Abstract
This paper introduces the Disturbance Description Language (DDL), an XML dialect intended to describe a number of anomalous elements that can occur in a given scenario (including people, vehicles, fire or focus of pollution) and their respective properties, such as temporal availability, location, motion pattern and details for individual components, such as growth pattern and detectability. This dialect is part of a framework to support the execution of cooperative missions by a group of vehicles, in a simulated, augmented or real environment. An interface was incorporated into the framework, for creating and editing XML files following the defined schema. Once the information is correctly specified, it can be used in the framework, thus facilitating the process of environment disturbances specification and deployment. A survey answered by both practitioners and researchers shows that the degree of satisfaction with DDL is elevated (the overall evaluation of DDL achieved a 4.14 score (out of 5), with 81.1% of the answers being equal to or above 4); also, the usability of the interface was evaluated, having achieved a score of 83.6 in the SUS scale. These results imply that DDL is flexible enough to represent several types of disturbances, through a user-friendly interface.

Abstract
For the project of an Agile/Virtual Enterprise (A/V E) the resources selection is a key factor. The resources systems (output of the selection process) should be prepared to guarantee quality, efficiency and cost-attractiveness, in order to ensure the agility and integrability of the A/V E. This is a difficult matter because it can be a combinatorial and multi-criteria problem. Despite the potential of Value Analysis (VA), none of the resources selection models found in the literature incorporates the evaluation of the resources value. They approach mainly the factors cost and/or time. So, our model constitutes an innovative approach because it gives the highest importance to the value of the resources systems, through the incorporation of VA. The main objective is to quantify the selection process performance with VA integrated into the pre-selection of resources in accordance with the developed model. The paper contribution is the positive confirmation, through the simulation results analysis, of the benefits of VA integration in the resources selection process: greater applicability domain for candidate resources and number of tasks; and reduction of the selection time. In conclusion, the increased efficiency and the superior applicability domain of the model are demonstrated.

Abstract
Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors' research group has developed a multi-agent system: Multi-Agent System for Competitive Electricity Markets (MASCEM), which simulates the electricity markets environment. MASCEM is integrated with Adaptive Learning Strategic Bidding System (ALBidS) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network (ANN), originating promising results: an effective electricity market price forecast in a fast execution time. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Abstract
The overwhelming amount of news content published online every day has made it increasingly difficult to perform macro-level analysis of the news landscape. Visual exploration tools harness both computing power and human perception to assist in making sense of large data collections. In this paper, we employed three visualization tools to explore a dataset comprising one million articles published by news organizations and blogs. The visual analysis of the dataset revealed that 1) news and blog sources evaluate very differently the importance of similar events, granting them distinct amounts of coverage, 2) there are both dissimilarities and overlaps in the publication patterns of the two source types, and 3) the content's direction and diversity behave differently over time.

Abstract
We present D'accord, a generative music system for creating harmonically compatible accompaniments of symbolic and musical audio inputs with any number of voices, instrumentation, and complexity. The main novelty of our approach centers on offering multiple ranked solutions between a database of pitch configurations and a given musical input based on tonal pitch relatedness and consonance indicators computed in a perceptually motivated Tonal Interval Space. Furthermore, we detail a method to estimate the key of symbolic and musical audio inputs based on attributes of the space, which underpins the generation of key-related pitch configurations. The system is controlled via an adaptive interface implemented for Ableton Live, MAX, and Pure Data, which facilitates music creation for users regardless of music expertise and simultaneously serves as a performance, entertainment, and learning tool. We perform a threefold evaluation of D'accord, which assesses the level of accuracy of our key-finding algorithm, the user enjoyment of generated harmonic accompaniments, and the usability and learnability of the system.

Abstract