Emiliano Tramontana

Simplified firefly algorithm for 2D image key-points search

In order to identify an object, human eyes firstly search the field of view for points or areas which have particular properties. These properties are used to recognise an image or an object. Then this process could be taken as a model to develop computer algorithms for images identification. This paper proposes the idea of applying the simplified firefly algorithm to search for key-areas in 2D images. For a set of input test images the proposed version of firefly algorithm has been examined. Research results are presented and discussed to show the efficiency of this evolutionary computation method.

Using Modularity Metrics to Assist Move Method Refactoring of Large Systems

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 can 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.

Automatically Characterising Components with Concerns and Reducing Tangling

Developing large systems exhibiting a high degree of modularity can be a difficult task even for experienced developers. Hindering modularity has several armful effects, such as decreased readability, higher complexity and difficulties to reuse and evolve components. This paper assists developers to achieve modularity of components by providing a way to automatically characterise the concerns within components according to the APIs they are based on. This allows finding the degree of tangling and scattering of concerns over methods and classes. Moreover, by means of the proposed approach developers are given suggestions on how to reduce tangling of some components, thanks to the use of a metric and refactoring techniques. For systems comprising thousand of classes this is a valuable support, since unassisted developers could miss appropriate refactoring opportunities, due to the large number of details they should take into account.

A Cascade Neural Network Architecture Investigating Surface Plasmon Polaritons Propagation for Thin Metals in OpenMP

Surface plasmon polaritons (SPPs) confined along metal-dielectric interface have attracted a relevant interest in the area of ultracompact photonic circuits, photovoltaic devices and other applications due to their strong field confinement and enhancement. This paper investigates a novel cascade neural network (NN) architecture to find the dependance of metal thickness on the SPP propagation. Additionally, a novel training procedure for the proposed cascade NN has been developed using an OpenMP-based framework, thus greatly reducing training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand.

A Hybrid NeuroWavelet Predictor for QoS Control and Stability

For distributed systems to properly react to peaks of requests, their adaptation activities would benefit from the estimation of the amount of requests. This paper proposes a solution to produce a short-term forecast based on data characterising user behaviour of online services. We use wavelet analysis, providing compression and denoising on the observed time series of the amount of past user requests; and a recurrent neural network trained with observed data and designed so as to provide well-timed estimations of future requests. The said ensemble has the ability to predict the amount of future user requests with a root mean squared error below 0.06%. Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. Moreover, reliable provision lets users enjoy a level of availability of services unaffected by load variations.

A Cloud-Distributed GPU Architecture for Pattern Identification in Segmented Detectors Big-Data Surveys

Physical surveys in many fields use segmented detectors to sense some physical quantities. In many cases, physical phenomena produce raw data with a rate higher than that possible to identify interesting events and organize data when using a single host. This paper proposes a distributed and parallel processing system that greatly reduces the time needed for identifying events and organizing data. Moreover, our solution can be scaled according to the experiment at hand. This opens the possibility to analyse a wider range of physical phenomena, increases the accuracy of observations, and makes it possible to organize widely distributed experiments involving different laboratories or facilities at the same time. The proposed solution consists of a parallel algorithm able to quickly process patterns sensed by a segmented detector, hence achieving identification and data cataloguing. Moreover, a distributed software architecture properly taps into cloud-based resources to handle the massive amount of raw data generated by an experiment. The results in terms of performances for the proposed distributed and parallel solution have shown a relevant speed up for the required processing.

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.

A novel cloud-distributed toolbox for optimal energy dispatch management from renewables in IGSs by using WRNN predictors and GPU parallel solutions

Integrated generation systems (IGSs) are today increasingly considered to exploit renewable energy in order to supply load for remote areas, less developed countries and small isolated communities. The IGS investigated in this paper enclose a PV park with battery energy storage system and this configuration is considered as case study for the campus called Cittadella at the University of Catania. A novel cloud distributed toolbox for the purpose of an optimal energy dispatch management by using Wavelet Recurrent Neural Networks (WRNN) predictors and Graphic Processing Units (GPU) parallel solutions in the IGS considered as case study is proposed. Results coming from the implementation of the proposed cloud architecture are here presented.

Suggesting Extract Class Refactoring Opportunities by Measuring Strength of Method Interactions

For improving the modularity of a large software system, metrics can be valuable to help finding refactoring opportunities for classes. We define a novel metric that is intended to suggest how closely connected are the elements of a class. The metric characterises the strength of the coupling between methods of a class, based on invocations and the size of the parameters involved, as well as attribute accesses. The assessment of the strength of interactions turns out to be valuable in providing an indication on the possible changes that classes need to become more modular and prone to be reused. According to the computed metric and the assessment of system-wide relationships between classes, we are able to suggest Extract Class refactoring opportunities. The capability of the proposed approach to evaluate object-oriented systems is demonstrated by analysing a large software system.

Providing QoS strategies and cloud-integration to web servers by means of aspects

The main responsibilities of a web server are to listen from the communication channel and to prepare replies to requests. Additional responsibilities include adapting processing activities, for example, through scheduling or request filtering, so as to satisfy Quality of Service (QoS) requirements. Typical QoS‐related concerns address behavioural constraints (e.g. response time bounds, satisfiable by scheduling the most urgent requests first) and resource monitoring, for optimal use. Although such concerns are spread across several web server components, they should be handled separately from communication‐related ones, for the sake of modularity.