Zobrazeno 1 - 10
of 23 806
pro vyhledávání: '"A. A. Ghanem"'
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
This work presents a novel framework for physically consistent model error characterization and operator learning for reduced-order models of non-equilibrium chemical kinetics. By leveraging the Bayesian framework, we identify and infer sources of mo
Externí odkaz:
http://arxiv.org/abs/2410.12925
This paper investigates the application of Deep Reinforcement Learning (DRL) for attributing malware to specific Advanced Persistent Threat (APT) groups through detailed behavioural analysis. By analysing over 3500 malware samples from 12 distinct AP
Externí odkaz:
http://arxiv.org/abs/2410.11463
The task of action spotting consists in both identifying actions and precisely localizing them in time with a single timestamp in long, untrimmed video streams. Automatically extracting those actions is crucial for many sports applications, including
Externí odkaz:
http://arxiv.org/abs/2410.01304
Autor:
Chaitanya, Krishna, Damasceno, Pablo F., Fadnavis, Shreyas, Mobadersany, Pooya, Parmar, Chaitanya, Scherer, Emily, Zemlianskaia, Natalia, Surace, Lindsey, Ghanem, Louis R., Cula, Oana Gabriela, Mansi, Tommaso, Standish, Kristopher
Accurate assessment of disease severity from endoscopy videos in ulcerative colitis (UC) is crucial for evaluating drug efficacy in clinical trials. Severity is often measured by the Mayo Endoscopic Subscore (MES) and Ulcerative Colitis Endoscopic In
Externí odkaz:
http://arxiv.org/abs/2410.00536
Autor:
Cioppa, Anthony, Giancola, Silvio, Somers, Vladimir, Joos, Victor, Magera, Floriane, Held, Jan, Ghasemzadeh, Seyed Abolfazl, Zhou, Xin, Seweryn, Karolina, Kowalczyk, Mateusz, Mróz, Zuzanna, Łukasik, Szymon, Hałoń, Michał, Mkhallati, Hassan, Deliège, Adrien, Hinojosa, Carlos, Sanchez, Karen, Mansourian, Amir M., Miralles, Pierre, Barnich, Olivier, De Vleeschouwer, Christophe, Alahi, Alexandre, Ghanem, Bernard, Van Droogenbroeck, Marc, Gorski, Adam, Clapés, Albert, Boiarov, Andrei, Afanasiev, Anton, Xarles, Artur, Scott, Atom, Lim, ByoungKwon, Yeung, Calvin, Gonzalez, Cristian, Rüfenacht, Dominic, Pacilio, Enzo, Deuser, Fabian, Altawijri, Faisal Sami, Cachón, Francisco, Kim, HanKyul, Wang, Haobo, Choe, Hyeonmin, Kim, Hyunwoo J, Kim, Il-Min, Kang, Jae-Mo, Tursunboev, Jamshid, Yang, Jian, Hong, Jihwan, Lee, Jimin, Zhang, Jing, Lee, Junseok, Zhang, Kexin, Habel, Konrad, Jiao, Licheng, Li, Linyi, Gutiérrez-Pérez, Marc, Ortega, Marcelo, Li, Menglong, Lopatto, Milosz, Kasatkin, Nikita, Nemtsev, Nikolay, Oswald, Norbert, Udin, Oleg, Kononov, Pavel, Geng, Pei, Alotaibi, Saad Ghazai, Kim, Sehyung, Ulasen, Sergei, Escalera, Sergio, Zhang, Shanshan, Yang, Shuyuan, Moon, Sunghwan, Moeslund, Thomas B., Shandyba, Vasyl, Golovkin, Vladimir, Dai, Wei, Chung, WonTaek, Liu, Xinyu, Zhu, Yongqiang, Kim, Youngseo, Li, Yuan, Yang, Yuting, Xiao, Yuxuan, Cheng, Zehua, Li, Zhihao
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field unde
Externí odkaz:
http://arxiv.org/abs/2409.10587
Autor:
Nigmatullin, Ramil, Hemery, Kevin, Ghanem, Khaldoon, Moses, Steven, Gresh, Dan, Siegfried, Peter, Mills, Michael, Gatterman, Thomas, Hewitt, Nathan, Granet, Etienne, Dreyer, Henrik
The utility of solving the Fermi-Hubbard model has been estimated in the billions of dollars. Digital quantum computers can in principle address this task, but have so far been limited to quasi one-dimensional models. This is because of exponential o
Externí odkaz:
http://arxiv.org/abs/2409.06789
Timeline Analysis (TA) plays a crucial role in Timeline Forensics (TF) within the field of Digital Forensics (DF). It focuses on examining and analyzing time-based digital artefacts, such as timestamps derived from event logs, file metadata, and othe
Externí odkaz:
http://arxiv.org/abs/2409.02572
Autor:
Banerjee, Oishi, Saenz, Agustina, Wu, Kay, Clements, Warren, Zia, Adil, Buensalido, Dominic, Kavnoudias, Helen, Abi-Ghanem, Alain S., Ghawi, Nour El, Luna, Cibele, Castillo, Patricia, Al-Surimi, Khaled, Daghistani, Rayyan A., Chen, Yuh-Min, Chao, Heng-sheng, Heiliger, Lars, Kim, Moon, Haubold, Johannes, Jonske, Frederic, Rajpurkar, Pranav
Given the rapidly expanding capabilities of generative AI models for radiology, there is a need for robust metrics that can accurately measure the quality of AI-generated radiology reports across diverse hospitals. We develop ReXamine-Global, a LLM-p
Externí odkaz:
http://arxiv.org/abs/2408.16208
Autor:
S, Gabriel Pérez, Pérez, Juan C., Alfarra, Motasem, Zarzar, Jesús, Rojas, Sara, Ghanem, Bernard, Arbeláez, Pablo
This paper presents preliminary work on a novel connection between certified robustness in machine learning and the modeling of 3D objects. We highlight an intriguing link between the Maximal Certified Radius (MCR) of a classifier representing a spac
Externí odkaz:
http://arxiv.org/abs/2408.13135