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Titlebook: Algorithms and Architectures for Parallel Processing; 20th International C Meikang Qiu Conference proceedings 2020 Springer Nature Switzerl

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樓主: AMUSE
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發(fā)表于 2025-3-30 09:59:00 | 只看該作者
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發(fā)表于 2025-3-30 12:49:32 | 只看該作者
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發(fā)表于 2025-3-30 17:04:24 | 只看該作者
QoS-Aware and Fault-Tolerant Replica Placementnt network systems with high Quality of Service (.) guarantee. In this paper, an optimal replica placement problem is formulated in terms of minimizing the replica placement cost subject to both . and fault-tolerant constraints. Based on the generalized graph model, the optimal replica placement pro
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發(fā)表于 2025-3-30 21:38:16 | 只看該作者
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發(fā)表于 2025-3-31 03:19:10 | 只看該作者
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發(fā)表于 2025-3-31 08:10:04 | 只看該作者
Edge-Assisted Federated Learning: An Empirical Study from Software Decomposition Perspectivethe computing ability of the terminal device, the training efficiency becomes an issue when training some complex deep neural network models. On the other hand, edges, the nearby stationary devices with higher computational capacity, might serve as a help. This paper presents the design of a compone
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發(fā)表于 2025-3-31 11:36:26 | 只看該作者
A Dynamic Partitioning Framework for Edge-Assisted Cloud Computingen dedicated to algorithms and optimizations in the cloud-edge data cache and computation offloading schemes, while there is a blank in practical implementation to facilitate such a paradigm. In this work, we proposed a component-based framework that facilitates dynamic partitioning of a software pr
58#
發(fā)表于 2025-3-31 14:29:17 | 只看該作者
Deep Reinforcement Learning for Intelligent Migration of Fog Services in Smart Citiesd high quality of service (QoS) requirements. However, the mobility of end users in smart city systems can result in considerable network performance and QoS degradation, hence interrupting fog services provisioning. . is considered an effective solution to avoid service interruption and ensure serv
59#
發(fā)表于 2025-3-31 19:18:36 | 只看該作者
60#
發(fā)表于 2025-3-31 23:12:10 | 只看該作者
Fast Segmentation-Based Object Tracking Model for Autonomous Vehicles achieved through single vehicle sensors, such as a camera or LiDAR. Consider the low cost and wide application of optical cameras, a simple image segmentation-based on-road object tracking model is proposed. Different from the detection-based tracking with bounding box, our model improves tracking
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