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检索条件"任意字段=17th International Conference on Distributed Computing and Intelligent Technology"

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Function Scheduling with Data Security in Serverless computing Systems

Function Scheduling with Data Security in Serverless Computi...

引用

international Communication Systems and Networks and Workshops, COMSNETS

作者： Srestha Saha Arvind Pandey Sourav Kanti Addya Shubha Brata Nath Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. this work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. the experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user’s QoS.

关键词： Processor scheduling distributed ledger Data security Serverless computing Systems architecture Quality of service Simulated annealing Virtual machines Fabrics Time factors

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A Meta-reinforcement Learning Framework for Adaptive Quadrotor UAV Attitude Control 25th

A Meta-reinforcement Learning Framework for Adaptive Quadro...

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25th international conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Zhao, Kaidong Li, Yanjie Liu, Zihan Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China School of Mechanical Engineering and Automation The Harbin Institute of Technology Shenzhen Shenzhen518055 China

ISBN: (纸本)9789819642069

this paper presents a meta-reinforcement learning approach for quadrotor UAV attitude control, addressing the limitations of traditional control methods in complex, dynamic, and uncertain environments. Our method integrates the advantages of context learning, Gaussian sampling, and multi-task training to rapidly adapt to diverse aircraft parameters and environmental conditions. Specifically, we utilize historical information quadruples as contextual data to accurately estimate current task characteristics through real-time observation and learning. In the latent space, we employ Gaussian sampling to effectively handle unknown UAV parameters. To validate the method’s efficacy, we conducted a series of experiments in the Bullet simulator, including single attitude tracking, resultant acceleration control, and continuous trajectory tracking. Experimental results demonstrate that our approach significantly outperforms traditional PID controllers and domain randomization-based SAC methods in terms of stability, rapid response, and trajectory tracking precision. through multi-task training, our controller has developed the ability to swiftly adapt to novel situations. this research provides a robust and flexible solution for adaptive control of quadrotor UAVs, showing promise for more efficient and stable flight control in complex and dynamically changing environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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A Federated Learning Method for Non-intrusive Load Monitoring Based on Fed-Prox and Bi-GRU 5th

A Federated Learning Method for Non-intrusive Load Monitorin...

引用

5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Xu, Jinghao Li, Ding Hu, Wenkai Cheng, Xudong China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

In recent studies, Non-Intrusive Load Monitoring (NILM) methods based on deep learning have received widespread attentions and achieved promising results. Most existing NILM models are typically trained on data from an individual user, and thus may have poor performance when applied for load recognition of other users. Achieving load recognition across different households requires joint training of models with data collected from different users. However, due to concerns regarding user data privacy, it is challenging to directly access and utilize electricity consumption data from various users in practice. Motivated by such a problem, this study designs a federated learning method for NILM based on Fed-Prox and Bi-GRU. By aggregating and optimizing models trained by various local households at the central server multiple times, the resulting global model achieves load recognition across different households. Experiments are conducted using the UK-DALE and REFIT datasets to validate the proposed framework's effectiveness in load recognition across multiple users. the experimental results demonstrate that compared to NILM methods tailored for an individual user, the proposed approach exhibits better generalization performance for load recognition across multiple diverse users.

关键词： Non-intrusive load monitoring Federated learning Load recognition Deep neural network

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PEbfs: Implement High-Performance Breadth-First Search on PEZY-SC3s 24th

PEbfs: Implement High-Performance Breadth-First Search on P...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Guo, Weihao Wang, Qinglin Liu, Xiaodong Peng, Muchun Yang, Shun Liang, Yaling Shi, Yongzhen Cao, Ligang Liu, Jie Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China Engineering Research Center for National Fundamental Software National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615506

the breadth-first search (BFS) algorithm is a fundamental algorithm in graph theory, and it’s parallelization can significantly improve performance. therefore, there have been numerous efforts to leverage the powerful parallel computing capabilities of hardware like GPGPU to implement high-performance BFS algorithms. However, the energy efficiency is relatively low due to the high power consumption of the platforms on which the algorithm is adapted to. To deal with these challenges, this paper introduces PEbfs that is a high-performance BFS algorithm based on the PEZY-SC3s efficient processor. We integrated three search algorithms, two algorithm optimization strategies, and a directional optimization scheme into PEbfs. through multiple evaluations of the performance of PEbfs on the public SNAP dataset, the results demonstrate that the average energy efficiency ratio of PEbfs is higher than that of Enterprise and Tigr, the two most advanced implementations on Nvidia’s GPGPU: It achieves 3.08× the average energy efficiency ratio of Enterprise and 4.53× that of Tigr. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Fair and Communication-Efficient Personalized Federated Learning 18th

Fair and Communication-Efficient Personalized Federated Lea...

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18th international conference on Wireless Artificial intelligent computing Systems and Applications, WASA 2024

作者： Zheng, Yifu Zheng, Xu Wang, Tingqi Mu, Chong Zhakiyev, Nurkhat School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9783031714665

Personalized Federated Learning (pFL) is among the most popular tasks in distributed deep learning, which compensates for mutual knowledge and enables device-specific model personalization. However, the effectiveness of pFL is severely impeded by challenges on fairness and significant communication overhead, where devices holding essential samples have to devote extensive resources for model training. To address these issues, we introduce the Fair and Communication-Efficient Personalized Federated Learning (FCE-PFL) framework, which harmonizes performance and device fairness while maintaining communication costs. Based on the Dempster-Shafer theory, FCE-PFL employs assistance and contribution metrics to quantify the auxiliary information a client receives and provides. then FCE-PFL balances and adjusts the involvement of devices in each training iteration, so as to facilitate fair training by constraining the maximum resource consumption per device and reduce the overall communication overhead. Our framework has been proven to be superior to existing methods in accuracy through rigorous experiments on the CIFAR10 and CIFAR100 datasets, demonstrating its potential as a fair and efficient pFL solution. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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international Communication Systems and Networks and Workshops, COMSNETS

作者： Anuj Kakde Shubha Brata Nath Soumya K. Ghosh Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors" at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS’s potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. this system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives.

