Browsing by Issue Date, starting with "2024-02-16"
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- Balancing Plug-In for Stream-Based ClassificationPublication . de Arriba-Pérez, Francisco; García-Méndez, Silvia; Leal, Fátima; Malheiro, Benedita; Burguillo-Rial, Juan CarlosThe 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 plug-in 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.
- Smart Stress Relief – An EPS@ISEP 2022 ProjectPublication . Cifuentes, Gema Romera; Camps, Jacobine; Nascimento, Júlia Lopes; Bode, Julian Alexander; Duarte, Abel J.; Malheiro, Benedita; Ribeiro, Cristina; Justo, Jorge; Silva, Manuel F.; Ferreira, Paulo; Guedes, PedroMild 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.
- Citizen Engagement in Urban Planning – An EPS@ISEP 2022 ProjectPublication . Cardani, Carla G.; Couzyn, Carmen; Degouilles, Eliott; Benner, Jan M.; Engst, Julia A.; Duarte, Abel J.; Malheiro, Benedita; Ribeiro, Cristina; Justo, Jorge; Silva, Manuel F.; Ferreira, Paulo; Guedes, PedroInvolving people in urban planning offers many benefits, but current methods are failing to get a large number of citizens to participate. People have a high participation barrier when it comes to public participation in urban planning – as it requires a lot of time and initiative, only a small non-diverse group of citizens take part in governmental initiatives. In this paper, a product is developed to make it as easy as possible for citizens to get involved in construction projects in their community at an early stage. As a solution, a public screen is proposed, which offers citizens the opportunity to receive information, view 3D models, vote and comment at the site of the construction project via smartphone – the solution was named Parcitypate. To explain the functions of the product, a prototype was created and tested. In addition, concepts for branding, marketing, ethics, and sustainability are presented.
- Explainable Classification of Wiki StreamsPublication . García-Méndez, Silvia; Leal, Fátima; de Arriba-Pérez, Francisco; Malheiro, Benedita; Burguillo-Rial, Juan CarlosWeb 2.0 platforms, like wikis and social networks, rely on crowdsourced data and, as such, are prone to data manipulation by ill-intended 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.