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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 149804 条记录，以下是4631-4640 订阅

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Advancements in distributed Deep Learning: Federated Learning, AutoML Integration, and Beyond

Advancements in Distributed Deep Learning: Federated Learnin...

引用

2024 ieee international conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024

作者： Kavitha, T. Manikandan, S.P. Patil, Bhimaraya Patil, Anita Dept. of Computer Science and Engineering New Horizon College of Engineering Bangalore India CMR University Bengaluru India Dept. of Computer Science Engineering New Horizon College of Engg. Bangalore India Dept. of Information Science and Engg. City Engineering College Bengaluru India

ISBN: (纸本)9798331505134

This paper present advancements in distributed deep learning, focusing on federated learning, AutoML integration, and beyond. Leveraging the latest developments in machine learning (ML) and artificial intelligence (AI), our research explores novel approaches to enhance the efficiency, scalability, and security of distributed deep learning systems. This paper introduces federated learning techniques to enable collaborative model training across decentralized edge devices, thereby minimizing data privacy concerns and reducing communication overhead. Additionally, Proposed work investigate the integration of automated machine learning (AutoML) capabilities into distributed training pipelines, streamlining the model selection and hyperparameter tuning processes. Furthermore, Paper also explores cutting-edge advancements beyond traditional deep learning paradigms, such as generative adversarial networks (GANs) and reinforcement learning (RL), to unlock new opportunities in distributed AI. Through experimental evaluations and case studies, Paper demonstrate the effectiveness and potential impact of our proposed advancements, paving the way for future breakthroughs in distributed deep learning research. © 2024 ieee.

关键词： Federated learning

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Characterization of Microservice Response Time in Kubernetes: A Mixture Density Network Approach 19

Characterization of Microservice Response Time in Kubernetes...

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19th international conference on Network and Service Management (CNSM) - Network and Service Management in the Era of Generative AI and Digital Twins

作者： Manca, Lorenzo Borsatti, Davide Poltronieri, Filippo Zaccarini, Mattia Scotece, Domenico Davoli, Gianluca Foschini, Luca Grabarnik, Genady Ya. Shwartz, Larisa Stefanelli, Cesare Tortonesi, Mauro Cerroni, Walter Univ Bologna Bologna Italy Univ Ferrara Distributed Syst Res Grp Ferrara Italy St Johns Univ Dept Math & Comp Sci Queens NY USA IBM TJ Watson Res Ctr Operat Innovat New York NY USA

ISBN: (纸本)9783903176591

The use of microservice-based applications is becoming more prominent also in the telecommunication field. The current 5G core network, for instance, is already built around the concept of a "Service Based Architecture", and it is foreseeable that 6G will push even further this concept to enable more flexible and pervasive deployments. However, the increasing complexity of future networks calls for sophisticated platforms that could help network providers with their deployments design. In this framework, a central research trend is the development of digital twins of the physical infrastructures. These digital representations should closely mimic the behavior of the managed system, allowing the operators to test new configurations, analyze what-if scenarios, or train their reinforcement learning algorithms in safe environments. Considering that Kubernetes is becoming the de-facto standard platform for container orchestration and microservice-based application life cycle management, the implementation of a Kubernetes digital twin requires an accurate characterization of the microservice response time, possibly leveraging suitable Machine Learning techniques trained with measurement data collected in the field. In this paper we introduce a new methodology, based on Mixture Density Networks, to accurately estimate the statistical distribution of the response time of microservice-based applications. We show the improvement in performance with respect to simulation-based inference procedures proposed in literature.

关键词： Service Management and Orchestration Kubernetes Simulation Optimization Digital twins

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Development of a Load Balancing Method for Information Processing Centers of distributed computing systems Based on Algorithms Consistent Hashing 4

Development of a Load Balancing Method for Information Proce...

