

作者： Cui, Kainan Zheng, Xiaolong Zeng, Daniel Dajun Zhang, Zhu Luo, Chuan He, Saike School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songshan Lake Dongguan 523808 China

ISBN: (纸本)9783642395260

Understanding the rapid information diffusion process in social media is critical for crisis management. Most of existing studies mainly focus on information diffusion patterns under the word-of-mouth spread mechanism. However, to date, the mass-media spread mechanism in social media is still not well studied. In this paper, we take the emergency event of Wenzhou train crash as a case and conduct an empirical analysis, utilizing geospatial correlation analysis and social network analysis, to explore the mass-meida spread mechanism in social media. By using the approach of agent-based modeling, we further make a quantativiely comparison with the information diffusion patterns under the word-of-mouth spread mechanism. Our exprimental results show that the mass-meida spread mechanism plays a more important role than that of the word-of-mouth in the information diffusion process during emergency events. The results of this paper can provide significant potential implications for crisis management. © 2013 Springer-Verlag.

关键词： Information management

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A computer-supported collaborative learning platform based on clouds

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Journal of Computational Information systems 2011年第11期7卷 3811-3818页

作者： Li, Jiandun Peng, Junjie Zhang, Wu Han, Fangfang Yuan, Qin Joint Lab of Cloud Computing School of Computer Engineering and Science Shanghai University Shanghai 200072 China Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Computer-supported collaborative learning (CSCL) is an emerging branch of learning science concerned with studying how people can learn together with the help of computers. As an indispensable ingredient, computer mediation evolves through couples of phases, e.g. centralized server, peer-to-peer network, grid computing. Nevertheless, with daily rising trend on requirement's dynamic changes in service, existing models fall short to respond on demand. Thus, in this paper, by taking advantage of cloud computing, we propose a feasible CSCL platform and the experiments show that it can not only fulfill the basic requirement of CSCL, but also respond to learner's dynamic need on demand. Copyright © 2011 Binary Information Press.

关键词： Peer to peer networks

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A GPU based trafficparallel simulation module of artificial transportation systems

A GPU based trafficparallel simulation module of artificial ...

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2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012

作者： Wang, Kai Shen, Zhen Changsha Hunan China State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing 100190 China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songshan Lake Dongguan 523808 Guangdong Province China

ISBN: (纸本)9781467324007

Traffic micro-simulation is an important tool in the Intelligent Transportation systems (ITS) research. In the micro-simulation, a bottom up system can be built up by the interactions of vehicle agents, road agents, traffic lights agents, etc. The Artificial societies, Computational experiments, and Parallel execution (ACP) approach suggests integrating other metropolitan systems such as logistic, infrastructure, legal and regulatory, weather and environmental systems to build an Artificial Transportation system (ATS) to help solve ITS problems. This is reasonable as the transportation system is complex that is affected by many systems interacting with each other. However, there is a challenge that the computing burden can be very heavy as there can be many agents of different kinds interacting in parallel in ATS. In recent years, the Graphics Processing Units (GPUs) have been applied successfully in many areas for parallel computing. Compared with the traditional CPU cluster, GPU has an obvious advantage of low cost of hardware and electricity consumption. In this paper, we build a parallel traffic simulation module of ATS with GPU. The simulation results are reasonable and a maximum speedup factor of 105 is obtained compared with the CPU implementations. © 2012 IEEE.

关键词： Graphics processing unit

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A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Das, Polash Kumar Hu, Bin Liu, Chang Cui, Kaixin Ranjan, Prabhat Xiong, Gang School of Electronic and Information Engineering South China University of Technology Guangzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Cloud Computing Center Chinese Academy of Science Dongguan China

ISBN: (纸本)9781728128535

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature. © 2019 IEEE.

关键词： Local binary pattern

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GPU based ordinal optimization for traffic signal coordination

GPU based ordinal optimization for traffic signal coordinati...

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2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012

ISBN: (纸本)9781467324007

Traffic signal coordination has long been a hot topic in Intelligent Transportation systems (ITS) research. Simulation-based optimization is an important method to optimize the coordination designs as the traffic systems are not easily described by mathematical models accurately. For the traffic simulator in the method, micro-simulation is becoming more and more popular than the macroscopic or mesoscopic simulation in recent years, as the micro-simulation can describe drivers' decision-making process and their driving behaviors in the framework of ITS. However, the computing burden for the micro-simulation is usually very heavy as a large number of vehicles are modeled and simulated separately. For the optimizer in the method, iterative algorithms such as Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) are widely used to find the global optimal solutions. However, these algorithms usually take too much time to converge when a large-scale road network is encountered. To alleviate the computing burden and speed up the convergence, we build an agent-based traffic simulator and employ Ordinal Optimization (OO) as the optimizer. We accelerate both the simulator and the optimizer with Graphics Processing Unit (GPU), which has been applied successfully in many areas for parallel computing. Simulation results show the effectiveness of the OO method and the power of GPU for parallel computing. © 2012 IEEE.

关键词： Graphics processing unit

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cloud operating system for industrial application

Cloud operating system for industrial application

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Gang Xiong Tongkai Ji Xipeng Zhang Fenghua Zhu Wenjing Liu State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Guangdong China

ISBN: (纸本)9781467384810

With the rapid development of latest information technology, it is inevitable to apply IoT (Internet of Things), cloud computing and big data into the industrial fields of national key sectors including transportation, electricity, metallurgy, petroleum, chemical, manufacturing, military and so on. Wireless sensor network, industrial Internet, embedded system, software for industrial control and management, and smart terminal are gradually introduced into the industrial systems, which would make the past relatively closed industrial systems more open and intelligent, and contribute to the coming forth industrial revolution. In this paper, the authors mainly discuss issues about cloud Operating system (OS) for industrial application, including cloud computing and cloud operating system introduction, current status analysis of cloud OS and the transformation trend to industrial 4.0. Then, we independently design the main content of this cloud OS, and its application prospect and expected result are given. The study provides theoretical guidance and practical challenge for the development of cloud OS oriented to industrial area.

关键词： cloud computing Security Industries Hardware Operating systems Manufacturing Servers

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