Abstract A digital twin is a virtual replica of a physical system that has to interact with it in real-time in order to facilitate decision-making, to reduce failures and costs, and to ensure a coherent and safe system execution. We call effectiveness the ability of the digital twin to direct the physical counterpart. In this paper we provide the means to evaluate the effectiveness of a digital twin in the case that the physical system is operating under uncertainty, and it is therefore subject to perturbations . Specifically, we present the DT-Stark tool, that extends Stark , a tool for modelling and verification of systems operating under uncertainty, with feedback , a special mechanism that allow us to model the communications, and their effects, between the digital and the physical (perturbed) twin in a concise, clean fashion. We can then exploit the features of Stark to compare the behaviour of the twins, to verify properties over them, and to measure effectiveness. We provide some examples of the use of our tool by applying it to the evaluation of the effectiveness of digital twins in two robotic scenarios: an industrial plant and a smart hospital.

Obstructive sleep apnea (OSA) is a chronic disease characterized by intermittent hypoxemia during sleep related to snoring. It affects the quality of life and increases the risk of severe health conditions, including cardiovascular diseases. The gold standard for diagnosing OSA is polysomnography (PSG), which requires an overnight hospital stay while physically connected to 10-15 measurement channels. PSG is costly, inconvenient, and requires the involvement of a sleep technologist. Such as, over 80% of affected individuals remain undiagnosed. Therefore, cost-effective and non-invasive screening methods for OSA play a fundamental role in improving people’s file quality. Approaches based on deep learning techniques have achieved evaluable results. However, such results are not reproducible due to the lack of code and dataset, making it difficult to evaluate the impact of these methods on first-level diagnosis scenarios.In this work, we face apnea detection as a classification image problem. The introduced method exploits the Mel-spectrograms of snoring and VGG19, an architecture based on Convolutional Neural Networks (CNN), to detect apnea. We test our approach on a public dataset that stores data related to polysomnography with simultaneous audio recordings for sleep apnea studies. On this dataset, our methods archive 95, 4% of accuracy. The analysis of the performance values shows that our method reaches competitive results.

We present the Software Tool for the Analysis of Robustness in the unKnown environment (Stark), our Java tool for the specification, analysis and verification of robustness properties of Cyber-Physical Systems (CPSs). Stark includes: (i) a specification language for systems behaviour, perturbations, distances on systems behaviours, and requirements on systems behaviour expressed in the Robustness Temporal Logic (RobTL), a temporal logic for the specification and verification of properties on the evolution of distances between the behaviours of CPSs, and thus also of robustness properties; (ii) a module for the simulation of system behaviours and their perturbed versions; (iii) a module for the evaluation of distances between behaviours; (iv) a statistical model checker for RobTL formulae.

We propose two step-by-step approaches to the analysis of robustness in biochemical networks. Our aim is to measure the ability of the network to exhibit step-by-step limited variations on the concentration of a species of interest at varying of the initial concentration of other species. The first approach we propose is reaction-by-reaction, i.e. we compare the states reached by nominal and perturbed networks after they have performed the same number of reactions. We provide a statistical technique allowing for estimating robustness, we implement it in a tool called spebnr ( a Simple Python Environment for statistical estimation of Biochemical Network Robustness ) and showcase it on three case studies: the EnvZ/OmpR osmoregulatory signaling system of Escherichia Coli, the mechanism of bacterial chemotaxis of Escherichia Coli, and enzyme activity at saturation. Then, we consider a time-by-time approach, in which networks are compared on the basis of the states they reached at the same time point, regardless of how many reactions occurred. This approach is implemented in Stark , and we apply it to the study the robustness of the EnvZ/OmpR osmoregulatory signaling system and the Lotka-Volterra equations.

