Optimal discrimination between transient and permanent faults

An important practical problem in fault diagnosis is discriminating between permanent faults and transient faults. In many computer systems, the majority of errors are due to transient faults. Many heuristic methods have been used for discriminating between transient and permanent faults; however, we have found no previous work stating this decision problem in clear probabilistic terms. We present an optimal procedure for discriminating between transient and permanent faults, based on applying Bayesian inference to the observed events (correct and erroneous results). We describe how the assessed probability that a module is permanently faulty must vary with observed symptoms. We describe and demonstrate our proposed method on a simple application problem, building the appropriate equations and showing numerical examples. The method can be implemented as a run-time diagnosis algorithm at little computational cost; it can also be used to evaluate any heuristic diagnostic procedure by compariso

Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities

Critical Infrastructures (CIs), such as smart power grids, transport systems, and financial infrastructures, are more and more vulnerable to cyber threats, due to the adoption of commodity computing facilities. Despite the use of several monitoring tools, recent attacks have proven that current defensive mechanisms for CIs are not effective enough against most advanced threats. In this paper we explore the idea of a framework leveraging multiple data sources to improve protection capabilities of CIs. Challenges and opportunities are discussed along three main research directions: i) use of distinct and heterogeneous data sources, ii) monitoring with adaptive granularity, and iii) attack modeling and runtime combination of multiple data analysis techniques.Comment: EDCC-2014, BIG4CIP-201

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR

Factors affecting the spread of "Bois Noir" disease in north Italy vineyards

To define control strategies for “Bois Noir” disease (BN) it is necessary to know factors favouring its spreading by the vector Hyalesthes obsoletus Signoret. During 2003-2006 a research was carried out in 18 vineyards of a grape-growing area of North Italy to assess the influence of insecticides, applied on grapevine canopies, and environment surrounding the vineyards on disease spreading. The vector population density was higher outside than in the centre of the vineyards. Insecticides applied to grapevine canopies did not significantly influence the vector population level in the centre of the vineyards. The majority of vineyards showed randomized distribution of symptomatic grapevines. Seven vineyards had an aggregate distribution due to an edge effect from a border side with nettle. The incidence of border sides not contiguous to other grapevine rows on vineyard surface was positively related to higher levels of BN. The incidence of border sides with nettle on vineyard surface was positively correlated to disease incidence in the vineyards with aggregate distribution of symptomatic grapevines. All the data support the importance of surrounding vegetation as source of inoculum of BN phytoplasma. Molecular analyses on ribosomal and tuf genes show that 16 out of the 18 vineyards were affected only by BN: in 13 only tuf-type I was identified, in 2 only tuf-type II, in 1 both tuf-types, and in 2 it was not possible to identify the tuf-type of phytoplasmas detected. In the weeds tested only tuftype II phytoplasmas were identified while H. obsoletus was carrying both phytoplasma tuf-types.