Abstract
The widespread usage of smart devices and sensors together with the ubiquity of the Internet access is behind the exponential growth of data streams. Nowadays, there are hundreds of machine learning algorithms able to process high-speed data streams. However, these algorithms rely on human expertise to perform complex processing tasks like hyper-parameter tuning. This paper addresses the problem of data variability modelling in data streams. Specifically, we propose and evaluate a new parameter tuning algorithm called Self Parameter Tuning (SPT). SPT consists of an online adaptation of the Nelder & Mead optimisation algorithm for hyper-parameter tuning. The method explores a dynamic size sample method to evaluate the current solution, and uses the Nelder & Mead operators to update the current set of parameters. The main contribution is the adaptation of the Nelder-Mead algorithm to automatically tune regression hyper-parameters for data streams. Additionally, whenever concept drifts occur in the data stream, it re-initiates the search for new hyper-parameters. The proposed method has been evaluated on regression scenario. Experiments with well known time-evolving data streams show that the proposed SPT hyper-parameter optimisation outperforms the results of previous expert hyper-parameter tuning efforts. © 2018, Springer Nature Switzerland AG.

Abstract
This paper presents an overview of the development of SetSun, an outdoor intelligent shader, by a team of five Erasmus students within the framework of the European Project Semester at Instituto Superior de Engenharia do Porto, in the spring of 2018. The major goal of this project-based learning experience was to design a new type of parasol, granting a novel wellness and luxury experience, by combining the functionalities of smart electronics with that of a traditional parasol, while providing the participants with a meaningful learning experience for their future professional life. The Team conducted multiple studies, including scientific, technical, sustainability, marketing, ethics and deontological analyses, and discussions to derive the requirements, design the structure, specify the list of materials and components and develop a functional system. Following these studies, the Team assembled, debugged and tested the SetSun prototype successfully.

Abstract
E-commerce platforms explore the interaction between users and digital content – user generated streams of events – to build and maintain dynamic user preference models which are used to make mean-ingful recommendations. However, the accuracy of these incremental models is critically affected by the choice of hyper-parameters. So far, the incremental recommendation algorithms used to process data streams rely on human expertise for hyper-parameter tuning. In this work we apply our Self Hyper-Parameter Tuning (SPT) algorithm to incremental recommendation algorithms. SPT adapts the Melder-Mead optimi-sation algorithm to perform hyper-parameter tuning. First, it creates three models with random hyper-parameter values and, then, at dynamic size intervals, assesses and applies the Melder-Mead operators to update their hyper-parameters until the models converge. The main contribu-tion of this work is the adaptation of the SPT method to incremental matrix factorisation recommendation algorithms. The proposed method was evaluated with well-known recommendation data sets. The results show that SPT systematically improves data stream recommendations.

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Abstract
Monitoring of environmental radioactivity for the purpose of earthquake prediction requires the discrimination of anomalies of non-tectonic origin from seismically-induced anomalies. This is a challenging task as time series of environmental radioactivity display a complex temporal pattern reflecting a wide range of different physical processes, including meteorological and surface effects. The present study is based on the detailed time series of gamma radiation from the Eastern North Atlantic (ENA) site in the Azores, and on very high resolution precipitation intensity and soil moisture time series. The results show that an abrupt shift in the average level of the gamma radiation time series previously reported as a potential earthquake precursor can also be explained by a corresponding abrupt change in soil moisture. It was concluded that the reduction of false positive earthquake precursors requires the detailed assessment of both precipitation and soil moisture conditions at high temporal resolution.