Abstract

Abstract
Cities are becoming increasingly complex to manage, as they increase in size and must provide higher living standards for their populations. New technology-based solutions must be developed towards attending this growth and ensuring that it is socially sustainable. This paper puts forward the notion that these solutions must share some properties: they should be anthropocentric, holistic, horizontal, multi-dimensional, multi-modal, and predictive. We propose an architecture in which streaming data sources that characterize the city context are used to feed a real-time graph of the city's assets and states, as well as to train predictive models that hint into near future states of the city. This allows human decision-makers and automated services to take decisions, both for the present and for the future. To achieve this, multiple data sources about a city were gradually connected to a message broker, that enables increasingly rich decision-support. Results show that it is possible to predict future states of a city, in aspects such as traffic, air pollution, and other ambient variables. The key innovative aspect of this work is that, as opposed to the majority of existing approaches which focus on a real-time view of the city, we also provide insights into the near-future state of the city, thus allowing city services to plan ahead and adapt accordingly. The main goal is to optimize decision-making by anticipating future states of the city and make decisions accordingly.

Abstract
Population aging brings increased social problems. Solutions for this new reality must be devised. Providing care services at home may benefit patients, health service providers, and social security systems and needs to be seen as a possible solution for those social problems. By maintaining the patient at home, in his or her own environment, care services costs can be diminished and, at the same time, the comfort and well-being of the person in need are significantly increased. To pursue this goal, we explore the advantages that ambient assisted living can bring to people in a home environment, focusing on the problems of health care services at home. Specifically, in this chapter, we present a framework focused on the monitoring and assistance of the elderly that are living alone, focusing on those elderly with memory disabilities. We believe that this approach will enable the challenges that the current trend of population aging poses to be tackled. © 2015 by Taylor & Francis Group, LLC.

Abstract
Artificial Intelligence is a hot topic and Machine Learning is one of the most fluent approaches and practices. The problem with many AI models is that they can be useful for predicting but they are bad at explaining why they behave a certain way. In some contexts, the explanation may even be more important than the prediction itself, mainly in systems in which decisions are made based on their predictions. Therefore, it is increasingly necessary to provide a forecast accompanied by an explanation, when decisions are made automatically. This paper aims to contribute to the solution of problem based on human mobility research, or at least, to be a starting point for its solution.

Abstract
The evaluation is a determining factor in developing successful strategies for learning. In a classroom context, the teacher can observe the behavior of students and identify different ways to facilitate the assessment without inducing stress, avoiding the negative consequences of this on the result of learning. However, in learning environments eLearning direct contact is impossible and, therefore, there should be alternative ways to provide both detection and prevention of stress during the evaluation. It is therefore appropriate stress analysis and identification of strategies for solving problems arising from its appearance. In this work, a stress analysis module for use is proposed for the online student assessment, which is capable of indicating to the teaching moments more propitious time to intervene and the contents that cause greater difficulties. In this way, the teacher can effectively assist students who need it most.

Abstract
In recent years, the development and use of mobile devices such as smartphones and tablets grew significantly. They are used for virtually every activity of our lives, from communication or online shopping to e-banking or gaming, just to name a few. As a consequence, these devices contribute significantly to make our lives more digital, with all the perks and risks that this encompasses. One of the most serious risk is that of an authorized individual gaining physical access to our mobile device and, potentially, to all the applications and personal data it contains. Most of mobile devices are protected using some kind of password, that can be easily spotted by unauthorized users or event guessed. In the last years, new authentication mechanisms have been proposed, such as those using traditional biometrics or behavioral biometrics. In this paper we propose a new continuous authentication mechanism for mobile devices based on behavioral biometrics that monitors user interaction behavior for classifying the identity of the user. © Springer Nature Switzerland AG 2020.