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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是301-310 订阅

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SMVAR: A Novel RNN Accelerator Based on Non-blocking data Distribution Structure 25

SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Di...

引用

25th IEEE international conferences on High Performance computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xu, Jinwei Jiang, Jingfei Xu, Shiyao Gao, Lei National University of Defense Technology Science and Technology on Parallel and Distributed Laboratory Hunan Changsha China

ISBN: (纸本)9798350330014

Recurrent neural networks (RNNs) have become common models in the field of artificial intelligence to process temporal sequence task, such as speech recognition, text analysis, natural language processing, etc. To speedup RNNs inference, previous research proposed model sparse pruning techniques. However, the pruning rate of existing sparse pruning algorithms will be affected by pruning granularity and hardware friendliness. In order to approximate nonstructured pruning algorithm, this paper proposes Large Region Balanced Sparse (LRBS) pruning method, which does not limit sub-matrix shape and effectively improves pruning rate. Furthermore, we propose Sparse Matrix Vector Multiplication Accelerator for RNNs (SMVAR), which adopt non-blocking data distribution structure to solve the problem of efficient execution of large region irreg-ular matrix multiplication. To further improve the accelerator performance, SMVAR fine-grained adjusts the pipeline between macro-operations to reduce the idle of compute components. In addition, according to the coarse-grained block characteristics of LRBS algorithm, we develop the coarse-grained parallelism of accelerator with multiply compute units(CUs) structure. Experiments show that the pruning rate of our proposed LRBS is 1.25x-2.5x higher than that of the existing pruning algorithms. Compared with the existing work, the execution efficiency is improved by more than 2.02x-35.9x in the same application scenario. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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EOCSim: A CloudSim-Based Simulator for Earth Observation data processing in Clouds 14th

EOCSim: A CloudSim-Based Simulator for Earth Observation Dat...

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14th international conference on Large-Scale Scientific Computations (LSSC)

作者： Lalayan, Arthur Astsatryan, Hrachya Giuliani, Gregory Natl Acad Sci Armenia Inst Informat & Automat Problems 1 Paruyr Sevak Yerevan 0014 Armenia Natl Polytech Univ Armenia 105 Teryan Yerevan 0009 Armenia Univ Geneva Inst Environm Sci GRID Geneva Bd Carl Vogt 66 CH-1211 Geneva Switzerland

ISBN: (纸本)9783031562075;9783031562082

Advanced computing resources are necessary to efficiently and accurately process Earth Observation data for environmental monitoring. However, selecting the optimal infrastructure for distributed data processing can be costly and time-consuming due to the required historical simulation dataset. The article proposes the Earth Observation Cloud Simulator (EOCSim) model to address the challenge of utilizing the CloudSim environment to optimize performance and reduce resource usage costs. The model's accuracy is demonstrated by its ability to emulate the actual experiments' behavior, achieving a high level of accuracy in the weekly NDVI for the territory of Armenia.

关键词： Earth observation CloudSim distributed computing performance-cost optimization Dask Kubernetes

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Design of a Free Trade Zone data Management and Analysis Platform Based on Computer Machine Learning Technology 3

Design of a Free Trade Zone Data Management and Analysis Pla...

引用

3rd international Signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Liu, Chunyang Liu, Yi Wang, Shuli School of Economics Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350307023

This study is dedicated to architecting a data management and analysis platform, integrating advanced computer technologies and machine learning algorithms. Traditional approaches often fall short in handling and analyzing vast data sets. Thus, this paper proposes an innovative solution: enhancing data processing efficiency and precision through machine learning technologies. The research thoroughly examines the application of machine learning in data management, including the selection of appropriate algorithms, effective model training and optimization, and their integration into a user-friendly platform. The platform's design aims to deliver more accurate data analysis, supporting informed decision-making. © 2023 IEEE.

关键词： Information management

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AFFIRM: Privacy-by-Design Blockchain for Mobility data in Web3 using Information Centric Fog Networks with Collaborative Learning

AFFIRM: Privacy-by-Design Blockchain for Mobility Data in We...