关键词： Navigation Simulation Urban areas Green products Emergency services Delays Time factors Reliability Vehicle dynamics Testing

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A Review of Human Mesh Reconstruction: Beyond 2D Video Object Segmentation 16th

A Review of Human Mesh Reconstruction: Beyond 2D Video Obje...

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16th international conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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Enhancing Healthcare Recommendations for Special-Need Patients Using Deep Learning and Blockchain Security 15th

Enhancing Healthcare Recommendations for Special-Need Patien...

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15th international conference on intelligent computing and Networking, IC-ICN 2024

作者： Mudhafar, Mustafa Raparthi, Mohan thota, Vijayakumar Sivakumar, B.N. Degadwala, Sheshang Department of Medical Physics College of Applied Medical Sciences University of Kerbala Karbala56001 Iraq Alphabet Life Science DallasTX75063 United States NSB Business School Bangalore University Bangalore India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9789819786305

Special needs patients deserve increasingly specialised and secure healthcare supervision as the healthcare environment evolves. Our ground-breaking approach combines deep learning and blockchain security to enhance healthcare recommendations for people with special needs. this novel approach might assist with the difficulties of providing tailored healthcare advice. To get the best results, we mix two efficient technologies. Deep learning allows you to detect specific patterns in large patient datasets. Modern neural networks and algorithms are what power it. In the recommended approach, deep learning insights resulted in 92% accuracy, 89% precision, 94% recall, and a 91% F1 score. Deep learning allows for extremely accurate and customised healthcare suggestions. Special needs individuals need customised treatment regimens due to their specific medical challenges. Blockchain security is critical for ensuring patient data validity and confidentiality. To secure patient data, blockchain technology generates a distributed, immutable ledger and an open, auditable database. Encryption protects data's privacy, prohibiting unauthorised access to patient data. these developments in healthcare advice may increase treatment quality and patient trust in the system. Our analysis showed positive and game-changing findings, with the suggested technique outperforming traditional methods. these results illustrate the system's effectiveness and potential to revolutionise special-needs healthcare. this study reveals that the proposed approach is durable and has the potential to significantly alter healthcare recommendations. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Deep learning Healthcare Integration Patients Recommendations Security Special needs technology Treatment

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17th international conference on distributed computing and Internet technology, ICDCIT 2021

17th International Conference on Distributed Computing and I...

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17th international conference on distributed computing and Internet technology, ICDCIT 2021

ISBN: (纸本)9783030656201

the proceedings contain 20 papers. the special focus in this conference is on distributed computing and Internet technology. the topics include: Parameterized complexity of defensive and offensive alliances in graphs;a reconstructive model for identifying the global spread in a pandemic;cost effective method for ransomware detection: An ensemble approach;exploring Alzheimer’s disease network using social network analysis;stroke prediction using machine learning in a distributed environment;Automated diagnosis of breast cancer with roi detection using YOLO and heuristics;an efficient approach for event prediction using collaborative distance score of communities;A distributed system for optimal scale feature extraction and semantic classification of large-scale airborne LiDAR point clouds;load balancing approach for a mapreduce job running on a heterogeneous hadoop cluster;Model development in the tool use: Explorative, consolidating and analytic steps for UML and OCL models;Study the significance of ML-ELM using combined pagerank and content-based feature selection;reLink: Open information extraction by linking phrases and its applications;energy-efficient scheduling of deadline-sensitive and budget-constrained workflows in the cloud;an efficient renewable energy-based scheduling algorithm for cloud computing;a revenue-based service management algorithm for vehicular cloud computing;interference reduction in directional wireless networks;automated deadlock detection for large java libraries;DNet: An efficient privacy-preserving distributed learning framework for healthcare systems.

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Feature Selection and Classification Using an Ensemble Learning Model on Leh Ladakh Water Data 12th

Feature Selection and Classification Using an Ensemble Lea...

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12th international conference on Frontiers of intelligent computing: theory and Applications, FICTA 2024

作者： Sahu, Nitesh Kumar Sahu, Mridu Golchha, Roopa Jhariya, D.C. Shrivastav, Mayank Kumar Singh, Chandan Department of Information Technology National Institute of Technology Raipur Chhattisgarh India Department of Applied Geology National Institute of Technology Raipur Chhattisgarh India Department of Civil Engineering National Institute of Technology Raipur Chhattisgarh India

ISBN: (纸本)9789819601387

the problem of safe drinking water is the biggest challenge faced by Leh, which is the northernmost part of India. Leh City is facing a problem of non-potable water and water scarcity. To address this problem, there is a need for effective water harvesting techniques and water quality management. this study examines water quality parameters: pH, temperature, potassium, calcium, chloride, magnesium, total hardness, and alkalinity for total dissolved solids prediction. the proposed framework utilizes interpolation techniques to increase the dataset population and investigate relevant features using mutual information importance and recursive feature elimination. A machine-learning model called Random Forest is used to predict the total dissolved solids. Random forest with RFE achieved an R2 value of 0.991 with a lower MAE of 0.0128 and an MSE of 0.0003, outperforming random forest with mutual information, which achieved an R2 value of 0.990 with a lower MAE of 0.014 and an MSE of 0.0004. this finding indicates that the Random Forest model with recursive feature elimination is more suitable for the Leh Ladakh data set. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision trees

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