引用

4th international conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2022

作者： Myo, Aung Kyaw Portnov, Evgeni M Bain, Alexander M Department of Informatics and Computer Software System Zelenograd Moscow Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

Cache-aware algorithms are based on the fair assumption that requests from the same user need shared data that can be cached locally on the information processing center. So, caching is an effective way to improve performance, in connection with which cache-aware load-sharing algorithms have been developed that provide a high frequency of cache hits. A mathematical model of the request processing process has been developed using a cache-aware load balancing algorithm for information processing centers in distributed computing systems. Modifications of the consistent hashing algorithm are proposed, which provide the best uniformity of loads for information processing centers, as well as preventing possible overload. © 2022 ieee.

关键词： Loading Process control distributed databases Information processing Load management Mathematical models Energy efficiency

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Exploring factors affecting tertiary students' stickiness with Edu-Tech GPT

Exploring factors affecting tertiary students' stickiness wi...

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2024 ieee international conference on computing, Applications and systems, COMPAS 2024

作者： Nahid, Mehzabul Hoque Rabbani, Golam Mustafa Md. Nurullah Mona, Lagina Anwar, Md. Aftab Baijed, Mohammad American International University-Bangladesh Dept. of Mis Dhaka Bangladesh American International University-Bangladesh Dept. of Hrm Dhaka Bangladesh

ISBN: (纸本)9798331529765

Students from Generation Z receive individualized help through brainstorming and suggestions, as well as access to quality knowledge through Edu-Tech GPT. This study investigates the factors that contribute to the engagement and adherence of tertiary students with these tools, particularly focusing on the use of explainable AI in academic research among digitally divided nations. A conceptual framework with four reflective dimensions derived from literature on adapted Diffusion of Innovation theories and verified through focus group discussions. Initially, this research conducted a focus group interview of 141 Generation Z students from 10 different universities from Bangladesh who regularly use web-based Edu-Tech GPT services for their research projects. The process was able to extract four study dimensions after that conduct a survey with 26 items using purposing sampling technique. The data has been processed using Smart-PLS 4 for hypothesis testing. The results demonstrated that hedonic motivation, perceived utility, and usability have the most impact on students' engagement with Edu-Tech GPT services. This unique research empirically examines the impact of AI-powered technologies on Edu-Tech innovation and also proposed a value proposition framework for academician and practitioners. © 2024 ieee.

关键词： Students

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Proceedings - 2023 international conference on Cyber-Enabled distributed computing and Knowledge Discovery, CyberC 2023

Proceedings - 2023 International Conference on Cyber-Enabled...

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15th international conference on Cyber-Enabled distributed computing and Knowledge Discovery, CyberC 2023

ISBN: (纸本)9798350308693

The proceedings contain 57 papers. The topics discussed include: on the basin of attraction and capacity of restricted Hopfield network as an auto-associative memory;a type of recharging scheduling strategy based on adjustable request threshold in WRSNs;classification and identification of phishing websites based on machine learning;patch-based multi-level attention mechanism for few-shot multi-label medical image classification;exploring event-based dynamic topic modeling;improvement of passkey entry protocol for secure simple pairing;application of Voformer-EC clustering algorithm to stock multivariate time series data;and multimodal deep learning for enhanced arrhythmia detection using ECG time series and image data.

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Comparison of Neural Network Models for Short-Term Load Forecasting

Comparison of Neural Network Models for Short-Term Load Fore...

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2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Surmon, Shinead Ahmad, Ahmad Xiao, Xun Mo, Huadong School of Engineering and Technology University of New South Wales Canberra Australia University of Otago Department of Mathematics and Statistics Dunedin New Zealand School of Systems and Computing University of New South Wales Canberra Australia

ISBN: (纸本)9781665410205

Balancing supply and demand is crucial for efficient energy distribution. To achieve it, accurate short-term electrical load forecasting is essential. This study investigates the applicability of various machine learning architectures for short-term load forecasting, using an NSW load dataset from the Australian Energy Market Operator. The key finding is the superior performance of a hybrid model, which integrates LSTM and GRU layers, on the NSW load dataset. This study demonstrates hybrid neural network models can significantly improve the accuracy and reliability of energy load predictions, thereby suggesting a viable pathway for enhancing future utility management practices. © 2024 ieee.

关键词： Neural network models

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection 31

Towards Effective and Efficient Error Handling Code Fuzzing ...

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31st ieee international conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin Jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China

ISBN: (纸本)9798350330663

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL-F I are effective that contribute significantly to its overall performance. © 2024 ieee.

