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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

共 1769 条记录，以下是461-470 订阅

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Never Alone: a Quality of Context-aware Monitoring System for Aging in Place Smart Home

Never Alone: a Quality of Context-aware Monitoring System fo...

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IEEE international symposium on Network computing and Applications

作者： Hajer Nabli Ines Regaig Raoudha Ben Djemaa Layth Sliman Higher Institute of Computer Science and Communication Technologies of H. Sousse Sousse University of Sousse Tunisia Engineering School of Information and Digital Technologies EFREI Paris Villejuif France

ISBN: (纸本)9798350397314

Technology is always improving, which raises standards of living and makes life easier for people. A significant portion of these advancements are aimed at healthcare systems, with a particular emphasis in recent years on assisting elderly people in maintaining their independence in their homes. Data is collected continuously from various sensors and is used for contextual anomaly detection. As a result, the amount of sensor contextual data grows exponentially. These sensor data’s ability to be shared, reused, and interpreted was, however, constrained by the lack of syntactic or semantic integrity. On the other hand, understanding the Quality of Context (QoC) is acknowledged as a crucial component for the health monitoring system’s performance. The QoC has a significant impact on how contextual data behaves in health monitoring systems. As a result, health monitoring systems need to manage the QoC they rely on carefully and effectively. To address these problems, the main goal of this study is to develop a Context and Quality of Context (CQoC) ontology, which offers a framework for handling and utilizing contextual data, any QoC criteria, as well as context interpretation in a health monitoring system. Another purpose of this paper is the development of a Semantic QoC-aware Health Monitoring System (SQoCHMS) for the elderly that assures their safety and issues early alerts to help them deal with minor everyday problems related to such disorders.

关键词： Support vector machines Semantics Smart homes Ontologies Syntactics Safety Older adults

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A Dynamic Parameter Tuning Method for high performance SpMM 21st

A Dynamic Parameter Tuning Method for High Performance SpMM

引用

21st international Conference on Parallel and Distributed computing, Applications, and Technologies, PDCAT 2020

作者： Qi, Bin Komatsu, Kazuhiko Sato, Masayuki Kobayashi, Hiroaki Tohoku University Sendai Miyagi Japan

ISBN: (纸本)9783030692438

Sparse matrix-matrix multiplication (SpMM) is a basic kernel that is used by many algorithms. Several researches focus on various optimizations for SpMM parallel execution. However, a division of a task for parallelization is not well considered yet. Generally, a matrix is equally divided into blocks for processes even though the sparsities of input matrices are different. The parameter that divides a task into multiple processes for parallelization is fixed. As a result, load imbalance among the processes occurs. To balance the loads among the processes, this paper proposes a dynamic parameter tuning method by analyzing the sparsities of input matrices. The experimental results show that the proposed method improves the performance of SpMM for examined matrices by up to 39.5% and 12.3% on average. © 2021, Springer Nature Switzerland AG.

关键词： Matrix algebra

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Scaling Simulation of Continuous Urban Traffic Model for high performance computing System 21st

Scaling Simulation of Continuous Urban Traffic Model for Hig...

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21st international Conference on Computational Science, ICCS 2021

作者： Najdek, Mateusz Xie, Hairuo Turek, Wojciech AGH University of Science and Technology Krakow Poland University of Melbourne Melbourne Australia

ISBN: (纸本)9783030779603

Urban traffic simulation of extensive areas with complex driver models poses a significant computational challenge. Developing highly scalable parallel simulation algorithms is the only feasible way to provide useful results in this case. In this paper, we present extensions of the SMARTS system, a traffic simulation tool, which provides efficient scalability with a large number of parallel processes. The presented extensions enabled its scalability for HPC-grade systems. The extended version has been thoroughly tested in strong and weak scalability scenarios for up to 2400 computing cores of a supercomputer. The satisfactory scalability has been achieved by introducing several significant improvements, which have been discussed in details. © 2021, Springer Nature Switzerland AG.