引用

international conference on computing, Networking and Communications (ICNC)

作者： Khan, Junaid Ahmed Ozbay, Kaan Western Washington Univ Bellingham WA 98225 USA New York Univ New York NY USA

ISBN: (纸本)9781665457194

Micromobility IoT devices and Connected Vehicles generate massive mobility data, crucial for time-critical safety-related data analytics. It is challenging to study and understand such data without compromising user privacy. We propose AFFIRM, a secure privacy-preserving blockchain framework for efficient, scalable and lightweight mobility data generation, validation, storage and retrieval in future Web3 applications. AFFIRM enables nearby devices to self-organize as a fog network and collaboratively train machine learning algorithms locally to securely generate, validate, store and retrieve mobility data via consensus leveraging Information Centric Networking as the underlying architecture. The proposed collaborative learning enables nodes to learn and adapt with respect to parameters related to scalability, timeliness, security, privacy, and resource consumption. We evaluate AFFIRM using mobility data from New York city and results shows it to scalably store mobility data from up to 700 devices with lower delays and overhead.

关键词： Blockchain Web3 ICN Fog computing IoT Privacy preservation

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Optimizing Large-Scale data processing with Artificial Intelligence 3

Optimizing Large-Scale Data Processing with Artificial Intel...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Khurana, Saniya Kumar, Ritesh Beemkumar, N. Shukla, Anupam Nirmala, D. Meshram, Vidula V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

massive-scale statistics processing presents precise demanding situations in extracting insights, as the sheer amount of records can be overwhelming for classic analytics systems. Artificial Intelligence (AI) can offer a powerful answer for coping with large-scale records units, because it leverages system mastering algorithms to construct sophisticated fashions and automate complex evaluation. AI algorithms are also adaptive and incredibly green, which reduces the time it takes to mine valuable insights from big information units. This paper will study the diverse approaches that AI can enhance large-scale information processing and explore the numerous advantages of deploying AI in statistics processing workflows. The dialogue will include the advantages of leveraging AI for both supervised and unsupervised getting to know tasks in addition to the technical concerns around deciding on the proper AI technology for information processing jobs. Subsequently, the paper will present actual- world applications of AI in huge-scale records processing and evaluate the ability for groups to gain an aggressive aspect by deploying AI of their information pipelines. © 2023 IEEE.

关键词： Aggressive Applications Information Numerous Present

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A Novel Cost-aware data Placement Strategy for Edge-Cloud Collaborative Smart Systems 16

A Novel Cost-aware Data Placement Strategy for Edge-Cloud Co...

引用

IEEE 16th international conference on Cloud computing (IEEE CLOUD)

作者： Zhang, Yifei Xu, Jia Liu, Xiao Pan, Wuzhen Li, Xuejun Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Deakin Univ Sch Informat Technol Geelong Vic Australia

ISBN: (纸本)9798350304817

Currently, smart systems are widely used in various scenarios such as smart cities, smart healthcare and smart logistics. Meanwhile, centralized cloud computing has become the mainstream platform for smart systems due to the strong demand for huge data storage and processing capacities. Edge computing can effectively reduce network latency and improve service response time for smart systems. However, edge servers are usually limited with data storage and processing capacities. Therefore, how to utilize both cloud and edge servers effectively is an open issue. In this paper, we focus on the problem of data placement in an edge-cloud collaborative smart system. Specifically, we propose a Differential Evolution Particle Swarm Optimization based data placement strategy (called DE-PSO) to optimize the data storage cost under deadline constraints. DE-PSO considers different features, requirements and environments of edge and cloud data storage services to generate an optimized data placement scheme. The comprehensive experimental results show that the proposed strategy proves its superior performance compared with several representative data placement strategies.

关键词： edge computing cloud computing data placement smart systems workflow

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Homomorphic Encryption-Based System Design for Secure data processing

Homomorphic Encryption-Based System Design for Secure Data P...

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2023 IEEE international conference on Signal processing, Communications and computing, ICSPCC 2023

作者： Liu, Yihao Hung, Patrick Nekouei, Ehsan Cheung, Ray C.C. City University of Hong Kong Department of Electrical Engineering Hong Kong

ISBN: (纸本)9798350316728

Homomorphic Encryption (HE) is a useful data encryption technique that can support computations on encrypted data. For example, it allows users to add and multiply numbers within encrypted database, using custom-designed arithmetic circuits. Its main applications include cloud computing, data aggregation, machine learning, and multi-party communications. This research project implements a secure Encrypted Query System, supporting multiple clients using Homomorphic Encryption. In order to improve computational speed, we use High-Level Synthesis (HLS) to develop a hardware accelerated Number Theoretic Transform (NTT) based polynomial multiplier. Comparing with some existing methods in the literature, our proposed design can achieve markedly lower latency. © 2023 IEEE.

