Abstract
The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players' behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM - Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP - Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles' batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Abstract
This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network's execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network's integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). © 2012 IEEE.

Abstract
Finding an effective control interface to manipulate complex geometric objects has traditionally relied on experienced users to place the animation controls. This process, whether for key framed or for motion captured animation, takes a lot of time and effort. We introduce a novel sketching interface control system inspired in the way artists draw, in which a stroke defines the shape of an object and reflects the user's intention. We also introduce the canvas, a 2D drawing region where the users can make their strokes, which determines the domain of interaction with the object. We show that the combination of strokes and canvases provides a new way to manipulate the shape of an implicit volume in space. And most importantly, it is independent from the 3D model rig. The strokes can be easily stored and reused in other characters, allowing retargeting of poses. Our interactive approach is illustrated using facial models of different styles. As a result, we allow rapid manipulation of 3D faces on the fly in a very intuitive and interactive way. Our informal study showed that first time users typically master the system within seconds, creating appealing 3D poses and animations in just a few minutes. © 2011 ACM.

Abstract
Good design and implementation are necessary but not sufficient pre-requisites for successfully reusing object-oriented frameworks. Although not always recognized, good documentation is crucial for effective framework reuse, and often hard, costly, and tiresome, coming with many issues, especially when we are not aware of the key problems and respective ways of addressing them. Based on existing literature, case studies and lessons learned, the authors have been mining proven solutions to recurrent problems of documenting object-oriented frameworks, and writing them in pattern form, as patterns are a very effective way of communicating expertise and best practices. This paper presents a small set of patterns addressing problems related to the framework documentation itself, here seen as an autonomous and tangible product independent of the process used to create it. The patterns aim at helping non-experts on cost-effectively documenting object-oriented frameworks. In concrete, these patterns provide guidance on choosing the kinds of documents to produce, how to relate them, and which contents to include. Although the focus is more on the documents themselves, rather than on the process and tools to produce them, some guidelines are also presented in the paper to help on applying the patterns to a specific framework. © 2011 Springer-Verlag Berlin Heidelberg.

Abstract

Abstract
In real-world information retrieval systems, the underlying document collection is rarely stable or definitive. This work is focused on the study of signals extracted from the content of documents at different points in time for the purpose of weighting individual terms in a document. The basic idea behind our proposals is that terms that have existed for a longer time in a document should have a greater weight. We propose 4 term weighting functions that use each document's history to estimate a current term score. To evaluate this thesis, we conduct 3 independent experiments using a collection of documents sampled from Wikipedia. In the first experiment, we use data from Wikipedia to judge each set of terms. In a second experiment, we use an external collection of tags from a popular social bookmarking service as a gold standard. In the third experiment, we crowdsource user judgments to collect feedback on term preference. Across all experiments results consistently support our thesis. We show that temporally aware measures, specifically the proposed revision term frequency and revision term frequency span, outperform a term-weighting measure based on raw term frequency alone.