Abstract
The number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. Therefore, the online processing of these data streams requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated. Like their batch-learning counterparts, stream-based learning algorithms require careful hyperparameter settings. However, this problem is exacerbated in online learning settings, especially with the occurrence of concept drifts, which frequently require the reconfiguration of hyperparameters. In this article, we present SSPT, an extension of the Self Parameter Tuning (SPT) optimisation algorithm for data streams. We apply the Nelder-Mead algorithm to dynamically-sized samples, converging to optimal settings in a single pass over data while using a relatively small number of hyperparameter configurations. In addition, our proposal automatically readjusts hyperparameters when concept drift occurs. To assess the effectiveness of SSPT, the algorithm is evaluated with three different machine learning problems: recommendation, regression, and classification. Experiments with well-known data sets show that the proposed algorithm can outperform previous hyperparameter tuning efforts by human experts. Results also show that SSPT converges significantly faster and presents at least similar accuracy when compared with the previous double-pass version of the SPT algorithm.

Abstract

Abstract
In spite of their growing maturity, telecommunication operators lack complete client characterisation, essential to improve quality of service. Additionally, studies show that the cost to retain a client is lower than the cost associated to acquire new ones. Hence, understanding and predicting future client actions is a trend on the rise, crucial to improve the relationship between operator and client. In this paper, we focus in pay-as-you-go clients with uneven top-ups. We aim to determine to what extent we are able to predict the individual frequency and average value of monthly top-ups. To answer this question, we resort to a Portuguese mobile network operator data set with around 200 000 clients, and nine-month of client top-up events, to build client profiles. The proposed method adopts sliding window multiple linear regression and accuracy metrics to determine the best set of features and window size for the prediction of the individual top-up monthly frequency and monthly value. Results are very promising, showing that it is possible to estimate the upcoming individual target values with high accuracy.

Abstract
The European Project Semester (EPS) offered by the Instituto Superior de Engenharia do Porto (ISEP) provides engineering, business and product design undergraduates with a project-based learning experience in a multicultural and multidisciplinary teamwork environment. This paper reports the research and development of a reconfigurable and ergonomic three-level desk, for people who live in small spaces, by a multicultural and multidisciplinary team of five students. The main objective of the project was to integrate ethics- and sustainability-driven practices in the design, simulation and test an ergonomic, transformable desk. The FREE desk proposal aims to create a comfortable and dynamic working environment for people while providing a transformable space for different daily activities. This goal was pursued by designing a reconfigurable product, a smart desk that offers the user three levels of adjustability: bench level, sitting desk level, and standing desk level. The desk includes a folding light-sensor lamp into the table top and an integrated battery, in order to create a proper working space. The selected materials have a low environmental impact. The solution comes with different options regarding the table top lifting mechanism. This paper describes the state-of-the-art research, the ethics, sustainability, and marketing analyses, the design and simulation of the FREE desk as well as the obtained results.

Abstract
Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters - Memory-based and Model-based - using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

Abstract
The European Project Semester (EPS) is a multicultural, multidisciplinary teamwork and project-based learning framework offered to engineering, business and product design undergraduates by a network of European Higher Education institutions, including the Instituto Superior de Engenharia do Porto (ISEP). In the spring of 2021, five EPS@ISEP students from distinct countries and fields of study joined efforts to address the smart and sustainable food production issue. This paper reports their research and development of Wormify, a solution based on vermicomposting. The main goal of the project was to design, simulate, test and build a prototype following ethical and sustainable practices. Wormify aims to minimize the problem of feeding the growing global population, and to prevent food waste from going to landfills. These objectives were pursued by designing a smart modular system for urban rooftops or small balconies. Several modules can be connected to form a place for residents to meet and socialize. The smart system allows monitoring through an app/website. This paper presents the background studies, the concept and design, the development and final results.