An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

Using Modularity Metrics to assist Move Method Refactoring of Large System

For large software systems, refactoring activities can be a challenging task, since for keeping component complexity under control the overall architecture as well as many details of each component have to be considered. Product metrics are therefore often used to quantify several parameters related to the modularity of a software system. This paper devises an approach for automatically suggesting refactoring opportunities on large software systems. We show that by assessing metrics for all components, move methods refactoring an be suggested in such a way to improve modularity of several components at once, without hindering any other. However, computing metrics for large software systems, comprising thousands of classes or more, can be a time consuming task when performed on a single CPU. For this, we propose a solution that computes metrics by resorting to GPU, hence greatly shortening computation time. Thanks to our approach precise knowledge on several properties of the system can be continuously gathered while the system evolves, hence assisting developers to quickly assess several solutions for reducing modularity issues

Are liquids an efficient vehicle of healthcare associated infections? A review of reported cases in Italy (2000- 2014)

INTRODUCTION: In the field of healthcare-associated infections (HCAIs), one of the most reported, studied and discussed sources of infections is water, partly due to its controllability, but also because healthcare facilities, especially hospitals, require a significant quantity of water per day. In addition to water, during healthcare procedures, other liquids can serve as source of infections. The present study reports a review of those HCAIs associated to liquid vehicles occurred in Italy during the period 2000-2014. METHOD: The review focused on cases of liquid-associated HCAIs in both sporadic cases and outbreaks according to the definition provided by both Word Health Organization and United States' Centers for Disease Control and Preventions in 2011. The review included all original papers published in peer-reviewed journals, in which the association between the infection and the exposure to contaminated water/other fluid was demonstrated by epidemiological and/or molecular methods. Articles describing cases due to parenteral transmitted pathogens (by blood or blood-derived fluids) were excluded. RESULTS: During the period 2000-2014, 34 episodes have been described for a total of about 400 cases of infection. Isolations included genus Legionella, Pseudomonas, Serratia, Ralstonia, Burkolderia, Klebsiella and other pseudomonadaceae. The results confirm that HCAIs can be associated also to liquids other than piped water. The large majority of articles refers to hospital wards where patients with high risk of infections are usually admitted. DISCUSSION: The review highlights a great number of HCAIs, but if we consider that the large part of HCAIs are not reported in literature, it is clear that the burden of this phenomenon is by far higher. Many cases of HCAI were identified in the context of local surveillance systems, demonstrating their role in HCAI control. With regard to diagnosis, the isolation and identification of the etiological agent is critical to reach the source of infection and to plan the necessary disinfection measures. Therefore, it is possible to conclude that, through a multiple approach of engineering and hygiene measures, as well as surveillance ad management of hospital liquids, the risk for contracting "water born" HCAIs may be controlled

Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature

Improving files availability for BitTorrent using a diffusion model

The BitTorrent mechanism effectively spreads file fragments by copying the rarest fragments first. We propose to apply a mathematical model for the diffusion of fragments on a P2P in order to take into account both the effects of peer distances and the changing availability of peers while time goes on. Moreover, we manage to provide a forecast on the availability of a torrent thanks to a neural network that models the behaviour of peers on the P2P system. The combination of the mathematical model and the neural network provides a solution for choosing file fragments that need to be copied first, in order to ensure their continuous availability, counteracting possible disconnections by some peers

Is swarm intelligence able to create mazes?

In this paper, the idea of applying Computational Intelligence in the process of creation board games, in particular mazes, is presented. For two different algorithms the proposed idea has been examined. The results of the experiments are shown and discussed to present advantages and disadvantages

Human tularemia in Italy. Is it a re-emerging disease?

In order to evaluate whether tularemia is re-emerging in Italy, data on mortality and morbidity (obtained by the National Institute of Statistics; ISTAT), Italian cases described in the scientific literature and data concerning hospitalizations for tularemia (obtained by the National Hospital Discharge Database) were analysed. From 1979 to 2010, ISTAT reported 474 cases and no deaths. The overall number of cases obtained from the literature review was at least 31% higher than that reported by ISTAT. Moreover, the number of cases reported by ISTAT was 3·5 times smaller than hospitalized cases. The real frequency of the disease should be carefully investigated and taken into account in order to implement specific prevention measures.Tularemia is a contagious infectious disease due to Francisiella tularensis that can cause serious clinical manifestations and significant mortality if untreated. Although the frequency and significance of the disease has diminished over the last decades in Central Europe, over the past few years, there is new evidence suggesting that tularemia has re-emerged worldwide. To know the real epidemiology of the disease is at the root of correct control measures. In order to evaluate whether tularemia is re-emerging in Italy, data on mortality and morbidity (obtained by the National Institute of Statistics; ISTAT), Italian cases described in the scientific literature and data concerning hospitalizations for tularemia (obtained by the National Hospital Discharge Database) were analysed. From 1979 to 2010, ISTAT reported 474 cases and no deaths. The overall number of cases obtained from the literature review was at least 31% higher than that reported by ISTAT. Moreover, the number of cases reported by ISTAT was 3·5 times smaller than hospitalized cases. In Italy tularemia is sporadic, rarely endemic and self-limiting; but, although the trend of reported tularemia does not support the hypothesis of a re-emerging disease, the study demonstrates a wide underreporting of the disease. The real frequency of the disease should be carefully investigated and taken into account in order to implement specific prevention measures

A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I--V and P--V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported

Searching Design Patterns Fast by Using Tree Traversals

Large software systems need to be modified to remain useful. Changes can be more easily performed when their design has been carefully documented. This paper presents an approach to quickly find design patterns that have been implemented into a software system. The devised solution greatly reduces the performed checks by organising the search for a design pattern as tree traversals, where candidate classes are carefully positioned into trees. By automatically tagging classes with design pattern roles we make it easier for developers to reason with large software systems. Our approach can provide documentation that lets developers understand the role each class is playing, assess the quality of the code, have assistance for refactoring and enhancing the functionalities of the software system.