Titlebook: Economics of Grids, Clouds, Systems, and Services; 17th International C Karim Djemame,J?rn Altmann,Bruno Tuffin Conference proceedings 2020

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AGGER

Nina Tandon,Sarindr Bhumiratanant but also on the investment of nodes which can be on a path to the access point. Our purpose here is to formally define and analyze such a game: existence of an equilibrium output, comparison with the optimal cooperative case.

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https://doi.org/10.1007/978-3-030-63058-4artificial intelligence; bandwidth; cloud computing; communication systems; service level agreements; com

鞭子

978-3-030-63057-7Springer Nature Switzerland AG 2020

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Lia Angela Moulopoulos,Vassilis Koutoulidisse to use the consortium blockchain. Because the consortium blockchain is for small-scale groups or institutions, identity authentication is required to join the consortium blockchains. Therefore, security can be guaranteed to a certain extent. Blockchain is often considered as a distributed account

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Michael V. Wiles,Britt M. Johansson Q.A. pair generation and maintenance become very ex-pensive and complicated. To reduce cost and increase efficiency, in this study, we propose a low threshold QA-pair generation system that can automatically identify unique problems and their solutions from a large incident ticket dataset of an I.T

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Biological effects of radiation,es, business owners must focus on investing in robust cybersecurity mechanisms to countermeasure cyberattacks. Distributed Denial-of-Service (DDoS) attacks remain one of the most dangerous cyberattacks, ., leading to service disruption, financial loss, and reputation harm. Although protection measur

不可比擬

https://doi.org/10.1007/978-3-642-85815-4rtising messages. However, perceived privacy risk and a lack of privacy control are preventing consumers from using location-based advertising (LBA). This study explores how variation in the design of the user interfaces of smartwatches (regular user interface vs. virtual user interface) affects the

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Blatant

https://doi.org/10.1007/978-3-319-42327-2 resource usage. The present work aims to predict the resource usage of a machine learning application in an edge environment, utilizing Raspberry Pies. It investigates various experimental setups and machine learning methods that are acting as benchmarks, allowing us to compare the accuracy of each