关键词： Scalability

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A Collaborative Peer Review Process for Grading Coding Assignments 1

引用

21st international Conference on Computational Science, ICCS 2021

作者： Nayak, Pratik Göbel, Fritz Anzt, Hartwig Steinbuch Center for Computing Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9783030779801

ISBN: (纸本)9783030779795

With software technology becoming one of the most important aspects of computational science, it is imperative that we train students in the use of software development tools and teach them to adhere to sustainable software development workflows. In this paper, we showcase how we employ a collaborative peer review workflow for the homework assignments of our course on Numerical Linear Algebra for high performance computing (HPC). In the workflow we employ, the students are required to operate with the git version control system, perform code reviews, realize unit tests, and plug into a continuous integration system. From the students’ performance and feedback, we are optimistic that this workflow encourages the acceptance and usage of software development tools in academic software development. © 2021, Springer Nature Switzerland AG.

关键词： students

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Mobility-aware Multi-Access Edge computing for Multiplayer Augmented and Virtual Reality Gaming

Mobility-aware Multi-Access Edge Computing for Multiplayer A...

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IEEE international symposium on Network computing and Applications

作者： Ramesh Singh Radhika Sukapuram Suchetana Chakraborty IIIT Computer Science & Engineering Guwahati India IIT Computer Science & Engineering Jodhpur India

ISBN: (纸本)9798350397314

Augmented Reality (AR) and Virtual Reality (VR) games are some of the emerging use cases of 5G in the area of ultra-Reliable and Low Latency Communications (uRLLC). A multiplayer AR/VR game broadly consists of compute-intensive tasks which convert the raw data generated from sensory sources such as wearables, smartphones, etc., to action data such as location, orientation, intention, etc., and services that process the action data. Services generate a common response to all players by taking action data as input. The total response time must be as low as 20 milliseconds for a good user experience and to prevent motion sickness. While considering these aspects, the multiplayer game must be scalable, and users should be able to move. Multi-access edge computing (MEC) helps to improve performance by partially/fully offloading such tasks from mobile devices and latency-sensitive services from the cloud to a server at the edge called the MEC host. We propose, for the first time, an online mobility-aware heuristic in a Multi-access Edge computing Network (MEN) to reduce the response time, specifically the Game Frame Time (GFT), consistently, for an improved Quality of Experience (QoE), for such games. This is done by jointly offloading tasks and placing services, and migrating both whenever required. Additionally, for improved response, the network is partitioned into regions, and a service instance is placed on a MEC host, called the Region Coordinator (RC), in each region, in a decentralized manner. When a new player joins, an old player leaves, or old players move, the number of players and their mobility patterns change in a particular region. This may require allocating or moving tasks from one MEC host to another and migrating services to a new RC. While tasks and services are migrated, the associated state and data must be moved to the destination MEC host. Our experiments demonstrate that the standard deviation for the mean GFT is 0 ms in the best case and 9.26 ms

关键词： Multi-access edge computing Wearable computers Games Ultra reliable low latency communication User experience Time factors Quality of experience

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Enhanced Intrusion Detection in Wireless Sensor Networks: A Voting and Particle Swarm Optimization Approach

Enhanced Intrusion Detection in Wireless Sensor Networks: A ...

引用

international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Ameera Jaradat Salem Alhatamleh Ahmad Nasayreh Hasan Gharaibeh Dept. of Computer Sciences Yarmouk University Irbid Jordan

