检索结果-内蒙古大学图书馆

Blockchain Computing and applications (BCCA), international conference on

作者： Mpyana Mwamba Merlec Nday Kabulo Sinai Seng-Phil Hong Hoh Peter In Computer Science and Eng. Korea University Seoul South Korea School of Computing and AI Hanshin University Seoul South Korea Korea University DAO Solution Inc. Seoul South Korea

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

The proliferation of digital technologies has led to an exponential growth in personal data generation, raising significant privacy, security, and ownership control challenges. However, existing centralized identity and data management models present concerns leading to data breaches, unauthorized data sharing, and loss of user ownership control. To address these issues, this article proposes a novel Personal Data-as-a-Service (PDaaS) platform, which leverages the robustness of a permissioned blockchain to ensure data integrity and access control, while using IPFS for decentralized and resilient data storage. It enables a secure and decentralized user-centric framework for managing personal data while preserving user privacy and data sovereignty. Leveraging a suite of advanced technologies including decentralized identifiers, verifiable credentials, enhanced encryption techniques (i.e., quantum-resistant encryption), zero-knowledge proofs, and dynamic consent management, the PDaaS platform enables individuals to retain ownership and control over their data while facilitating secure and privacy-preserving data processing, sharing, exchange, and monetization. This paper presents the design considerations and framework architecture of the proposed platform, which was built using the Hyperledger Besu to assess its feasibility and performance. It also discusses the implications, use cases, and challenges of deploying the PDaaS platform.

关键词： Data privacy distributed ledger Data integrity Memory Data collection Data breach Robustness Data models Blockchains Encryption

来源：评论

学校读者我要写书评

暂无评论

Towards Automatic, Predictable and High-Performance parallel Code Generation 30th

Towards Automatic, Predictable and High-Performance Paralle...

引用

30th international conference on parallel and distributed processing techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Mullin, Lenore Hains, Gaétan 1400 Washington Ave AlbanyNY12222 United States LACL Université Paris-Est Créteil94000 France

ISBN: (纸本)9783031856372

High-performance architectures have complex features so that reliable production of parallel software is beyond the reach of many Computer Science graduates. Compilers alone cannot guarantee the highest performance and multiple APIs with complex performance features are difficult to master. As a first step towards more comprehensive solutions we are building key elements of a pre-compiler system that will automatically produce predictable, scalable and high-performance code from declarative tensor expressions. In this paper we summarize and analyze a large set of timing experiments of matrix multiplication variants that are mapped to vectorized and multithread code. The analysis covers two high-end target architectures and exhaust a whole space of code, compiler, pragma and parallelism parameters. Our analysis shows how the best choice of parameters is produced from a small set of tests that can converge in a matter of seconds and then predict performance of larger instances to within 25% or much less. Inefficient choices of parameters is also shown to be reliably predicted from small tests, so that our design for a precompiler is guaranteed to be a realistic and portable tool. The generality of our Mathematics of Arrays tensor algebra, and very broad applicability of tensor operations (signal processing, scientific computing, AI, etc.) supports our claim that these experiments and design can be generalized to a general purpose parallel programming tool. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

ScaDL 2022 Invited Talk 1: Design of secure power monitors for accelerators, by exploiting ML techniques, in the Euro-HPC TEXTAROSSA project

ScaDL 2022 Invited Talk 1: Design of secure power monitors f...

引用

IEEE international Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： William Fornaciari Politecnico di Milano � DEIB Italy

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The evolution of High Performance Computing (HPC) has to face with several obstacles, including power/thermal management of the cores and the presence of heterogeneous computing platforms. Within the Euro-HPC project TEXTAROSSA, started in spring 2021, several HPC applications exploiting AI and with the need of processing big chunks of data in a secure context, are properly accelerated by leveraging ad-hoc designed accelerators to be implemented in hardware. Such customized heterogeneity of execution has many benefits, but increases the problem of power management, since the accelerators, possibly generated through high-level-synthesis, are neither providing run-time information on their power consumption, nor allows to control the security of the information flow against implementation attacks. In such scenario, any global power manager / resource orchestrator, can operate only with a partial picture of the overall systems and not in real-time, with the risk of being trapped in poor power optimizations and unbalanced resource exploitation. The goal of the talk is to show how is possible to exploit popular ML techniques for a twofold purpose: •Automatically generate an on-line power monitor, to augment the hardware description of any piece of hardware, in particular that of cores and accelerators, capable to provide on-line power estimated in less that few milliseconds. •Select, in the space of all the possible power monitors, those that are not leaking information that can be used to mount side-channel attacks.

