Inferring Anomaly Situation from Multiple Data Sources in Cyber Physical Systems

AbstractCyber physical systems are becoming ubiquitous devices in many fields thus creating the need for effective security measures. We propose to exploit their intrinsic dependency on the environment in which they are deployed to detect and mitigate anomalies. To do so, sensor measurements, network metrics, and contextual information are fused in a unified security architecture. In this paper, the model of the proposed framework is presented and a first proof of concept involving a telecommunication infrastructure case study is provided

Thread on testbeds

This deliverable presents a set of possible scenarios in which personalization plays a fundamental role in the Semantic Web. These scenarios have been collected with the contribution of many partners of the working group A3, and have then been analysed with two aims. First of all, we have identified the key concepts of personalization scenarios, and subsequently we have analysed the currently available tools and languages supplied by the (Semantic) Web in order to define a set of requirements to be passed to the other working groups of the network.European Commissionpeer-reviewe

Sensitivity and specificity of in vivo COVID-19 screening by detection dogs: Results of the C19-Screendog multicenter study

Trained dogs can recognize the volatile organic compounds contained in biological samples of patients with COVID-19 infection. We assessed the sensitivity and specificity of in vivo SARS-CoV- 2 screening by trained dogs. We recruited five dog-handler dyads. In the operant conditioning phase, the dogs were taught to distinguish between positive and negative sweat samples collected from volunteers’ underarms in polymeric tubes. The conditioning was validated by tests involving 16 positive and 48 negative samples held or worn in such a way that the samples were invisible to the dog and handler. In the screening phase the dogs were led by their handlers to a drive-through facility for in vivo screening of volunteers who had just received a nasopharyngeal swab from nursing staff. Each volunteer who had already swabbed was subsequently tested by two dogs, whose responses were recorded as positive, negative, or inconclusive. The dogs’ behavior was constantly monitored for attentiveness and wellbeing. All the dogs passed the conditioning phase, their responses showing a sensitivity of 83-100% and a specificity of 94-100%. The in vivo screening phase involved 1251 subjects, of whom 205 had a COVID-19 positive swab and two dogs per each subject to be screened. Screeningsensitivity and specificity were respectively 91.6-97.6% and 96.3-100% when only one dog was involved, whereas combined screening by two dogs provided a higher sensitivity. Dog wellbeing was also analysed: monitoring of stress and fatigue suggested that the screening activity did not adversely impact the dogs’ wellbeing. This work, by screening a large number of subjects, strengthen recent findings that trained dogs can discriminate between COVID-19 infected and healthy human subjects and introduce two novel research aspects: i) assessement of signs of fatigue and stress in dogs during training and testing, and ii) combining screening by two dogs to improve detection sensitivity and specificity. Using some precautions to reduce the risk of infection and spillover, in vivo COVID-19 screening by a dog-handler dyad can be suitable to quickly screen large numbers of people: it is rapid, non- invasiveand economical, since it does not involve actual sampling, lab resources or waste management, and is suitable to screen large numbers of people

Comparison and combination of a hemodynamics/biomarkers-based model with simplified PESI score for prognostic stratification of acute pulmonary embolism: findings from a real world study

