Abstract
The latest technological advances drive the emergence of countless real-time data streams fed by users, sensors, and devices. These data sources can be mined with the help of predictive and classification techniques to support decision-making in fields like e-commerce, industry or health. In particular, stream-based classification is widely used to categorise incoming samples on the fly. However, the distribution of samples per class is often imbalanced, affecting the performance and fairness of machine learning models. To overcome this drawback, this paper proposes Bplug, a balancing plug-in for stream-based classification, to minimise the bias introduced by data imbalance. First, the plugin determines the class imbalance degree and then synthesises data statistically through non-parametric kernel density estimation. The experiments, performed with real data from Wikivoyage and Metro of Porto, show that Bplug maintains inter-feature correlation and improves classification accuracy. Moreover, it works both online and offline.

Abstract
This work applies Natural Language Processing (NLP) techniques, specifically transformer models, for the emotional evaluation of open-ended responses. Today's powerful advances in transformer architecture, such as ChatGPT, make it possible to capture complex emotional patterns in language. The proposed transformer-based system identifies the emotional features of various texts. The research employs an innovative approach, using prompt engineering and existing context, to enhance the emotional expressiveness of the model. It also investigates spaCy's capabilities for linguistic analysis and the synergy between transformer models and this technology. The results show a significant improvement in emotional detection compared to traditional methods and tools, highlighting the potential of transformer models in this domain. The method can be implemented in various areas, such as emotional research or mental health monitoring, creating a much richer and complete user profile.

Abstract
Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80% accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.

Abstract
Small living spaces require ingenious solutions that are functional, ergonomic and, above all, reconfigurable. This project for smart, ergonomic and adjustable furniture was embraced by a team of students from different countries, universities and study areas enrolled in the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP). EPS is a design project where international students work in teams to create a solution to a real problem from scratch, analysing the state of the art, the market and the associated ethical and sustainability issues. As a project-based learning process, EPS aims to prepare engineering students to work together in multidisciplinary teams, develop personal skills and address the challenges of the contemporary world. The current project aims to design, simulate and test an ethically and sustainability-driven safe and transformable furniture. Amplea is the adjustable furniture solution developed by five EPS students in spring 2023. It transforms into a kitchen counter, dining table or standing desk. By transforming easily, Amplea’s design provides more comfort and saves space in small living spaces. This paper summarises the research, the design of the solution and the development and testing of the proof-of-concept prototype. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
This paper describes the project based learning experience of a multidisciplinary and multicultural team of students enrolled in the spring of 2023 on the European Project Semester at the Instituto Superior de Engenharia do Porto (EPS@ISEP). Animo is an original blimp based concept that aims to help farmers better manage their livestock. Its development was motivated by the difficulty to effectively monitor cattle herds over vast areas, especially in remote locations where locating animals is challenging. This environmentally friendly solution offers real-time livestock monitoring without thermal engines. Real-time monitoring is achieved through the blimp’s extensive animal data collection. Farmers may discover and handle quickly herd welfare issues by accessing information via a user-friendly App. With an emphasis on accessibility and environmental sustainability, Animo seeks to increase agricultural productivity and profitability. The user controls the blimp motion through the app to obtain a comprehensive farm view. Targeting Australia’s large cattle stations, it aims to enhance productivity while minimising the environmental impact. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The technological revolution experienced over the last two decades, together with changes in shopping behaviour, has led supermarkets to consider smart shopping trolleys. Recently, several companies have tested and implemented smart services and devices, such as smart shopping carts with scanners, automatic payment methods, or self-payment locations, to maximise supermarket profits by reducing staff and improving the customer experience. In the spring of 2023, a team of six students enrolled in the European Project Semester at Instituto Superior de Engenharia do Porto (ISEP) proposed FESmarket, an innovative smart shopping cart solution. The user-centred design focused on making the shopping interaction and experience more efficient, comfortable, and satisfactory. Form (balancing aesthetics with innovation), function (selecting functionalities based on the most disruptive technologies), market (fulfilling the identified needs), sustainability (minimising the use of resources), and ethics (respecting human values) are the pillars of the project. FESmarket proposes a smart shopping trolley equipped a built-in touch screen for real-time information on products and their location, cameras for product identification, an audio assistance system, a refrigeration chamber, and a mobile app interface for the customer. Finally, a proof-of-concept prototype was assembled and tested to validate the viability of the designed solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.