Abstract
Social innovation is presented as a viable alternative to incite systemic changes related to sustainability in its three dimensions (social, environmental and economic), to involve the government, companies and, above all, civil society. In order to understand the development of social innovation in Brazil and Portugal, and to promote cross-sector collaboration, we have conducted several case studies involving centers for social innovation in both countries. The data analysis demonstrates that collaboration between sectors and the construction of collaborative networks is as a key element in the development and sustainability of social innovation. In spite of this, in practice the construction of these networks is not trivial, there is the need to manage these networks innovatively. The literature review points out the key enablers for cross-sector collaboration in the context of social innovation, the alignment of values and goals, mutual trust, commitment and bridge leadership.

Abstract
This paper describes the application of the IVY workbench to the formal analysis of a user interface for a safety-critical aerospace system. The operation manual of the system was used as a requirement document, and this made it possible to build a reference model of the user interface, focusing on navigation between displays, the information provided by each display, and how they are interrelated. Usability-related property specification patterns were then used to derive relevant properties for verification. This paper discusses both the modeling strategy and the analytical results found using the IVY workbench. The purpose of the reference model is to provide a standard against which future versions of the interface may be assessed.

Abstract
In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of mining concept-drifting data streams. However, most of these approaches can only be applied to uni-dimensional classification problems where each input instance has to be assigned to a single output class variable. The problem of mining multi-dimensional data streams, which includes multiple output class variables, is largely unexplored and only few streaming multi-dimensional approaches have been recently introduced. In this paper, we propose a novel adaptive method, named Locally Adaptive-MB-MBC (LA-MB-MBC), for mining streaming multi-dimensional data. To this end, we make use of multi-dimensional Bayesian network classifiers (MBCs) as models. Basically, LA-MB-MBC monitors the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a concept drift is detected, LA-MB-MBC adapts the current MBC network locally around each changed node. An experimental study carried out using synthetic multi-dimensional data streams shows the merits of the proposed method in terms of concept drift detection as well as classification performance.

Abstract
Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast cancer datasets, which makes research in its prediction more difficult. Objectives: To evaluate the performance of machine learning techniques applied to the prediction of breast cancer recurrence. Material and Methods: Revision of published works that used machine learning techniques in local and open source databases between 1997 and 2014. Results: The revision showed that it is difficult to obtain a representative dataset for breast cancer recurrence and there is no consensus on the best set of predictors for this disease. High accuracy results are often achieved, yet compromising sensitivity. The missing data and class imbalance problems are rarely addressed and most often the chosen performance metrics are inappropriate for the context. Discussion and Conclusions: Although different techniques have been used, prediction of breast cancer recurrence is still an open problem. The combination of different machine learning techniques, along with the definition of standard predictors for breast cancer recurrence seem to be the main future directions to obtain better results.

Abstract

Abstract
The selection of metafeatures for metalearning (MtL) is often an ad hoc process. The lack of a proper motivation for the choice of a metafeature rather than others is questionable and may originate a loss of valuable information for a given problem (e.g., use of class entropy and not attribute entropy). We present a framework to systematically generate metafeatures in the context of MtL. This framework decomposes a metafeature into three components: meta-function, object and post-processing. The automatic generation of metafeatures is triggered by the selection of a meta-function used to systematically generate metafeatures from all possible combinations of object and post-processing alternatives. We executed experiments by addressing the problem of algorithm selection in classification datasets. Results show that the sets of systematic metafeatures generated from our framework are more informative than the non-systematic ones and the set regarded as state-of-the-art.