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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411-420 订阅

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CryptoASCII-ASCII based cryptosystem for cloud data storage

CryptoASCII-ASCII based cryptosystem for cloud data storage

引用

international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Pavithra, M. Prakash, M. Vennila, V. Tamil Nadu Komarapalayam India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

Cloud computing plays a huge role in IT industry, Education sector, government sector, medical field, defense management, Natural disaster control, weather reporting, road traffic maintenances, e-money transaction etc. The cloud provides wide amount of services can include delivery of data, software, hardware, infrastructure, platform, networking and so on. The merits of cloud computing are numerous to the user for data storing, accessing, maintenance in the cloud from anywhere at any time at low cost. On the other hands, the data in the cloud were not maintained by the direct owner. Despite, the data security has always been big challenge in cloud storage. data integrity, confidentiality and privacy protection are the prominent factors of the user’s side from the cloud environment. In this proposed paper, we introducing a new method CryptoASCII to provide data security in the cloud storage. By CryptoASCII, we confirmed that the user’s data are maintained with integrity and confidentiality. CryptoASCII can provide encryption and decryption algorithms to ensure data privacy as well as it provide redundancy checking proof for data integrity. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Redundancy

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Fast Prototyping of Distributed Stream processing Applications with stream2gym 43

Fast Prototyping of Distributed Stream Processing Applicatio...

引用

43rd IEEE international conference on Distributed computing Systems (ICDCS)

作者： Ifath, Md. Monzurul Amin Neves, Miguel Haque, Israat Dalhousie Univ Halifax NS Canada

ISBN: (纸本)9798350339864

Stream processing applications have been widely adopted due to real-time data analytics demands, e.g., fraud detection, video analytics, IoT applications. Unfortunately, prototyping and testing these applications is still a cumbersome process for developers that usually requires an expensive testbed and deep multi-disciplinary expertise, including in areas such as networking, distributed systems, and data engineering. As a result, it takes a long time to deploy stream processing applications into production and yet users face several correctness and performance issues. In this paper, we present stream2gym, a tool for the fast prototyping of large-scale distributed stream processing applications. stream2gym builds on Mininet, a widely adopted network emulation platform, and provides a high-level interface to enable developers to easily test their applications under various operating conditions. We demonstrate the benefits of stream2gym by prototyping and testing several applications as well as reproducing key findings from prior research work in video analytics and network traffic monitoring. Moreover, we show stream2gym presents accurate results compared to a hardware testbed while consuming a small amount of resources (enough to be supported in a single commodity laptop even when emulating a dozen of processing nodes).

关键词： data Analytics

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Performance Estimation and Prototyping of Reconfigurable Near-Memory computing Systems 33

Performance Estimation and Prototyping of Reconfigurable Nea...

引用

33rd international conference on Field-Programmable Logic and Applications (FPL)

作者： Iskandar, Veronia Abd El Ghany, Mohamed A. Goehringer, Diana Tech Univ Dresden Chair Adapt Dynam Syst Dresden Germany German Univ Cairo Dept Elect IES Lab Cairo Egypt Tech Univ Darmstadt Darmstadt Germany Ctr Scalable Data Analyt & Artificial Intelligenc Leipzig Germany

ISBN: (纸本)9798350341515

The concept of near-memory computing (NMC) has emerged as a promising solution to address the memory wall challenges faced by future computing architectures. By utilizing modern systems that integrate 3D-stacked DRAM memory, the NMC paradigm minimizes unnecessary data movement between the memory subsystem and the CPU. FPGA vendors have incorporated 3D-stacked memories into their products to meet the increasing bandwidth requirements of memory-intensive applications, enabling FPGAs to compete with GPU solutions in terms of speed and energy efficiency. Recent NMC proposals focus on different data processing workloads, including graph processing and machine learning. This work addresses the research questions of how to leverage the full bandwidth of 3D-stacked high-bandwidth memory and how to facilitate the adoption of the near-memory computing paradigm.

关键词： High bandwidth memories Near memory computing Parallel Architectures Prediction

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data Stream Scheduling Algorithm Based on Link Congestion Probability Optimization

Data Stream Scheduling Algorithm Based on Link Congestion Pr...

引用

2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Wang, Pengyi Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781510671881

Aiming at the problem of increased network delay due to congestion caused by link collision caused by improper traffic scheduling in the industrial Internet, this paper proposes a flow scheduling strategy based on probability-based data flow path planning within a period of time. On the basis of the software-defined network centralized control architecture, the controller collects the switches and edges’ node information in the topological network to meet the QoS of different services as much as possible while ensuring that more services are responded. In the scheduling period, the congestion probability of each link is introduced, and the relative delay is obtained by combining the delay required by the link. The optimal path under the current strategy is selected to optimize the result of data flow scheduling and reduce data flow transmission delay. © 2023 SPIE. All rights reserved.

关键词： Controllers

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

引用

26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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An Approach to Intent Recognition and Classification Based on sEMG Signals Using Features Extracted and Evaluated by data Index Values 9

An Approach to Intent Recognition and Classification Based o...