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Wireless sensor networks (WSNs) are of great importance in advanced technologies in the modern developing world as they are widely used in many applications. Although encryption and authentication protocols in WSNs are widely adopted, there are continuous expectations of new sophisticated attack strategies that are spreading, which pose high risks and threats. This work addresses these challenges using Intrusion Detection Systems (IDS) that will be supported by machine learning algorithms. Specifically, it explores the WSN-DS dataset that includes network activities and attack types. In addition, it initializes the importance of feature selection and proposes data balancing methods such as Synthetic Minority Over-sampling Technique SMOTE, it is a statistical method for correcting class imbalance in datasets by creating synthetic samples for the minority class to enhance the detection rate. To increase the performance of the machine learning model, we use a voting classifier consisting of Random Forest (RF), K-Nearest Neighbors (KNN) and Extreme Gradient Boosting (XGBoost) and optimize them using Particle Swarm Optimization (PSO) by selecting the best hyperparameters. This ensemble method aims to maximize the performance of the used ensemble models. The proposed model is demonstrated by improving the PSO voting classifier model which gives a remarkable accuracy of 99.99% making it a highly effective approach in predicting and filtering different types of attacks in wireless sensor networks. This work will help in promoting further research in protecting wireless sensor networks from invasive intrusions in various applications to ensure security in communication networks.

关键词： Wireless sensor networks Accuracy Smart cities Intrusion detection Nearest neighbor methods Predictive models Robot sensing systems Security Particle swarm optimization Random forests

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performance Improvement of DDoS Intrusion Detection Model Using Hybrid Deep Learning Method in the SDN Environment

Performance Improvement of DDoS Intrusion Detection Model Us...

引用

IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Ameni Chetouane Kamel Karoui RIADI Laboratory National School of Computer Science Manouba Tunisia National Institute of Applied Sciences and Technologies Tunis Tunisia

Software-Defined Networking (SDN) is a networking paradigm that has the potential to revolutionize the way we develop and operate network infrastructures. SDN enables network engineers to quickly monitor networks, centrally manage networks, and quickly and accurately detect malicious traffic and link failures. In addition to its flexibility, SDN is also susceptible to various attacks like Distributed Denial of Service (DDoS) that can bring down the entire network. In recent years, Deep Learning (DL) approaches have been applied for reliable and highly accurate traffic anomaly detection. Therefore, this paper proposes a method based on DL to classify the benign traffic from the DDoS attack traffic. The major contribution of this paper is to propose a novel hybrid DL method to perform the classification. In addition, we compare different deep learning methods using attack severity, which is integrated into the standard metrics to differentiate the quality of the results of DL methods. The attack severity will be calculated using an appropriate weighting of undiscovered intrusions (FN and FP) discovered during the testing phase. Besides, various DDoS attack detection research projects employ public datasets that are not specific to the SDN environment. We propose a method to determine the adequacy of a selected dataset using two proposed metrics based on quality and quantity conformity evaluation.

关键词： Deep learning Measurement Denial-of-service attack Ubiquitous computing Virtual machining Reliability computer crime

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Joint Job Assignment and Resource Allocation for Multi-Job Wireless Federated Learning

Joint Job Assignment and Resource Allocation for Multi-Job W...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Tan Li Zeheng Wei Hai Liu Zhiyong Lin Tse-Tin Chan Department of Computer Science The Hang Seng University of Hong Kong Hong Kong SAR China School of Computer Science Guangdong Polytechnic Normal University China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Wireless Federated Learning (WFL) marks a significant evolution in edge artificial intelligence (AI), allowing for collaborative learning while preserving the privacy of edge devices. Recently, the enhanced data collection and storage capability of edge devices have precipitated a paradigm shift from training an individual model (single-job) to multiple AI models (multi-job). This shift poses a new challenge in maintaining high system performance while managing resource management among multiple jobs. In this work, we address the challenge of a multi-job WFL framework by optimizing its dual efficiency. We formulate a multi-objective optimization problem with the goal of minimizing energy consumption and execution time to enhance system efficiency, while ensuring that all AI jobs meet their predefined performance criteria, thereby guaranteeing learning efficiency. To solve the problem, we propose an algorithm that jointly optimizes job assignment and resource allocation, by considering the triple-heterogeneity of data, devices, and jobs in the multi-job WFL framework. Specifically, a matching game-based method is utilized to assign jobs to capable devices, considering their respective contributions and costs, while convex optimization techniques are employed to refine the resource allocation toward computing frequency and transmission power. The performance of the algorithm is evaluated through numerical simulations in terms of system and learning performance over multiple AI model training jobs, showing that our proposed algorithm ensures time and energy efficiency under the same learning performance constraints.

