Abstract
This paper focus on the classification, in real-time and under uncontrolled lighting, of fabric textures for the automotive industry. Many industrial processes have spatial constraints that limit the effective control of illumination of their vision based systems, hindering their effectiveness. The ability to overcome these problems using robust classification methods with suitable pre-processing techniques and choice of characteristics will increase the efficiency of this type of solutions with obvious production gains and thus economical. For this purpose, this paper studied and analyzed various pre-processing techniques, and selected the most appropriate fabric characteristics for the considered industrial case scenario. The methodology followed was based on the comparison of two different machine learning classifiers, ANN and SVM, using a large set of samples with a large variability of lightning conditions to faithfully simulate the industrial environment. The obtained solution shows the sensibility of ANN over SVM considering the number of features and the size of the training set, showing the better effectiveness and robustness of the last. The characteristics vector uses histogram equalization, Laws filter and Sobel filter, and multi-scale analysis. By using a correlation based method was possible to reduce the number of features used, achieving a better balanced between processing time and classification ratio.

Abstract
Mobile applications have some specific characteristics not found on web and desktop applications. The mobile testing tools available may not be prepared to detect problems related to those specificities. So, it is important to assess the quality of the test cases generated/executed by mobile testing tools in order to check if they are able to find those specific problems. One way to assess the quality of a test suite is through mutation testing. This paper presents new mutation operators created to inject faults leading to known failures related to the non-preservation of users transient UI state when mobile applications go to background and then come back to foreground. A set of mutation operators is presented and the rational behind its construction is explained. A case study illustrates the approach to evaluate a mobile testing tool. In this study, the tool used is called iMPAcT tool, however any other mobile testing tool could be used. The experiments are performed over mobile applications publicly available on the Google Play store. The results are presented and discussed. Finally, some improvements are suggested for the iMPAcT tool in order to be able to generate test cases that can kill more mutants and so, hopefully, detect more failures in the future.

Abstract
Viticulture has a major impact in the European economy and over the years the intensive grapevine production led to the proliferation of many varieties. Traditionally these varieties are manually catalogued in the field, which is a costly and slow process and being, in many cases, very challenging to classify even for an experienced ampelographer. This article presents a cost-effective and automatic method for grapevine varieties classification based on the analysis of the leaf’s images, taken with an RGB sensor. The proposed method is divided into three steps: (1) color and shape features extraction; (2) training and; (3) classification using Linear Discriminant Analysis. This approach was applied in 240 leaf images of three different grapevine varieties acquired from the Douro Valley region in Portugal and it was able to correctly classify 87% of the grapevine leaves. The proposed method showed very promising classification capabilities considering the challenges presented by the leaves which had many shape irregularities and, in many cases, high color similarities for the different varieties. The obtained results compared with manual procedure suggest that it can be used as an effective alternative to the manual procedure for grapevine classification based on leaf features. Since the proposed method requires a simple and low-cost setup it can be easily integrated on a portable system with real-time processing to assist technicians in the field or other staff without any special skills and used offline for batch classification.

Abstract

Abstract
Large-scale password data breaches are becoming increasingly commonplace, which has enabled researchers to produce a substantial body of password security research utilising real-world password datasets, which often contain numbers of records in the tens or even hundreds of millions. While much study has been conducted on how password composition policies-sets of rules that a user must abide by when creating a password-influence the distribution of user-chosen passwords on a system, much less research has been done on inferring the password composition policy that a given set of user-chosen passwords was created under. In this paper, we state the problem with the naive approach to this challenge, and suggest a simple approach that produces more reliable results. We also present pol-infer, a tool that implements this approach, and demonstrates its use in inferring password composition policies.

Abstract
Students can explore playful environments offered by serious games to simulate challenges in the process of launching and managing a start-up, which can improve their strategic planning and management skills. In this sense, this paper identifies and explores the benefits and limitations of the use of serious games in entrepreneurship education. Subsequently, it is discussed how these elements are approached in the context of nine entrepreneurship serious games. The findings indicate that all considered games create an active learning environment, although it is not clear how they can be integrated into a didactical system and how the students' performance could be evaluated and assessed. Additionally, there were also globally identified accessibility, interoperability and usability issues.