引用

9th international conference on Intelligent computing and Signal processing, ICSP 2024

作者： Ye, Sijia Meng, Jingyan Ma, Yehao Zuo, Guokun Shi, Changcheng Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China University of Chinese Academy of Sciences Beijing China Ningbo Cixi Institute of Biomedical Engineering Ningbo China Ningbo University of Technology Ningbo China

ISBN: (纸本)9798350376548

The accuracy of real-time surface electromyography (sEMG) based intention recognition still needs to be improved. However, most of the proposed improved feature extraction algorithms would be more computationally complex and increase computation time than traditional methods, which is not conducive to get result of real-time classification quickly. In this study, we collected sEMG signals from 14 subjects and used eight feature extraction algorithms to derive the offline and real-time classification accuracy, as well as evaluate the data after feature extraction. Furthermore, correlation between accuracy and index values was analyzed. The results show that there are a number of features with high correlation between accuracy and index values for real-time data, and a few features with significant correlation between real-time accuracy and index values of offline data. Therefore, the index value of the feature data can be used to predict the real-time classification accuracy, and then the best feature extraction algorithm can be selected for this subject. © 2024 IEEE.

关键词： feature index intent classification real-time recognition surface electromyography

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Improved Feature Extraction for Time Series data Using Sliding Window: A Case Study of Carnatic Tala 5

Improved Feature Extraction for Time Series Data Using Slidi...

引用

5th international conference on Advancements in computing, ICAC 2023

作者： Puvanendran, Rukshani Dayarathna, Oshani Erunika Kartheeswaran, Thangathurai University of Vavuniya Department of ICT Sri Lanka University of Peradeniya Department of Statistics and Computer Science Sri Lanka University of Vavuniya Department of Physical Science Sri Lanka

ISBN: (纸本)9798350358131

Human action patterns are typically temporal. Since the data collected is periodic, processing time series data into a feature matrix for further learning and pattern recognition is a challenging task. Periodic data must be processed in a sequential manner. Here we have tested the case study to identify the patterns related to the Carnatic Sapta Tala (the Seven Tala System) used in Carnatic music. Accelerometer, gyroscope, gravity sensor, and rotation vector are some of the sensors embedded in smartphones and used for data collection. The minimum window size to extract distinct characteristics of the selected Sapta tala was determined to be 20 through the experiment performed. This study employed a feature extraction method with a 20-second window and 99.95% overlap to create a feature matrix for identifying Sapta tala patterns in the Carnatic music system. The findings demonstrate an improved association between these rhythmic patterns of movement and can be tracked using smartphone sensors when utilizing a method that involves 20-frame overlapping windows. Future work could incorporate more advanced machine learning algorithms and data from additional sensors to further enhance the proposed methodology to categorize tala patterns. The optimal window size of 20 frames can be further enhanced during the application of classification algorithms, subject to the relevant metric. © 2023 IEEE.

关键词： Time series

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

引用

9th international conference on Big data computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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PimCity: A Compute in Memory Substrate featuring both Row and Column Parallel computing 8

PimCity: A Compute in Memory Substrate featuring both Row an...

引用

8th IEEE international conference on Rebooting computing, ICRC 2023

作者： Resch, Salonik Cilasun, Husrev Zabihi, Masoud Chowdhury, Zamshed Zhao, Zhengyang Wang, Jian-Ping Sapatnekar, Sachin S. Karpuzcu, Ulya University of Minnesota Twin Cities United States

ISBN: (纸本)9798350382044

processing-in-memory (PIM) substrates can perform parallel computation directly within the memory array. As a result, throughput performance and energy efficiency can reach unprecedented levels, however, there are limitations. Typical PIM architectures only support parallel computing in one dimension of the memory array: Computation is performed along either multiple rows or multiple columns (but not both). This restricts data layout and makes intra-array intermediate data transfers during computation inevitable - which require reads and writes, even for short-distance data movement. Inter-array data transfers, on the other hand, become a problem for larger scale algorithms which use more than one PIM array. Such data transfers incur large communication overheads and increase the complexity of the peripheral circuitry and interconnection network between arrays. Intermediate data transfers of any form limit scalability and efficiency. To overcome this limitation, we introduce PimCity, a new PIM substrate which can compute in both the rows and the columns of the memory array. PimCity replaces intraarray data transfer (memory) operations with lower overhead logic operations inside the memory array. Further, PimCity can perform logic operations directly across neighboring memory arrays, which in turn enables low-cost inter-array data transfers. PimCity hence represents a scalable PIM architecture suitable for both HPC and embedded IoT style applications. © 2023 IEEE.

关键词： data transfer

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Design of "data Structure" Resource Platform for Computer Major Based on Cloud computing 4

Design of "Data Structure" Resource Platform for Computer Ma...

引用

4th international conference on Computers, Information processing and Advanced Education, CIPAE 2023

作者： Zhao, Lishuang Liu, Yanchun Bohai University College of Information Science and Technology Jinzhou China

ISBN: (纸本)9798350342710

Resource management based on cloud computing is essentially to deliver resource services to corresponding user terminals through virtualization processing of scattered resources in the network space through the "lease"mode. In order to meet the needs of the "online+offline"hybrid teaching mode reform, this paper studies the cloud computing resource scheduling model based on improved particle swarm optimization algorithm. The architecture model is composed of user access layer, application service layer, platform service layer and resource pool layer. The organizational resources are composed of "data Structure"resources of the computer specialty. This platform has the characteristics of software architecture unification, function modularization, service standardization and personalization, effectively integrates the high-quality teaching resources of "data Structure"for computer specialty, and realizes the unified planning, deployment and management of teaching resources. © 2023 IEEE.

关键词： Cloud computing

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