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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

共 9660 条记录，以下是301-310 订阅

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High-Speed Transcript Collection on Multimedia Platforms: Advancing Social Media Research through parallel Processing

High-Speed Transcript Collection on Multimedia Platforms: Ad...

引用

1st international Conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Cakmak, Mert Can Agarwal, Nitin Univ Arkansas COSMOS Res Ctr Little Rock AR 72204 USA

ISBN: (纸本)9798350364613;9798350364606

In the digital era, time is a precious commodity that equates to money, and parallelization techniques have been pivotal in maximizing time efficiency. This research underscores the critical importance of quick and effective transcription retrieval from multimedia-rich platforms, leveraging parallel processing and advanced methodologies for significant speed enhancements. The ability to swiftly process vast amounts of data not only streamlines research efforts but also offers extensive benefits for various fields of study. By demonstrating substantial gains in processing speed, this study showcases the revolutionary potential of efficient data handling techniques, marking a significant contribution to the broader research community's ability to analyze digital media's impact on society.

关键词： parallel processing transcription collection parallelization strategies multimedia platforms

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7th IEEE Workshop on parallel and distributed Processing for Computational Social systems (ParSocial 2023)

2023 IEEE International Parallel and Distributed Processing ...

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2023 IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2023 2023年 818-819页

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Collecting Clustering Coefficient of distributed Graph Data with Shuffled Differential Privacy

Collecting Clustering Coefficient of Distributed Graph Data ...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Hongfa Ding Peiwang Fu Yingxuan Luo Heling Jiang Hai Liu School of Information Guizhou University of Fiance and Economics Guiyang China Data Security Certification (Guizhou) Co. Ltd Guiyang China Jiangxi Datang International Fuzhou Power Generation Co. Ltd Fuzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The intricate properties and relevance of graph data make it difficult to collect graph statistics privately via differential privacy (DP). Traditional centralized or local DP on graph data, face challenges like third-party threats and low data utility when collecting the clustering coefficient. In this regard, we introduce GCC-SDP, a scheme for collecting distributed Graph Clustering Coefficient with Shuffled DP (SDP). GCC-SDP gathers the local wedge lists of all edges and adjacency bit vectors through SDP and random response for calculating the noisy local triangle counts. It then collects the local degree values of all users by using Laplace mechanism, followed by estimating the global clustering coefficient of the global graph data by data collector. We provide specific steps of GCC-SDP and demonstrate through theoretical analysis that GCC-SDP conforms to various DPs, with unbiased results. Empirical experiments show that GCC-SDP performs better than existing local DP-based techniques across most accuracy metrics.

关键词： Differential privacy distributed processing Accuracy distributed databases Vectors Noise measurement Faces

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Research on Power Big Data Fusion Method Based on Graph Convolutional Neural Network

Research on Power Big Data Fusion Method Based on Graph Conv...

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Runyang Ji State Grid Nantong Power Supply Company Nantong Jiangsu

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

In recent years, in order to accelerate the construction of new power systems, power companies have carried out a series of information system construction, generating a massive amount of power related business data, which brings problems such as low query efficiency and inaccurate results to the real-time query performance of the system. Therefore, this article proposes a power big data fusion method based on graph convolutional neural networks, using association rule algorithms to horizontally connect various fragmented basic wide tables, indicators, and labels related to the perspective of power business, and aggregate them to form a logical business view. The association integrates more data information of power business objects, providing data support for power big data analysis, mining, and inference.

关键词： Companies Big Data parallel processing Real-time systems Inference algorithms Power systems Association rule learning Convolutional neural networks Business Information systems

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Outlier Detection Approach in Sensor-to-Microcontroller Interfaces

Outlier Detection Approach in Sensor-to-Microcontroller Inte...

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international symposium on Industrial Electronics (INDEL)

作者： Zivko Kokolanski Vladimir Dimcev Faculty of Electrical Engineering and Information Technologies Ss. Cyril and Methodius University in Skopje Skopje North Macedonia

ISBN: (数字)9798350352320

ISBN: (纸本)9798350352337

This paper presents the implementation of an outlier detection method based on the Grubbs test, specifically designed for microcontroller applications. The theoretical background for outlier detection is outlined, and experimental validation is conducted using a direct sensor-to-microcontroller interface circuit. Results demonstrate that this approach improves the accuracy of measurement systems affected by Gaussian noise or other normal-distributed external interference signals.

关键词： Industrial electronics Accuracy Embedded systems Microcontrollers Gaussian noise distributed databases Interference Reliability theory Noise measurement Anomaly detection

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POCA: A PYNQ Offloaded Cryptographic Accelerator on Embedded FPGA-Based systems

POCA: A PYNQ Offloaded Cryptographic Accelerator on Embedded...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Roberto A. Bertolini Filippo Carloni Davide Conficconi Marco Domenico Santambrogio Dipartimento di Elettronica Informatica e Bioingegneria Politecnico di Milano Milan Italy

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

The pervasiveness of embedded systems across edge and connected devices is challenging their design, as sharing data securely via AES encryption is computationally intensive. This work proposes POCA, an FPGA-accelerated HW/SW library that performs AES cryptographic primitives with different modes and the most used key sizes. The HW/SW approach of POCA showcases the benefits of accelerated cryptographic primitives and the flexibility of a unified platform interface across different embedded systems by exploiting the Python PYNQ framework. POCA matches the multicore ARM crypto extensions on an Ultrascale+ ZU3EG while overwhelming SW performance.

