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.

Abstract
The European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) is a capstone engineering design programme in which students, organised in multidisciplinary and multicultural teams, develop a solution for a proposed problem, taking into account sustainability, ethical and market concerns. This paper describes a research project aimed at raising awareness and changing behaviour in relation to waste disposal, carried out by a team of EPS@ISEP students during spring 2023. BinIt, as the project is named, targets young adults who want to live in a cleaner city. Unlike other campaigns, it simplifies and stimulates proper waste disposal and recycling, tackling the root of the problem and creating a new social norm. BinIt includes a campaign, a web app and the Garbage Gladiator bin. The app consists of a city map where users can pin and check bin locations, and an educational platform with information on waste disposal and recycling issues. Gamification is incorporated through a ranking system. The Garbage Gladiator is a physical container for urban public spaces specially designed to encourage people to dispose of their waste correctly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

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
Mild is a smart stress relief solution created by DSTRS, an European Project Semester student team enrolled at the Instituto Superior de Engenharia do Porto in the spring of 2022. This paper details the research performed, concerning ethics, marketing, sustainability and state-of-the-art, the ideas, concept and design pursued, and the prototype assembled and tested by DSTRS. The designed kit comprises a bracelet, pair of earphones with case, and a mobile app. The bracelet reads the user heart beat and temperature to automatically detect early stress signs. The case and mobile app command the earphones to play sounds based on the user readings or on user demand. Moreover, the case includes a tactile distractor, a scent diffuser and vibrates. This innovative multi-sensory output, combining auditory, olfactory, tactile and vestibular stimulus, intends to sooth the user.

Abstract
Web 2.0 platforms, like wikis and social networks, rely on crowdsourced data and, as such, are prone to data manipulation by illintended contributors. This research proposes the transparent identification of wiki manipulators through the classification of contributors as benevolent or malevolent humans or bots, together with the explanation of the attributed class labels. The system comprises: (i) stream-based data pre-processing; (ii) incremental profiling; and (iii) online classification, evaluation and explanation. Particularly, the system profiles contributors and contributions by combining features directly collected with content- and side-based engineered features. The experimental results obtained with a real data set collected from Wikivoyage - a popular travel wiki - attained a 98.52% classification accuracy and 91.34% macro F-measure. In the end, this work seeks to address data reliability to prevent information detrimental and manipulation.