Deep Learning for Abstraction, Control and Monitoring of Complex Cyber-Physical Systems

Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant amount of uncertainty.Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant amount of uncertainty

Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes

We consider the problem of predictive monitoring (PM), i.e., predicting at runtime the satisfaction of a desired property from the current system's state. Due to its relevance for runtime safety assurance and online control, PM methods need to be efficient to enable timely interventions against predicted violations, while providing correctness guarantees. We introduce \textit{quantitative predictive monitoring (QPM)}, the first PM method to support stochastic processes and rich specifications given in Signal Temporal Logic (STL). Unlike most of the existing PM techniques that predict whether or not some property $\phi$ is satisfied, QPM provides a quantitative measure of satisfaction by predicting the quantitative (aka robust) STL semantics of $\phi$. QPM derives prediction intervals that are highly efficient to compute and with probabilistic guarantees, in that the intervals cover with arbitrary probability the STL robustness values relative to the stochastic evolution of the system. To do so, we take a machine-learning approach and leverage recent advances in conformal inference for quantile regression, thereby avoiding expensive Monte-Carlo simulations at runtime to estimate the intervals. We also show how our monitors can be combined in a compositional manner to handle composite formulas, without retraining the predictors nor sacrificing the guarantees. We demonstrate the effectiveness and scalability of QPM over a benchmark of four discrete-time stochastic processes with varying degrees of complexity

Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems

We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Generative Adversarial Networks. The method learns two Neural Networks: the first one aims at generating troubling scenarios for the controller, while the second one aims at enforcing the safety constraints. We test the proposed method on a variety of case studies

Neural Predictive Monitoring

Neural State Classification (NSC) is a recently proposed method for runtime predictive monitoring of Hybrid Automata (HA) using deep neural networks (DNNs). NSC trains a DNN as an approximate reachability predictor that labels a given HA state x as positive if an unsafe state is reachable from x within a given time bound, and labels x as negative otherwise. NSC predictors have very high accuracy, yet are prone to prediction errors that can negatively impact reliability. To overcome this limitation, we present Neural Predictive Monitoring (NPM), a technique based on NSC and conformal prediction that complements NSC predictions with statistically sound estimates of uncertainty. This yields principled criteria for the rejection of predictions likely to be incorrect, without knowing the true reachability values. We also present an active learning method that significantly reduces both the NSC predictor\u2019s error rate and the percentage of rejected predictions. Our approach is highly efficient, with computation times on the order of milliseconds, and effective, managing in our experimental evaluation to successfully reject almost all incorrect predictions

The COVID-19 pandemic and its global effects on dental practice : An International survey

Objectives: A multicentre survey was designed to evaluate the impact of COVID-19 outbreak on dental practice worldwide, estimate the COVID-19 related symptoms/signs, work attitudes and behaviour and the routine use of protective measures and Personal Protective Equipment (PPE). Methods: A global survey using a standardized questionnaire with research groups from 36 countries was designed. The questionnaire was developed and pretested during April 2020 and contained three domains: 1) Personal data; 2) COVID-19 positive rate and symptoms/signs presumably related to the coronavirus; 3) Working conditions and PPE adopted after the outbreak. Countries' data were grouped by the Country Positive Rate (CPR) during the survey period and by Gross-National-Income per capita. An ordinal multinomial logistic regression model was carried out with COVID-19 self-reported rate referred by dental professionals as dependent variable to assess the association with questionnaire items. Results: A total of 52,491 questionnaires were returned with a male/female ratio of 0.63. Out of the total respondents, 7,859 dental professionals (15%) reported symptoms/signs compatible with COVID-19. More than half of the sample (n = 27,818; 53%) stated to use FFP2/N95 masks, while 21,558 (41.07%) used eye protection. In the bivariate analysis, CPR and N95/FFP2 were significantly associated (OR = 1.80 95% =5.20 95% 95% CI = 1.60/2.82 and OR CI = 1.44/18.80, respectively), while Gross-National-Income was not statistically associated with CPR (OR = 1.09 CI = 0.97/1.60). The same significant associations were observed in the multivariate analysis. Conclusions: Oral health service provision has not been significantly affected by COVID-19, although access to routine dental care was reduced due to country-specific temporary lockdown periods. While the dental profession has been identified at high-risk, the reported rates of COVID-19 for dental professionals were not significantly different to those reported for the general population in each country. These findings may help to better plan oral health care for future pandemic events

