Abstract
In a lung nodule detection task, parenchyma segmentation is crucial to obtain the region of interest containing all the nodules. Thus, the challenge is to devise a methodology that includes all the lung nodules, particularly those close to the walls, as the juxtapleural nodules. In this paper, different region growing approaches are proposed for the automatic segmentation of the lung parenchyma. The methodology is organized in five different steps: first, the image intensity is corrected to improve the contrast of the lungs. With that, the fat area is obtained, automatically deriving the interior of the lung region. Then, the traquea is extracted by a 3D region growing, being subtracted from the lung region results. The next step is the division of the two lungs to guarantee that both are separated. And finally, the lung contours are refined to provide appropriate final results. The methodology was tested in 50 images taken from the LIDC image database, with a large variability and, specially, including different types of lung nodules. In particular, this dataset contains 158 nodules, from which 40 are juxtapleural nodules. Experimental results demonstrate that the method provides accurate lung regions, specially including the centers of 36 of the juxtapleural nodules. For the other 4, although the centers are not included, parts of their areas are retained in the segmentation, which is useful for lung nodule detection.

Abstract

Abstract
Symbolic Data Analysis (SDA) provides a framework for the representation and analysis of data that comprehends inherent variability. While in Data Mining and classical Statistics the data to be analyzed usually presents one single value for each variable, that is no longer the case when the entities under analysis are not single elements, but groups gathered on the basis of some given criteria. Then, for each variable, variability inherent to each group should be taken into account. Also, when analysing concepts, such as botanic species, disease descriptions, car models, and so on, data entail intrinsic variability, which should be explicitly considered. To this purpose, new variable types have been introduced, whose realizations are not single real values or categories, but sets, intervals, or, more generally, distributions over a given domain. SDA provides methods for the (multivariate) analysis of such data, where the variability expressed in the data representation is taken into account, using various approaches. (C) 2014 John Wiley & Sons, Ltd.

Abstract
In this paper we present a system, AutoMashUpper, for making multi-song music mashups. Central to our system is a measure of "mashability" calculated between phrase sections of an input song and songs in a music collection. We define mashability in terms of harmonic and rhythmic similarity and a measure of spectral balance. The principal novelty in our approach centres on the determination of how elements of songs can be made fit together using key transposition and tempo modification, rather than based on their unaltered properties. In this way, the properties of two songs used to model their mashability can be altered with respect to transformations performed to maximize their perceptual compatibility. AutoMashUpper has a user interface to allow users to control the parameterization of the mashability estimation. It allows users to define ranges for key shifts and tempo as well as adding, changing or removing elements from the created mashups. We evaluate AutoMashUpper by its ability to reliably segment music signals into phrase sections, and also via a listening test to examine the relationship between estimated mashability and user enjoyment.

Abstract
Scheduling plays an important role in the companies' competiveness, dealing with complex combinatorial problems subject to uncertainty and emergence. In particular, in the ramp-up phase of small lot-sizes of complex products, scheduling is more demanding, e.g. due to late requests and immature technology products and processes. This paper presents the principles of a distributed scheduling architecture based on holonic and swarm principles and implemented using multi-agent system technology. In particular, it is described the coordination among the network of the swarm of schedulers and analysed the impact of embedded self-organization mechanisms.

Abstract
Teaching and learning computer programming is as challenging as it is difficult. Assessing the work of students and providing individualised feedback is time-consuming and error prone for teachers and frequently involves a time delay. The existent tools prove to be insufficient in domains where there is a greater need to practice. At the same time, Massive Open Online Courses (MOOC) are appearing, revealing a new way of learning. However, this paradigm raises serious questions regarding the monitoring of student progress and its timely feedback. This chapter provides a conceptual design model for a computer programming learning environment. It uses the portal interface design model, gathering information from a network of services such as repositories, program evaluators, and learning management systems, a central piece in the MOOC realm. This model is not limited to the domain of computer programming and can be adapted to any area that requires evaluation with immediate feedback. © 2015, IGI Global.