Abstract
A comparison between co-training and self-training method for single-target regression based on multiples learners is performed. Data streaming systems can create a significant amount of unlabeled data which is caused by label assignment impossibility, high cost of labeling or labeling long duration tasks. In supervised learning, this data is wasted. In order to take advantaged from unlabeled data, semi-supervised approaches such as Co-training and Self-training have been created to benefit from input information that is contained in unlabeled data. However, these approaches have been applied to classification and batch training scenarios. Due to these facts, this paper presents a comparison between Co-training and Self-learning methods for single-target regression in data streams. Rules learning is used in this context since this methodology enables to explore the input information. The experimental evaluation consisted of a comparison between the real standard scenario where all unlabeled data is rejected and scenarios where unlabeled data is used to improve the regression model. Results show evidences of better performance in terms of error reduction and in high level of unlabeled examples in the stream. Despite this fact, the improvements are not expressive.

Abstract

Abstract
MATLAB is a high-level language used in various scientific and engineering fields. Deployment of well-Tested MATLAB code to production would be highly desirable, but in practice a number of obstacles prevent this, notably performance and portability. Although MATLAB-To-C compilers exist, the performance of the generated C code may not be sufficient and thus it is important to research alternatives, such as CPU parallelism, GPGPU computing and FPGAS. OpenCL is an API and programming language that allows targeting these devices, hence the motivation for MATLAB-To-OpenCL compilation. In this paper, we describe our recent efforts on offloading code to OpenCL devices in the context of our MATLAB to C/OpenCL compiler.

Abstract
ProcessPAIR is a novel tool for automating the performance analysis of software developers. Based on a performance model calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. We present the results of a controlled experiment involving 61 software engineering master students, half of whom used ProcessPAIR in a performance analysis assignment. The results show significant benefits in terms of students' satisfaction (average score of 4.78 out of 5 for ProcessPAIR users, against 3.81 for other users), quality of the analysis outcomes (average grades achieved of 88.1 out of 100 for ProcessPAIR users, against 82.5 for other users), and time required to do the analysis (average of 252 min for ProcessPAIR users, against 262 min for other users, but with much room for improvement).

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
This paper presents some teaching strategies applied in a curricular unit of the 3rd year of the Integrated Master of Engineering and Industrial Management at the University of Minho The goal was to teach theoretical concepts associated to automation topics. The practical aspects as designing an automated machine or developing the corresponding Ladder Logic diagram is often well accepted by students. On the other hand, topics related to instrumentation, sensor type and functioning for example, more theoretical, are usually less attractive to students. So, different tools have been developed and tested along the past years to overcome this concern. This paper presents the last experience tested: evaluating a previously developed APP Inventor tool (developed by students) as wondering if it is a suitable way to learn automation; an automation conquest, where the competition may promote the stimulus to learn.