Big Data Analytics for QoS Prediction Through Probabilistic Model Checking

As competitiveness increases, being able to guaranting QoS of delivered services is key for business success. It is thus of paramount importance the ability to continuously monitor the workflow providing a service and to timely recognize breaches in the agreed QoS level. The ideal condition would be the possibility to anticipate, thus predict, a breach and operate to avoid it, or at least to mitigate its effects. In this paper we propose a model checking based approach to predict QoS of a formally described process. The continous model checking is enabled by the usage of a parametrized model of the monitored system, where the actual value of parameters is continuously evaluated and updated by means of big data tools. The paper also describes a prototype implementation of the approach and shows its usage in a case study.Comment: EDCC-2014, BIG4CIP-2014, Big Data Analytics, QoS Prediction, Model Checking, SLA compliance monitorin

Interests Diffusion in Social Networks

Understanding cultural phenomena on Social Networks (SNs) and exploiting the implicit knowledge about their members is attracting the interest of different research communities both from the academic and the business side. The community of complexity science is devoting significant efforts to define laws, models, and theories, which, based on acquired knowledge, are able to predict future observations (e.g. success of a product). In the mean time, the semantic web community aims at engineering a new generation of advanced services by defining constructs, models and methods, adding a semantic layer to SNs. In this context, a leapfrog is expected to come from a hybrid approach merging the disciplines above. Along this line, this work focuses on the propagation of individual interests in social networks. The proposed framework consists of the following main components: a method to gather information about the members of the social networks; methods to perform some semantic analysis of the Domain of Interest; a procedure to infer members' interests; and an interests evolution theory to predict how the interests propagate in the network. As a result, one achieves an analytic tool to measure individual features, such as members' susceptibilities and authorities. Although the approach applies to any type of social network, here it is has been tested against the computer science research community. The DBLP (Digital Bibliography and Library Project) database has been elected as test-case since it provides the most comprehensive list of scientific production in this field.Comment: 30 pages 13 figs 4 table

Applying Extensions of Evidence Theory to Detect Frauds in Financial Infrastructures

The Dempster-Shafer (DS) theory of evidence has significant weaknesses when dealing with conflicting information sources, as demonstrated by preeminent mathematicians. This problem may invalidate its effectiveness when it is used to implement decision-making tools that monitor a great number of parameters and metrics. Indeed, in this case, very different estimations are likely to happen and can produce unfair and biased results. In order to solve these flaws, a number of amendments and extensions of the initial DS model have been proposed in literature. In this work, we present a Fraud Detection System that classifies transactions in a Mobile Money Transfer infrastructure by using the data fusion algorithms derived from these new models. We tested it in a simulated environment that closely mimics a real Mobile Money Transfer infrastructure and its actors. Results show substantial improvements of the performance in terms of true positive and false positive rates with respect to the classical DS theory

An Approach for Securing Cloud-Based Wide Area Monitoring of Smart Grid Systems

Computing power and flexibility provided by cloud technologies represent an opportunity for Smart Grid applications, in general, and for Wide Area Monitoring Systems, in particular. Even though the cloud model is considered efficient for Smart Grids, it has stringent constraints in terms of security and reliability. An attack to the integrity or confidentiality of data may have a devastating impact for the system itself and for the surrounding environment. The main security risk is represented by malicious insiders, i.e., malevolent employees having privileged access to the hosting machines. In this paper, we evaluate a powerful hardening approach that could be leveraged to protect synchrophasor data processed at cloud level. In particular, we propose the use of homomorphic encryption to address risks related to malicious insiders. Our goal is to estimate the feasibility of such a security solution by verifying the compliance with frame rate requirements typical of synchrophasor standards

An unusual outbreak of nontuberculous mycobacteria in hospital respiratory wards: Association with nontuberculous mycobacterial colonization of hospital water supply network

AbstractThe incidence and prevalence of pulmonary nontuberculous mycobacterial (NTM) infection is increasing worldwide arousing concerns that NTM infection may become a serious health challenge. We recently observed a significant increase of NTM-positive sputa samples from patients referred to respiratory disease wards of a large tertiary hospital in Rome. A survey to identify possible NTM contamination revealed a massive presence of NTM in the hospital water supply network. After decontamination procedures, NTM presence dropped both in water pipelines and sputa samples. We believe that this observation should encourage water network surveys for NTM contamination and prompt decontamination procedures should be considered to reduce this potential source of infection

All-sky search for gravitational-wave bursts in the second joint LIGO-Virgo run

We present results from a search for gravitational-wave bursts in the data collected by the LIGO and Virgo detectors between July 7, 2009 and October 20, 2010: data are analyzed when at least two of the three LIGO-Virgo detectors are in coincident operation, with a total observation time of 207 days. The analysis searches for transients of duration < 1 s over the frequency band 64-5000 Hz, without other assumptions on the signal waveform, polarization, direction or occurrence time. All identified events are consistent with the expected accidental background. We set frequentist upper limits on the rate of gravitational-wave bursts by combining this search with the previous LIGO-Virgo search on the data collected between November 2005 and October 2007. The upper limit on the rate of strong gravitational-wave bursts at the Earth is 1.3 events per year at 90% confidence. We also present upper limits on source rate density per year and Mpc^3 for sample populations of standard-candle sources. As in the previous joint run, typical sensitivities of the search in terms of the root-sum-squared strain amplitude for these waveforms lie in the range 5 10^-22 Hz^-1/2 to 1 10^-20 Hz^-1/2. The combination of the two joint runs entails the most sensitive all-sky search for generic gravitational-wave bursts and synthesizes the results achieved by the initial generation of interferometric detectors.Comment: 15 pages, 7 figures: data for plots and archived public version at https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=70814&version=19, see also the public announcement at http://www.ligo.org/science/Publication-S6BurstAllSky

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects