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View article: Monitoring Spatially Distributed Cyber-Physical Systems with Alternating Finite Automata
Monitoring Spatially Distributed Cyber-Physical Systems with Alternating Finite Automata Open
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 CPS, these connections can be dynamically dependent on the sp…
View article: stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic
stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic Open
Integrating symbolic knowledge and data-driven learning algorithms is a longstanding challenge in Artificial Intelligence. Despite the recognized importance of this task, a notable gap exists due to the discreteness of symbolic representat…
View article: ARCH-COMP 2024 Category Report: Falsification
ARCH-COMP 2024 Category Report: Falsification Open
This report presents the results from the falsification category of the 2024 competition in the Applied Verification for Continuous and Hybrid Systems (ARCH) workshop. The report summarizes the competition rules and settings, the benchmark…
View article: Context, Composition, Automation, and Communication: The C <sup>2</sup> AC Roadmap for Modeling and Simulation
Context, Composition, Automation, and Communication: The C <sup>2</sup> AC Roadmap for Modeling and Simulation Open
Simulation has become, in many application areas, a sine qua non . Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work …
View article: stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic
stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic Open
Integrating symbolic knowledge and data-driven learning algorithms is a longstanding challenge in Artificial Intelligence. Despite the recognized importance of this task, a notable gap exists due to the discreteness of symbolic representat…
View article: ECATS: Explainable-by-design concept-based anomaly detection for time series
ECATS: Explainable-by-design concept-based anomaly detection for time series Open
Deep learning methods for time series have already reached excellent performances in both prediction and classification tasks, including anomaly detection. However, the complexity inherent in Cyber Physical Systems (CPS) creates a challeng…
View article: BUSTLE: A Versatile Tool for the Evolutionary Learning of STL Specifications from Data
BUSTLE: A Versatile Tool for the Evolutionary Learning of STL Specifications from Data Open
Describing the properties of complex systems that evolve over time is a crucial requirement for monitoring and understanding them. Signal Temporal Logic (STL) is a framework that proved to be effective for this aim because it is expressive…
View article: Context, Composition, Automation, and Communication -- The C2AC Roadmap for Modeling and Simulation
Context, Composition, Automation, and Communication -- The C2AC Roadmap for Modeling and Simulation Open
Simulation has become, in many application areas, a sine-qua-non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work f…
View article: WebMonitor: Verification of Web User Interfaces
WebMonitor: Verification of Web User Interfaces Open
Application development for the modern Web involves sophisticated engineering workflows which include user interface aspects. Those involve Web elements typically created with HTML/CSS markup and JavaScript-like languages, yielding Web doc…
View article: Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes
Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes Open
We introduce a similarity function on formulae of signal temporal logic (STL). It comes in the form of a kernel function, well known in machine learning as a conceptually and computationally efficient tool. The corresponding kernel trick a…
View article: Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes
Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes Open
We introduce a similarity function on formulae of signal temporal logic (STL). It comes in the form of a kernel function , well known in machine learning as a conceptually and computationally efficient tool. The corresponding kernel trick …
View article: Online monitoring of spatio-temporal properties for imprecise signals
Online monitoring of spatio-temporal properties for imprecise signals Open
From biological systems to cyber-physical systems, monitoring the behavior of such dynamical systems often requires reasoning about complex spatiooral properties of physical and computational entities that are dynamically interconnected an…
View article: Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming Open
Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stat…
View article: Posterior predictive model checking using formal methods in a spatio-temporal model
Posterior predictive model checking using formal methods in a spatio-temporal model Open
We propose an interdisciplinary framework, Bayesian formal predictive model checking (Bayes FPMC), which combines Bayesian predictive inference, a well established tool in statistics, with formal verification methods rooting in the compute…
View article: Bayesian Machine Learning meets Formal Methods: An application to spatio-temporal data
Bayesian Machine Learning meets Formal Methods: An application to spatio-temporal data Open
We propose an interdisciplinary framework that combines Bayesian predictive inference, a well-established tool in Machine Learning, with Formal Methods rooted in the computer science community. Bayesian predictive inference allows for cohe…
View article: Online Monitoring of Spatio-Temporal Properties for Imprecise Signals
Online Monitoring of Spatio-Temporal Properties for Imprecise Signals Open
From biological systems to cyber-physical systems, monitoring the behavior of such dynamical systems often requires to reason about complex spatio-temporal properties of physical and/or computational entities that are dynamically interconn…
View article: Mining Interpretable Spatio-temporal Logic Properties for Spatially\n Distributed Systems
Mining Interpretable Spatio-temporal Logic Properties for Spatially\n Distributed Systems Open
The Internet-of-Things, complex sensor networks, multi-agent cyber-physical\nsystems are all examples of spatially distributed systems that continuously\nevolve in time. Such systems generate huge amounts of spatio-temporal data, and\nsyst…
View article: MoonLight: A Lightweight Tool for Monitoring Spatio-Temporal Properties
MoonLight: A Lightweight Tool for Monitoring Spatio-Temporal Properties Open
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, space is represented as a weighted graph, describing the topol…
View article: A kernel function for Signal Temporal Logic formulae
A kernel function for Signal Temporal Logic formulae Open
We discuss how to define a kernel for Signal Temporal Logic (STL) formulae. Such a kernel allows us to embed the space of formulae into a Hilbert space, and opens up the use of kernel-based machine learning algorithms in the context of STL…
View article: Analysis of Spatio-temporal Properties of Stochastic Systems Using TSTL
Analysis of Spatio-temporal Properties of Stochastic Systems Using TSTL Open
In this article, we present Three-Valued spatio-temporal Logic (TSTL), which enriches the available spatiotemporal analysis of properties expressed in Signal spatio-temporal Logic (SSTL), to give further insight into the dynamic behavior o…
View article: A Logic-Based Learning Approach to Explore Diabetes Patient Behaviors
A Logic-Based Learning Approach to Explore Diabetes Patient Behaviors Open
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 control a variety of behavioral factors that affect glycemic cont…
View article: Modelling and Analysing Resilient Cyber-Physical Systems
Modelling and Analysing Resilient Cyber-Physical Systems Open
From smart buildings to medical devices to smart nations, software systems increasingly integrate computation, networking, and interaction with the physical environment. These systems are known as Cyber-Physical Systems (CPS). While these …
View article: Qualitative and Quantitative Monitoring of Spatio-Temporal Properties with SSTL
Qualitative and Quantitative Monitoring of Spatio-Temporal Properties with SSTL Open
In spatially located, large scale systems, time and space dynamics interact and drives the behaviour. Examples of such systems can be found in many smart city applications and Cyber-Physical Systems. In this paper we present the Signal Spa…
View article: Signal Convolution Logic
Signal Convolution Logic Open
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 the percentage of time a formula is satisfied in a bounded i…