Abstract
The von Mises-Fisher distribution is widely used for modelling directional data. In this paper we propose goodness-of-fit methods for a concentrated von Mises-Fisher distribution and we analyse by simulation some questions concerning the application of these tests. We analyse the empirical power of the Kolmogorov-Smirnov test for several dimensions of the sphere, supposing as alternative hypothesis a mixture of two von Mises-Fisher distributions with known parameters. We also compare the empirical power of the Kolmogorov-Smirnov test with the Rao's score test for data on the sphere, supposing as alternative hypothesis, a mixture of two Fisher distributions with unknown parameters replaced by their maximum likelihood estimates or a 5-parameter Fisher-Bingham distribution. Finally, we give an example with real spherical data.

Abstract
This paper presents a web portal for the certification of open source software. The portal aims at helping programmers in the internet age, when there are (too) many open source reusable libraries and tools available. Our portal offers programmers a web-based and easy setting to analyze and certify open source software, which is a crucial step to help programmers choosing among many available alternatives, and to get some guarantees before using one piece of software. The paper presents our first prototype of such web portal. It also describes in detail a domain specific language that allows programmers to describe with a high degree of abstraction specific open source software certifications. The design and implementation of this language is the core of the web portal. © Springer-Verlag Berlin Heidelberg 2014.

Abstract
Several approaches exist to automatically derive test cases that check the conformance of the implementation of abstract data types (ADTs) with respect to their specification. However, they lack support for the testing of implementations of ADTs defined by generic classes. In this paper, we present a novel technique to automatically derive, from specifications, unit test cases for Java generic classes that, in addition to the usual testing data, encompass implementations for the type parameters. The proposed technique relies on the use of Alloy Analyzer to find model instances for each test goal. JUnit test cases and Java implementations of the parameters are extracted from these model instances. © 2012 Springer-Verlag.

Abstract
This paper presents the classification of power quality (PQ) disturbances using modular probabilistic neural network (MPNN), support vector machines (SVMs) and least square support vector machines (LS-SVMs) in grid-connected wind energy systems. Different types of sag and swell disturbances due to the change in load and wind speed are created using MATLAB/Simulink. Classification scheme encompasses suitable features extracted by S-transform (ST) and is subsequently trained with MPNN, SVM and LS-SVM to effectively classify the PQ disturbances. The accuracy and reliability of the proposed classifier are also validated on signals with noise content. A comparative study is also carried out to determine the efficacy of the proposed techniques. © 2012 IEEE.

Abstract
Many real world data mining applications involve analyzing geo-referenced data. Frequently, this type of data sets are incomplete in the sense that not all geographical coordinates have measured values of the variable(s) of interest. This incompleteness may be caused by poor data collection, measurement errors, costs management and many other factors. These missing values may cause several difficulties in many applications. Spatial imputation/interpolation methods try to fill in these unknown values in geo-referenced data sets. In this paper we propose a new spatial imputation method based on machine learning algorithms and a series of data preprocessing steps. The key distinguishing factor of this method is allowing the use of data from faraway regions, contrary to the state of the art on spatial data mining. Images (e. g. from a satellite or video surveillance cameras) may also suffer from this incompleteness where some pixels are missing, which again may be caused by many factors. An image can be seen as a spatial data set in a Cartesian coordinates system, where each pixel (location) registers some value (e. g. degree of gray on a black and white image). Being able to recover the original image from a partial or incomplete version of the reality is a key application in many domains (e. g. surveillance, security, etc.). In this paper we evaluate our general methodology for spatial interpolation on this type of problems. Namely, we check the ability of our method to fill in unknown pixels on several images. We compare it to state of the art methods and provide strong experimental evidence of the advantages of our proposal.

Abstract