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

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

Efficient generation of NN-photon generalized binomial states in a cavity

Extending a previous result on the generation of two-photon generalized binomial field states, here we propose an efficient scheme to generate with high-fidelity, in a single-mode high-Q cavity, N-photon generalized binomial states with a maximum number of photons N>2. Besides their interest for classical-quantum border investigations, we discuss the applicative usage of these states in realizing universal quantum computation, describing in particular a scheme that performs a controlled-NOT gate by dispersive interaction with a control atom. We finally analyze the feasibility of the proposed schemes, showing that they appear to be within the current experimental capabilities.Comment: 8 pages, 2 figure

MRI-guided focused ultrasound surgery in musculoskeletal diseases: the hot topics

MRI-guided focused ultrasound surgery (MRgFUS) is a minimally invasive treatment guided by the most sophisticated imaging tool available in today's clinical practice. Both the imaging and therapeutic sides of the equipment are based on non-ionizing energy. This technique is a very promising option as potential treatment for several pathologies, including musculoskeletal (MSK) disorders. Apart from clinical applications, MRgFUS technology is the result of long, heavy and cumulative efforts exploring the effects of ultrasound on biological tissues and function, the generation of focused ultrasound and treatment monitoring by MRI. The aim of this article is to give an updated overview on a "new" interventional technique and on its applications for MSK and allied sciences

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

Design, simulation and development of a decentralized control for a robotic manipulator

The objective of this thesis is to design, develop and test a decentralized control for the YouBot. This robot is composed by an omnidirectional mobile platform on which a five – axis robot arm with a two – finger gripper is installed. In the first step the YouBot arm’s performance is analysed and the problems are verified and mentioned by the community. The stat of the art is assessed. The available hardware and software is analysed, as well as the temporal performances, the responses of the system, the optimal sampling time and the torque characterization. Subsequently the COM (center of mass) for each link, is estimated, through a least squares algorithm, and the gravity compensation is implemented. After this a hybrid motion/force decentralized control based on Newton – Eulero dynamics is realized. The control algorithm is tested in simulation and on the real Youbot Manipulator. The obtained results have been analyzed and discussed

Validation of Geant4 nuclear reaction models for hadrontherapy and preliminary results with SMF and BLOB

Reliable nuclear fragmentation models are of utmost importance in hadrontherapy, where Monte Carlo (MC) simulations are used to compute the input parameters of the treatment planning software, to validate the deposited dose calculation, to evaluate the biological effectiveness of the radiation, to correlate the bþ emitters production in the patient body with the delivered dose, and to allow a non- invasive treatment verification. Despite of its large use, the models implemented in Geant4 have shown severe limitations in reproducing the measured secondaries yields in ions interaction below 100 MeV/A, in term of production rates, angular and energy distributions [1–3]. We will present a benchmark of the Geant4 models with double-differential cross sec- tion and angular distributions of the secondary fragments produced in the 12C fragmentation at 62 MeV/A on thin carbon target, such a benchmark includes the recently implemented model INCL++ [4,5]. Moreover, we will present the preliminary results, obtained in simulating the same interaction, with SMF [6] and BLOB [7]. Both, SMF and BLOB are semiclassical one-body approaches to solve the Boltzmann-Langevin equation. They include an identical treatment of the mean-field propagation, on the basis of the same effective interaction, but they differ in the way fluctuations are included. In particular, while SMF employs a Uehling-Uhlenbeck collision term and introduces fluctuations as projected on the density space, BLOB introduces fluctuations in full phase space through a modified collision term where nucleon-nucleon correlations are explicitly involved. Both of them, SMF and BLOB, have been developed to sim- ulate the heavy ion interactions in the Fermi-energy regime. We will show their capabilities in describing 12C fragmentation foreseen their implementation in Geant4

Wind- and tide-induced currents in the Stagnone Lagoon (Sicily)

The hydrodynamic circulation is analyzed in the coastal lagoon of Stagnone di Marsala, a natural reserve located in the north-western part of Sicily, using both experimental measurements and numerical simulations. Field measurements of velocities and water levels, carried out using an ultrasound sensor (3D), are used to validate the numerical model. A 3D finite-volume model is used to solve the Reynolds-averaged momentum and mass balance differential equations on a curvilinear structured grid, employing the k–ε turbulence model for the Reynolds stresses. The numerical analysis allows to identify the relative contribution of the forces affecting the hydrodynamic circulation inside the lagoon. In the simulations only wind and tide forces are considered, neglecting the effects of water density changes. Two different conditions are considered. In the first both the wind stress over the free-surface and the tidal motion are imposed. In the second the wind action is neglected, to separately analyze the tide-induced circulation. The comparison between the two test cases highlights the fundamental role of the wind on the hydrodynamics of the Stagnone lagoon, producing a strong vertical recirculation pattern that is not observed when the flow is driven by tides only