Abstract
This paper deliberates some of the motivations for contemplating Kits in the theoretical-practical class of a Curricular Unit of Process Control to first year students of a Master Degree in Mechanical Engineering, alongside their purpose. Also, the perceptions of these students about the use of these kits in their learning process are discussed based on an online questionnaire developed for that purpose. According to students' feedback, gathered by an anonymous online questionnaire, it was possible to investigate the effectiveness of the use of didactics kits in the learning of Process Control topics. The obtained results from the students perception are clearly positive and motivating to further uses of this type kit as portable laboratories. © 2023 IEEE.

Abstract

Abstract
Low-cost, small-sized portable laboratories, or take-home laboratories, have been increasing in popularity worldwide. One example of such a successful Arduino-based kit is the Temperature Control Laboratory (TCLab), originally proposed by [1]. This kit has been used in Portugal for control engineering education since 2018. This paper proposes a TCLab demo session, reflecting the use of this kit in Portugal across different educational contexts. © 2023 IEEE.

Abstract
The recent release of ChatGPT-3 by OpenAI may have been a major disruptive mark in terms of Artificial Intelligence based tools. The testing and rapid user adoption rate of ChatGPT-3 was massive with a worldwide impact. Despite its recent public release ChatGPT-3 is already eliciting a mix of positive reactions revealing outstanding positive aspects as well as some negative ones. A short evaluation of ChatGPT-3 is presented, using the context of genetic algorithms, a topic lectured in introductory artificial intelligence courses. Examples outlining potential advantages of adopting ChatGPT and disadvantages which raise ethical issues and may limit its use are presented. © 2023 IEEE.

Abstract

Abstract
This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often require excessive time, labour and are hazardous for humans, provide ample room for improvement through collaboration with robots. However, the integration of humans in-the-loop raises open challenges due to the uncertainty that comes with the ambiguous nature of human behaviour. Such uncertainty makes it difficult to represent high-level human behaviour based on low-level sensory input data. The proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) approach addresses this challenge by fuzzifying ambiguous sensory data and developing a combined activity recognition and sequence modelling system using state machines and the LSTM deep learning method. The evaluation process compares the traditional LSTM approach with raw sensory data inputs, a Fuzzy-LSTM approach with fuzzified inputs, and the proposed FS-LSTM approach. The results show that the use of fuzzified inputs significantly improves accuracy compared to traditional LSTM, and, while the fuzzy state machine approach provides similar results than the fuzzy one, it offers the added benefits of ensuring feasible transitions between activities with improved computational efficiency.