T. Mancini, F. Mari, A. Massini, I. Melatti, and E. Tronci. "Anytime system level verification via parallel random exhaustive hardware in the loop simulation." Microprocessors and Microsystems 41 (2016): 12–28. ISSN: 0141-9331. DOI: 10.1016/j.micpro.2015.10.010.
Abstract: Abstract System level verification of cyber-physical systems has the goal of verifying that the whole (i.e., software + hardware) system meets the given specifications. Model checkers for hybrid systems cannot handle system level verification of actual systems. Thus, Hardware In the Loop Simulation (HILS) is currently the main workhorse for system level verification. By using model checking driven exhaustive HILS, System Level Formal Verification (SLFV) can be effectively carried out for actual systems. We present a parallel random exhaustive HILS based model checker for hybrid systems that, by simulating all operational scenarios exactly once in a uniform random order, is able to provide, at any time during the verification process, an upper bound to the probability that the System Under Verification exhibits an error in a yet-to-be-simulated scenario (Omission Probability). We show effectiveness of the proposed approach by presenting experimental results on SLFV of the Inverted Pendulum on a Cart and the Fuel Control System examples in the Simulink distribution. To the best of our knowledge, no previously published model checker can exhaustively verify hybrid systems of such a size and provide at any time an upper bound to the Omission Probability.
Keywords: Model Checking of Hybrid Systems; Model checking driven simulation; Hardware in the loop simulation
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L. Tortora, G. Meynen, J. Bijlsma, E. Tronci, and S. Ferracuti. "Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective." Frontiers in Psychology 11 (2020): 220. ISSN: 1664-1078. DOI: 10.3389/fpsyg.2020.00220.
Abstract: Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.
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Giuseppe Della Penna, Benedetto Intrigila, Igor Melatti, Michele Minichino, Ester Ciancamerla, Andrea Parisse, Enrico Tronci, and Marisa Venturini Zilli. "Automatic Verification of a Turbogas Control System with the Mur$\varphi$ Verifier." In Hybrid Systems: Computation and Control, 6th International Workshop, HSCC 2003 Prague, Czech Republic, April 3-5, 2003, Proceedings, edited by O. Maler and A. Pnueli, 141–155. Lecture Notes in Computer Science 2623. Springer, 2003. ISSN: 3-540-00913-2. DOI: 10.1007/3-540-36580-X.
Abstract: Automatic analysis of Hybrid Systems poses formidable challenges both from a modeling as well as from a verification point of view. We present a case study on automatic verification of a Turbogas Control System (TCS) using an extended version of the Mur$\varphi$ verifier. TCS is the heart of ICARO, a 2MW Co-generative Electric Power Plant. For large hybrid systems, as TCS is, the modeling effort accounts for a significant part of the whole verification activity. In order to ease our modeling effort we extended the Mur$\varphi$ verifier by importing the C language long double type (finite precision real numbers) into it. We give experimental results on running our extended Mur$\varphi$ on our TCS model. For example using Mur$\varphi$ we were able to compute an admissible range of values for the variation speed of the user demand of electric power to the turbogas.
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B. Leeners, T. H. C. Kruger, K. Geraedts, E. Tronci, T. Mancini, F. Ille, M. Egli, S. Röblitz, L. Saleh, K. Spanaus et al. "Lack of Associations between Female Hormone Levels and Visuospatial Working Memory, Divided Attention and Cognitive Bias across Two Consecutive Menstrual Cycles." Frontiers in Behavioral Neuroscience 11 (2017): 120. ISSN: 1662-5153. DOI: 10.3389/fnbeh.2017.00120.
Abstract: Background: Interpretation of observational studies on associations between prefrontal cognitive functioning and hormone levels across the female menstrual cycle is complicated due to small sample sizes and poor replicability. Methods: This observational multisite study comprised data of n=88 menstruating women from Hannover, Germany, and Zurich, Switzerland, assessed during a first cycle and n=68 re-assessed during a second cycle to rule out practice effects and false-positive chance findings. We assessed visuospatial working memory, attention, cognitive bias and hormone levels at four consecutive time-points across both cycles. In addition to inter-individual differences we examined intra-individual change over time (i.e., within-subject effects). Results: Oestrogen, progesterone and testosterone did not relate to inter-individual differences in cognitive functioning. There was a significant negative association between intra-individual change in progesterone and change in working memory from pre-ovulatory to mid-luteal phase during the first cycle, but that association did not replicate in the second cycle. Intra-individual change in testosterone related negatively to change in cognitive bias from menstrual to pre-ovulatory as well as from pre-ovulatory to mid-luteal phase in the first cycle, but these associations did not replicate in the second cycle. Conclusions: There is no consistent association between women's hormone levels, in particular oestrogen and progesterone, and attention, working memory and cognitive bias. That is, anecdotal findings observed during the first cycle did not replicate in the second cycle, suggesting that these are false-positives attributable to random variation and systematic biases such as practice effects. Due to methodological limitations, positive findings in the published literature must be interpreted with reservation.
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Benedetto Intrigila, Ivano Salvo, and Stefano Sorgi. "A characterization of weakly Church-Rosser abstract reduction systems that are not Church-Rosser." Information and Computation 171, no. 2 (2001): 137–155. Academic Press, Inc.. ISSN: 0890-5401. DOI: 10.1006/inco.2001.2945.
