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检索条件"任意字段=5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019"

共 603 条记录，以下是61-70 订阅

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Event-based Attack Tolerant for A Class of False data Injection in Networked Control Systems 5

Event-based Attack Tolerant for A Class of False Data Inject...

引用

5th ieee international conference on Cloud computing and intelligence Systems (CCIS)

作者： Wu, Jiancun Yang, Mingjin Peng, Chen Shanghai Univ Sch Mechatron Engn & Automat Dept Automat Shanghai 200072 Peoples R China

ISBN: (纸本)9781538660058

this paper addresses an event-based false data injection (FDI) tolerance problem for networked control systems (NCSs). the NCSs are said to be tolerable for attacks, if some extent of state divergence caused by attacks can be tolerated for the studied system. Under the scenario that the measurement (sensor-to-controller) channels are jammed, the average duration of NCSs is well characterized under consideration of the FDI attacks;and the characteristic of m-tolerant for NCSs is refined, according to the specific FDI under an event-triggered control scheme. Moreover, the effects of these FDI attacks to the evolution of systems are deeply analyzed. Examples are given to show the secure feature of the proposed method.

关键词： Networked control systems False data injection Security Attack tolerant

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Exploring the potential of elastic computing clusters in geo-distributed data centers with fast fabric interconnection 21

Exploring the potential of elastic computing clusters in geo...

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21st ieee international conference on High Performance computing and Communications, 17th ieee international conference on Smart City and 5th ieee international conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Chen, Shouwei Wang, Wensheng Rodero, Ivan *** Inc. China Rutgers Discovery Informatics Institute United States

ISBN: (纸本)9781728120584

Large-scale enterprise computing systems are growing rapidly, to address the increasing demand for data processing;however, in many cases, the computing resources in a single data center may not be sufficient for critical data-centric workloads, and important factors, such as space limitations, power availability, or company policies, limit the possibilities of expanding the data center's resources. In this paper, we explore the potential of harvesting spare computing resources across geo-distributed data centers with fast fabric interconnection for real-world enterprise applications. We specifically characterize the computing resource utilization of four large-scale production data centers, and we show how to efficiently combine local storage and computing clusters with remote and elastic computation resources. the primary challenge is incorporating the available remote computing resources efficiently. To achieve this goal, we propose leveraging the capabilities of Kubernetes-based elastic computing clusters to utilize the spare computing resources across geo-distributed data centers for big data applications. We also provide an experimental performance evaluation based on real-use case scenarios via an empirical execution and a simulation, which shows that the proposed system can accelerate big data services by employing existing computing resources more efficiently across geo-distributed data centers. © 2019 ieee.

关键词： big data

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A Survey of Available Information Recovery of Binary Programs Based on Machine Learning 5

A Survey of Available Information Recovery of Binary Program...

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5th ieee international conference on Artificial intelligence and big data, ICAIBD 2022

作者： Shao, Wenqiang Yang, Qichao Guo, Xixi Cai, Ruijie State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665499132

A binary program is an executable file obtained by compiling the source code. In order to reduce the size of software or prevent reverse engineering, software publishers usually only release executable files that have stripped a lot of source information. the rich source-level information is very helpful for software hardening and malware analysis. In recent years, the rapid development of artificial intelligence has promoted the research of binary program information recovery. this paper summarizes the relevant research on effective information recovery of binary programs since 2016. Firstly, the importance of recovering source-level information from binary programs is introduced. Next, the problems are divided into three categories, compiler traceability, signature information recovery and variable information recovery of source program. Finally, based on the latest technology in this field, the future development direction is analyzed. © 2022 ieee.

关键词： Machine learning

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Message from the datacom 2019 Chairs

Proceedings - 2019 IEEE 5th International Conference on Big ...

