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
We investigate the relationship between Functional Encryption (FE) and Fully Homomorphic Encryption (FHE), demonstrating that, under certain assumptions, a Functional Encryption scheme supporting evaluation on two ciphertexts implies Fully Homomorphic Encryption. We first introduce the notion of Randomized Functional Encryption (RFE), a generalization of Functional Encryption dealing with randomized functionalities of interest in its own right, and show how to construct an RFE from a (standard) semantically secure FE. For this we define the notion of entropically secure FE and use it as an intermediary step in the construction. Finally we show that RFEs constructed in this way can be used to construct FHE schemes thereby establishing a relation between the FHE and FE primitives. We conclude the paper by recasting the construction of RFE schemes in the context of obfuscation. © 2013 Springer-Verlag Berlin Heidelberg.

Abstract
Functional encryption (FE) is a powerful cryptographic primitive that generalizes many asymmetric encryption systems proposed in recent years. Syntax and security definitions for FE were proposed by Boneh, Sahai, and Waters (BSW) (TCC 2011) and independently by O'Neill (ePrint 2010/556). In this paper we revisit these definitions, identify several shortcomings in them, and propose a new definitional approach that overcomes these limitations. Our definitions display good compositionality properties and allow us to obtain new feasibility and impossibility results for adaptive token-extraction attack scenarios that shed further light on the potential reach of general FE for practical applications.

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.