关键词： Search engines

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Multimodal Sensor data Fusion and Ensemble Modeling for Human Locomotion Activity Recognition

Multimodal Sensor Data Fusion and Ensemble Modeling for Huma...

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ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable computing (UbiComp/ISWC)

作者： Oh, Se Won Jeong, Hyuntae Chung, Seungeun Lim, Jeong Mook Noh, Kyoung Ju Elect & Telecommun Res Inst Daejeon Daejeon South Korea

ISBN: (纸本)9798400702006

The primary research objective of this study is to develop an algorithm pipeline for recognizing human locomotion activities using multimodal sensor data from smartphones, while minimizing prediction errors due to data differences between individuals. The multimodal sensor data provided for the 2023 SHL recognition challenge comprises three types of motion data and two types of radio sensor data. Our team, 'HELP,' presents an approach that aligns all the multimodal data to derive a form of vector composed of 106 features, and then blends predictions from multiple learning models which are trained using different number of feature vectors. The proposed neural network models, trained solely on data from a specific individual, yield F1 scores of up to 0.8 in recognizing the locomotion activities of other users. Through post-processing operations, including the ensemble of multiple learning models, it is expected to achieve a performance improvement of 10% or greater in terms of F1 score.

关键词： Activity recognition Neural networks Machine learning Multimodal sensors Human locomotion SHL dataset

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Ethernet data Distribution Service data processing Method 2

Ethernet Data Distribution Service Data Processing Method

引用

2nd international conference on algorithms, Microchips, and Network Applications, AMNA 2023

作者： Wu, Yu Li, Xiaoya Hu, Huiyang China Flight Test Research Institute Testing Institute China

ISBN: (纸本)9781510664791

With the rapid development of aviation electromechanical technology, new aircraft are equipped with a large number of sensors and electronic control devices. In order to meet the data transmission requirements of avionics systems, Ethernet data Distribution (DDS) protocol has gradually become the standard solution for published/subscribed data in distributed real-time systems. Facing the new requirements of bus data processing using DDS protocol, starting from the characteristics and format of Ethernet DDS data recording, this paper gives the overall architecture and core processing method of the processing software. This paper analyzes the key problems of Ethernet DDS data processing, and proposes solutions for DDS bus interface control document (ICD) plane structure, multi-level parallel data processing algorithm, data detection and filtering. The method is validated and can correctly process Ethernet DDS data. © 2023 SPIE.

关键词： Ethernet

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Research on the Methods of data Mining based on the Edge computing for the IoT

Research on the Methods of Data Mining based on the Edge Com...

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2023 IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Huang, Yan Zhang, Weimeng Xi'an Traffic Engineering Institute Shaanxi China Xian Technician College Shaanxi China

ISBN: (纸本)9798350398465

With the advent of the internet of everything, the number of network edge devices has increased rapidly, causing the data generated by such devices to increase rapidly. In order to enable the internet of things to provide users with information services and reliable decision-making more intelligently, valuable knowledge can be fully mined from these massive data. At present, the use of cloud computing models for centralized processing of big data generated by the internet of things has begun to show many drawbacks. Compared with cloud computing, edge computing migrates data computing or storage to the 'edge' of the network close to users, reducing the delay brought by network transmission. In addition, edge computing mode can better protect users' privacy data and reduce the risk of sensitive data privacy leakage. Edge computing has become a new paradigm to solve the needs of Internet of things and localized computing. The thesis studies the method of image data classification processing of the Internet, the edge agent is established by deploying the shallow part of the neural network on the edge device, and the cloud agent is established by deploying the deep part of the neural network on the cloud server device. The multi-agent is constructed by the way of edge cloud cooperation. The reasoning execution of convolutional neural network can complete the classification and exit on the local terminal for fast local inference;when additional processing is needed, the data can be transmitted to the cloud server for further processing by using the deep neural network in the cloud to improve the performance accuracy of the system. © 2023 IEEE.

关键词： Sensitive data

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