Due to the diffusion of IoT, modern software systems are often thought to control and coordinate smart devices in order to manage assets and resources, and to guarantee efficient behaviours. For this class of systems, which interact extensively with humans and with their environment, it is thus crucial to guarantee their correct behaviour in order to avoid unexpected and possibly dangerous situations. In this paper we will present a framework that allows us to measure the robustness of systems. This is the ability of a program to tolerate changes in the environmental conditions and preserving the original behaviour. In the proposed framework, the interaction of a program with its environment is represented as a sequence of random variables describing how both evolve in time. For this reason, the considered measures will be defined among probability distributions of observed data. The proposed framework will be then used to define the notions of adaptability and reliability. The former indicates the ability of a program to absorb perturbation on environmental conditions after a given amount of time. The latter expresses the ability of a program to maintain its intended behaviour (up-to some reasonable tolerance) despite the presence of perturbations in the environment. Moreover, an algorithm, based on statistical inference, is proposed to evaluate the proposed metric and the aforementioned properties. We use two case studies to the describe and evaluate the proposed approach.

Collective Adaptive Systems often consist of many heterogeneous components typically organised in groups. These entities interact with each other by adapting their behaviour to pursue individual or collective goals. In these systems, the distribution of these entities determines a space that can be either physical or logical. The former is defined in terms of a physical relation among components. The latter depends on logical relations, such as being part of the same group. In this context, specification and verification of spatial properties play a fundamental role in supporting the design of systems and predicting their behaviour. For this reason, different tools and techniques have been proposed to specify and verify the properties of space, mainly described as graphs. Therefore, the approaches generally use model spatial relations to describe a form of proximity among pairs of entities. Unfortunately, these graph-based models do not permit considering relations among more than two entities that may arise when one is interested in describing aspects of space by involving interactions among groups of entities. In this work, we propose a spatial logic interpreted on simplicial complexes. These are topological objects, able to represent surfaces and volumes efficiently that generalise graphs with higher-order edges. We discuss how the satisfaction of logical formulas can be verified by a correct and complete model checking algorithm, which is linear to the dimension of the simplicial complex and logical formula. The expressiveness of the proposed logic is studied in terms of the spatial variants of classical bisimulation and branching bisimulation relations defined over simplicial complexes.

Abstract We present MoonLight, a tool for monitoring temporal and spatio-temporal properties of mobile, spatially distributed, and interacting entities such as biological and cyber-physical systems. In MoonLight the space is represented as a weighted graph describing the topological configuration in which the single entities are arranged. Both nodes and edges have attributes modeling physical quantities and logical states of the system evolving in time. MoonLight is implemented in Java and supports the monitoring of Spatio-Temporal Reach and Escape Logic (STREL). MoonLight can be used as a standalone command line tool, such as Java API, or via Matlab™ and Python interfaces. We provide here the description of the tool, its interfaces, and its scripting language using a sensor network and a bike sharing example. We evaluate the tool performances both by comparing it with other tools specialized in monitoring only temporal properties and by monitoring spatio-temporal requirements considering different sizes of dynamical and spatial graphs.

Cyber-Physical Systems (CPS) consist of inter-wined computational (cyber) and physical components interacting through sensors and/or actuators. Computational elements are networked at every scale and can communicate with each other and with humans. Nodes can join and leave the network at any time or they can move to different spatial locations. In this scenario, monitoring spatial and temporal properties plays a key role in the understanding of how complex behaviors can emerge from local and dynamic interactions. We revisit here the Spatio-Temporal Reach and Escape Logic (STREL), a logic-based formal language designed to express and monitor spatio-temporal requirements over the execution of mobile and spatially distributed CPS. STREL considers the physical space in which CPS entities (nodes of the graph) are arranged as a weighted graph representing their dynamic topological configuration. Both nodes and edges include attributes modeling physical and logical quantities that can evolve over time. STREL combines the Signal Temporal Logic with two spatial modalities reach and escape that operate over the weighted graph. From these basic operators, we can derive other important spatial modalities such as everywhere, somewhere and surround. We propose both qualitative and quantitative semantics based on constraint semiring algebraic structure. We provide an offline monitoring algorithm for STREL and we show the feasibility of our approach with the application to two case studies: monitoring spatio-temporal requirements over a simulated mobile ad-hoc sensor network and a simulated epidemic spreading model for COVID19.

