Abstract
This paper develops a computational tool, based on two-stage stochastic programming, for a wind power producer bidding in the electricity market. The uncertainty related to electricity market prices and wind power production is taken into account. A hybrid intelligent technique, combining wavelet transform, particle swarm optimization and adaptive-networkbased fuzzy inference system, enables the generation of scenarios. Besides, risk aversion is considered using the conditional value-at-risk methodology. Computer simulation results are provided and analyzed. Finally, conclusions are duly drawn.

Abstract
Web interfaces are used nowadays for virtually every kind of computer application. The proliferation of web interfaces created the need to collect and analyze data on how users interact with them. Many web applications used for this purpose rely on what can be called a meta web interface. Meta web interfaces are used for different purposes but they share a set of common features: a web interface based on the subject interface with a second layer interface for collecting data, a central repository for persisting the collected data, and an API for retrieving aggregated data on user interaction. This paper describes Z-Web - a framework for developing meta web interfaces that provides these three features. To create a second layer in the meta web interface a Z-Web server is placed as a proxy between the web client and the subject web server and injects modifications while forwarding HTTP requests. These modifications are typically JavaScript libraries that collect and store data related to user interaction. The framework caches the pages it proxies and provides persistent storage for the collected data. An application interface (API) makes this data available to client application supported by Z-Web. This paper presents an overview of Z-Web, with the general architecture of a web application based on this framework, and describes the design and implementation issues of its main components. Two systems developed with Z-Web are also presented to evaluate the applicability of the framework and its overhead when compared with similar systems.

Abstract

Abstract
The LMS plays a decisive role in most eLearning environments. Although they integrate many useful tools for managing eLearning activities, they must also be effectively integrated with other specialized systems typically found in an educational environment such as Repositories of Learning Objects or ePortfolio Systems. Both types of systems evolved separately but in recent years the trend is to combine them, allowing the LMS to benefit from using the ePortfolio assessment features. This paper details the most common strategies for integrating an ePortfolio system into an LMS: the data, the API and the tool integration strategies. It presents a comparative study of strategies based on the technical skills, degree of coupling, security features, batch integration, development effort, status and standardization. This study is validated through the integration of two of the most representative systems on each category - respectively Mahara and Moodle.

Abstract
Data streams are ubiquitous and have in the last two decades become an important research topic. For their predictive non-parametric analysis, Hoeffding-based trees are often a method of choice, offering a possibility of any-time predictions. However, one of their main problems is the delay in learning progress due to the existence of equally discriminative attributes. Options are a natural way to deal with this problem. Option trees build upon regular trees by adding splitting options in the internal nodes. As such they are known to improve accuracy, stability and reduce ambiguity. In this paper, we present on-line option trees for faster learning on numerical data streams. Our results show that options improve the any-time performance of ordinary on-line regression trees, while preserving the interpretable structure of trees and without significantly increasing the computational complexity of the algorithm. Copyright 2011 by the author(s)/owner(s).

Abstract
Performance management in collaborative networks requires approaches suitable to deal with a larger domain than in traditional organisations, including new processes, new stakeholders and a less clear border between internal and external. The proposed approach addresses objectives and strategies setting, performance measurement and evaluation, monitoring, learning and improvement. It relies on the identification of key stakeholders and on their key success factors which provide the external perspective driving the performance evaluation and improvement. The work starts from the consideration of different natures and life times of Business Communities and Virtual Organisations, analyses the stakeholders' relations and emphasises the clear identification of value creation for stakeholders as an essential component to align organisations.