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Embedded systems including IoT devices are designed for specialized functions; thus, changes in functions are not considered following their release. For this reason, changing functions to satisfy the requirements of IoT systems is difficult. In this study, we focus on updating existing embedded systems without modifying them. We investigate the design of new functions and their implementation with limited resources. This paper describes an evolution mechanism for updating the functionalities of existing embedded systems. The evolution mechanism uses a control unit that is deployed outside the embedded system. To guide the steady implementation of the evolution mechanism, we define an evolution process that effectively uses the state machine diagram at the design time and runtime to update the embedded systems. The programming framework implemented in this study supports the evolution process. We evaluate the evolution mechanism based on the results from two experiments. The first experiment involved applying the evolution mechanism to a cleaning robot, this demonstrated that the evolution mechanism systematically enables the injection of new functions into an embedded system in the real world. The second experiment, on the probabilistic model checking, demonstrated that the mechanism provides almost the same performance as the ordinary embedded system with an improved robustness.

A system of permanent magnet quadrupoles (PMQs) is going to be realized by INFNLNS to be used as a collection system for the injection of laser driven ion beams up to 60 AMeV in an energy selector based on four resistive dipoles. This system is the first element of the ELIMED (ELI-Beamlines MEDical and Multidisciplinary applications) beam transport, dosimetry and irradiation line that will be developed by INFN-LNS (It) and installed at the ELI-Beamlines facility in Prague (Cz). ELIMED will be the first users open transport beam-line where a controlled laser-driven ion beam will be used for multidisciplinary researches. The definition of well specified characteristics, both in terms of performances and field quality, of the magnetic lenses is crucial for the system realization, for the accurate study of the beam dynamics and for the proper matching with the magnetic selection system which will be designed in the next months. Here, we report the design of the collection system and the adopted solutions in order to realize a robust system form the magnetic point of view. Moreover, the first preliminary transport simulations are also described.

Although a key driver of Earth's climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate net radiation, latent and sensible heat and their uncertainties. The resulting FLUXCOM database comprises 147 global gridded products in two setups: (1) 0.0833${\deg}$ resolution using MODIS remote sensing data (RS) and (2) 0.5${\deg}$ resolution using remote sensing and meteorological data (RS+METEO). Within each setup we use a full factorial design across machine learning methods, forcing datasets and energy balance closure corrections. For RS and RS+METEO setups respectively, we estimate 2001-2013 global (${\pm}$ 1 standard deviation) net radiation as 75.8${\pm}$1.4 ${W\ m^{-2}}$ and 77.6${\pm}$2 ${W\ m^{-2}}$, sensible heat as 33${\pm}$4 ${W\ m^{-2}}$ and 36${\pm}$5 ${W\ m^{-2}}$, and evapotranspiration as 75.6${\pm}$10 ${\times}$ 10$^3$ ${km^3\ yr^{-1}}$ and 76${\pm}$6 ${\times}$ 10$^3$ ${km^3\ yr^{-1}}$. FLUXCOM products are suitable to quantify global land-atmosphere interactions and benchmark land surface model simulations.

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.

Tailoring the properties of correlated oxides is accomplished by chemical doping, pressure, temperature or magnetic field. Photoexcitation is a valid alternative to reach out-of-equilibrium states otherwise inaccessible. Here, we quantitatively estimate the coupling between a lattice distortion and the charge-transfer excitation in (La$_2$CuO$_{4+\delta}$). We photoinduce a coherent La ion vibration and monitor the response of the optical constants in a broad energy range, providing quantitative information on the electron-phonon matrix element that can be compared to theoretical models. We propose the same methodology to probe electron-electron interactions in other materials.

Modern spreadsheet systems can be used to implement complex spreadsheet applications including data sheets, customized user forms and executable procedures written in a scripting language. These applications are often developed by practitioners that do not follow any software engineering practice and do not produce any design documentation. Thus, spreadsheet applications may be very difficult to be maintained or restructured. In this position paper we present in a nutshell two reverse engineering techniques and a tool that we are currently realizing for the abstraction of conceptual data models and business logic models., Comment: In Proceedings of the 2nd Workshop on Software Engineering Methods in Spreadsheets (http://spreadsheetlab.org/sems15/)

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., Comment: Published version on: 2014 IEEE Symposium on Computational Intelligence for Human-like Intelligence

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, 2014

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.

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 \emph{wavelet analysis}, providing compression and denoising on the observed time series of the amount of past user requests; and a \emph{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.

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.

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.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems13. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales314. Here we use empirical models based on eddy covariance data15 and process-based models16,17 to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance36,9,11,12,14. Our study indicates that spatial climate covariation drives the global carbon cycle response.