Zobrazeno 1 - 10
of 9 500
pro vyhledávání: '"VASSILEV, A"'
Publikováno v:
The 11th IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS-2024)
This Research proposes a Novel Reinforcement Learning (RL) model to optimise malware forensics investigation during cyber incident response. It aims to improve forensic investigation efficiency by reducing false negatives and adapting current practic
Externí odkaz:
http://arxiv.org/abs/2410.15028
Autor:
Vassilev, Katja D.
Although wave kinetic equations have been rigorously derived in dimension $d \geq 2$, both the physical and mathematical theory of wave turbulence in dimension $d = 1$ is less understood. Here, we look at the one-dimensional MMT (Majda, McLaughlin, a
Externí odkaz:
http://arxiv.org/abs/2408.13693
Autor:
Kutchartt, Erico, González-Olabarria, José Ramón, Aquilué, Núria, Garcia-Gonzalo, Jordi, Trasobares, Antoni, Botequim, Brigite, Hauglin, Marius, Palaiologou, Palaiologos, Vassilev, Vassil, Cardil, Adrian, Navarrete, Miguel Ángel, Orazio, Christophe, Pirotti, Francesco
Canopy fuels and surface fuel models, topographic features and other canopy attributes such as stand height and canopy cover, provide the necessary spatial datasets required by various fire behaviour modelling simulators. This is a technical note rep
Externí odkaz:
http://arxiv.org/abs/2409.00008
This research focused on enhancing post-incident malware forensic investigation using reinforcement learning RL. We proposed an advanced MDP post incident malware forensics investigation model and framework to expedite post incident forensics. We the
Externí odkaz:
http://arxiv.org/abs/2408.01999
Autor:
Aehle, Max, Nguyen, Xuan Tung, Novák, Mihály, Dorigo, Tommaso, Gauger, Nicolas R., Kieseler, Jan, Klute, Markus, Vassilev, Vassil
We have applied an operator-overloading forward-mode algorithmic differentiation tool to the Monte-Carlo particle simulation toolkit Geant4. Our differentiated version of Geant4 allows computing mean pathwise derivatives of user-defined outputs of Ge
Externí odkaz:
http://arxiv.org/abs/2407.02966
Autor:
Aehle, Max, Novák, Mihály, Vassilev, Vassil, Gauger, Nicolas R., Heinrich, Lukas, Kagan, Michael, Lange, David
Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Computing them via algorithmic differentiation typically does not requir
Externí odkaz:
http://arxiv.org/abs/2405.07944
Detecting out of policy speech (OOPS) content is important but difficult. While machine learning is a powerful tool to tackle this challenging task, it is hard to break the performance ceiling due to factors like quantity and quality limitations on t
Externí odkaz:
http://arxiv.org/abs/2310.15019
Autor:
Garofalo, Nicola, Vassilev, Dimiter
In this paper we formulate some conjectures in sub-Riemannian geometry concerning a characterisation of the Koranyi-Kaplan ball in a group of Heisenberg type through the existence of a solution to suitably overdetermined problems. We prove an integra
Externí odkaz:
http://arxiv.org/abs/2309.12567
Publikováno v:
Forensic Science International: Digital Investigation, 2024
In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. Howeve
Externí odkaz:
http://arxiv.org/abs/2309.07064
Autor:
Barbara Krzemińska, Izabela Borkowska, Maria Malm, Dorota Tchórzewska, Jaco Vangronsveld, Andon Vassilev, Katarzyna Dos Santos Szewczyk, Małgorzata Wójcik
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Plants belonging to the genus Cotoneaster can be valuable sources of phytochemicals with potential therapeutic properties. The natural habitats of most of these species are situated in Asia, Africa and southern Europe. Introducing them into
Externí odkaz:
https://doaj.org/article/ceacb21f6f2b477cb14ac37c066e541d