关键词： Errors

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Deep Analysis: Apple Fruit Disease Detection Using Deep Learning

Deep Analysis: Apple Fruit Disease Detection Using Deep Lear...

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2024 ieee international conference on Information Technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Kavya, P. Nischitha, S. Nivedita, N.S. Prabhu, Akshatha Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (纸本)9798350382693

Vision systems have played an important role infruit processing lines. Integrating them in such processes improves efficiency and reduces human errors. Fruit industries are susceptible to losses due to defect detection and lack of timely measures. In this work, various defects in apples -scab, rot, blotch are considered. As a part of the dataset collection, healthy images also form part of datset. Convolution neural networks - VGG 16, VGG-19, Resnet 50, Inception, DenseNet, and MobileNet are used for the classification of 3 defects. The data augmentation methods are applied to create a balanced dataset. A total of 2000 images of the 4 categories are used for training/testing with a ratio of 80:20. The performance metrics such as accuracy, precision, recall, and F-score are evaluated. It is observed that MobileNEt performs better than other models with an accuracy of 98%. The misclassifications are represented inthe confusion matrix for each model. © 2024 ieee.

关键词： Fruits

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Designing distributed Chi-Fuzzy Rule based Classification System

Designing Distributed Chi-Fuzzy Rule based Classification Sy...

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ieee international conference on Fuzzy systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / international Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Varshney, Ayush K. Torra, Vicenc Umea Univ Dept Comp Sci Umea Sweden

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

Fuzzy Rule based Classification systems (FRBCSs) are an important area of fuzzy logic and fuzzy sets. FRBCSs provides interpretable models with good classification rate. In the presence of large number of instances with high dimension in training data, the classical Chi's FRBCSs' rulebase becomes huge causing the classical model to be computationally expensive which also leads to the degraded interpretability. In this work, we propose the 'distributed Chi Fuzzy Rule Based Classification systems' (DCHI-FRBCSs). The proposed approach distributes the n-dimensional data into n-nodes where each node forms its own 1-dimensional rules for the dimension. The output from each node is the rule weights which are then aggregated to form the final rule weights, the class with highest final rule weight is the final output of the model. Aggregators play an important role in combining the information from several sources. Andness-directed OWA selects desired level of andness between attributes for OWA aggregator. In this paper, we explore an extension of DCHI-FRBCSs called 'Andess-directed distributed Chi Fuzzy Rule Based Classification System' (alpha-DCHI-FRBCS) which incorporates andness-directed OWA operator as the aggregator function. Results over UCI datasets verify the superiority of our methodology.

关键词： Fuzzy Rule Based Classification System distributed system Andness OWA Aggregators

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Hybrid Centralized-Decentralized Economic Dispatch Based on a distributed Finite-Step Consensus Algorithm with Divided Regional Incremental Costs 14

Hybrid Centralized-Decentralized Economic Dispatch Based on ...

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14th international conference on Information Science and Technology, ICIST 2024

作者： Zhang, Hanwen Yang, Hanqing Li, Tieshan Long, Yue School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350353334

The economic dispatch problem of power systems, which is typically based on multi-agent networks, often necessitates conditions like full graph connectivity. However, as the scale of power systems expands, maintaining full graph connectivity in communication systems could result in increased costs and communication burdens. Consequently, this paper investigates a distributed finite-step c o nsensus m e thod t h at incorporates geographical region division, aiming to achieve economical power allocation without the need for full graph connectivity. In this method, we initially derive a Laplace matrix from the distributed topology of generators in multi-agent power systems. Subsequently, we construct another Laplace matrix by geographically dividing regions, thereby illustrating the communication relationships between these regions. Subsequently, we calculate the incremental cost of each region, taking into account the power constraints of the generators. Moreover, to expedite convergence, we employ a distributed finite-step c o nsensus algorithm to address the economic dispatch problem, leveraging the divided regional incremental costs. Finally, we validate the effectiveness and accuracy of the proposed method through results on various multi-agent topologies and comparisons with other iterative algorithms. © 2024 ieee.

关键词： Consensus algorithm

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