Abstract

Abstract
The expectations of Machine Learning systems are becoming increasingly demanding, namely in what concerns the diversity of applications, the expected accuracy, and the pressure for results. However, there are cases in which Human experts are needed to label the data, which may have a significant cost in terms of human resources and time. In these cases, it is often best to learn on-the-fly, without expecting for the whole data to be labeled. Often, it is desirable to guide the Human annotators into focusing on the more relevant instances: this constitutes the so-called active learning. In this paper we propose an approach in which a clustering algorithm is used to find groups of similar instances. Then, the procedure is guided with the objective of favoring the annotation of the groups that are under-represented in the labeled dataset. Results show that this approach leads to models that are, over time, more accurate and reliable.

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
In the last years, developments in data collection, storing, processing and analysis technologies resulted in an unprecedented use of data by organizations. The volume and variety of data, combined with the velocity at which decisions must now be taken and the dynamism of business environments, pose new challenges to Machine Learning. Namely, algorithms must now deal with streaming data, concept drift, distributed datasets, among others. One common task nowadays is to update or re-train models when data changes, as opposed to traditional one-shot batch systems, in which the model is trained only once. This paper addresses the issue of when to update or re-train a model, by proposing an approach to predict the performance metrics of the model if it were trained at a given moment, with a specific set of data. We validate the proposed approach in an interactive Machine Learning system in the domain of fraud detection. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Emotion is an essential part of what means to be human, but it is still disregarded by most technical fields as something not to be considered in scientific or engineering projects. However, the understanding of emotion as an aspect of decision-making processes and of modelling of human behavior is essential to create a better connection between humans and their tools and machines. With this work we focus on the measurement of emotion of users through the use of non-intrusive methods, like measuring inputs and reactions to stimuli, along with the creation of a tool that measures the emotional changes caused by visual output created by the tool itself. Usage of the tool in a test environment and the subsequent analysis of the data obtained will allow for conclusions about the effectiveness of the method, and if it is possible to apply it to future studies on human emotions by investigators in the fields of psychology and computation.