引用

Proceedings - 2019 ieee 5th international conference on big data intelligence and computing, datacom 2019 2019年 XI页

作者： Hwang, Ren-Hung Zomaya, Albert Chen, Jiming Lee, Che-Rung Abawajy, Jemal

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Precise subsidization grants for college students over big data optimized random forest 12

Precise subsidization grants for college students over big d...

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12th ieee international conference on Internet of things, 15th ieee international conference on Green computing and Communications, 12th ieee international conference on Cyber, Physical and Social computing and 5th ieee international conference on Smart data, ithings/GreenCom/CPSCom/Smartdata 2019

作者： Dai, Ranran Cheng, Shi Chen, Fengmei Huang, Tao Cheng, Xianyi Silicon Lake College JiangSu Kunshan215322 China School of Information Science and Technology NanTong University NanTong JiangSu22601 9 China

ISBN: (纸本)9781728129808

It is difficult to accurate identify and ascertain college students' grants. Open 'poorer than poor' funding mode lacks humanistic care. How to subsidize has always been a difficult problem in the education of University grants. In the past, due to the lack of data and the high cost of acquisition data, how to introduce big data processing technology and machine learning method into the accurate prediction of college student grants has been not achieved good results. We establish a prediction model in this paper, by optimizing the characteristic selection and running parameters for random forest model, also, we can precise subsidization grants for college students' based on balanced corpus strategy and big data analysis. the experiments on real data sets show that the accuracy can reach 91%, although not the most final determinative result, to improve the precision and quality of college student grant work, has the very vital significance. © 2019 ieee.

关键词： big data

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3D Model Retrieval Based on Vision Feature Fusion 5

3D Model Retrieval Based on Vision Feature Fusion

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5th ieee international conference on Cloud computing and intelligence Systems (CCIS)

作者： Zhang, Mandun Ma, Yingshi Wang, Xiaofang Wei, Wei Xiao, Zhidong Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Tianjin Int Joint Ctr Virtual Real & Visual Comp Tianjin 300401 Peoples R China Hebei Prov Key Lab Big Data Calculat Tianjin 300401 Peoples R China Weier Kebao Tianjin Sci & Technol Co Ltd Tianjin 300401 Peoples R China Bournemouth Univ NCCA Poole BH12 5BB Dorset England

ISBN: (纸本)9781538660058

By applying multi-view features of a 3D model as the whole feature descriptors to match the 3D model feature, this paper presents a modified 3D model retrieval algorithm which is based on the fusion of contour features and texture features of the model. After two-dimensional depth images of the 3D model are obtained under the different views of a spherical bounding box, the contour feature and the texture features of the model images are fused for realizing the constitute of the 3D model. the experiment results show that the proposed method gains great improvement in retrieval speed and effective rate in comparison with other view-based 3D model retrieval methods.

关键词： 3D model Feature descriptor View projection Contour feature Texture feature

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Recommendation Technology Summarize on Multi-agent Game Confrontation 5

Recommendation Technology Summarize on Multi-agent Game Conf...

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5th ieee international conference on Cloud computing and intelligence Systems (CCIS)

作者： Zhang, Wanpeng Yang, Zhen Yang, Lipeng Liu, Hongfu Natl Univ Def Technol Coll Intelligence Sci & Engn Changsha 410000 Hunan Peoples R China

ISBN: (纸本)9781538660058

Game confrontation is an important research direction of multi-agent system, which will form massive strategic data. How to excavate and discover useful strategies to make game confrontation more efficient becomes a meaningful research direction. Firstly, this article introduces the mainstream recommendation algorithms, which are demographic-based recommendation algorithm, content-based recommendation algorithms, collaborative filtering based recommendation algorithm and combinatorial recommendation algorithm. Secondly, the implementation process of the recommendation algorithm is described, and then the most commonly used similarity calculation method for finding nearest neighbors is Jintroduced. Finally, the difficulties and solutions in the current recommendation algorithms are introduced, such as sparsity of matrix, algorithm scalability, new user cold start problem and so on. Future recommendation research direction on data diversification and further improvement of recommendation algorithm.

