Abstract
This paper describes the application of the IVY workbench to the formal analysis of a user interface for a safety-critical aerospace system. The operation manual of the system was used as a requirement document, and this made it possible to build a reference model of the user interface, focusing on navigation between displays, the information provided by each display, and how they are interrelated. Usability-related property specification patterns were then used to derive relevant properties for verification. This paper discusses both the modeling strategy and the analytical results found using the IVY workbench. The purpose of the reference model is to provide a standard against which future versions of the interface may be assessed.

Abstract
In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of mining concept-drifting data streams. However, most of these approaches can only be applied to uni-dimensional classification problems where each input instance has to be assigned to a single output class variable. The problem of mining multi-dimensional data streams, which includes multiple output class variables, is largely unexplored and only few streaming multi-dimensional approaches have been recently introduced. In this paper, we propose a novel adaptive method, named Locally Adaptive-MB-MBC (LA-MB-MBC), for mining streaming multi-dimensional data. To this end, we make use of multi-dimensional Bayesian network classifiers (MBCs) as models. Basically, LA-MB-MBC monitors the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a concept drift is detected, LA-MB-MBC adapts the current MBC network locally around each changed node. An experimental study carried out using synthetic multi-dimensional data streams shows the merits of the proposed method in terms of concept drift detection as well as classification performance.

Abstract
Presently, in several countries, the wind and solar power capacity connected to the distribution network is increasing steadily. In parallel, the investment in smart grid technologies is growing, which will support the integration of renewable energy and represents an opportunity to develop advanced management functions. Forecasting is a key input for several management functions of distribution system operators (DSOs). This chapter describes modeling approaches allowing to take advantage of the wealth of power measurements available in near real-time in a smart grid context [e.g., smart meters and SCADA (supervisory control and data acquisition) system], in order to improve the quality of renewable energy forecasting in a computationally efficient manner. As a basis, the power generation vector is to be considered as a multivariate one, that is, by simultaneously focusing on all sites of interest, instead of trying to build and estimate models at every location, individually. Two real-world test cases with wind and solar generation are used to show the improvement in accuracy from exploring distributed information in a probabilistic forecasting framework. © 2016 John Wiley & Sons, Ltd. All rights reserved.

Abstract
The selection of metafeatures for metalearning (MtL) is often an ad hoc process. The lack of a proper motivation for the choice of a metafeature rather than others is questionable and may originate a loss of valuable information for a given problem (e.g., use of class entropy and not attribute entropy). We present a framework to systematically generate metafeatures in the context of MtL. This framework decomposes a metafeature into three components: meta-function, object and post-processing. The automatic generation of metafeatures is triggered by the selection of a meta-function used to systematically generate metafeatures from all possible combinations of object and post-processing alternatives. We executed experiments by addressing the problem of algorithm selection in classification datasets. Results show that the sets of systematic metafeatures generated from our framework are more informative than the non-systematic ones and the set regarded as state-of-the-art.

Abstract
This paper presents a detailed study of the energy consumption of the different Java Collection Framework (JFC) implementations. For each method of an implementation in this framework, we present its energy consumption when handling different amounts of data. Knowing the greenest methods for each implementation, we present an energy optimization approach for Java programs: based on calls to JFC methods in the source code of a program, we select the greenest implementation. Finally, we present preliminary results of optimizing a set of Java programs where we obtained 6.2% energy savings.

Abstract
Micro grid represents an emergent paradigm to address the challenges of recent smart electrical grid visions, where several small-scale and distributed electrical units cooperate to achieve higher levels of energy self-sustainability, by reducing the main grid dependence. Nevertheless, the realization of this paradigm requires advanced intelligent approaches that are able to effectively manage the micro grid infrastructure and its elements. Multi-agent systems provide a suitable framework to support the development of such systems, where autonomous agents endowed with predictive data analysis capabilities take advantage of the large amount of data produced to predict the renewable energy production and consumption. In this context, this paper presents a predictive data analysis driven multi-agent system for the management of micro grids renewable energy production. The proposed approach was applied to an experimental case study, considering different predictive algorithms and data sources for the short and midterm forecasting of the production of wind and photovoltaic energy-based units.