From biological systems to cyber-physical systems, monitoring the behavior of such dynamical systems often requires reasoning about complex spatio-temporal properties of physical and computational entities that are dynamically interconnected and arranged in a particular spatial configuration. Spatio-Temporal Reach and Escape Logic (STREL) is a recent logic-based formal language designed to specify and reason about spatio-temporal properties. STREL considers each system's entity as a node of a dynamic weighted graph representing its spatial arrangement. Each node generates a set of mixed-analog signals describing the evolution over time of computational and physical quantities characterizing the node's behavior. While there are offline algorithms available for monitoring STREL specifications over logged simulation traces, here we investigate for the first time an online algorithm enabling the runtime verification during the system's execution or simulation. Our approach extends the original framework by considering imprecise signals and by enhancing the logics' semantics with the possibility to express partial guarantees about the conformance of the system's behavior with its specification. Finally, we demonstrate our approach in a real-world environmental monitoring case study.

No abstract available.

Building open, distributed systems while guaranteeing a specific behaviour is difficult because of the dynamicity of the operating environments and the complexity of the interactions of their components. The AbC calculus provides a novel communication mechanism to select interacting partners based on their runtime capabilities, making it naturally to model complex interactions and adaptive behaviour in such systems. The formal account of this calculus has enabled constructing formally verifiable models and proving their properties. In this paper, we i) propose an implementation of AbC using the Erlang language ii) formalize the operational semantics of our implementation; iii) propose a set of rules that given an AbC specification, automatically generate Erlang executable code; and iv) prove that the proposed translation is correct by establishing a simulation relation between source and target specifications. This enables us to guarantee that any property proved for a given AbC specification is preserved by the corresponding implementation.

Collective adaptive systems are new emerging computational systems consisting of a large number of interacting components and featuring complex behaviour. These systems are usually distributed, heterogeneous, decentralised and interdependent, and are operating in dynamic and possibly unpredictable environments. Finding ways to understand and design these systems and, most of all, to model the interactions of their components, is a difficult but important endeavour. In this article we propose a language-based approach for programming the interactions of collective-adaptive systems by relying on attribute-based communication; a paradigm that permits a group of partners to communicate by considering their run-time properties and capabilities. We introduce AbC, a foundational calculus for attribute-based communication and show how its linguistic primitives can be used to program a sophisticated variant of the well-known problem of Stable Allocation in Content Delivery Networks. In our variant, content providers are assigned to clients based on collaboration and by taking into account the preferences of both parties in a fully anonymous and distributed settings. We also illustrate the expressive power of attribute-based communication by showing the natural encoding of group-based, publish/subscribe-based and channel-based communication paradigms into AbC.

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 Spatio-Temporal Logic (SSTL), a modal logic that can be used to specify spatio-temporal properties of linear time and discrete space models. The logic is equipped with a Boolean and a quantitative semantics for which efficient monitoring algorithms have been developed. As such, it is suitable for real-time verification of both white box and black box complex systems. These algorithms can also be combined with stochastic model checking routines. SSTL combines the until temporal modality with two spatial modalities, one expressing that something is true somewhere nearby and the other capturing the notion of being surrounded by a region that satisfies a given spatio-temporal property. The monitoring algorithms are implemented in an open source Java tool. We illustrate the use of SSTL analysing the formation of patterns in a Turing Reaction-Diffusion system and spatio-temporal aspects of a large bike-sharing system. Comment: 36 pages with 13 figures

Cyber-Physical Systems (CPS) consist of collaborative, networked and tightly intertwined computational (logical) and physical components, each operating at different spatial and temporal scales. Hence, the spatial and temporal requirements play an essential role for their correct and safe execution. Furthermore, the local interactions among the system components result in global spatio-temporal emergent behaviors often impossible to predict at the design time. In this work, we pursue a complementary approach by introducing STREL a novel spatio-temporal logic that enables the specification of spatio-temporal requirements and their monitoring over the execution of mobile and spatially distributed CPS. Our logic extends the Signal Temporal Logic [15]with two novel spatial operators reach and escape from which is possible to derive other spatial modalities such as everywhere, somewhere and surround. These operators enable a monitoring procedure where the satisfaction of the property at each location depends only on the satisfaction of its neighbours, opening the way to future distributed online monitoring algorithms. We propose both a qualitative and quantitative semantics based on constraint semirings, an algebraic structure suitable for constraint satisfaction and optimisation. We prove that, for a subclass of models, all the spatial properties expressed with reach and escape, using euclidean distance, satisfy all the model transformations using rotation, reflection and translation. Finally, we provide an offline monitoring algorithm for STREL and, to demonstrate the feasibility of our approach, we show its application using the monitoring of a simulated mobile ad-hoc sensor network as running example.