关键词： Hardware Monitoring Technology transfer Side-channel attacks Power system management distributed processing conferences

来源：评论

学校读者我要写书评

暂无评论

Indian Sign Language Alphanumeric Hand Gestures Recognition Using Deep Learning techniques

Indian Sign Language Alphanumeric Hand Gestures Recognition ...

引用

Advanced Research in Computing (ICARC), international conference on

作者： Shakana Mahendran Ramya Mahendran Department of Physical Science FAS Vavuniya Campus of the University of Jaffna Vavuniya Sri Lanka Information Technology Center Trincomalee Campus EUSL. Trincomalee Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Alphanumeric sign language recognition plays a vital role in enabling effective communication between individuals who are deaf and the general population. This research focuses on classifying alphanumeric hand gestures in Indian Sign Language using real-time image processing. The study collects diverse hand shape images captured under varying backgrounds and lighting conditions, making it suitable for real-world applications. In the preprocessing stage, images are cropped and resized to a standardized 128×128 pixel format to ensure uniformity in the dataset. A two-pronged approach is adopted to classify the collected images. Sequential and parallel CNNs are employed to capture spatial and channel information effectively. Additionally, a channel attention module enhances the model’s ability to identify crucial channel information. The Deep Convolutional Neural Network (DCNN) employed in this study achieved remarkable accuracy, reaching 99.976%. This outcome demonstrates the superior performance of this approach compared to other classification methods, whether utilizing real-time or non-laboratory experimental data. This research contributes to developing an efficient, accurate, and cost-effective system for classifying alphanumeric signs in Indian Sign Language. It facilitates easy and fast communication between individuals who are deaf and those who are not, promoting inclusive interactions.

关键词： Hands Training Deep learning Sign language Accuracy Shape Computational modeling Real-time systems Stability analysis Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Improved parallel ZF-VBLAST Detector for MIMO System 4

Improved Parallel ZF-VBLAST Detector for MIMO System

引用

4th international conference on Communications, Signal processing, and their applications, ICCSPA 2020

作者： Alfawaz, Oruba Eddin, Maha Alaa Alnajjar, Khawla A. El-Moursy, Ali A. University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Sharjah Department of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9781728165356

The rapid growth of data traffic in the last decade is expected to continue in the next generation (5G) system. In order to service the high demand of data rates in 5G, complex communication architectures are needed such as multiple input multiple output (MIMO) technology. The main idea of MIMO is to increase the number of antennas in both sides (transmitter Tx and receiver Rx) that can achieve high throughput and energy efficiency. Receivers complexity and speed are some of the important requirements in MIMO systems. We focus on Vertical Bell Laboratories Layered Space Time (VBLAST) receiver. Using large number of antennas with VBLAST technique can increase the complexity of the system and the required time to recover the original signal. However, parallel computing can be used to divide the tasks of complex algorithm and distribute them among different microprocessors to run in an acceptable execution time. In this paper, we perform deep performance and time analysis to decide the opportunities of parallelization. We achieved speed up of 11.4X in the computation time of ZF-VBLAST algorithm for different sizes of MIMO systems using multi-threaded implementation. © 2021 IEEE.

关键词： MIMO systems

来源：评论

学校读者我要写书评

暂无评论

Owl: Differential-Based Side-Channel Leakage Detection for CUDA applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

引用

international conference on Dependable Systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Optimal Placement of Grid-Connected Solar Photovoltaic Systems Using Artificial Intelligence Methods 14

Optimal Placement of Grid-Connected Solar Photovoltaic Syste...

引用

14th international conference on Developments in eSystems Engineering (DeSE)

作者： Ansari, Sam Hamid, Abdul-Kadir Al Hindawi, Noor Ahmad Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665408882

Solar energy is a sustainable, clean, and free energy source used to supply heat, electricity, and even fuel and chemical energy to residential, commercial, and industrial centers. The problems associated with fossil resources and the consequences of environmental and global climate change have created good opportunities for solar energy to compete with fossil fuels, especially in countries with high radiation potential. Scientific and technical weaknesses, variations in irradiance level due to climate and seasonal changes, radiation angles, geographical location, etc., have confined the solar energy-related applications. The optimal placement of the photovoltaic (PV) power stations providing maximum performance is critical to be addressed. This study intends to empower grid-connected solar PV systems by investigating various constraints and influencing factors related to the location of solar farms. We exploit selected machine learning algorithms such as k-means, k-medoids, fuzzy c-means (FCM) methods, as well as a new proposed algorithm based on an image processing approach to optimize the system by detecting the appropriate clusters and site locations. Finally, by performing several simulations, we compare the results and the efficiency of the algorithms. Our results further designate the significant superiority of the proposed techniques.