Abstract: Basic properties of rewriting systems can be stated in the framework of abstract reduction systems (ARS). Properties like confluence (or Church-Rosser, CR) and weak confluence (or weak Church-Rosser, WCR) and their relationships can be studied in this setting: as a matter of fact, well-known counterexamples to the implication WCR CR have been formulated as ARS. In this paper, starting from the observation that such counterexamples are structurally similar, we set out a graph-theoretic characterization of WCR ARS that is not CR in terms of a suitable class of reduction graphs, such that in every WCR not CR ARS, we can embed at least one element of this class. Moreover, we give a tighter characterization for a restricted class of ARS enjoying a suitable regularity condition. Finally, as a consequence of our approach, we prove some interesting results about ARS using the mathematical tools developed. In particular, we prove an extension of the Newman’s lemma and we find out conditions that, once assumed together with WCR property, ensure the unique normal form property. The Appendix treats two interesting examples, both generated by graph-rewriting rules, with specific combinatorial properties.
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Benedetto Intrigila, Igor Melatti, Alberto Tofani, and Guido Macchiarelli. "Computational models of myocardial endomysial collagen arrangement." Computer Methods and Programs in Biomedicine 86, no. 3 (2007): 232–244. Elsevier North-Holland, Inc.. ISSN: 0169-2607. DOI: 10.1016/j.cmpb.2007.03.004.
Abstract: Collagen extracellular matrix is one of the factors related to high passive stiffness of cardiac muscle. However, the architecture and the mechanical aspects of the cardiac collagen matrix are not completely known. In particular, endomysial collagen contribution to the passive mechanics of cardiac muscle as well as its micro anatomical arrangement is still a matter of debate. In order to investigate mechanical and structural properties of endomysial collagen, we consider two alternative computational models of some specific aspects of the cardiac muscle. These two models represent two different views of endomysial collagen distribution: (1) the traditional view and (2) a new view suggested by the data obtained from scanning electron microscopy (SEM) in NaOH macerated samples (a method for isolating collagen from the other tissue). We model the myocardial tissue as a net of spring elements representing the cardiomyocytes together with the endomysial collagen distribution. Each element is a viscous elastic spring, characterized by an elastic and a viscous constant. We connect these springs to imitate the interconnections between collagen fibers. Then we apply to the net of springs some external forces of suitable magnitude and direction, obtaining an extension of the net itself. In our setting, the ratio forces magnitude /net extension is intended to model the stress /strain ratio of a microscopical portion of the myocardial tissue. To solve the problem of the correct identification of the values of the different parameters involved, we use an artificial neural network approach. In particular, we use this technique to learn, given a distribution of external forces, the elastic constants of the springs needed to obtain a desired extension as an equilibrium position. Our experimental findings show that, in the model of collagen distribution structured according to the new view, a given stress /strain ratio (of the net of springs, in the sense specified above) is obtained with much smaller (w.r.t. the other model, corresponding to the traditional view) elasticity constants of the springs. This seems to indicate that by an appropriate structure, a given stiffness of the myocardial tissue can be obtained with endomysial collagen fibers of much smaller size.
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V. Bono, and I. Salvo. "A CuCh Interpretation of an Object-Oriented Language." Electronic Notes in Theoretical Computer Science 50, no. 2 (2001): 159–177. Elsevier. Notes: BOTH 2001, Bohm’s theorem: applications to Computer Science Theory (Satellite Workshop of ICALP 2001). DOI: 10.1016/S1571-0661(04)00171-9.
Abstract: CuCh machine extends pure lambda–calculus with algebraic data types and provides a the possibility of defining functions over the disjoint sum of algebras. We exploit such natural form of overloading to define a functional interpretation of a simple, but significant fragment of a typical object-oriented language.
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Benedetto Intrigila, Daniele Magazzeni, Igor Melatti, and Enrico Tronci. "A Model Checking Technique for the Verification of Fuzzy Control Systems." In CIMCA '05: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06), 536–542. Washington, DC, USA: IEEE Computer Society, 2005. ISSN: 0-7695-2504-0-01. DOI: 10.1109/CIMCA.2005.1631319.
Abstract: Fuzzy control is well known as a powerful technique for designing and realizing control systems. However, statistical evidence for their correct behavior may be not enough, even when it is based on a large number of samplings. In order to provide a more systematic verification process, the cell-to-cell mapping technology has been used in a number of cases as a verification tool for fuzzy control systems and, more recently, to assess their optimality and robustness. However, cell-to-cell mapping is typically limited in the number of cells it can explore. To overcome this limitation, in this paper we show how model checking techniques may be instead used to verify the correct behavior of a fuzzy control system. To this end, we use a modified version of theMurphi verifier, which ease the modeling phase by allowing to use finite precision real numbers and external C functions. In this way, also already designed simulators may be used for the verification phase. With respect to the cell mapping technique, our approach appears to be complementary; indeed, it explores a much larger number of states, at the cost of being less informative on the global dynamic of the system.
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Q. M. Chen, A. Finzi, T. Mancini, I. Melatti, and E. Tronci. "MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks." Fundamenta Informaticae 174 (2020): 229–258. IOS Press. ISSN: 1875-8681. DOI: 10.3233/FI-2020-1941.
Abstract: In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes . As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the overall cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance ). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy.
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S. Sinisi, V. Alimguzhin, T. Mancini, E. Tronci, F. Mari, and B. Leeners. "Optimal Personalised Treatment Computation through In Silico Clinical Trials on Patient Digital Twins." Fundamenta Informaticae 174 (2020): 283–310. IOS Press. ISSN: 1875-8681. DOI: 10.3233/FI-2020-1943.
Abstract: In Silico Clinical Trials (ISCT), i.e. clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine ). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.
Keywords: Artificial Intelligence; Virtual Physiological Human; In Silico Clinical Trials; Simulation; Personalised Medicine; In Silico Treatment Optimisation
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