Service-oriented computing is calling for novel computational models and languages with well-disciplined primitives for client–server interaction, structured orchestration and unexpected events handling. We present CaSPiS, a process calculus where the conceptual abstractions of sessioning and pipelining play a central role for modelling service-oriented systems. CaSPiS sessions are two-sided, uniquely named and can be nested. CaSPiS pipelines permit orchestrating the flow of data produced by different sessions. The calculus is also equipped with operators for handling (unexpected) termination of the partner's side of a session. Several examples are presented to provide evidence of the flexibility of the chosen set of primitives. One key contribution is a fully abstract encoding of Misra et al.'s orchestration language Orc. Another main result shows that in CaSPiS it is possible to program a ‘graceful termination’ of nested sessions, which guarantees that no session is forced to hang forever after the loss of its partner.

The autonomic computing paradigm has been proposed to cope with size, complexity, and dynamism of contemporary software-intensive systems. The challenge for language designers is to devise appropriate abstractions and linguistic primitives to deal with the large dimension of systems and with their need to adapt to the changes of the working environment and to the evolving requirements. We propose a set of programming abstractions that permit us to represent behaviors, knowledge, and aggregations according to specific policies and to support programming context-awareness, self-awareness, and adaptation. Based on these abstractions, we define SCEL (Software Component Ensemble Language), a kernel language whose solid semantic foundations lay also the basis for formal reasoning on autonomic systems behavior. To show expressiveness and effectiveness of SCEL;’s design, we present a Java implementation of the proposed abstractions and show how it can be exploited for programming a robotics scenario that is used as a running example for describing the features and potential of our approach.

Action-labelled transition systems (LTSs) have proved to be a fundamental model for describing and proving properties of concurrent systems. In this paper we introduce Multiple-Labelled Transition Systems (MLTSs) as generalisations of LTSs that enable us to deal with system features that are becoming increasingly important when considering languages and models for network-aware programming. MLTSs enable us to describe not only the actions that systems can perform but also their usage of resources and their handling (creation, revelation . . .) of names; these are essential for modelling changing evaluation environments. We also introduce MoMo, which is a logic inspired by Hennessy–Milner Logic and the μ-calculus, that enables us to consider state properties in a distributed environment and the impact of actions and movements over the different sites. MoMo operators are interpreted over MLTSs and both MLTSs and MoMo are used to provide a semantic framework to describe two basic calculi for mobile computing, namely μKlaim and the asynchronous π-calculus.

We describe the approach that has been followed by the authors while teaching the CS1 laboratory course on Java programming at the University of Florence. In particular, we focus on the assessment method that has been utilized: by making use of specific software developed by the teachers themselves, the method allowed them to automatically obtain a preliminary evaluation of the students' performance, which could subsequently be analyzed and modified after a manual exploration of the students' work.

In this paper we propose a new environment for allowing students of a first programming undergraduate course to test their Java code. This environment allows the student to learn the basics of the Java language without necessarily knowing the object-oriented features of the language itself, and the teacher to propose new tests by making use of a graphical test editor. Moreover, the client-server architecture of the Web-based version of the environment is designed so that the student does not even need a Java virtual machine on its computing device, but only a Web browser. This latter feature makes our environment a useful tool for ubiquitous testing of Java programming skills.

Klaim is an experimental programming language that supports a programming paradigm where both processes and data can be moved across different computing environments. The language relies on the use of explicit localities. This paper presents a temporal logic for specifying properties of Klaim programs. The logic is inspired by Hennessy-Milner Logic (HML) and the μ-calculus, but has novel features that permit dealing with state properties and impact of actions and movements over the different sites. The logic is equipped with a complete proof system that enables one to prove properties of mobile systems.