检索结果-内蒙古大学图书馆

IEEE International Conference on Intelligent computing and Information Systems

作者： Aya Naser Manal Tantawi Howida A. Shedeed Mohamed F. Tolba Scientific Computing Dept. Ain Shames University Cairo Egypt Scientific Computing Dept. Faculty of Computer and Information Science Ain Shames University Cairo Egypt

Epilepsy is the 4th prevalent neurological disorder, which distinguishes itself by frequent seizures. These seizures are unprovoked and can be detected by investigating the electroencephalogram (EEG) of the subject. In this paper, an automatic method is proposed to discriminate between normal and epileptic (interictal & ictal) classes. Approximation entropy (APEN) has been utilized as a single informative feature. Multiple classifiers such as: support vector machine (SVM), Probabilistic Neural Network (PNN) and Soft-max regression along with different strategies for classification have been examined. Experiments were carried out by the most popular dataset used in this domain. 96.875%, 53.75% and 65% are the best results achieved by soft-max regression classifier using multi-class strategy for normal, interictal and ictal classes respectively.

关键词： Conferences Information systems

来源：评论

学校读者我要写书评

暂无评论

Virtual machine provisioning, code management, and data movement design for the Fermilab HEPCloud Facility 22

Virtual machine provisioning, code management, and data move...

引用

22nd International Conference on computing in High Energy and Nuclear Physics, CHEP 2016

作者： Timm, S. Cooper, G. Fuess, S. Garzoglio, G. Holzman, B. Kennedy, R. Grassano, D. Tiradani, A. Krishnamurthy, R. Vinayagam, S. Raicu, I. Wu, H. Ren, S. Noh, S.-Y. Scientific Computing Division Fermilab BataviaIL60563 United States Computer Science Dept. Illinois Institute of Technology ChicagoIL60616 United States Korea Institute of Science and Technology Information Daejeon Korea Republic of

The Fermilab HEPCloud Facility Project has as its goal to extend the current Fermilab facility interface to provide transparent access to disparate resources including commercial and community clouds, grid federations, and HPC centers. This facility enables experiments to perform the full spectrum of computing tasks, including data-intensive simulation and reconstruction. We have evaluated the use of the commercial cloud to provide elasticity to respond to peaks of demand without overprovisioning local resources. Full scale data-intensive workflows have been successfully completed on Amazon Web Services for two High Energy Physics Experiments, CMS and NOνA, at the scale of 58000 simultaneous cores. This paper describes the significant improvements that were made to the virtual machine provisioning system, code caching system, and data movement system to accomplish this work. The virtual image provisioning and contextualization service was extended to multiple AWS regions, and to support experiment-specific data configurations. A prototype Decision Engine was written to determine the optimal availability zone and instance type to run on, minimizing cost and job interruptions. We have deployed a scalable on-demand caching service to deliver code and database information to jobs running on the commercial cloud. It uses the frontiersquid server and CERN VM File System (CVMFS) clients on EC2 instances and utilizes various services provided by AWS to build the infrastructure (stack). We discuss the architecture and load testing benchmarks on the squid servers. We also describe various approaches that were evaluated to transport experimental data to and from the cloud, and the optimal solutions that were used for the bulk of the data transport. Finally, we summarize lessons learned from this scale test, and our future plans to expand and improve the Fermilab HEP Cloud Facility. © Published under licence by IOP Publishing Ltd.

关键词： Virtual machine

来源：评论

学校读者我要写书评

暂无评论

Layer-parallel training of deep residual neural networks

arXiv

引用

arXiv 2018年

作者： Günther, S. Ruthotto, L. Schroder, J.B. Cyr, E.C. Gauger, N.R. Scientific Computing Group TU Kaiserslautern Germany Department of Mathematics and Computer Science Emory University AtlantaGA United States Dept. of Mathematics and Statistics University of New Mexico AlbuquerqueNM United States Computational Mathematics Department Sandia National Laboratories AlbuquerqueNM United States

Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification and recognition. Mathematically, ResNet architectures can be interpreted as forward Euler discretizations of a nonlinear initial value problem whose time-dependent control variables represent the weights of the neural network. Hence, training a ResNet can be cast as an optimal control problem of the associated dynamical system. For similar time-dependent optimal control problems arising in engineering applications, parallel-in-time methods have shown notable improvements in scalability. This paper demonstrates the use of those techniques for efficient and effective training of ResNets. The proposed algorithms replace the classical (sequential) forward and backward propagation through the network layers by a parallel nonlinear multigrid iteration applied to the layer domain. This adds a new dimension of parallelism across layers that is attractive when training very deep networks. From this basic idea, we derive multiple layer-parallel methods. The most efficient version employs a simultaneous optimization approach where updates to the network parameters are based on inexact gradient information in order to speed up the training process. Using numerical examples from supervised classification, we demonstrate that the new approach achieves similar training performance to traditional methods, but enables layer-parallelism and thus provides speedup over layer-serial methods through greater concurrency. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimal control systems

来源：评论

学校读者我要写书评

暂无评论

Virtualization detection strategies and their outcomes in public clouds

Virtualization detection strategies and their outcomes in pu...

