Abstract
We present a pilot study where spectral unmixing is applied to hyperspectral images captured in a controlled environment with a threefold purpose in mind: validation of our experimental setup, of the data processing pipeline, and of the usage of spectral unmixing algorithms for the aforementioned research avenue. Results from this study show that classical techniques such as VCA and FCLS can be used to distinguish between plastic and nonplastic materials, but struggle significantly to distinguish between spectrally similar plastics, even in the presence of multiple pure pixels. © 2024 IEEE.

Abstract
This paper explores the potential use of acoustic imaging and the use of a multi-frequency multibeam-echosounder (MBES) for monitoring marine litter in the water column. The main goal is to perform a test and validation setup using a simulation and actual experimental setup to determine if the MBES data can detect marine litter in a water column image (WCI) and if using multi-frequency MBES data will allow to better distinguish and characterize marine litter debris in detection applications. Results using simulated HoloOcean Environment and actual marine litter data revealed the successful detection of objects commonly found in ocean litter hotspots at various ranges and frequencies, enablingthe pursue of novel means of automatic detection and classification in MBES WCI data while using multi-frequency capabilities. © 2024 IEEE.

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.

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.