Abstract
P2P networks endowed individuals with the means to easily and efficiently distribute digital media over the Internet, but user legal liability issues may be raised as they also facilitate the unauthorized distribution and reproduction of copyrighted material. Traditional P2P file sharing systems focus on performance and scalability, disregarding any privacy or legal issues that may arise from their use. Lacking alternatives, and unaware of the privacy issues that arise from relaying traffic of insecure applications, users have adopted anonymity systems for P2P file sharing. This work aims at hiding user content interests from malicious peers through plausible deniability. The Mistrustful P2P model is built on the concept of mistrusting all the entities participating in the P2P network, hence its name. It provides a deterministic and configurable privacy protection that relies on cover content downloads to hide user content interests, has no trust requirements, and introduces several mechanisms to prevent user legal liability and reduce network overhead while enabling timely content downloads. We extend previous work on the Mistrustful P2P model by discussing its legal and ethical framework, assessing its feasibility for more use cases, providing a security analysis, comparing it against a traditional P2P file sharing model, and further defining and improving its main mechanisms.

Abstract
In this paper, an optimal scheduling of thermal and wind power plants is presented by using a stochastic programming approach to cover the uncertainties of the forecasted generation of wind farms. Uncertainties related to wind forecast error, consequently wind generation outage power and also system load demand are modeled through scenario generation. Then, with regard to day-ahead and real-time energy markets and taking into account the relevant constraints, the thermal unit commitment problem is solved considering wind energy injection into the system. Besides, in order to assess impacts of Demand Response (DR) on the problem, a load reduction demand response model has been applied in the base model. In this approach, self and cross elasticity is used for modeling the customers' behavior modeling. The results indicate that the DR Programs (DRPs) improves the market efficiency especially in peak hours when the thermal Gencos become critical suppliers and the combination of DRPs and wind farm can be so efficient.

Abstract
In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of several new proposed stream algorithms, ensembles play an important role, in particular for 'non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semi supervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research. Published by Elsevier B.V.

Abstract
In the last decades power systems witnessed the implementation of an organizational and operational restructuring that lead to the introduction of competitive mechanisms in some activities of the value chain. This is the case of generation and retailing with the development of wholesale and retail markets. These developments together with a renewed emphasis on the adoption of more sustainable solutions while maintaining adequate security of supply levels contributed to increase the interest of generation companies for models enabling the optimization of the use of generation assets or for models and tools to help them to prepare and test bidding strategies to the day-ahead markets. Having in mind the increased complexity of the operation of power systems, Agent-Based Models, ABM, are been used to complement the traditional optimization and equilibrium models, taking advantage of the interaction between agents acting in a simulation environment. In this scope, this paper describes an ABM model that uses Q-learning to provide knowledge for the agents to behave in an optimal way. This model is designed to mimic the main features of the common electricity market between Portugal and Spain, the MIBEL. Apart from describing the developed model, this paper also includes preliminary results from its application to the MIBEL case.

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