关键词： clustering data mining grid-connected solar photovoltaic systems image processing machine learning

来源：评论

学校读者我要写书评

暂无评论

Efficient Multi-GPU Graph processing with Remote Work Stealing

Efficient Multi-GPU Graph Processing with Remote Work Steali...

引用

international conference on Data Engineering

作者： Ke Meng Liang Geng Xue Li Qian Tao Wenyuan Yu Jingren Zhou Alibaba Group

Graph algorithms support a broad spectrum of big data applications. A typical approach to scale graph algorithms is to run in a distributed and parallel setting with multiple processing devices. The approach requires balanced and effective utilization of computation, memory, and communication resources across devices. To address the problem, a large number of studies have been conducted, such as graph partitioning and asynchronous computation. However, there are still many outstanding issues yet to be solved. For example, the workloads can be skewed differently across devices, and between iterations, even with the state-of-the-art graph partitioners. As the graph partitions are typically static, they fall short in capturing the dynamic characteristics with different algorithms, inputs, and progress, leading to poor utilization of resources. Recently, GPUs have been increasingly used to accelerate various graph algorithms. Their highly efficient interconnection technologies, such as NVLink, open new opportunities for us to achieve better resource utilization. In this paper, we analyze the dynamic load-imbalance (DLB) problem and the long tail (LT) problem in multi-GPUs and solve them by adaptive remote work stealing on-the-fly. We first introduce a frontier stealing algorithm to solve the DLB problem, then an ownership stealing algorithm to solve the LT problem. Based on these two algorithms, we developed Gum — a multi-GPU graph processing system with high device utilization. We evaluated Gum on four typical graph algorithms (BFS, WCC, PR, SSSP). The results show that Gum can run up to an order of magnitude faster than Gunrock and Groute, with fewer stragglers and less synchronization overhead.

关键词：

来源：评论

学校读者我要写书评

暂无评论

distributed-Memory parallel Algorithms for Sparse Times Tall-Skinny-Dense Matrix Multiplication 21

Distributed-Memory Parallel Algorithms for Sparse Times Tall...

引用

35th ACM international conference on Supercomputing (ICS)

作者： Selvitopi, Oguz Brock, Benjamin Nisa, Israt Tripathy, Alok Yelick, Katherine Buluc, Aydin Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450383356

Sparse times dense matrix multiplication (SpMM) finds its applications in well-established fields such as computational linear algebra as well as emerging fields such as graph neural networks. In this study, we evaluate the performance of various techniques for performing SpMM as a distributed computation across many nodes by focusing on GPU accelerators. We examine how the actual local computational performance of state-of-the-art SpMM implementations affect computational efficiency as dimensions change when we scale to large numbers of nodes, which proves to be an unexpectedly important bottleneck. We consider various distribution strategies, including A-Stationary, B-Stationary, and C-Stationary algorithms, 1.5D and 2D algorithms, and RDMA-based and bulk synchronous methods of data transfer. Our results show that the best choice of algorithm and implementation technique depends not only on the cost of communication for particular matrix sizes and dimensions, but also on the performance of local SpMM operations. Our evaluations reveal that with the involvement of GPU accelerators, the best design choices for SpMM differ from the conventional algorithms that are known to perform well for dense matrix-matrix or sparse matrix-sparse matrix multiplies.

关键词： Sparse linear algebra Sparse matrices Graphics accelerators parallel algorithms RDMA

来源：评论

学校读者我要写书评

暂无评论

Predictable remote invocations for distributed stream processing

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF ESCIENCE 2020年 107卷 716-729页

作者： Basanta-Val, P. Fernandez-Garcia, N. Sanchez-Fernandez, L. Univ Carlos III Madrid UC3M BS Inst Financial Big Data IFiBiD Dept Ingn Telemat Madrid 28911 Spain Ctr Univ Defensa Escuela Naval Mil Plaza Espana S-N Marin 36920 Spain

Typical infrastructure for big-data includes multiple machines with data accessed remotely with request-response patterns from different remote locations. Currently, most of the state-of-the-art remote invocation techniques are focused on models for distributed interactions, which have not explored the advantages given by parallel computing, such as those offered to run on distributed stream processors. In this context, the article is focused on the definition of a predictable remote procedure call (RPC) able to take advantage from the distributed stream processing technology. Potentially, this type of infrastructure enables efficient parallel computations, reducing the effective response-time of end-to-end invocations linearly with the number of resources assigned to the system, as the data increases. The article describes a predictable model which defines maximum response-times for different types of applications described in the context of Apache Storm. Evaluation also offers clues on the performance one may expect from this type of infrastructure. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Big-data distributed stream processing Predictable infrastructure Apache storm Time-critical big-data Efficient big-data

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：