关键词： Games of Strategy Recommendation Algorithm Sparseness Matrix User Interest Model Participle

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Novel data-Based Model for Future Epidemiology 5

Novel Data-Based Model for Future Epidemiology

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Mathebula, Dephney Univ South Africa Decis Sci Pretoria South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Epidemiological studies aim at predicting the outbreak of diseases as epidemics and pandemics. this goal is often realized using closed form expressions that significantly utilize systems of differential equations. these systems of equations are often derived by groups of researchers working in global collaborative efforts. However, groups of researchers can experience a high workload in scenarios when there is large number of non - active participating researchers in a collaborative study. In addition, data explosion and increased availability can also undermine the efforts of hardworking researchers. this is because of the high velocity and variety associated with big data availability. Hence, an approach that helps researchers to address these challenges is required the discussion in this research proposes a suitable solution in this regard the proposed solution introduces the notion of cognitive epidemiology and epidemiological crawlers in a novel computing framework In the proposed computing framework, crawlers and existing closed form expressions used in epidemiological studies interact Prior to this interaction, epidemiological closed form expressions are formatted in a manner to enable the incorporation of intelligence capability. the research presents execution paths and discusses the incorporation alongside the integration of the proposed mechanism in a manner suitable for integration with the internet.

关键词： Artificial intelligence Goals Epidemiology Internet Crawlers Machine learning computing Framework Conventional Epidemiological Cycle COVID-19 bi-directional communications

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Research on Power Grid Topology Analysis Based on Graph database 5

Research on Power Grid Topology Analysis Based on Graph Data...

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5th ieee international conference on Cloud computing and intelligence Systems (CCIS)

作者： Zhou, Aihua Qiu, Hongbin Pan, Sen Hu, Bin Global Energy Interconnect Res Inst Co Ltd Beijing Peoples R China Power Syst Artificial Intelligence Joint Lab SGCC Beijing Peoples R China

ISBN: (纸本)9781538660058

In order to better solve the problems of low efficiency and slow service startup in power grid topology analysis, this paper uses graph database technology to efficiently solve the data modeling, storage, and processing problems in power grid asset data management represented by power grid topology analysis systems. the power grid topology analysis application framework based on graph database is constructed, and the key technologies and difficulties of power grid topology analysis based on graph database are analyzed in detail from the aspects of graph database management for power grid topology management and power grid topology analysis based on a graph database in order to provide theoretical and technical support for the application of graph database technology in power grid topology analysis.

关键词： Graph database Power grid Topology analysis

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Short-Term Photovoltaic Output Forecasting Based on Multivariable Phase Space Reconstruction and Support Vector Regression 5

Short-Term Photovoltaic Output Forecasting Based on Multivar...

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5th ieee international conference on Cloud computing and intelligence Systems (CCIS)

作者： Dou, Chunxia Bian, Zhongwei Zhang, Tengfei Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Jiangsu Peoples R China Jiangsu Engn Lab Big Data Anal & Control Act Dist Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Artifical Intelligence Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781538660058

A comprehensive forecasting method of the short-term photovoltaic output is presented in this paper to forecast the short-term output of two types: the days with weak weather fluctuates (sunny days) and the days with strong weather fluctuations. Firstly, the support vector regression (SVR) is used to forecast and track the reconstructed irradiated intensity and temperature, the forecasted data of the radiation intensity and temperature are used as the input of the forecasting model. At last, the high nonlinear fitting of the photovoltaic output is realized by the SVR model also. through the analysis of the actual data of photovoltaic power plants, the rationality and effectiveness of this method are verified in this paper. Compared with the traditional forecasting method using SVR only, the useful information contained in the historical data of photovoltaic output can be further explored and the accuracy of the PV output forecasting is also improved.

关键词： short-term photovoltaic output multivariable phase space reconstruction the support vector regression

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