Abstract
The way students learn changed significantly over the past two years, due to the current pandemic. However, this change was neither desired not planed beforehand. As a result, in many cases, it may have been undertaken without the appropriate care. In this paper we propose a framework for online education tailored for Computer Science degrees. Its goals are twofold: to avoid disruptive changes by providing a familiar and supportive structure for teaching/learning activities, and to motivate Students to learn autonomously, despite their reduced contact with their peers or the Teacher.

Abstract
Artificial Intelligence (AI), in all its different sub-fields, has grown significantly over the past years. When compared with other scientific or technological fields, this can almost be seen as a revolution. Nonetheless, as in other revolutions, not all that revolves around AI evolved at the same pace. As a consequence, many serious legal and ethical issues on the use of Artificial Intelligence are presently being raised. This paper addresses the main root causes for these problems from a technical standpoint, and then analyzes the legal and ethical framework. Finally, the paper describes a range of techniques and methods that can be used to address the identified problems, namely by ensuring transparency, fairness, equality, explanability and avoiding bias or discrimination. The field is presently at a tipping point, which can either lead to an avoidance of Artificial Intelligence due to fear or lack of regulation, or to a wide adoption supported by increased transparency and more human-centered approaches. Given the recent developments addressed in this paper, the paper argues in favor of a tendency towards the latter.

Abstract
Water loss is one of the factors that most affect a concessionaire's financial sustainability. Early detection of any anomaly in water consumption is very valuable. This article aims to carry out a preliminary study to detect change points in consumption associated with water meter malfunction. The dataset is composed of water consumption measurements of two different companies (a hotel and a hospital) located in the north of Portugal, obtained during a complete year. Different methods were implemented in order to study its effectiveness in the detection of change points in the time series related to a sharp decrease in water consumption. Results suggest that the Seasonal Decomposition of Time Series by Loess method (STL) and the combination of several breakpoint detection methods is a suitable approach to be implemented in a software system, in order to help the company in anomaly detection and in the decision-making process of substituting the water meters.

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Abstract
A product development framework called LeanDfX has been conceived at INEGI, aiming at organizing the product design and development process benefitting from lean thinking and DfX paradigms. The design of the metallic structure for an automated guided vehicle (AGV) focusing on its static, dynamic and fatigue characteristics was a recent opportunity to enhance and further develop the framework through the consideration and integration into the process of several existing tools such as FMEA (failure mode and effect analysis), QFD (quality function deployment) or fuzzy logic. This paper describes the integration of those tools in the LeanDfX framework and an application to the design of an AGV structure. The methodology presented involves systematic consideration of a substantial number of design requirements and more detailed product specification characterization. Such a number might be seen as delaying the development process, but the present case study showed that the inverse was true, thanks to the structured systematic approach and timely elimination of less desirable alternatives.