Abstract
Mental fatigue is a concern for a range of reasons, including its negative impact on productivity and quality of life in general. The maximal working capacity and performance of an individual, whether physical or mental, generally also decreases as the day progresses. The loss of these capabilities is associated with the emergence of fatigue, which is particularly visible in long and demanding tasks or repetitive jobs. However, good management of working time and of the effort invested in each task, as well as the effect of breaks at work can result in better performance and better mental health, delaying the effects of fatigue. In this paper a model and prototype are proposed to detect and monitor fatigue, based on behavioral biometrics (Keystroke Dynamics and Mouse Dynamics). Using this approach, the aim is to develop leisure and work context-aware environments that may improve quality of life and individual performance, as well as productivity in organizations. © Springer International Publishing Switzerland 2016.

Abstract
In a time in which streaming data becomes the new normal in Machine Learning problems, to the detriment of batch data, new challenges arise. In the past, a data source would be static in the sense that all data were known at the moment of the training of the model. A model would be trained and it would be in use for relatively long periods of time. Nowadays, data arrive in real-time and their statistical properties may also change over time, rendering trained models outdated. In this paper we propose an approach to deal with the concept drift problem with minimal computational effort. Specifically, we continuously update an ensemble with new weak learners and adjust their weights according to their performance. This approach is suitable to be used in real-time in the form of an ever-evolving model that adapts to change in the data.

Abstract
Machine Learning is a field in which significant steps forward have been taken in the last years, resulting in a wide variety of available algorithms, for many different problems. Nonetheless, most of these algorithms focus on the training of static models, in the sense that the model stops evolving after the training phase. This is increasingly becoming a limitation, especially in an era in which datasets are increasingly larger and may even arrive as sequential streams of data. Frequently retraining a model, in these scenarios, is not realistic. In this paper we propose evoRF: a combination of a Random Forest with an evolutionary approach. Its key innovative aspect is the evolution of the weights of the Random Forest over time, as new data arrives, thus making the forest's voting scheme adapt to the new data. Older trees can also be replaced by newly trained ones, according to their accuracy, ensuring that the ensemble remains up to date without requiring a whole retraining.

Abstract
In recent years, mobile devices and applications have known a growth that is unprecedented in any other technological field, reaching virtually all aspects of our lives including sports, leisure, social relationships or health. This paper describes the development of an environment to assess auditory emotional recognition based on a mobile application. The primary aim of this work is to provide a valuable instrument that can be used both in research and clinical settings, responding to the strong need of validated measures of emotional processing in Portugal. The secondary aim is to study behavioral features, acquired unobtrusively from the interaction of the participant with the device, in search for a relationship with medical conditions, cognitive impairments, auditory emotional recognition or sociodemographic indicators. This will establish the foundation for the prediction of such aspects based on the analysis of people's interaction with technological devices, providing new potentially interesting diagnostic tools.

Abstract
Machine Learning problems are significantly growing in complexity, either due to an increase in the volume of data, to new forms of data, or due to the change of data over time. This poses new challenges that are both technical and scientific. In this paper we propose a Distributed Learning System that runs on top of a Hadoop cluster, leveraging its native functionalities. It is guided by the principle of data locality. Data are distributed across the cluster, so models are also distributed and trained in parallel. Models are thus seen as Ensembles of base models, and predictions are made by combining the predictions of the base models. Moreover, models are replicated and distributed across the cluster, so that multiple nodes can answer requests. This results in a system that is both resilient and with high availability. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The main focus of an Intelligent environment, as with other applications of Artificial Intelligence, is generally on the provision of good decisions towards the management of the environment or the support of human decision-making processes. The quality of the system is often measured in terms of accuracy or other performance metrics, calculated on labeled data. Other equally important aspects are usually disregarded, such as the ability to produce an intelligible explanation for the user of the environment. That is, asides from proposing an action, prediction, or decision, the system should also propose an explanation that would allow the user to understand the rationale behind the output. This is becoming increasingly important in a time in which algorithms gain increasing importance in our lives and start to take decisions that significantly impact them. So much so that the EU recently regulated on the issue of a “right to explanation”. In this paper we propose a Human-centric intelligent environment that takes into consideration the domain of the problem and the mental model of the Human expert, to provide intelligible explanations that can improve the efficiency and quality of the decision-making processes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.