| Records |
| Author |
Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.; Mari, F.; Leeners, B. |
| Title |
Optimal Personalised Treatment Computation through In Silico Clinical Trials on Patient Digital Twins |
Type |
Journal Article |
| Year |
2020 |
Publication |
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Abbreviated Journal |
Fundamenta Informaticae |
| Volume |
174 |
Issue |
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Pages |
283-310 |
| Keywords |
Artificial Intelligence; Virtual Physiological Human; In Silico Clinical Trials; Simulation; Personalised Medicine; In Silico Treatment Optimisation |
| 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. |
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| Publisher |
IOS Press |
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1875-8681 |
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no |
| Call Number |
MCLab @ davi @ |
Serial |
187 |
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| Author |
Chen, Q.M.; Finzi, A.; Mancini, T.; Melatti, I.; Tronci, E. |
| Title |
MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks |
Type |
Journal Article |
| Year |
2020 |
Publication |
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Abbreviated Journal |
Fundamenta Informaticae |
| Volume |
174 |
Issue |
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Pages |
229-258 |
| Keywords |
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| 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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IOS Press |
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1875-8681 |
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Approved |
no |
| Call Number |
MCLab @ davi @ |
Serial |
188 |
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| Author |
Mancini, T.; Melatti, I.; Tronci, E. |
| Title |
Any-horizon uniform random sampling and enumeration of constrained scenarios for simulation-based formal verification |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Software Engineering |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1-1 |
| Keywords |
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| Abstract |
Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding </i>vacuity</i> in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution (e.g., uniformly), and can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking. |
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Edition |
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| ISSN |
1939-3520 |
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Conference |
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| Notes |
To appear |
Approved |
no |
| Call Number |
MCLab @ davi @ ref9527998 |
Serial |
191 |
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| Author |
Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E. |
| Title |
On Checking Equivalence of Simulation Scripts |
Type |
Journal Article |
| Year |
2021 |
Publication |
Journal of Logical and Algebraic Methods in Programming |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
100640 |
| Keywords |
Formal verification, Simulation based formal verification, Formal Verification of cyber-physical systems, System-level formal verification |
| Abstract |
To support Model Based Design of Cyber-Physical Systems (CPSs) many simulation based approaches to System Level Formal Verification (SLFV) have been devised. Basically, these are Bounded Model Checking approaches (since simulation horizon is of course bounded) relying on simulators to compute the system dynamics and thereby verify the given system properties. The main obstacle to simulation based SLFV is the large number of simulation scenarios to be considered and thus the huge amount of simulation time needed to complete the verification task. To save on computation time, simulation based SLFV approaches exploit the capability of simulators to save and restore simulation states. Essentially, such a time saving is obtained by optimising the simulation script defining the simulation activity needed to carry out the verification task. Although such approaches aim to (bounded) formal verification, as a matter of fact, the proof of correctness of the methods to optimise simulation scripts basically relies on an intuitive semantics for simulation scripting languages. This hampers the possibility of formally showing that the optimisations introduced to speed up the simulation activity do not actually omit checking of relevant behaviours for the system under verification. The aim of this paper is to fill the above gap by presenting an operational semantics for simulation scripting languages and by proving soundness and completeness properties for it. This, in turn, enables formal proofs of equivalence between unoptimised and optimised simulation scripts. |
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Series Issue |
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Edition |
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| ISSN |
2352-2208 |
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| Notes |
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Approved |
no |
| Call Number |
MCLab @ davi @ Mancini2021100640 |
Serial |
183 |
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| Author |
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| Title |
Charme |
Type |
Conference Article |
| Year |
2003 |
Publication |
Lecture Notes in Computer Science |
Abbreviated Journal |
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| Volume |
2860 |
Issue |
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Pages |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
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Editor |
Geist, D.; Tronci, E. |
| Language |
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3-540-20363-X |
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Approved |
yes |
| Call Number |
Sapienza @ mari @ editor-charme03 |
Serial |
37 |
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| Author |
Martinelli, Marco; Tronci, Enrico; Dipoppa, Giovanni; Balducelli, Claudio |
| Title |
Electric Power System Anomaly Detection Using Neural Networks |
Type |
Conference Article |
| Year |
2004 |
Publication |
8th International Conference on: Knowledge-Based Intelligent Information and Engineering Systems (KES) |
Abbreviated Journal |
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Issue |
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Pages |
1242-1248 |
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The aim of this work is to propose an approach to monitor and protect Electric Power System by learning normal system behaviour at substations level, and raising an alarm signal when an abnormal status is detected; the problem is addressed by the use of autoassociative neural networks, reading substation measures. Experimental results show that, through the proposed approach, neural networks can be used to learn parameters underlaying system behaviour, and their output processed to detecting anomalies due to hijacking of measures, changes in the power network topology (i.e. transmission lines breaking) and unexpected power demand trend. |
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Springer |
Place of Publication |
Wellington, New Zealand |
Editor |
Negoita, M.G.; Howlett, R.J.; Jain, L.C. |
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Series Title |
Lecture Notes in Computer Science |
Abbreviated Series Title |
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| Series Volume |
3213 |
Series Issue |
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Edition |
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| ISSN |
3-540-23318-0 |
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| Notes |
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Approved |
yes |
| Call Number |
Sapienza @ mari @ kes04 |
Serial |
35 |
| Permanent link to this record |