引用

Asia Pacific Conference on Postgraduate Research in Microelectronics & Electronics (PrimeAsia)

作者： B. Asvija R. Eswari M B. Bijoy Centre for Development of Advanced Computing Bangalore India Dept. of Computer Applications National Institute of Technology Tiruchirappalli India

Virtualization detection on publicly exposed computing resources can have serious security implications. It can lead to the exploitation of known vulnerabilities in the virtualization software and also to carrying out attacks on shared virtual resources. The impact of such attacks can be very severe on public clouds, as numerous clients who subscribe to the cloud services share the infrastructure. Yet the threat of virtualization detection has been often overlooked by many cloud service providers. In this paper, we show how the three popular public clouds namely the Amazon EC2, Google computing Engine and the Microsoft Azure clouds are vulnerable to this risk. We summarize the various approaches used for virtualization detection and present the results obtained on the public clouds from an attacker's perspective. We demonstrate that the publicly exposed guest instances are not hardened to prevent this information leakage to the attackers. In addition to the prior known approaches, we also propose and demonstrate a new approach for detecting virtualization, based on the location and size of the descriptor tables.

关键词： Cloud computing Virtual machine monitors Virtualization Registers Google Engines Tools

来源：评论

学校读者我要写书评

暂无评论

A method for contactless palm ROI extraction 11

A method for contactless palm ROI extraction

引用

11th International Conference on computer Engineering and Systems, ICCES 2016

作者： ElSayed, Ahmed S. Ebeid, Hala M. Roushdy, Mohamed Fayed, Zaki T. Computer Science Dept. Faculty of Computers and Information Sciences Ain Shams University Cairo Egypt Scientific Computing Department Faculty of Computers and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9781509032679

Palmprint can be extracted from a hand using a low-cost webcam in a contactless manner. Using a webcam makes the enrollment process fast and convenient for users. Being contactless solve the hygiene issue and avoid copying the latent prints from sensor's surface. However, a number of challenges arise in such environment;geometric transformations, the existence of finger rings, hand accessories, and other false objects. This paper proposes a palm ROI extraction method that is robust to these challenges. The method is based on blob analysis, morphological and geometrical operations without a need to pre-Train or parameter adjustment. It's tested on three available hand databases that cover these challenges;namely, Sfax, IITD and PolyU 3D/2D. The palm ROI is considered to be wrongly extracted if it contains part of the background. The method achieves an extraction error of 0%, 0.27% and 0.26% for the three DBs, respectively. Applying a massive rotation and scaling tests leads to a minor increase in the extraction errors by 0.24%, 0.35% and 0.84% for the three DBs, respectively. © 2016 IEEE.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Parallel transposition of sparse data structures 16

Parallel transposition of sparse data structures

引用

30th International Conference on Supercomputing, ICS 2016

作者： Wang, Hao Liu, Weifeng Hou, Kaixi Feng, Wu-Chun Dept. of Computer Science Virginia Tech. BlacksburgVA United States Niels Bohr Institute University of Copenhagen Copenhagen Denmark Scientific Computing Department STFC Rutherford Appleton Laboratory Harwell Oxford United Kingdom

ISBN: (纸本)9781450343619

Many applications in computational sciences and social sciences exploit sparsity and connectivity of acquired data. Even though many parallel sparse primitives such as sparse matrix-vector (SpMV) multiplication have been extensively studied, some other important building blocks, e.g., parallel transposition for sparse matrices and graphs, have not received the attention they deserve. In this paper, we first identify that the transposition operation can be a bottleneck of some fundamental sparse matrix and graph algorithms. Then, we revisit the performance and scalability of parallel transposition approaches on x86-based multi-core and many-core processors. Based on the insights obtained, we propose two new parallel transposition algorithms: ScanTrans and MergeTrans. The experimental results show that our ScanTrans method achieves an average of 2.8-fold (up to 6.2-fold) speedup over the parallel transposition in the latest vendor-supplied library on an Intel multicore CPU platform, and the MergeTrans approach achieves on average of 3.4-fold (up to 11.7-fold) speedup on an Intel Xeon Phi many-core processor. © 2016 ACM.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

Compressive sensing based Q-space resampling for handling fast bulk motion in hardi acquisitions

Compressive sensing based Q-space resampling for handling fa...

