Abstract
This poster presents a pilot project on non-conventional learning strategies based on students' active participation in real-life FLOSS projects. The aim of the project is to validate the hypothesis that the peer-production model, which underlies most FLOSS projects, can enhance the learning-teaching process based on extensive and systematic collaborative practices. Consisting of a semester-long experiment with a class of 23 Master Information and Technology (MSc IT) students, organized in small groups, this pilot project aims at exploiting FLOSS communities and the respective FLOSS projects as learning opportunities. This pilot project is part of a course assignment on Teaching Software Engineering in the Master in Informatics taking place during the spring semester of 2013, at the University of Minho, in Portugal. The students participate in real FLOSS projects by choosing one of the following roles: 1) requirement analyst, writing software requirements; 2) programmer, developing source code; or 3) tester, writing bug reports. As a real-life experience conducted with students in Portugal, the project enables the systematic study of the dynamics of peer-to-peer learning and assessment of the didactic value and potential of this kind of non-standard learning experiences. Moreover, as a beneficial side effect, students have the opportunity to experience full immersion in a real FLOSS community and learn about project management and organized participation.

Abstract
Software connectors encapsulate interaction patterns between services in complex, distributed service-oriented applications. Such patterns evolve over time, in response to faults, changes in the expected QoS levels, emergent requirements or the reassessment of contextual conditions. This paper builds up on a model for connector reconfiguration to introduce notions of reconfiguration equivalence and refinement allowing for reasoning about them. This paves the way towards a (still missing) calculus of connector reconfigurations. Copyright 2013 ACM.

Abstract
It is said that due to the peculiar dynamics of FLOSS communities, effective participation in their projects is a privileged way to acquire the relevant skills and expertise in software development. Such is probably the reason for a number of higher education institutions to include in their Software Engineering curricula some form of contact with the FLOSS reality. This paper explores such a perspective through an on-going case study on university students' collaboration in FLOSS projects. The aim of this research is to 1) identify what should be learnt about software development through regular participation in a FLOSS project/community, and 2) assess the didactic potential of this kind of non-standard learning experiences. To this aim we resorted to a participatory research action approach and qualitative methods, namely case studies combining direct observation and interviews.

Abstract
Models for exogenous coordination provide powerful glue-code, in the form of software connectors, to express interaction protocols between services in distributed applications. Connector reconfiguration mechanisms play, in this setting, a major role to deal with change and adaptation of interaction protocols. This paper introduces a model for connector reconfiguration, based on a collection of primitives as well as a language to specify connectors and their reconfigurations. © 2013 Springer-Verlag.

Abstract
Label Ranking (LR) problems, such as predicting rankings of financial analysts, are becoming increasingly important in data mining. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consider conventional discretization methods used in classification, by simply treating each unique ranking as a separate class. In this paper, we show that such an approach has several disadvantages. As an alternative, we propose an adaptation of an existing method, MDLP, specifically for LR problems. We illustrate the advantages of the new method using synthetic data. Additionally, we present results obtained on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and in some cases improves the results of the learning algorithms.

Abstract
The aim of this work is to describe an exploratory study on the use of a SAX-based Multiresolution Motif Discovery method for Heart Sound Classification. The idea of our work is to discover relevant frequent motifs in the audio signals and use the discovered motifs and their frequency as characterizing attributes. We also describe different configurations of motif discovery for defining attributes and compare the use of a decision tree based algorithm with random forests on this kind of data. Experiments were performed with a dataset obtained from a clinic trial in hospitals using the digital stethoscope DigiScope. This exploratory study suggests that motifs contain valuable information that can be further exploited for Heart Sound Classification. © 2013 ACM.