Abstract
One part of demonstrating that a device is acceptably safe, often required by regulatory standards, is to show that it satisfies a set of requirements known to mitigate hazards. This paper is concerned with how to demonstrate that a user interface software design is compliant with use-related safety requirements. A methodology is presented based on the use of formal methods technologies to provide guidance to developers about addressing three key verification challenges: 1) how to validate a model, and show that it is a faithful representation of the device; 2) how to formalize requirements given in natural language, and demonstrate the benefits of the formalization process; and 3) how to prove requirements of a model using readily available formal verification tools. A model of a commercial device is used throughout the paper to demonstrate the methodology. A representative set of requirements are considered. They are based on US Food and Drug Administration (FDA) draft documentation for programmable medical devices, and on best practice in user interface design illustrated in relevant international standards. The methodology aims to demonstrate how to achieve the FDA's agenda of using formal methods to support the approval process for medical devices.

Abstract
At the celebration of its 30th anniversary, Erasmus is recognised as the most successful exchange program ever implemented. The prospects of attaining a common European consciousness challenged the program's ability to blend together knowledge, attitudes and skills in a winning combination. It is no longer sufficient to communicate and integrate: mobility should actively foster skills to support students's professional career at national and international levels. Although literature on mobility is vast and interesting, studies on the impact of the mobility experience in the students' future employability profile rarely provide first-hand data on their expectations in this regard. This exploratory research comprises a qualitative focus group approach with Erasmus students during their exchange period in a Portuguese university and collected some insightful data on how students consider their mobility in terms of new learning outcomes, the professional value of the experience and the development of new skills. Results indicate that students seem to be quite aware of the positive implications of mobility in their professional careers and of the set of skills developed during that period. Overall, this article contributes to demonstrating the importance of assessing skills development during Erasmus mobility experiences. Managerial implications and suggestions for future research are provided.

Abstract
In a single-target regression context, some important systems based on data streaming produce huge quantities of unlabeled data (without output value), of which label assignment may be impossible, time consuming or expensive. Semi-supervised methods, that include the co-training approach, were proposed to use the input information of the unlabeled examples in the improvement of models and predictions. In the literature, the co-training methods are essentially applied to classification and operate in batch mode. Due to these facts, this work proposes a co-training online algorithm for single-target regression to perform model improvement with unlabeled data. This work is also the first-step for the development of online multi-target regressor that create models for multiple outputs simultaneously. The experimental framework compared the performance of this method, when it rejects unalabeled data and when it uses unlabeled data with different parametrization in the training. The results suggest that the co-training method regressor predicts better when a portion of unlabeled examples is used. However, the prediction improvements are relatively small. © Springer International Publishing AG 2017.

Abstract
A previous version of this article contained minor errors in Tables 2, 3 and 4. This has now been corrected, the publisher apologises for the error. © 2016 European Crohn's and Colitis Organisation (ECCO).

Abstract

Abstract
Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TENSORCAST, a novel method that forecasts time-evolving networks more accurately than current state of the art methods by incorporating multiple data sources in coupled tensors. TENSORCAST is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple real-world networks, including DBLP and a Twitter temporal network with over 310 million non-zeros, where we predict the evolution of the activity of the use of political hashtags.