31 citations · 38 across the 8 of their papers we have counts for
8 papers
Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semantics
Irene Ferfoglia, Simone Silvetti, Gaia Saveri +2
Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box dee…
Towards Interpretable Concept Learning over Time Series via Temporal Logic Semantics
Irene Ferfoglia, Simone Silvetti, Gaia Saveri +2
Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box dee…
Monitoring Spatially Distributed Cyber-Physical Systems with Alternating Finite Automata
Anand Balakrishnan, Sheryl Paul, Simone Silvetti +2
Modern cyber-physical systems (CPS) can consist of various networked components and agents interacting and communicating with each other. In the context of spatially distributed CP…
MoonLight: A Lightweight Tool for Monitoring Spatio-Temporal Properties
Ezio Bartocci, Luca Bortolussi, Michele Loreti +2
We present MoonLight, a tool for monitoring temporal and spatio-temporal properties of mobile and spatially distributed cyber-physical systems (CPS). In the proposed framework, spa…
A Logic-Based Learning Approach to Explore Diabetes Patient Behaviors
Josephine Lamp, Simone Silvetti, Marc Breton +2
Type I Diabetes (T1D) is a chronic disease in which the body's ability to synthesize insulin is destroyed. It can be difficult for patients to manage their T1D, as they must contro…
Signal Convolution Logic
Simone Silvetti, Laura Nenzi, Ezio Bartocci +1
We introduce a new logic called Signal Convolution Logic (SCL) that combines temporal logic with convolutional filters from digital signal processing. SCL enables to reason about t…
A Robust Genetic Algorithm for Learning Temporal Specifications from Data
Laura Nenzi, Simone Silvetti, Ezio Bartocci +1
We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system. We assume the traini…
An Active Learning Approach to the Falsification of Black Box Cyber-Physical Systems
Simone Silvetti, Alberto Policriti, Luca Bortolussi
Search-based testing is widely used to find bugs in models of complex Cyber-Physical Systems. Latest research efforts have improved this approach by casting it as a falsification p…