Abstract
Each one of us has a particular way to behave before a conflict. We can assume a cooperative and collaborative attitude or we can behave in a more selfish or competitive way. Given our personality traits or our past experiences, each one has a tendency to behave more towards one or the other. Nonetheless, there are also external factors that influence our conflict handling style including the individuals we are conflicting with, our level of stress or fatigue, the level of escalation of the conflict or even the context or the setting in which it is being settled. Undeniably, the personal conflict resolution style of the parties is preponderant for the outcome of the conflict resolution process. The mediator, while knowing the style of each party, may better conduct the process, namely by pointing out and changing unrealistic, selfish or overly competitive behaviors. Traditionally, the mediator makes use of questionnaires to assess the conflict resolution style of the parties. In this chapter we detail a new view on the problem in which this knowledge is built in real-time, in a non-invasive way, based on the behavior evidenced by the parties during the conflict resolution process. The level of escalation of the conflict is also analyzed similarly, through the non-invasive analysis of the level of stress of the parties during the actual negotiation process. The relationship between stress and the personal conflict handling style is also studied.

Abstract

Abstract
Ambient Intelligence emerged more than two decades ago, with the exciting promise of technologically empowered environments that would be everywhere, cater to all our needs, be constantly available, know who we are and what we like, and allow us to make explicit requests using natural means instead of the traditional mouse and keyboard. At a time in which this technological unravelling was expected to have already happened, we still use the mouse and the keyboard. In this paper we make a brief analysis of why is this evolution taking more than initially expected. We then move on to analyse several different projects that are innovative, in the sense that they encompass fields of application that go beyond the initially envisioned, and show the diverse areas that AmI systems may potentially come to change. © Springer International Publishing Switzerland 2014.

Abstract
In the last years, the amount of devices that can be connected to a network grew significantly allowing to, among other tasks, collect data about the environment or the people in it in a non-intrusive way. This generated nowadays well-known topics such as Big Data or the Internet of Things. This also opened the door to the development of novel and interesting applications. In this paper we propose a distributed system for acquiring data about the users of technological devices in a non-intrusive way. We describe how this data can be collected and transformed to produce meaningful interaction features, that reveal the state of the individuals. We analyse the requirements of such a system, namely in terms of storage and speed, and describe three prototypes currently being used in three different domains of application.

Abstract
The collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students' level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.

Abstract