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pro vyhledávání: '"Sharifi, Sepehr"'
In learning-enabled autonomous systems, safety monitoring of learned components is crucial to ensure their outputs do not lead to system safety violations, given the operational context of the system. However, developing a safety monitor for practica
Externí odkaz:
http://arxiv.org/abs/2405.13254
In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis. Given that such boundary captures the conditions in terms of MLC behavior and system cont
Externí odkaz:
http://arxiv.org/abs/2301.13807
Autor:
Mozhdehi, Amir Mohammad, Sharifi, Amir Hossein, Ganjali, Ahmad, Morsali, Ali, Sharifi, Sepehr, Naghavi, Farnaz, Bamoharram, Fatemeh F., Sillanpää, Mika
Publikováno v:
In Journal of Molecular Liquids 15 March 2021 326
Autor:
Mozhdehi, Amir Mohammad, Bamoharram, Fatemeh F., Morsali, Ali, Sharifi, Amir Hossein, Sharifi, Sepehr, Ganjali, Ahmad
Publikováno v:
In Journal of Molecular Liquids 1 January 2020 297
Akademický článek
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Autor:
Parvizimosaed, Alireza1 (AUTHOR), Sharifi, Sepehr1 (AUTHOR), Amyot, Daniel1 (AUTHOR) damyot@uottawa.ca, Logrippo, Luigi1,2 (AUTHOR), Roveri, Marco3 (AUTHOR), Rasti, Aidin1 (AUTHOR), Roudak, Ali4 (AUTHOR), Mylopoulos, John1 (AUTHOR)
Publikováno v:
Software & Systems Modeling. Dec2022, Vol. 21 Issue 6, p2395-2427. 33p.