Bayesian Abstraction of Markov Population Models

Markov Population Models are a widespread formalism, with applications in Systems Biology, Performance Evaluation, Ecology, and many other fields. The associated Markov stochastic process in continuous time is often analyzed by simulation, which can be costly for large or stiff systems, particularly when simulations have to be performed in a multi-scale model (e.g. simulating individual cells in a tissue). A strategy to reduce computational load is to abstract the population model, replacing it with a simpler stochastic model, faster to simulate. Here we pursue this idea, building on previous work [3] and constructing an approximate kernel for a Markov process in continuous space and discrete time, capturing the evolution at fixed dt time steps. This kernel is learned automatically from simulations of the original model. Differently from [3], which relies on deep neural networks, we explore here a Bayesian density regression approach based on Dirichlet processes, which provides a principled way to estimate uncertainty

Clinical Decision Support Using Colored Petri Nets: a Case Study on Cancer Infusion Therapy

We consider a drug infusion scenario in which a drug is delivered through an infusion pump to a patient, whose vital parameters are monitored via a bedside monitor. Drug infusion therapies are based on clinical protocols that are drug-specific and very diversified. The burden of their proper application on several patients lies, most of the times, on nursing staff alone. With the aim of making the choices safe and prompt and limiting human errors, we build a system that suggests the proper action based on the protocol and the status of the patient. Given the high variability of protocols, it is important to choose a flexible structure. We choose Hierarchical Colored Petri Nets (HCPN), a mathematical formalism for describing discrete event dynamic systems, which is, in fact, modular, expressive and admits a graphic representation. Cancer infusion therapy is the case study considered, as that clinical scenario is likely to become critical from a staff/patient ratio point of view, since the number of patients is continuously growing

Stochastic Variational Smoothed Model Checking

Model-checking for parametric stochastic models can be expressed as checking the satisfaction probability of a certain property as a function of the parameters of the model. Smoothed model checking (smMC) leverages Gaussian Processes (GP) to infer the satisfaction function over the entire parameter space from a limited set of observations obtained via simulation. This approach provides accurate reconstructions with statistically sound quantification of the uncertainty. However, it inherits the scalability issues of GP. In this paper, we exploit recent advances in probabilistic machine learning to push this limitation forward, making Bayesian inference of smMC scalable to larger datasets, enabling its application to larger models in terms of the dimension of the parameter set. We propose Stochastic Variational Smoothed Model Checking (SV-smMC), a solution that exploits stochastic variational inference (SVI) to approximate the posterior distribution of the smMC problem. The strength and flexibility of SVI make SV-smMC applicable to two alternative probabilistic models: Gaussian Processes (GP) and Bayesian Neural Networks (BNN). Moreover, SVI makes inference easily parallelizable and it enables GPU acceleration. In this paper, we compare the performances of smMC against those of SV-smMC by looking at the scalability, the computational efficiency and at the accuracy of the reconstructed satisfaction function

Model Predictive Control of glucose concentration based on Signal Temporal Logic specifications

Insulin is a peptide hormone produced by the pancreas to regulate the cells intake of glucose in the blood. Type 1 diabetes compromises this particular capacity of the pancreas. Patients with this disease inject insulin to regulate the level of glucose in the blood, thus reducing the risk of longterm complications. Artificial Pancreas (AP) is a wearable device developed to provide automatic delivery of insuline, allowing a potentially significant improvement in the quality of life of patients. In this paper we apply to the AP a Model Predictive Controller able to generate state trajectories that meet constraints expressed through Signal Temporal Logic (STL). Such a form of constraints is indeed appropriate for the AP, in which some requirements result in hard constraints (absolutely avoid hypoglycaemia) and some other in soft constraints (avoid a prolonged hyperglycaemia). We rely on the BluSTL toolbox, which allows to automatically generate controllers using STL specifications. We perform simulations on two different scenarios: an MPC controller that uses the same constraints as [1] and an MPC-STL controller in both deterministic and adversarial environment (robust control). We show that the soft constraints permitted by STL avoid unnecessary restriction, providing safe trajectories in correspondence of higher disturbance