Abstract
It is a common affair to settle disputes out of courts nowadays, through negotiation, mediation or any other mean. This has also been implemented over telecommunication means under the so-called Online Dispute Resolution methods. However, this new technology-supported approach is impersonal and cold, leaving aside important issues such as the disputants' body language, stress level or emotional response while being based on forms, e-mails or chat rooms. To overcome this shortcoming, in this paper, it is proposed the creation of intelligent environments for conflict resolution that can complement the existing tools with important knowledge about the context of interaction. This will allow decision-makers to take better framed decisions based not only on figures but also on important contextual information, similar to what happens when parties communicate in the physical presence of each other.

Abstract
Monitoring and managing performance in the workplace is nowadays an important aspect, in a time in which methodologies like Agile push individual and team limits further. Current performance monitoring approaches are either intrusive or based on productivity measures and are thus often dreaded by workers. Moreover, these approaches do not take into account the importance and role of the numerous external factors that influence productivity. We present a non-intrusive performance monitoring environment based on behavioral biometrics and real time analytics. It monitors and analyzes 15 features extracted from the workers' interaction with the computer and can provide a measure of performance that is completely transparent. This measure is sensitive to external factors such as mental fatigue, stress or emotional valence. We validate this environment by assessing the effects of musical selection on Human-Computer Interaction. Results show a significant improvement on mouse motion when participants listen to the selected auditory stimuli and a negative effect on typing performance, especially with stimuli with positive tension. This work will enable the development of performance monitoring and management environments, with benefits for both organizations and individuals.

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.

Abstract
Machine Learning has emerged in the last years as the main solution to many of nowadays' data-based decision problems. However, while new and more powerful algorithms and the increasing availability of computational resources contributed to a widespread use of Machine Learning, significant challenges still remain. Two of the most significant nowadays are the need to explain a model's predictions, and the significant costs of training and re-training models, especially with large datasets or in streaming scenarios. In this paper we address both issues by proposing an approach we deem predictive and user-centric. It is predictive in the sense that it estimates the benefit of re-training a model with new data, and it is user centric in the sense that it implements an explainable interface that produces interpretable explanations that accompany predictions. The former allows to reduce necessary resources (e.g. time, costs) spent on re-training models when no improvements are expected, while the latter allows for human users to have additional information to support decision-making. We validate the proposed approach with a group of public datasets and present a real application scenario.

Abstract
The process of devising solutions for conflict resolution generally configures a challenging task. There exist different approaches to address the problem, namely the use of case-based models or even relying on the parties themselves to perform the task. From a computational point of view, these problems generally represent a NP-complete problem. In order to surpass this shortcoming, in this paper it is presented a biologically inspired method to deal with the problem in which genetic algorithms are used to create possible solutions for a given dispute. The approach presented is able to generate a broad number of diverse solutions that cover virtually the whole search space for a given problem. This approach provides better results than a case-based approach since: (1) it is independent of the legal domain and (2) it does not depend on the number and quality of cases present in a database. The results of this work are being applied in a negotiation tool that is part of the UMCourt conflict resolution platform.

Abstract
Human performance, in all its different dimensions, is a very complex and interesting topic. In this paper we focus on performance in the workplace which, asides from complex is often controversial. While organizations and generally competitive working conditions push workers into increasing performance demands, this does not necessarily correlates positively to productivity. Moreover, existing performance monitoring approaches (electronic or not) are often dreaded by workers since they either threat their privacy or are based on productivity measures, with specific side effects. We present a new approach for the problem of performance monitoring that is not based on productivity measures but on the workers' movements while sitting and on the performance of their interaction with the machine. We show that these features correlate with mental fatigue and provide a distributed architecture for the non -intrusive and transparent collection of this data. The easiness in deploying this architecture, its non -intrusive nature, the potential advantages for better human resources management and the fact that it is not based on productivity measures will, in our belief, increase the willingness of both organizations and workers to implement this kind of performance management initiatives.