Abstract

Abstract
Even in a liberalized environment, managing the security of supply associated to the generating system continues to be a major task of the System Operators. The increased use of renewable energy, in particular wind power, adds new challenges to the process, namely in countries like Portugal and Spain, where strong investments in wind power have been done and are foreseen for the next years. In order to tackle this issue, REN (the Portuguese TSO), REE (the Spanish TSO) and INESC Porto (a RandD institute) joined together to develop a project where Monte Carlo simulation is used to evaluate the risk associated with specific future configurations of the generating system, until the horizon of 2025, in the framework of medium and long term generation planning of MIBEL (the Iberian electricity market). Probabilistic simulation was chosen because deterministic approaches to this problem, although simple to understand and easy to implement, are unable to tackle the complex relations between different uncertain variables. In this project, simulation is organized chronologically, in order to preserve the relations between load and the different variables associated to generation and produce meaningful risk indices of generation adequacy. A new feature is the analysis of the operational reserve, through a process that estimates, in each simulated state, the unforeseen change in load and wind generation. These unexpected changes are then compared with the total available operational reserve, defined by the secondary reserve plus the tertiary reserve units with lead time up to one hour. It is important to point out that the proposed simulation is a tool that is able to quantify the adequacy of different reserve requirements, solutions for reserve enhancement, etc. , in order to support the decision making process. This paper describes the concepts and assumptions of the simulation model and presents results and conclusions of some of the case studies carried out in the project.

Abstract
More and more current software systems rely on non trivial coordination logic for combining autonomous services typically running on different platforms and often owned by different organizations. Often, however, coordination data is deeply entangled in the code and, therefore, difficult to isolate and analyse separately. COORDINSPECTOR is a software tool which combines slicing and program analysis techniques to isolate all coordination elements from the source code of an existing application. Such a reverse engineering process provides a clear view of the actually invoked services as well as of the orchestration patterns which bind them together. The tool analyses Common Intermediate Language (CIL) code, the native language of Microsoft Net Framework. Therefore, the scope of application Of COORDINSPECTOR is quite large: potentially any piece of code developed in any of the programming languages which compiles to the Net Framework. The tool generates graphical representations of the coordination layer together and identifies the underlying business process orchestrations, rendering them as Ore specifications.

Abstract
Classification is a quite relevant task within data analysis field. This task is not a trivial task and different difficulties can arise depending on the nature of the problem. All these difficulties can become worse when the datasets are too large or when new information can arrive at any time. Incremental learning is an approach that can be used to deal with the classification task in these cases. It must alleviate, or solve, the problem of limited time and memory resources. One emergent approach uses concentration bounds to ensure that decisions are made when enough information supports them. IADEM is one of the most recent algorithms that use this approach. The aim of this paper is to improve the performance of this algorithm in different ways: simplifying the complexity of the induced models, adding the ability to deal with continuous data, improving the detection of noise, selecting new criteria for evolutionating the model, including the use of more powerful prediction techniques, etc. Besides these new properties, the new system, IADEM-2, preserves the ability to obtain a performance similar to standard learning algorithms independently of the datasets size and it can incorporate new information as the basic algorithm does: using short time per example.

Abstract
With the increasing number of devices carried by users and the emergence of more dynamic network scenarios, new solutions have to be developed to support ubiquitous communications. The need for dynamic and transparent auto-configuration and adaptation of applications, devices and networks of devices to a wide variety of events is one of the major challenges. This paper presents a protocol to be used with notification systems, to support the registration, subscription and distribution of generic events among heterogeneous and independent entities, thus addressing the requirements of future communication scenarios.

Abstract
If, as a well-known aphorism states, modelling is for reasoning, this paper is an attempt to define and apply a formal semantics to UML sequence diagrams in order to enable rigourous reasoning about them. Actually, model transformation plays a fundamental role in the process of software development, in general, and in model driven engineering in particular. Being a de facto standard in this area, UML is no exception, even if the number and diversity of diagrams expressing UML models makes it difficult to base its semantics on a single framework. This paper builds on previous attempts to base UML semantics in a coalgebraic setting and illustrates the application of the proposed framework to reason about composition and refactoring of sequence diagrams. © 2008 IEEE.