Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks

Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.

Heating the Early Universe : Numerical Methods and Their Analysis

During the epoch when the first collapsed structures formed (6<z<50) our Universe went through an extended period of changes. Some of the radiation from the first stars and accreting black holes in those structures escaped and changed the state of the Intergalactic Medium (IGM). The era of this global phase change in which the state of the IGM was transformed from cold and neutral to warm and ionized, is called the Epoch of Reionization.In this thesis we focus on numerical methods to calculate the effects of this escaping radiation. We start by considering the performance of the cosmological radiative transfer code C2-Ray. We find that although this code efficiently and accurately solves for the changes in the ionized fractions, it can yield inaccurate results for the temperature changes. We introduce two new elements to improve the code. The first element, an adaptive time step algorithm, quickly determines an optimal time step by only considering the computational cells relevant for this determination. The second element, asynchronous evolution, allows different cells to evolve with different time steps. An important constituent of methods to calculate the effects of ionizing radiation is the transport of photons through the computational domain or ``ray-tracing''. We devise a novel ray tracing method called PYRAMID which uses a new geometry - the pyramidal geometry. This geometry shares properties with both the standard Cartesian and spherical geometries. This makes it on the one hand easy to use in conjunction with a Cartesian grid and on the other hand ideally suited to trace radiation from a radially emitting source. A time-dependent photoionization calculation not only requires tracing the path of photons but also solving the coupled set of photoionization and thermal equations. Several different solvers for these equations are in use in cosmological radiative transfer codes. We conduct a detailed and quantitative comparison of four different standard solvers in which we evaluate how their accuracy depends on the choice of the time step. This comparison shows that their performance can be characterized by two simple parameters and that the C2-Ray generally performs best.

Algorithms and tools of big data: A bibliographic review

66 p.

Learning with MOOCs: promises and problems

Massive Open Online Courses (MOOCs) may be considered to be a new form of virtual technology enhanced learning environments. Since their first appearance in 2008, the increase in the number of MOOCs has been dramatic. The hype about MOOCs was accompanied by great expectations: 2012 was named the Year of the MOOCs and it was expected that MOOCs would revolutionise higher education. Two types of MOOCs may be distinguished: cMOOCs as proposed by Siemens, based on his ideas of connectivism, and xMOOCs developed in institutions such as Stanford and MIT. Although MOOCs have received a great deal of attention, they have also met with criticism. The time has therefore come to critically reflect upon this phenomenon.

Possibilities and problems of teaching the history of the present time through media and information literacy.

Le système éducatif encourage une histoire positiviste, ordonnée, unilatérale et universelle; par l´incorporation de le découpage chronologique de l´histoire en quatre étapes. Mais, est-ce qu´il serait posible que les élèves puissent étudier leur propre présent? Mon commuication poursuit d´exposer, comme Saab affirmait, le présent est “le point de départ et d´arrivée de l´enseignement de l´histoire détermine les allers et les retours au passé”. La façon d´approcher l´enseignement de l´histoire est confortable. Il n´y a pas de questions, il n´y a pas de discussions. Cette vision de l´histoire interprétée par l´homme blancoccidental-hétérosexuel s´inscrit dans le projet de la modernité du Siècle des Lumières. Par conséquent, cette histoire obvie que nous vivons dans una société postmoderne de la suspicion, de la pensée débile. En ce qui concerne la problématique autour de la pollution audiovisuelle et la façon dont les enseignants et les élèves sont quotidiennement confrontés à ce problème. Par conséquent, il est nécessaire de réfléchir à la question de l´enseignement de l´histoire quadripartite. Actuellement, les médias et les nouvelles technologies sont en train de changer la vie de l´humanité. Il est indispensable que l´élève connaisse son histoire presente et les scénarioshistoriques dans l´avenir. Je pense en la nécessité d´adopter une didactique de l’histoire presente et par conséquent, nous devons utiliser la maîtrise des médias et de l´information. Il faut une formation des enseignants que pose, comme Gadamer a dit: “le passé y le présent se trouvent par une négociation permanente”. Una formation des enseignants qui permette de comprendre et penser l´histoire future / les histoires futures. À mon avis, si les élèves comprennent la complexité de leur monde et leurs multiples visions, les élèves seront plus tolérantes et empathiques.

Spatial and temporal downscaling of solar global radiation and mean air temperature from weather forecast data - an introductory numerical study and validation

This short paper presents a numerical method for spatial and temporal downscaling of solar global radiation and mean air temperature data from global weather forecast models and its validation. The final objective is to develop a prediction algorithm to be integrated in energy management models and forecast of energy harvesting in solar thermal systems of medium/low temperature. Initially, hourly prediction and measurement data of solar global radiation and mean air temperature were obtained, being then numerically downscaled to half-hourly prediction values for the location where measurements were taken. The differences between predictions and measurements were analyzed for more than one year of data of mean air temperature and solar global radiation on clear sky days, resulting in relative daily deviations of around -0.9±3.8% and 0.02±3.92%, respectively.

