Abstract

Abstract
Many of nowadays organizations can be said to be knowledge-based. That is, they have relevant decision-making processes that are supported by data and data mining processes. These data may be created/collected by the organization or acquired from external sources (e.g. open data portals). In any case, the quality of the data will, ultimately, be one of the main drivers of decision quality. In this context, it is important that data-producing organizations also produce relevant meta-information characterizing the provenance of the data, its context or the representation standards used. This paper presents a framework to facilitate this process, promoting the inclusion of information concerning representation standards, provenance, trust and permissions at the data level. The main goal is to promote data usability and, consequently, its value for the organizations. © Springer Nature Switzerland AG 2019.

Abstract
Nowadays, the world is getting increasingly competitive and the quality and the amount of the work presented are one of the decisive factors when choosing an employee. It is no longer necessary to only perform but, to achieve a product with quality, on time, at the lowest possible cost and with the minimum resources. For this reason, the employee must have a high score of attention when performing a task, and the factors that influence attention negatively must be reduced. This is true in many different domains, from the workplace to the classroom. In this paper, we present a nonintrusive smart environment for monitoring people's attention when working in teams. The presented system provides real time information about each individual and information about the team. It can be very useful for team managers to identify potentially distracting events or individuals because when the attention of an individual is not at its best when performing the proposed task, her/his performance will be negatively affected, with consequences for the individual and for the organization.

Abstract
There have been a variety of research approaches that have examined the stress issues related to human-computer interaction including laboratory studies, cross-sectional surveys, longitudinal case studies and intervention studies. A critical review of these studies indicates that there are important physiological, biochemical, somatic and psychological indicators of stress that are related to work activities where human-computer interaction occurs. In a medical or biological context, stress is a physical, mental, or emotional factor that causes bodily or mental tension, which can cause or influence the course of many medical conditions including psychological conditions such as depression and anxiety. In these cases, individuals are under an increasing demand for performance, driving them to be under constant pressure, and consequently to present variations in their levels of stress. To mitigate this condition, this paper proposes to add a new dimension in human-computer interaction through the development of a distributed multi-modal framework approach entitled X3S, which aims to monitor and assess the psychological stress of computer users during high-end tasks, in a non-intrusive and non-invasive way, through the access of soft sensors activity (e.g. task performance and human behaviour). This approach presents as its main innovative key the capacity to validate each stress model trained for each individual through the analysis of cortisol and stress assessment survey data. Overall, this paper discusses how groups of medical students can be monitored through their interactions with the computer. Its main aim is to provide a stress marker that can be effectively used in large numbers of users and without inconvenience.

Abstract
In today's society, there is a compelling need for innovative approaches for the solution of many pressing problems, such as understanding the fluctuations in the performance of an individual when involved in complex and high-stake tasks. In these cases, individuals are under an increasing demand for performance, driving them to be under constant pressure, and consequently to present variations in their levels of stress. Human stress can be viewed as an agent, circumstance, situation, or variable that disturbs the normal functioning of an individual, that when not managed can bring mental problems, such as chronic stress or depression. In this paper, we propose a different approach for this problem. The EUStress application is a non-intrusive and non-invasive performance monitoring environment based on behavioural biometrics and real time analysis, used to quantify the level of stress of individuals during online exams.

Abstract
Learning styles are strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms. The learner's attention affects learning results and can define the success or failure of a student. When students are carrying out learning activities using new technologies, it is extremely important that the teacher has some feedback from the students' work in order to detect potential learning problems at an early stage and then to choose the appropriate teaching methods. In this paper we present a nonintrusive distributed system for monitoring the attention level in students. It is especially suited for classes working at the computer. The presented system is able to provide real-time information about each student as well as information about the class, and make predictions about the best learning style for a student using an ensemble of neural networks. It can be very useful for teachers to identify potentially distracting events and this system might be very useful to the teacher to implement more suited teaching strategies. (C) 2017 Published by Elsevier B.V.