关键词： Performance evaluation distributed processing Embedded systems Multicore processing Conferences Information sharing Libraries

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Research on Intelligent Maneuvering Decision Making Based on Deep Reinforcement Learning

Research on Intelligent Maneuvering Decision Making Based on...

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Yao Wang Yi Jiang Huiqi Xu Yuan Zhang Naval Aviation University Yantai China

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

At present, the maneuvering environment is complex and changeable, and unmanned platforms are faced with a variety of uncertain factors when carrying out multiple tasks. In order to improve the efficient and safe maneuvering performance of unmanned platforms in complex scenarios, this paper proposed building a maneuvering decision network system by using deep reinforcement learning network DDPG network for guiding agents execute maneuvering decisions according to tasks and environments to achieve the purpose of safe and efficient maneuvering. The paper tested the maneuvering decision performance in dynamic changing scenarios and verified the autonomous intelligent maneuvering performance of network guided agents, which could effectively avoid threats and choose better paths, and had good robustness and generalization.

关键词： Training Decision making Buildings parallel processing Deep reinforcement learning Robustness Network systems

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parallelizing Accelerographic Records Processing

Parallelizing Accelerographic Records Processing

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Ronaldo Canizales Luis Mixco Jedidiah McClurg Department of Computer Science Colorado State University Colorado USA Observatorio de Amenazas y Recursos Naturales Ministerio de Medio Ambiente y Recursos Naturales San Salvador El Salvador

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Strong-motion processing holds paramount importance in earthquake engineering and disaster risk management systems. By leveraging parallel loops and task-parallelism techniques, we address computational challenges posed by large-scale accelerographic datasets. Through experimentation with more than one million data points from six real-world seismic events, our approach achieved speedups of up to 2.9x, demonstrating the effectiveness of parallel programming in accelerating seismic data processing. Our findings highlight the significance of parallel programming techniques in advancing seismological research and enhancing earthquake mitigation strategies.

关键词： distributed processing parallel programming Disasters Scalability Prevention and mitigation Earthquake engineering Seismology

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Byzantine Agreement with Unknown Participants and Failures 35

Byzantine Agreement with Unknown Participants and Failures

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Khanchandani, Pankaj Wattenhofer, Roger Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665440660

A set of mutually distrusting participants that want to agree on a common opinion must solve an instance of a Byzantine agreement problem. These problems have been extensively studied in the literature. However, most of the existing solutions assume that the participants are aware of n - the total number of participants in the system - and f - an upper bound on the number of Byzantine participants. In this paper, we show that most of the fundamental agreement problems can be solved without affecting resiliency even if the participants do not know the values of (possibly changing) n and f. Specifically, we consider a synchronous system where the participants have unique but not necessarily consecutive identifiers, and give Byzantine agreement algorithms for reliable broadcast, approximate agreement, rotor-coordinator, early terminating consensus and total ordering in static and dynamic systems, all with the optimal resiliency of n > 3f. Moreover, we show that some synchrony is necessary as an agreement with probabilistic termination is impossible in a semi-synchronous or asynchronous system if the participants are unaware of n and f.

关键词： distributed processing Upper bound Heuristic algorithms Probabilistic logic Approximation algorithms Reliability Dynamical systems

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Machine Learning Assisted HPC Workload Trace Generation for Leadership Scale Storage systems 22

Machine Learning Assisted HPC Workload Trace Generation for ...

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31st international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Paul, Arnab K. Choi, Jong Youl Karimi, Ahmad Maroof Wang, Feiyi Oak Ridge Natl Lab Oak Ridge TN 37830 USA BITS Pilani K K Birla Goa Campus Zuarinagar Goa India

ISBN: (纸本)9781450391993

Monitoring and analyzing a wide range of I/O activities in an HPC cluster is important in maintaining mission-critical performance in a large-scale, multi-user, parallel storage system. Center-wide I/O traces can provide high-level information and fine-grained activities per application or per user running in the system. Studying such large-scale traces can provide helpful insights into the system. It can be used to develop predictive methods for making predictive decisions, adjusting scheduling policies, or providing decisions for the design of next-generation systems. However, sharing real-world I/O traces to expedite such research efforts leaves a few concerns;i) the cost of sharing the large traces is expensive due to this large size, and ii) privacy concern is an issue. We address such issues by building an end-to-end machine learning (ML) workflow that can generate I/O traces for large-scale HPC applications. We leverage ML based feature selection and generative models for I/O trace generation. The generative models are trained on I/O traces collected by the darshan I/O characterization tool over a period of one year. We present a two-step generation process consisting of two deep-learning models, called the feature generator and the trace generator. The combination of two-step generative models provides robustness by reducing the bias of the model and accounting for the stochastic nature of the I/O traces across different runs of an application. We evaluate the performance of the generative models and show that the two-step model can generate time-series I/O traces with less than 20% root mean square error.

关键词： Darshan Generative Modeling Feature Selection Clustering parallel File System

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