E
Ezio Bartocci
Researcher at Vienna University of Technology
Publications - 206
Citations - 4152
Ezio Bartocci is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Temporal logic & Computer science. The author has an hindex of 32, co-authored 188 publications receiving 3337 citations. Previous affiliations of Ezio Bartocci include University of Camerino & Stony Brook University.
Papers
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Book ChapterDOI
Specification-Based Monitoring of Cyber-Physical Systems: A Survey on Theory, Tools and Applications
Ezio Bartocci,Jyotirmoy V. Deshmukh,Alexandre Donzé,Georgios Fainekos,Oded Maler,Dejan Nickovic,Sriram Sankaranarayanan +6 more
TL;DR: This chapter summarise the state-of-the-art techniques for qualitative and quantitative monitoring of CPS behaviours, and presents an overview of some of the important applications and describes the tools supporting CPS monitoring and compare their main features.
Book ChapterDOI
Introduction to runtime verification
TL;DR: The aim of this chapter is to act as a primer for those wanting to learn about Runtime Verification, providing an overview of the main specification languages used for RV and introducing the standard terminology necessary to describe the monitoring problem.
Journal ArticleDOI
Computational Modeling, Formal Analysis, and Tools for Systems Biology
Ezio Bartocci,Pietro Liò +1 more
TL;DR: It is believed that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.
Book ChapterDOI
Model repair for probabilistic systems
TL;DR: Using a new version of parametric probabilistic model checking, it is shown how the Model Repair problem can be reduced to a nonlinear optimization problem with a minimal-cost objective function, thereby yielding a solution technique.
Book ChapterDOI
Runtime verification with state estimation
Scott D. Stoller,Ezio Bartocci,Justin Seyster,Radu Grosu,Klaus Havelund,Scott A. Smolka,Erez Zadok +6 more
TL;DR: This work views event sequences as observation sequences of a Hidden Markov Model, uses an HMM model of the monitored program to "fill in" sampling-induced gaps in observation sequences, and extends the classic forward algorithm for HMM state estimation to compute the probability that the property is satisfied by an execution of the program.