Zobrazeno 1 - 10
of 1 710
pro vyhledávání: '"Harvey, Paul A."'
Autor:
Almasan, Paul, Ferriol-Galmés, Miquel, Paillisse, Jordi, Suárez-Varela, José, Perino, Diego, López, Diego, Perales, Antonio Agustin Pastor, Harvey, Paul, Ciavaglia, Laurent, Wong, Leon, Ram, Vishnu, Xiao, Shihan, Shi, Xiang, Cheng, Xiangle, Cabellos-Aparicio, Albert, Barlet-Ros, Pere
The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ultra-low deterministic latency)
Externí odkaz:
http://arxiv.org/abs/2205.14206
Autor:
Almasan, Paul, Ferriol-Galmés, Miquel, Paillisse, Jordi, Suárez-Varela, José, Perino, Diego, López, Diego, Perales, Antonio Agustin Pastor, Harvey, Paul, Ciavaglia, Laurent, Wong, Leon, Ram, Vishnu, Xiao, Shihan, Shi, Xiang, Cheng, Xiangle, Cabellos-Aparicio, Albert, Barlet-Ros, Pere
The proliferation of emergent network applications (e.g., AR/VR, telesurgery, real-time communications) is increasing the difficulty of managing modern communication networks. These applications typically have stringent requirements (e.g., ultra-low
Externí odkaz:
http://arxiv.org/abs/2201.01144
Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models while keeping all the original data generated on devices locally. Since devices may be resource constrained, offloading can be used to improve F
Externí odkaz:
http://arxiv.org/abs/2111.01516
Applying Federated Learning (FL) on Internet-of-Things devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are three challenges that need to be addressed to make FL efficient: (i) exe
Externí odkaz:
http://arxiv.org/abs/2107.04271
Autor:
Varghese, Blesson, de Lara, Eyal, Ding, Aaron, Hong, Cheol-Ho, Bonomi, Flavio, Dustdar, Schahram, Harvey, Paul, Hewkin, Peter, Shi, Weisong, Thiele, Mark, Willis, Peter
This article argues that low latency, high bandwidth, device proliferation, sustainable digital infrastructure, and data privacy and sovereignty continue to motivate the need for edge computing research even though its initial concepts were formulate
Externí odkaz:
http://arxiv.org/abs/2106.12224
Human fallibility, unpredictable operating environments, and the heterogeneity of hardware devices are driving the need for software to be able to adapt as seen in the Internet of Things or telecommunication networks. Unfortunately, mainstream progra
Externí odkaz:
http://arxiv.org/abs/2105.06973
Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data, and reducing the ingress bandwidth demand beyond the edge. However, fo
Externí odkaz:
http://arxiv.org/abs/2008.03523
Containers are becoming a popular workload deployment mechanism in modern distributed systems. However, there are limited software-based methods (hardware-based methods are expensive requiring hardware level changes) for obtaining the power consumed
Externí odkaz:
http://arxiv.org/abs/2006.00342
Autor:
Pescosolido, Anthony1 (AUTHOR), Harvey, Paul1 (AUTHOR)
Publikováno v:
Academy of Management Annual Meeting Proceedings. 2024, Vol. 2024 Issue 1, p1-4. 4p.
Characteristics and sources of Pb exposure via household dust from the urban area of Shanghai, China
Publikováno v:
In Science of the Total Environment 10 March 2022 811