关键词： performance evaluation Training Wireless communication Costs Federated learning System performance Data models Ad hoc networks Resource management Optimization

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A Deep Neural Network Approach for Fault Detection in Industrial Motors: Enhancing Reliability and Efficiency in Industry 4.0

A Deep Neural Network Approach for Fault Detection in Indust...

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international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Hassan Ali Mostafa Ahyen Raziq Yaqub Ahmed Badawi Noora Shifa Amith Khandakar Department of Electrical Engineering University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering and Computer Science Alabama A&M University Huntsville USA Department of Electrical Engineering Qatar University Doha Qatar

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

In the era of Industry 4.0, the integration of sophisticated monitoring and control technologies is essential for the reliability and efficiency of industrial equipment. This paper presents a deep neural network (DNN)-based approach for fault detection and classification in industrial motors. The approach identifies common motor faults, including bent shaft, loose vibration, loosely mounted components, electrical faults, and bearing vibration. The proposed DNN model demonstrated robust performance, achieving an overall training accuracy of 83.49% ± 0.43% and a test accuracy of 81.91 % ± 0.82%. The model also achieved a mean precision of 82.05% ± 0.77%, mean recall of 81.91 % ± 0.82%, and mean F1-Score of81.86 % ± 0.78%. The 95% confidence intervals for accuracy, precision, recall, and F1-Score were (80.77%, 83.04%), (80.98%, 83.11 %), (80.77%, 83.04%), and (80.78 % ,82.95 % ), respectively. These results demonstrate the DNN model's ability to precisely classify various motor faults with minimal variability, as evidenced by its narrow confidence intervals and low standard deviations. The model's reliability and efficacy for real-world applications in proactive maintenance and fault detection are confirmed by the consistent performance that is observed across cross-validation folds.

关键词： Vibrations Shafts Accuracy Fault detection Artificial neural networks Motors Maintenance Fourth Industrial Revolution standards Monitoring

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Workshops held at the 26th international Conference on Parallel and Distributed computing, Euro-Par 2020

Workshops held at the 26th International Conference on Paral...

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Workshops held at the 26th international Conference on Parallel and Distributed computing, Euro-Par 2020

ISBN: (纸本)9783030715922

The proceedings contain 27 papers. The special focus in this conference is on Parallel and Distributed computing. The topics include: Parallelization of the k-means Algorithm in a Spectral Clustering Chain on CPU-GPU Platforms;Management of Heterogeneous Cloud Resources with Use of the PPO;An Open-Source Virtualization Layer for CUDA Applications;high performance Portable Solver for Tridiagonal Toeplitz Systems of Linear Equations;highPerMeshes – A Domain-Specific Language for Numerical Algorithms on Unstructured Grids;Implementation and Evaluation of CUDA-Unified Memory in Numba;performance Evaluation of Java/PCJ Implementation of Parallel Algorithms on the Cloud;parallelizing Automatic Temporal Cognitive Tool for Large-Scale Online Learning Analytics;Experiments Using a Software-Distributed Shared Memory, MPI and 0MQ over Heterogeneous computing Resources;ants-Review: A Privacy-Oriented Protocol for Incentivized Open Peer Reviews on Ethereum;on the Provenance Extraction Techniques from Large Scale Log Files: A Case study for the Numerical Weather Prediction Models;Improving Existing WMS for Reduced Makespan of Workflows with Lambda;predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks;On the Detection of Silent Data Corruptions in HPC Applications Using Redundant Multi-threading;analysis of Genome architecture Mapping Data with a Machine Learning and Polymer-Physics-Based Tool;A New Parallel Methodology for the Network Analysis of COVID-19 Data;next Generation Blockchain-Based Financial Services;a Digital Voting System for the 21st Century;trustless, Censorship-Resilient and Scalable Votings in the Permission-Based Blockchain Model;p2T: Pay to Transport;Balanced and Compressed Coordinate Layout for the Sparse Matrix-Vector Product on GPUs.

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