A Comparative Analysis Between Genetic Algorithms and Complex Nonlinear Least Squares on Electrical Impedance Characterization

This paper compares the performance of the complex nonlinear least squares algorithm implemented in the LEVM/LEVMW software with the performance of a genetic algorithm in the characterization of an electrical impedance of known topology. The effect of the number of measured frequency points and of measurement uncertainty on the estimation of circuit parameters is presented. The analysis is performed on the equivalent circuit impedance of a humidity sensor.

Numerical modelling and structural optimization of multifunctional maritime structures aimed to protect harbours and produce energy

The main objective of this PhD thesis is to optimize a specific multifunctional maritime structure for harbour protection and energy production, named Overtopping Breakwater for Energy Conversion (OBREC), developed by the team of the University of Campania. This device is provided with a sloping plate followed by a unique reservoir, which is linked with the machine room (where the energy conversion occurs) by means of a pipe passing through the crown wall, provided with a parapet on top of it. Therefore, the potential energy of the overtopping waves, collected inside the reservoir located above the still water level, is then converted by means of low – head turbines. In order to improve the understanding of the wave – structure interactions with OBREC, several methodologies have been used and combined together: i. analysis of recent experimental campaigns on wave overtopping discharges and pressures at the crown wall on small – scale OBREC cross sections, carried out in other laboratories by the team of the University of Campania; ii. new experiments on cross sections similar to the OBREC device, planned and carried out in the hydraulic lab at the University of Bologna in the framework of this PhD work; iii. numerical modelling with a 1 – phase incompressible fluid model IH – 2VOF, developed by the University of Cantabria, and with a 2 – phase incompressible fluid model OpenFOAM, both available from the literature; iv. numerical modelling with a new 2 – phase compressible fluid model developed in the OpenFOAM environment within this PhD work; v. analysis of the data gained from the monitoring of the OBREC prototype installation.

Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.

Numerical modeling and experimental validation of the recrystallization behaviour in the extrusion of 6XXX aluminum alloys

The microstructure of 6XXX aluminum alloys deeply affects mechanical, crash, corrosion and aesthetic properties of extruded profiles. Unfortunately, grain structure evolution during manufacturing processes is a complex phenomenon because several process and material parameters such as alloy chemical composition, temperature, extrusion speed, tools geometries, quenching and thermal treatment parameters affect the grain evolution during the manufacturing process. The aim of the present PhD thesis was the analysis of the recrystallization kinetics during the hot extrusion of 6XXX aluminum alloys and the development of reliable recrystallization models to be used in FEM codes for the microstructure prediction at a die design stage. Experimental activities have been carried out in order to acquire data for the recrystallization models development, validation and also to investigate the effect of process parameters and die design on the microstructure of the final component. The experimental campaign reported in this thesis involved the extrusion of AA6063, AA6060 and AA6082 profiles with different process parameters in order to provide a reliable amount of data for the models validation. A particular focus was made to investigate the PCG defect evolution during the extrusion of medium-strength alloys such as AA6082. Several die designs and process conditions were analysed in order to understand the influence of each of them on the recrystallization behaviour of the investigated alloy. From the numerical point of view, innovative models for the microstructure prediction were developed and validated over the extrusion of industrial-scale profiles with complex geometries, showing a good matching in terms of the grain size and surface recrystallization prediction. The achieved results suggest the reliability of the developed models and their application in the industrial field for process and material properties optimization at a die-design stage.

Numerical Analysis and Redesign of a High Speed Linear Cascade Wind Tunnel for Aircraft Gas Turbine Engines Testing

Linear cascade testing serves a fundamental role in the research, development, and design of turbomachines as it is a simple yet very effective way to compute the performance of a generic blade geometry. These kinds of experiments are usually carried out in specialized wind tunnel facilities. This thesis deals with the numerical characterization and subsequent partial redesign of the S-1/C Continuous High Speed Wind Tunnel of the Von Karman Institute for Fluid Dynamics. The current facility is powered by a 13-stage axial compressor that is not powerful enough to balance the energy loss experienced when testing low turning airfoils. In order to address this issue a performance assessment of the wind tunnel was performed under several flow regimes via numerical simulations. After that, a redesign proposal aimed at reducing the pressure loss was investigated. This consists of a linear cascade of turning blades to be placed downstream of the test section and designed specifically for the type of linear cascade being tested. An automatic design procedure was created taking as input parameters those measured at the outlet of the cascade. The parametrization method employed Bézier curves to produce an airfoil geometry that could be imported into a CAD software so that a cascade could be designed. The proposal was simulated via CFD analysis and proved to be effective in reducing pressure losses up to 41%. The same tool developed in this thesis could be adopted to design similar apparatuses and could also be optimized and specialized for the design of turbomachines components.

The SUGAR Toolbox

The SUGAR Toolbox contains scripts coded in MATLAB for calculating various thermodynamic, kinetic, and geologic properties of substances occurring in the marine environment, particularly gas hydrate and seep systems. Brief descriptions of the toolbox scripts and some notes on the underlying basic theory as well as tables of additional property values can be found in the accompanying documentation.

A general-purpose tunable landscape generator

The research literature on metalieuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metalieuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.