Background: Prognostic stratification is of utmost importance for management of acute Pulmonary Embolism (PE) in clinical practice. Many prognostic models have been proposed, but which is the best prognosticator in real life remains unclear. The aim of our study was to compare and combine the predictive values of the hemodynamics/biomarkers based prognostic model proposed by European Society of Cardiology (ESC) in 2008 and simplified PESI score (sPESI).Methods: Data records of 452 patients discharged for acute PE from Internal Medicine wards of Tuscany (Italy) were analysed. The ESC model and sPESI were retrospectively calculated and compared by using Areas under Receiver Operating Characteristics (ROC) Curves (AUCs) and finally the combination of the two models was tested in hemodinamically stable patients. All cause and PE-related in-hospital mortality and fatal or major bleedings were the analyzed endpointsResults: All cause in-hospital mortality was 25% (16.6% PE related) in high risk, 8.7% (4.7%) in intermediate risk and 3.8% (1.2%) in low risk patients according to ESC model. All cause in-hospital mortality was 10.95% (5.75% PE related) in patients with sPESI score ≥1 and 0% (0%) in sPESI score 0. Predictive performance of sPESI was not significantly different compared with 2008 ESC model both for all cause (AUC sPESI 0.711, 95% CI: 0.661-0.758 versus ESC 0.619, 95% CI: 0.567-0.670, difference between AUCs 0.0916, p=0.084) and for PE-related mortality (AUC sPESI 0.764, 95% CI: 0.717-0.808 versus ESC 0.650, 95% CI: 0.598-0.700, difference between AUCs 0.114, p=0.11). Fatal or major bleedings occurred in 4.30% of high risk, 1.60% of intermediate risk and 2.50% of low risk patients according to 2008 ESC model, whereas these occurred in 1.80% of high risk and 1.45% of low risk patients according to sPESI, respectively. Predictive performance for fatal or major bleeding between two models was not significantly different (AUC sPESI 0.658, 95% CI: 0.606-0.707 versus ESC 0.512, 95% CI: 0.459-0.565, difference between AUCs 0.145, p=0.34). In hemodynamically stable patients, the combined endpoint in-hospital PE-related mortality and/or fatal or major bleeding (adverse events) occurred in 0% of patients with low risk ESC model and sPESI score 0, whilst it occurred in 5.5% of patients with low-risk ESC model but sPESI ≥1. In intermediate risk patients according to ESC model, adverse events occurred in 3.6% of patients with sPESI score 0 and 6.65% of patients with sPESI score ≥1.Conclusions: In real world, predictive performance of sPESI and the hemodynamic/biomarkers-based ESC model as prognosticator of in-hospital mortality and bleedings is similar. Combination of sPESI 0 with low risk ESC model may identify patients with very low risk of adverse events and candidate for early hospital discharge or home treatment.

Eventual leader election in infinite arrival message-passing system model with bounded concurrency

We study the failure detection problem in a message-passing system that may dynamically change over time, so that the number of processes which make progress during a computation may grow to infinity as time tends to infinity but the number of concurrently up processes do not exceed a known bound. We first propose the specification of a new oracle, called HB*, able to give hints on which processes are making progress in the system. A possible HB* implementation is given. Then, we show how to use HB * to implement the oracle Ω that eventually identifies a unique leader in the system. To the best of our knowledge this is the first implementation of Ω running in a message passing system with infinitely many processes. © 2010 IEEE

Eventual Leader Election in Infinite Arrival Message-Passing System Model with Bounded Concurrency

We study the the failure detection problem in a message-passing system that may dynamically change over time, so that the number of processes which make progress during a computation may grow to infinity as time tends to infinity. We first propose an oracle well-suited to such systems, called HB ∗ , by detailing its specification and a possible implementation. HB ∗ gives hints on what processes are alive in the system. Then, we show how to use HB ∗ to implement the oracle Ω that eventually identifies an unique leader in the system. To the best of our knowledge this is the first implementation of Ω running in a message passing system with infinitely many processes

Connectivity in Eventually Quiescent Dynamic Distributed Systems

A distributed dynamic system is a fully distributed system subject to a continual arrival/ departure of the very entities defining the system. Another characterizing dimension of these systems is their possible arbitrary large size (number of entities) and the possible arbitrary small part of the system a single entity directly interacts with. This interaction occurs through data exchange over logical links, and the constantly changing graph formed by all links connecting entities represents the overlay network of the dynamic distributed system. The connectivity of such overlay is of fundamental importance to make the whole system working. This paper gives a precise definition of the connectivity problem in dynamic distributed systems while formally defining assumptions on arrival/departure of entities and on the evolution of the system size along the time. The paper shows the impossibility of achieving connectivity when an arbitrary large number of entities may arrive/depart concurrently at any time, doing so for an arbitrarily long time. A solution is presented achieving overlay connectivity during quiescent periods of the system: periods in which no more arrivals and departures take place. The paper conveys the fact that the finite but not known duration of perturbed period before quiescence, makes the solution of the problem far from being trivial. The paper also provides a simulation study showing that the solution not only achieves connectivity in quiescent periods but it rearranges entities in an overlay that shows good scalability properties.