Abstract
Telecommunication operators compete not only for new clients, but, above all, to maintain current ones. The modelling and prediction of the top-up behaviour of prepaid mobile subscribers allows operators to anticipate customer intentions and implement measures to strengthen customer relationship. This research explores a data set from a Portuguese operator, comprising 30 months of top-up events, to predict the top-up monthly frequency and average value of prepaid subscribers using offline and online multi-target regression algorithms. The offline techniques adopt a monthly sliding window, whereas the online techniques use an event sliding window. Experiments were performed to determine the most promising set of features, analyse the accuracy of the offline and online regressors and the impact of sliding window dimension. The results show that online regression outperforms the offline counterparts. The best accuracy was achieved with adaptive model rules and a sliding window of 500,000 events (approximately 5 months). Finally, the predicted top-up monthly frequency and average value of each subscriber were converted to individual date and value intervals, which can be used by the operator to identify early signs of subscriber disengagement and immediately take pre-emptive measures.

Abstract

Abstract
Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90% values for all evaluation metrics (accuracy, precision, recall, and F-measure).

Abstract
Involving 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 possibleforcitizenstogetinvolvedinconstructionprojectsintheircommunity 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.

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.