检索结果-内蒙古大学图书馆

Workshop on Workflows in Support of Large-Scale Science, WORKS

作者： Tamas Kukla Tamas Kiss Gabor Terstyanszky Peter Kacsuk Centre for Parallel Computing School of Informatics University of Westminster London UK Laboratory of the Parallel and Distributed Systems Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Several widely utilized, grid workflow management systems emerged in the last decade. These systems were developed by different scientific communities for various purposes. Enhancing these systems with the capability of invoking and nesting the workflows of other systems within their native workflows makes these communities to be able to carry out cross-organizational experiments and share non-native workflows. The novel solution described in this paper allows the integration of different workflow engines and makes them accessible for workflow systems in order to achieve this goal. The solution is based on an application repository and submitter, which exposes different workflow engines and executes them using the computational resources of the grid. In contrast with other approaches, our solution is scalable in terms of both number of workflows and amount of data, easily extendable in the sense that the integration of a new workflow engine does not require code re-engineering, and general, since it can be adopted by numerous workflow systems.

关键词： Engines Workflow management software Risk management Petri nets Parallel processing Informatics Laboratories Concurrent computing distributed computing Automation

来源：评论

学校读者我要写书评

暂无评论

An Anisotropic Diffusion Filtering Implementation to Execute in Parallel distributed systems

An Anisotropic Diffusion Filtering Implementation to Execute...

引用

IEEE International Conference on Computational Science and Engineering Workshops (CSEWORKSHOPS)

作者： Antonio C. Sobieranski Leandro Coser M.A.R. Dantas Aldo v. Wangenheim Eros Comunello LAPIX-Image Processing and Graphic Computing Lab The Cyclops Group Federal University of Santa Catarina Brazil LaPeSD-Distributed Systems Research Laboratory Federal University of Santa Catarina Brazil Graduate Program Applied Computer Science Universidade do Vale do Itajaí Brazil

ISBN: (纸本)9780769532578

In this paper, we present a parallelization of a filtering algorithm related to non-linear anisotropic diffusion, used to enhance the performance of an application in a parallel distributed system. The anisotropic diffusion is a well-established technique for image enhancement by means of diffusivity functions, which act as border attenuators. However, it requires a high computational cost when a large amount of data is used. The proposed implementation was parallelized considering both point-to-point and collective communications, adopting the MPI paradigm. Results from both approaches indicate that the proposed algorithm has reached interesting levels of performance (81% and 93% of efficiency, respectively) when compared to the execution of one process in a single computer node. In addition, our results indicate an enhancement of around 21% utilizing the collective communication strategy when compared to point-to-point communication.

关键词： Filtering Magnetic separation Anisotropic magnetoresistance Ions Kernel Maximum likelihood detection Pixel

来源：评论

学校读者我要写书评

暂无评论

UNPCC: A novel unsupervised classification scheme for network intrusion detection

UNPCC: A novel unsupervised classification scheme for networ...

引用

18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006

作者： Xie, Zongxing Quirino, Thiago Shyu, Mei-Ling Chen, Shu-Ching Chang, LiWu Department of Electrical and Computer Engineering University of Miami Coral Gables FL 33124 United States Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL United States Center for High Assurance Computer Systems Naval Research Laboratory Washington DC United States

ISBN: (纸本)0769527280

The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (Unsu-pervised Principal Component Classifier) algorithm is a multi-class unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms. © 2006 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Collateral representative subspace projection modeling for supervised classification

Collateral representative subspace projection modeling for s...

引用

18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006

作者： Quirino, Thiago Xie, Zongxing Shyu, Mei-Ling Chen, Shu-Ching Chang, LiWu Department of Electrical and Computer Engineering University of Miami Coral Gables FL 33124 United States Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL 33199 United States Center for High Assurance Computer Systems Naval Research Laboratory Washington DC 20375 United States

ISBN: (纸本)0769527280

In this paper, a novel supervised classification approach called Collateral Representative Subspace Projection Modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements. In addition, C-RSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, Decision Table (DT), Nearest Neighbor (NN), KNN, Support Vector Machine (SVM), 1-NN Best Warping Window DTW, 1-NN DTW with no Warping Window, and the well-known classifier boosting method AdaBoost with SVM. © 2006 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Spotlights on network operations and management research in Latin America - A report on LANOMS 2005

Spotlights on network operations and management research in ...

引用

作者： Granville, Lisandro Zambenedetti Gaspary, Luciano Paschoal Schulze, Bruno De Souza, José Neuman Brazil Special Interest Group on Computer Networks and Distributed Systems Brazilian Computer Society Brazil Brazilian Laboratory on Computer Networks Brazil IFIP/IEEE im Brazil Graduate Program on Applied Computing Universidade do Vale do Rio dos Sinos Brazil IEEE Communications Society Japan National Lab. for Scientific Computing - LNCC Brazil Brazilian National Research Council CNPq Brazil ACM Brazil IEEE Brazil Brazilian Computing Society Brazil Federal University of Ceará Brazil

来源：评论

学校读者我要写书评

暂无评论

Tillarom: An AJAX based folk song search and retrieval system with gesture interface based on kodály hand

Tillarom: An AJAX based folk song search and retrieval syste...

