Abstract

Abstract
Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.

Abstract
This study aims to explore how higher education institutions respond to the challenge of incorporating soft skills into their curricula. It employs a mixed-methods approach in which the quantitative analysis of the disciplines addressing this issue is complemented by a thematic analysis of semi-structured interviews conducted with four higher education institutions in Portugal. The findings indicate that although the number of subjects specifically addressing soft skills is small, there is a growing concern to incorporate soft skills in pedagogical and evaluation methodologies in each course. Several challenges, good practices, and future perspectives are also explored in this work.

Abstract
In recent years, several regulatory initiatives have been carried out at the European Commission level to ensure the ethical use of Artificial Intelligence, including the General Data Protection Regulation, Data Governance Act, or the Artificial Intelligence Act. However, there is also a need for technological solutions that effectively enable the implementation of this regulation in a realistic and efficient way. The main goal of this work is to propose and implement such a technological solution, relying on the notion of observability. The hypothesis is that a set of ethics metrics can be implemented along a domain-agnostic Data Science/Artificial Intelligence pipeline. These metrics, when observed in real time, will allow not only to assess the level of compliance of the pipeline with ethics standards at different levels, but also allow for a timely reaction by the organization when the data, the model or any other artifact in the pipeline exhibits undesired behavior. In this way, some of the most important ethical principles of AI are guaranteed: responsibility and prevention of harm. This work aims to identify a large group of ethics metrics, implement them, map them onto the different stages of a typical Data Science / AI process, and determine whether the presence of these metrics ensures or contributes to the development of AI solutions that can be considered ethical according to the latest European regulation.

Abstract
The combination of class imbalance and overlap is currently one of the most challenging issues in machine learning. While seminal work focused on establishing class overlap as a complicating factor for classification tasks in imbalanced domains, ongoing research mostly concerns the study of their synergy over real-word applications. However, given the lack of a well-formulated definition and measurement of class overlap in real-world domains, especially in the presence of class imbalance, the research community has not yet reached a consensus on the characterisation of both problems. This naturally complicates the evaluation of existing approaches to address these issues simultaneously and prevents future research from moving towards the devise of specialised solutions. In this work, we advocate for a unified view of the problem of class overlap in imbalanced domains. Acknowledging class overlap as the overarching problem - since it has proven to be more harmful for classification tasks than class imbalance - we start by discussing the key concepts associated to its definition, identification, and measurement in real-world domains, while advocating for a characterisation of the problem that attends to multiple sources of complexity. We then provide an overview of existing data complexity measures and establish the link to what specific types of class overlap problems these measures cover, proposing a novel taxonomy of class overlap complexity measures. Additionally, we characterise the relationship between measures, the insights they provide, and discuss to what extent they account for class imbalance. Finally, we systematise the current body of knowledge on the topic across several branches of Machine Learning (Data Analysis, Data Preprocessing, Algorithm Design, and Meta-learning), identifying existing limitations and discussing possible lines for future research.

Abstract