most citedGOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems

20 citations · 22 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR202520 cited

GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems

Davide Maffiola, Stefano Longari, Michele Carminati +2

Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time dat…

cs.CR20252 cited

CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment

Stefano Longari, Alessandro Pozone, Jessica Leoni +4

In the last decades, Cyber-physical Systems (CPSs) have experienced a significant technological evolution and increased connectivity, at the cost of greater exposure to cyber-attac…

cs.LG2025

CoCoAFusE: Beyond Mixtures of Experts via Model Fusion

Aurelio Raffa Ugolini, Mara Tanelli, Valentina Breschi

Many learning problems involve multiple patterns and varying degrees of uncertainty dependent on the covariates. Advances in Deep Learning (DL) have addressed these issues by learn…

cs.CY2025

The epistemic dimension of algorithmic fairness: assessing its impact in innovation diffusion and fair policy making

Eugenia Villa, Camilla Quaresmini, Valentina Breschi +2

Algorithmic fairness is an expanding field that addresses a range of discrimination issues associated with algorithmic processes. However, most works in the literature focus on ana…

eess.SY2024

SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study

Aurelio Raffa Ugolini, Valentina Breschi, Andrea Manzoni +1

In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide…

cs.LG2024

Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models

Jessica Leoni, Valentina Breschi, Simone Formentin +1

Traditional models grounded in first principles often struggle with accuracy as the system's complexity increases. Conversely, machine learning approaches, while powerful, face cha…