引用

1st ACM International Workshop on Human-Centered Multimedia, HCM 2006, co-located with the 2006 ACM International Multimedia Conference

作者： Licsár, Attila Szirányi, Tamás Kovács, László Pataki, Balázs University of Pannonia Department of Image Processing and Neurocomputing Egyetem u. 10 H-8200 Veszprém Hungary Department of Distributed Systems Computer and Automation Research Institute Hungarian Academy of Sciences Lágymányosi u. 11 H-1111 Budapest Hungary Analogical and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Lágymányosi u. 11 H-1111 Budapest Hungary

ISBN: (纸本)1595935002

A digital folk song search and retrieval system with a hand gesture based interface is presented. Tillarom is a comprehensive collection of original Hungarian folk songs recorded using different technologies such as phonographs and/or stereo DAT cassettes. This digital archive contains professional quality metadata records as well as MIDI recordings for presenting the different types of clustered folk songs. An AJAX based search and retrieval interface was developed that can be used together with optically recognized Kodály's hand signs to formulate queries through a web browser. The appearance based recognition of hand gestures utilizes contour analysis and SVM based classification. We evaluated the performance of the recognition of hand signs and investigated the main problems of their usage in our system. Copyright 2006 ACM.

关键词： Information systems

来源：评论

学校读者我要写书评

暂无评论

Collateral Representative Subspace Projection Modeling for Supervised Classification

Collateral Representative Subspace Projection Modeling for S...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Thiago Quirino Zongxing Xie Mei-ling Shyu Shu-ching Chen Liwu Chang Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL USA Naval Research Laboratory Center for High Assurance Computer Systems Washington D.C. USA

In this paper, a novel supervised classification approach called collateral representative subspace projection modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements. In addition, C-RSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, decision table (DT), nearest neighbor (NN), KNN, support vector machine (SVM), I-NN best warping window DTW, I-NN DTW with no warping window, and the well-known classifier boosting method AdaBoost with SVM

关键词： Principal component analysis Support vector machines Support vector machine classification Intrusion detection Training data Classification tree analysis Bayesian methods Laboratories distributed computing Power system modeling

来源：评论

学校读者我要写书评

暂无评论

UNPCC: A Novel Unsupervised Classification Scheme for Network Intrusion Detection

UNPCC: A Novel Unsupervised Classification Scheme for Networ...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Zongxing Xie Thiago Quirino Mei-ling Shyu Shu-ching Chen Liwu Chang Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL USA Center for High Assurance Computer Systems Naval Research Laboratory Washington D.C. DC USA

The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (unsupervised principal component classifier) algorithm is a multiclass unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms

关键词： Intrusion detection Classification algorithms Clustering algorithms Computer networks distributed computing Laboratories Training data Traffic control Testing Machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Journal of Information Science and Engineering: Editorial notice

引用

Journal of Information Science and Engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

No abstract available

关键词：

来源：评论

学校读者我要写书评

暂无评论

Load-sensitive routing of long-lived IP flows 99

Load-sensitive routing of long-lived IP flows

引用

Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication

作者： Anees Shaikh Jennifer Rexford Kang G. Shin Real-Time Computing Laboratory Department of EECS University of Michigan Ann Arbor MI Network Mathematics Research Networking and Distributed Systems AT&T Labs - Research Florham Park NJ

ISBN: (纸本)9781581131352

Internet service providers face a daunting challenge in provisioning network resources, due to the rapid growth of the Internet and wide fluctuations in the underlying traffic patterns. The ability of dynamic routing to circumvent congested links and improve application performance makes it a valuable traffic engineering tool. However, deployment of load-sensitive routing is hampered by the overheads imposed by link-state update propagation, path selection, and signaling. Under reasonable protocol and computational overheads, traditional approaches to load-sensitive routing of IP traffic are ineffective, and can introduce significant route flapping, since paths are selected based on out-of-date link-state information. Although stability is improved by performing load-sensitive routing at the flow level, flapping still occurs, because most IP flows have a short duration relative to the desired frequency of link-state updates. To address the efficiency and stability challenges of load-sensitive routing, we introduce a new hybrid approach that performs dynamic routing of long-lived flows, while forwarding short-lived flows on static preprovisioned paths. By relating the detection of long-lived flows to the timescale of link-state update messages in the routing protocol, route stability is considerably improved. Through simulation experiments using a one-week ISP packet trace, we show that our hybrid approach significantly outperforms traditional static and dynamic routing schemes, by reacting to fluctuations in network load without introducing route flapping.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：