Zobrazeno 1 - 10
of 1 177
pro vyhledávání: '"DAMIANI, ERNESTO"'
Trees continue to fascinate with their natural beauty and as engineering masterpieces optimal with respect to several independent criteria. Pythagorean tree is a well-known fractal design that realistically mimics the natural tree branching structure
Externí odkaz:
http://arxiv.org/abs/2411.08024
Malware visualization analysis incorporating with Machine Learning (ML) has been proven to be a promising solution for improving security defenses on different platforms. In this work, we propose an integrated framework for addressing common problems
Externí odkaz:
http://arxiv.org/abs/2409.14439
Autor:
Colombo, Maurizio, Asal, Rasool, Damiani, Ernesto, AlQassem, Lamees Mahmoud, Almemari, Al Anoud, Alhammadi, Yousof
The massive deployment of Machine Learning (ML) models raises serious concerns about data protection. Privacy-enhancing technologies (PETs) offer a promising first step, but hard challenges persist in achieving confidentiality and differential privac
Externí odkaz:
http://arxiv.org/abs/2406.19418
Modern applications are increasingly driven by Machine Learning (ML) models whose non-deterministic behavior is affecting the entire application life cycle from design to operation. The pervasive adoption of ML is urgently calling for approaches that
Externí odkaz:
http://arxiv.org/abs/2311.12686
Autor:
Zhang, Zhibo, Li, Pengfei, Hammadi, Ahmed Y. Al, Guo, Fusen, Damiani, Ernesto, Yeun, Chan Yeob
This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning. While EEG signal analysis has attracted att
Externí odkaz:
http://arxiv.org/abs/2401.01896
Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. Often, the design of such models is based on some ad-hoc a
Externí odkaz:
http://arxiv.org/abs/2306.10341