Abstract
This study proposes two predictive models of classification that allow to identify, at the end of the 1st and 2nd semesters, the undergraduate students of a higher education institution more prone to academic dropout. The proposed methodology, which combines 3 popular data mining algorithms, such as random forest, support vector machines and artificial neural networks, in addition to contributing to predictive performance, allows to identify the main factors behind academic dropout. The empirical results show that it is possible to reduce to about 1/4 the 4 tens potential predictors of dropout, and show that there are essentially two predictors, concerning student’s curriculum context, that explain this propensity. This knowledge is useful for decision-makers to adopt the most appropriate strategic measures and decisions in order to reduce student dropout rates.

Abstract
Thin-film sensors surfaces are becoming popular to collect data in several specific and complex processes, namely plastic injection or metal stamping, allowing the digitization of such processes through the use of Internet of Things technologies. A particular challenge in such thin-film sensors surfaces is the data acquisition and signal conditioning system, which implementation is complex due to the characteristics of these sensors (e.g., low amplitude and noisy signals), but even more complex when implemented in real industrial processes, which are subject to harsh conditions, namely noise, dirt and aggressive elements. This work describes a modular data acquisition and signals conditioning system for thin-film sensors surfaces, meeting the requirements of scalability, robustness and low-cost, meaning that it can be easily expanded according to the number of sensors required for the application scenario.

Abstract

Abstract
The defense industry has unique features involving national sovereignty. Despite the characteristics that led to the separation of the military and civil spheres, since the 1990s, the number of dual-use projects has been growing. Taking into account that Portugal is a small European country, this paper analyzes the relationships within the defense industry in order to determine how university–industry–government relationships (the Triple Helix) function in this specific industry. The analysis of 145 projects of the Portuguese Ministry of Defense led to the following conclusions: first, academia was represented in more than 90% of the projects, and 40% of those projects have a dual-use application; second, there is a predominance of knowledge production, dissemination and application, for which the university’s institutional sphere is essential and third, the Triple Helix system evolves into a network of relationships that involve projects with both civil and military applications. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Abstract
Machine Learning systems are generally thought of as fully automatic. However, in recent years, interactive systems in which Human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained Machine Learning models which improve over time. In this paper we show that the order by which instances are evaluated by the auditors, and their feedback incorporated, influences the evolution of the performance of the system over time. The goal of this paper is to study of different instance selection strategies for Human evaluation and feedback can improve the learning speed. This information can then be used by the system to determine, at each moment, which instances would improve the system the most, so that these can be suggested to the users for validation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Deep brain stimulation (DBS) surgery is the gold standard therapeutic intervention in Parkinson's disease (PD) with motor complications, notwithstanding drug therapy. In the intraoperative evaluation of DBS's efficacy, neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different stimulation parameters and electrode positions. To tackle this subjectivity, we designed a wearable device to quantitatively evaluate the wrist rigidity changes during the neurosurgery procedure, supporting physicians in decision-making when setting the stimulation parameters and reducing surgery time. This system comprises a gyroscope sensor embedded in a textile band for patient's hand, communicating to a smartphone via Bluetooth and has been evaluated on three datasets, showing an average accuracy of 80%. In this work, we present a system that has seen four iterations since 2015, improving on accuracy, usability and reliability. We aim to review the work done so far, outlining the iHandU system evolution, as well as the main challenges, lessons learned, and future steps to improve it. We also introduce the last version (iHandU 4.0), currently used in DBS surgeries at SAo JoAo Hospital in Portugal.