Abstract
This chapter discusses a series of developments on predictive control for path following via a priori generated trajectory for autonomous aerial vehicles. The strategy partitions itself into offline and runtime procedures with the assumed goal of moving the computationally expensive part into the offline phase and of leaving only tracking decisions to the runtime. First, itwill be recalled that differential flatness represents a well-suited tool for generating feasible reference trajectory. © Springer International Publishing Switzerland 2015.

Abstract
In year 2015, we experience a proliferation of scientific publications, conferences and funding programs on KDD for medicine and healthcare. However, medical scholars and practitioners work differently from KDD researchers: their research is mostly hypothesis-driven, not data-driven. KDD researchers need to understand how medical researchers and practitioners work, what questions they have and what methods they use, and how mining methods can fit into their research frame and their everyday business. Purpose of this tutorial is to contribute to this learning process. We address medicine and healthcare; there the expertise of KDD scholars is needed and familiarity with medical research basics is a prerequisite. We aim to provide basics for (1) mining in epidemiology and (2) mining in the hospital. We also address, to a lesser extent, the subject of (3) preparing and annotating Electronic Health Records for mining.

Abstract
The early detection of melanoma is one of the greatest challenges in clinical practice of dermatology, and the reticular pattern is one of the most important dermoscopic structures to improve melanocytic lesion diagnosis. A texture-based approach is developed for the automatic detection of reticular patterns, whose output will assist clinical decision-making. Feature selection was based on the use of two algorithms by means of the classical graylevel co-occurrence matrix and Laws energy masks optimized on a set of 104 dermoscopy images. The AdaBoost (adaptive boosting) approach to machine learning was used within this strategy. Results suggest superiority of LEM for reticular pattern detection in dermoscopic images, achieving a sensitivity of 90.16% and a specificity of 86.67%. The use of automatic classification in dermoscopy to support clinicians is a strong tool to assist diagnosis; however, the use of automatic classification as a complementary tool in clinical routine requires algorithms with high levels of sensitivity and specificity. The results presented in this work will contribute to achieving this goal. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

Abstract
Learning programming is a complex task that poses significant challenges. Students face different kinds of difficulties at complex levels that traditional teaching/learning methods are nor able to cope with. For this reason, several authors have researched the pedagogical effectiveness of program visualization and animation, and developed some tools. Animation can help students on the analysis and understanding of given programs, and can also guide on the development of new ones. It is very important to give students the opportunity to practice solving programming exercises by themselves. Receiving feedback is essential for knowledge acquisition. New tools arose (especially in the area of programming contests) to allow for the submission of solutions (programs developed by the students) to the problem statements presented by the teacher and to assess them, returning immediately information about the submitted answer. These tools can be incorporated into teaching activities, allowing students to test their work getting immediate feedback. Automatic evaluation systems significantly improve students performance. In this article are shown these two approaches, animation and automatic assessment, and proposed a new pedagogical practice resulting from the combination of both.

Abstract
The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have developed/adapted to treat rankings as the target object follow two different approaches: distribution-based (e.g., using Mallows model) or correlation-based (e.g., using Spearman's rank correlation coefficient). Decision trees have been adapted for label ranking following both approaches. In this paper we evaluate an existing correlation-based approach and propose a new one, Entropy-based Ranking trees. We then compare and discuss the results with a distribution-based approach. The results clearly indicate that both approaches are competitive.

Abstract