引用

IEEE International Symposium on Biomedical Imaging

作者： Shireen Elhabian Clement Vachet Joseph Piven Martin Styner Guido Gerig Scientific Computing and Imaging Institute Salt Lake City UT USA Dept. of Psychiatry Dept. of Computer Science University of North Carolina NC USA Dept. of Computer Science & Engineering NYU Tandon School of Engineering NY USA

ISBN: (纸本)9781479923519

Diffusion-weighted (DW) MRI has become a widely adopted imaging modality to reveal the underlying brain connectivity. Long acquisition times and/or non-cooperative patients increase the chances of motion-related artifacts. Whereas slow bulk motion results in inter-gradient misalignment which can be handled via retrospective motion correction algorithms, fast bulk motion usually affects data during the application of a single diffusion gradient causing signal dropout artifacts. Common practices opt to discard gradients bearing signal attenuation due to the difficulty of their retrospective correction, with the disadvantage to lose full gradients for further processing. Nonetheless, such attenuation might only affect limited number of slices within a gradient volume. Q-space resampling has recently been proposed to recover corrupted slices while saving gradients for subsequent reconstruction. However, few corrupted gradients are implicitly assumed which might not hold in case of scanning unsedated infants or patients in pain. In this paper, we propose to adopt recent advances in compressive sensing based reconstruction of the diffusion orientation distribution functions (ODF) with under sampled measurements to resample corrupted slices. We make use of Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) basis functions which can analytically model ODF from arbitrary sampled signals. We demonstrate the impact of the proposed resampling strategy compared to state-of-art resampling and gradient exclusion on simulated intra-gradient motion as well as samples from real DWI data.

关键词： Image reconstruction Brain modeling Compressed sensing Measurement Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Virtual machine provisioning, code management, and data movement design for the Fermilab HEPCloud Facility

引用

Journal of Physics: Conference Series 2017年第5期898卷

作者： S Timm G Cooper S Fuess G Garzoglio B Holzman R Kennedy D Grassano A Tiradani R Krishnamurthy S Vinayagam I Raicu H Wu S Ren S-Y Noh Scientific Computing Division Fermilab Batavia IL 60563 USA Computer Science Dept. Illinois Institute of Technology Chicago IL 60616 USA Korea Institute of Science and Technology Information Daejeon Korea

关键词：

来源：评论

学校读者我要写书评

暂无评论

Ultra-fast NoC emulation on a single FPGA 25

Ultra-fast NoC emulation on a single FPGA

引用

25th International Conference on Field Programmable Logic and applications, FPL 2015

作者： Van Chu, Thiem Sato, Shimpei Kise, Kenji Dept. of Computer Science Tokyo Institute of Technology Tokyo Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9780993428005

Network-on-Chip (NoC) has become the de facto on-chip communication architecture for many-core systems. This paper proposes novel methods for emulating large-scale NoC designs on a single FPGA. Since FPGAS offer a highly parallel platform, FPGA-based emulation can be much faster than the software-based approach. However, emulating NoC designs with up to thousands of nodes is a challenging task due to the FPGA capacity constraints. We first describe how to accurately model synthetic workloads on FPGA by separating the time of the emulated network and the times of the traffic generation units. We next present a novel use of time-multiplexing in emulating the entire network using several physical nodes. Finally, we show the basic steps to apply the proposed methods to emulate different NoC architectures. The proposed methods enable ultrafast emulations of large-scale NoC designs with up to thousands of nodes using only on-chip resources of a single FPGA. In particular, more than 5,000× simulation speedup over BookSim, a widely used software-based NoC simulator, is achieved. © 2015 Imperial College.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

A framework for efficient transcription of historical documents using keyword spotting 15

A framework for efficient transcription of historical docume...

引用

3rd International Workshop on Historical Document Imaging and Processing, HIP 2015

作者： Zagoris, Konstantinos Pratikakis, Ioannis Gatos, Basilis Institute of Informatics and Telecommunications National Center for Scientific Research "Demokritos" Athens Greece Visual Computing Group Dept. of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece

ISBN: (纸本)9781450336024

Keyword spotting (KWS) has drawn the attention of the research community as the alternative means to solve hard cases of handwriting text recognition. In this paper, a framework is proposed that employs KWS to enhance the efficiency in the manual transcription process, thus, reducing drastically the cost of training data creation. The core principle relies upon the ability of robust document-specific descriptors to produce meaningful similarities between a chosen word image for transcription and the corresponding word images in the full dataset under consideration. In the proposed framework, KWS is coupled with a relevance feedback mechanism which further enhances retrieval performance while being independent to the chosen KWS algorithm. The efficiency of the proposed pipeline is showcased via a user-friendly web-based prototype. © 2015 ACM.

关键词： Transcription

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：