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

Abstract
Secondary Reserve Requirements (SRR) are usually estimated based upon unit failure rates, and demand and intermittent productions forecasting errors. These requirements are very often inputs to energy and reserve generation dispatch models. However, for the long term, the fact that renewable generation investments must also be computed, affects these requirements. This paper proposes a new Unit Commitment (UC) to represent the SRR in long-term electricity generation models as a function of the renewable investment decisions. Specifically, SRRs are computed as a function of the forecasting errors of renewable productions, and of the unavailability rates of the generation units, which are also outputs of the UC. The case studies show that, when SRRs are endogenous, investments in renewable generation can be lower than expected due to the additional reserve costs these technologies involve. © 2017 IEEE.

Abstract
Crowdsourcing has become an essential source of information for tourists and the tourism industry. Every day, large volumes of data are exchanged among stakeholders in the form of searches, posts, shares, reviews or ratings. This paper presents a tourist-centred analysis of crowd-sourced hotel information collected from the Expedia platform. The analysis relies on Data Mining methodologies to predict trends and patterns which are relevant to tourists and businesses. First, we propose an approach to reduce the crowd-sourced data dimensionality, using correlation and Multiple Linear Regression to identify the single most representative rating. Finally, we use this rating to model the hotel customers and predict hotel ratings, using the Alternating Least Squares algorithm. In terms of contributions, this work proposes: (i) a new crowd-sourced hotel data set; (ii) a crowd-sourced rating analysis methodology; and (iii) a model for the prediction of personalised hotel ratings.

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