Abstract
In a time in which a significant amount of interpersonal interactions take place online, one must enquire to which extent are these milieus suitable for supporting the complexity of our communication. This is especially important in more sensitive domains, such as the one of Online Dispute Resolution, in which inefficient communication environments may result in misunderstandings, poor decisions or the escalation of the conflict. The conflict manager, in particular, may find his skills severely diminished, namely in what concerns the accurate perception of the state of the parties. In this paper the development of a rich communication framework is detailed that conveys contextual information about their users, harnessed from the transparent analysis of their behaviour while communicating. Using it, the conflict manager may not only better perceive the conflict and how it affects each party but also take better contextualized decisions, closer to the ones taken in face-to-face settings.

Abstract
This paper proposes a mixed-integer nonlinear programming approach to maximize the total expected profit of a price-taker hydro producer operating in a pool-based electricity market. Head dependence, commitment decisions, discharge ramping, startup costs and forbidden zones are all effectively handled in our approach. Market uncertainty is modeled via price scenarios and risk management is suitably addressed using conditional value-at-risk. Appropriate offering strategies to the day-ahead market are developed, consisting of hourly supply functions generated for different risk levels. A realistic cascaded hydro system with seven reservoirs is considered as a case study for analyzing and comparing risk-neutral vs. risk-averse results. Conclusions are duly drawn.

Abstract
[No abstract available]

Abstract
In complexity theory, scheduling problem is considered as a NP-complete combinatorial optimization problem. Since Multi-Agent Systems manage complex, dynamic and unpredictable environments, in this work they are used to model a scheduling system subject to perturbations. Meta-heuristics proved to be very useful in the resolution of NP-complete problems. However, these techniques require extensive parameter tuning, which is a very hard and time-consuming task to perform. Based on Multi-Agent Learning concepts, this article propose a Case-based Reasoning module in order to solve the parameter-tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance. © 2013 Springer-Verlag Berlin Heidelberg.

Abstract
E-Learning, much like any other communication processes, has been significantly shaped by technological evolution. In its original form, e-Learning aimed to bring the education closer to people, making it more modular and personalized. However, in reality, we observe that it represents a separation between student and teacher, simplifying this relationship to the exchange of "text-based messages", leaving aside all the important contextual richness of the classroom. We are addressing this issue by devising a contextual layer for e-Learning platforms. Particularly, in this paper we describe a solution to convey information about the level of stress of the students so that the teacher can take better and more informed decisions concerning the management of the learning process.

Abstract
This paper studies complex coordination mechanisms based on constraint satisfaction. In particular, it focuses on data-sensitive connectors from the Reo coordination language. These connectors restrict how and where data can flow between loosely-coupled components taking into account the data being exchanged. Existing engines for Reo provide a very limited support for data-sensitive connectors, even though data constraints are captured by the original semantic models for Reo. When executing data-sensitive connectors, coordination constraints are not exhaustively solved at compile time but at runtime on a per-need basis, powered by an existing SMT (satisfiability modulo theories) solver. To deal with a wider range of data types and operations, we abstract data and reduce the original constraint satisfaction problem to a SAT problem, based on a variation of predicate abstraction. We show soundness and completeness of the abstraction mechanism for well-defined constraints, and validate our approach by evaluating the performance of a prototype implementation with different test cases, with and without abstraction. © Springer-Verlag Berlin Heidelberg 2013.