Abstract
It is common knowledge that history repeats itself! Maybe not literally, but patterns of behaviour likely dependent of the human nature, are probably prone to repeat themselves. So, one may wonder if looking back could help us prepare for a better future. Moreover, by looking back at the history of people and societies, we should all be able to have a better understanding of why things happen the way they do. This seldom happens, and when it does, it is happening within very limited circle of the society such as scholars and some politician circles, rarely overflowing to the whole society.The point is that, what we see today is not very different from what has happened in the past. Let us go back to November 13, 1460, the day Prince Henry the Navigator, passed away in Sagres, leaving Portugal with an enormous debt. Despite that fact, Prince Henry was the “guiding force behind Portugal’s assimilation of nautical knowledge and its vast extension of maritime exploration for nearly four decades” (Kock, 2003, p.59). It is interesting that by that time intellectual property was already being managed. (...)

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
The application of the Internet of Things to manufacturing is the driving force of the new industrial revolution (Industrie 4.0). In fact, most activities in the manufacturing industry can benefit from the data collected in the context of the industrial process. The Industrial Internet of Things (IIoT), whose pillars are the usage of IP communication between the devices and making the devices accessible through the Internet, can maximize the benefits of the information by the integration between multiple data sources, and by the ubiquitous fruition of the information itself. It is common belief that IIoT will transform companies and countries, opening up a new era of economic growth and competitiveness, since it has great potential for improving quality control, sustainable and green practices, supply chain traceability, and maintenance of the user in the loop. Anyway, a number of challenges arise in this context, related for example to adaptability and scalability, real-time communication and QoS, and system deployment and management. A communication middleware can support the IIoT vision by coping with these issues. This talk introduces the IIoT, discusses its benefits and challenges, and presents communication middleware developed in different sub-areas of IIoT (service-oriented industrial informatics [1], smart grids [2], maintenance of industrial machines [3]) that enable the IIoT vision.

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. © 2017 ACM.

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
Assessing the quality of aperture synthesis maps is relevant for benchmarking image reconstruction algorithms, for the scientific exploitation of data from optical long-baseline interferometers, and for the design/upgrade of new/existing interferometric imaging facilities. Although metrics have been proposed in these contexts, no systematic study has been conducted on the selection of a robust metric for quality assessment. This article addresses the question: what is the best metric to assess the quality of a reconstructed image? It starts by considering several metrics and selecting a few based on general properties. Then, a variety of image reconstruction cases are considered. The observational scenarios are phase closure and phase referencing at the Very Large Telescope Interferometer (VLTI), for a combination of two, three, four and six telescopes. End-to-end image reconstruction is accomplished with the MIRA software, and several merit functions are put to test. It is found that convolution by an effective point spread function is required for proper image quality assessment. The effective angular resolution of the images is superior to naive expectation based on the maximum frequency sampled by the array. This is due to the prior information used in the aperture synthesis algorithm and to the nature of the objects considered. The l(1)-norm is the most robust of all considered metrics, because being linear it is less sensitive to image smoothing by high regularization levels. For the cases considered, this metric allows the implementation of automatic quality assessment of reconstructed images, with a performance similar to human selection.