this paper studies the characteristic behavior mining of college students' new media literacy in the context of internet data retrieval. First of all, this article defines the four concepts of media, network media...
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the rapid advancements in high-performance computing (HPC) have made large-scale parallel computing feasible. As a commonly used parallel programming model, Message Passing Interface (MPI) plays a crucial role in HPC ...
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the operation, maintenance, and management level of the distribution network is an important means to improve power supply reliability, and the status of the distribution network depends on equipment operation status ...
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the internet of Vehicles (IoV) represents a critical component of modern mobile edge computing systems. IoV computing resources encompass backend cloud resources, roadside edge nodes, and vehicle-mounted units. Tradit...
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Convolutional Neural Networks make tasks of computer vision like image classification and object tracking possible. the advances in accelerator hardware have made the progress in neural networks possible. Accelerator ...
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Digital Twin (DT) technology is expected to cover a crucial role in a variety of 6G application scenarios, including smart automotive, smart home and smart city. By leveraging advanced Artificial Intelligence (AI) mod...
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ISBN:
(纸本)9798350387452;9798350387445
Digital Twin (DT) technology is expected to cover a crucial role in a variety of 6G application scenarios, including smart automotive, smart home and smart city. By leveraging advanced Artificial Intelligence (AI) modules alongside cutting-edge communication and networking architectures, DTs will be able to develop cognitive and social skills and build relationships with each other, thus facilitating the sharing of services and experience. However, in current implementations, DTs typically engage withtheir physical counterpart only, for predictive maintenance and optimization, while protocols for inter-twin communications and service discovery are still unexplored. In this paper, we focus on service discovery and provisioning in DT networks hosted at the network edge. In our design, the Social internet of things (SIoT) notion is applied to build social networks among DTs and, in parallel, name-based primitives, according to the Information Centric Networking (ICN) paradigm, are considered to support inter-twin interactions. Two distributed name-based service discovery mechanisms are envisioned: a social-driven scheme, leveraging friendship and similarities among DTs' names, and a network-driven scheme, leveraging the ICN forwarding fabric only. A performance evaluation shows the benefits of the conceived solution in terms of reduced discovery latency compared to legacy centralized approaches.
Using numerous devices while driving causes drivers to lose focus on the road, contributing to accidents in about one-third of cases. To address this issue, significant research is focused on developing interfaces bet...
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LoRa technology has shown to be a very promising option for implementing a variety of internet of things applications. One such application is the private farming remote monitoring. In this paper, the authors present ...
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As a distributed machine learning framework, federated learning has received considerable attention in recent years and has been researched and applied in various scenarios. However, the system heterogeneity due to th...
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ISBN:
(纸本)9798400716751
As a distributed machine learning framework, federated learning has received considerable attention in recent years and has been researched and applied in various scenarios. However, the system heterogeneity due to the physical characteristics of various terminal devices has led to the straggler effect, making the practical implementation of federated learning challenging. therefore, we propose a semi-asynchronous federated optimization method based on buffer pre-aggregation. this method allows every participant to engage in training through pre-aggregation and establishes a training time framework based on the pre-aggregation time. It updates the model adaptively using a semi-asynchronous communication method combined with lag factors, improving communication efficiency while maintaining stable accuracy. Experimental results on datasets demonstrate that our proposed method can effectively accelerate the training process of federated learning compared to existing federated optimization methods.
this paper introduces the internet of things technology and makes full use of the perceptual advantages of the internet of things at the end of the network to realize the seamless connection of the sensor network, the...
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