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共 4247 条记录，以下是41-50 订阅

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An Efficient Device Placement Method for distributed Training of Multi-branch Neural Network-Based Remote Sensing Interpretation 25th

An Efficient Device Placement Method for Distributed Traini...

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25th international conference on Web Information Systems Engineering, WISE 2024

作者： Long, Ao Wang, Yuewei Huang, Xiaohui Han, Wei Fan, Runyu Chen, Yunliang Li, Jianxin School of Computer Science China University of Geosciences Wuhan China School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9789819605699

Multi-branch neural networks are widely used in remote sensing intelligent interpretation because they can fuse multi-modal remote sensing data to improve interpretation accuracy. Meanwhile, in the current "big data era" of remote sensing, the application of distributed deep learning has been steadily growing to improve the training efficiency of remote sensing interpretation models. However, prevailing distributed training systems necessitate users to manually devise a parallelization plan or automatically generate one from a restricted space of model parallelism configurations. At the same time, these techniques are not suitable for extending the application of multi-branch neural network-based remote sensing interpretation on computing devices. In this paper, considering both the data and the model parallelism, we design and implement a fast and near-optimal device placement method for automatically placing branches in multi-branch neural networks across multiple devices. the core idea is to consider the trade-off between the computation and the inter-device communication to minimize the total training time. through careful formulation of the problem as an integer program, our method can offer optimal placement and scheduling of the branches. We conduct experiments to deploy and evaluate the proposed method on two multi-branch networks. the results indicate that the method finds non-trivial device placements outperforming baselines. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixed-integer linear programming

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Multiscale Temporal-Convolution and Attention Improved Neural Network for Biased Mental State Recognition 17th

Multiscale Temporal-Convolution and Attention Improved Neur...

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17th international conference on Brain Informatics, BI 2024

作者： Zuo, Guoyu Xiao, Erjun Zhang, Tielin Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing University of Technology Beijing100124 China Center for Excellence in Brain Science and Intelligence Technology University of Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9789819632930

Accurate detection of mental state is vital for sleep assessment and anesthesia monitoring. However, challenges exist, including 1) limited data sources caused by few modalities, few channels, biased sample distribution, or large individual differences;2) multiscale dynamic feature of mental state signals, containing complex and dynamic fluctuations. In this paper, we proposed a multiscale temporal convolution and attention improved neural network (MTA-NN) to handle these two challenges, including: a preprocessing method using a clustering-first and label-generation approach for data augmentation involves filtering the production samples to mitigate the impact of noise on model performance;multiscale temporal-convolution module for detecting different scales of temporal and frequency features;an attention module for decoding key features in different spatial and temporal channels, and for setting the importance of historical dependencies;a full-connection layer is designed to recognize different sleep stage. the proposed MTA-NN is then verified in three typical sleep-state recognition tasks, including SleepEDF-20, SleepEDF-78, and ISRUC3. Compared to some state-of-the-art algorithms, the proposed MTA-NN achieves higher performance. Further ablation experiments show the necessary and importance of these designed sub modules in MTA-NN. We consider the same algorithm can also be extended to further similar mental state recognition tasks such as anesthesia monitoring. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anesthesiology

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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 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...

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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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Learning to Program: Mapping Errors and Misconceptions of Python Novices to Support the Design of intelligent Programming Tutors

Learning to Program: Mapping Errors and Misconceptions of Py...

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17th international conference on Computer Supported Education, CSEDU 2025

作者： van der Heyden, Lisa Batur, Fatma Chounta, Irene-Angelica Department of Human-centered Computing and Cognitive Science University of Duisburg-Essen Duisburg Germany Computing Education Research Group University of Duisburg-Essen Essen Germany

ISBN: (纸本)9789897587467

Students often struggle with basic programming tasks after their first programming course. Adaptive tutoring systems can support students’ practice by generating tasks, providing feedback, and evaluating students’ progress in real-time. Here, we describe the first step for building such a system focusing on designing tasks that address common errors and misconceptions. To that end, we compiled a collection of Python tasks for novices. In particular, a) we identified errors occurring during introductory programming and mapped them to learning tasks;b) we conducted a survey to validate our mapping;c) we conducted semi-structured interviews with instructors to understand potential reasons for such errors and best practices for addressing them. Synthesizing our findings, we discuss the creation of a tasks’ corpus to serve as a basis for adaptive tutors. this work contributes to the standardization and systematization of computing education and provides insights regarding the design of learning tasks tailored to addressing errors. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Python

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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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Fast Memory Disaggregation with SwiftSwap 20th

Fast Memory Disaggregation with SwiftSwap

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Zhang, Xiangwei Wang, Desheng Zhang, Weizhe Yu, Zhiji Hao, Meng School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China School of Cyberspace Science Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819628292

With the continuous evolution of cloud computing technology, the memory requirements of cloud servers are increasing. Insufficient memory has become the performance bottleneck of many applications. Memory disaggregation has been proposed to solve this problem, which enables computing nodes to utilize the memory of remote memory nodes, thus not only effectively extending the capacity of logical memory, but also significantly improving the resource utilization and elasticity of the whole cluster, providing a more flexible resource allocation scheme for large-scale parallel computing and data processing. However, most of the existing memory disaggregation systems require modification of hardware, operating systems or applications, which are difficult to use. this paper proposes a high-performance memory disaggregation system SwiftSwap, which can directly access remote memory without any modification of hardware, operating system, or application. We design and implement a high performance cache, cache lookup algorithm LDA and cache elimination strategy RM_AwareLRU on the computing node to improve the performance of the system. through comprehensive evaluation, SwiftSwap has up to 5.1× performance advantage in throughput, 4.8× performance advantage in tail delay, and 6.4× performance advantage against applications running in containers. © IFIP international Federation for Information Processing 2025.

关键词： Resource allocation

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