Abstract
The recognition of emotions in spoken words is one of the most important aspects in human communication and social relationships. Traditional approaches to the study of vocal emotional recognition involve instructing listeners to choose which one of several words describing emotion categories best characterize linguistically neutral utterances or vocalizations uttered by actors portraying various emotional states. To this end, generic experiment control software is usually used, which has some disadvantages. In this paper, we present a system that digitalizes the whole process involved in understanding how people perceive and understand vocal emotions, improving data collection, processing and analysis. Moreover, this system provides a new group of features that allows a more comprehensive characterization of the behavioral dimension underlying vocal emotional recognition. In this paper we describe this system and analyze the relationship between emotional perception, gender, age and Human-Computer Interaction.

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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.