Abstract
The mature bulk power system requires to meet the needs of 21th century in terms of efficient and effective utilization of electric energy, together with the capability of accommodating recently growing renewable energy resources penetration. As a new idea of modernizing the current grid structure, the smart grid issue is a widely growing area of interest with investments from developed/developing country governments. As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest as such strategies provide consumers the chance for the real-time control of their consumption to reduce their bills, while utilities can lower the peak power value to be supplied to consumers. As a new type of consumer load in the electric market, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-grid (V2G) option instead of peak power procurement from utility. This study aims to discuss the impacts of different DR strategies and EV owner consumer preferences on the reduction of total electricity prices. Different case studies are conducted to better analyze the price reduction potential of different operating strategies.

Abstract
Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results. © 2014 Springer-Verlag.

Abstract
This paper presents an approach for testing mobile applications using reverse engineering and behavioural patterns. The goal of this research work is to ease the testing of mobile applications by automatically identifying and testing behaviour that is common in this type of applications, i.e., behaviour patterns. The approach includes a tool to automatically explore an Android application. This tool also identifies patterns in the behaviour of the application and apply tests previously associated with those patterns. The final results of this research work will be a catalogue of behavioural patterns and the tool which will output a report on the matched patterns and another one on the testing of those patterns.

Abstract
Online social networks present themselves as valuable information sources about their users and their respective behaviours and interests. Many researchers in data mining have analysed these types of data, aiming to find interesting patterns. This paper addresses the problem of identifying and displaying tweet profiles by analysing multiple types of data: spatial, temporal, social and content. The data mining process that extracts the patterns is composed by the manipulation of the dissimilarity matrices for each type of data, which are fed to a clustering algorithm to obtain the desired patterns. This paper studies appropriate distance functions for the different types of data, the normalization and combination methods available for different dimensions and the existing clustering algorithms. The visualization platform is designed for a dynamic and intuitive usage, aimed at revealing the extracted profiles in an understandable and interactive manner. In order to accomplish this, various visualization patterns were studied and widgets were chosen to better represent the information. The use of the project is illustrated with data from the Portuguese twittosphere.

Abstract
Having knowledge about social interactions as a basis for informed decision support in situations of conflict can be determinant. However, lower attention is given to the social network interpretation process in conflict management approaches. The main objective of the work presented here is to identify how the parties' social networks correlate to their negotiation performance and how this can be formalized. Therefore, an experiment was set up in which was tried to streamline all the relevant aspects of the interaction between the individual and its environment that occur in a rich sensory environment (where the contextual modalities were monitored). This research explicitly focuses on the idea that an Ambient Intelligence system can create scenarios that augment the possibilities of reaching a positive outcome taking into account the role of contextualized social relationships in various conflict management strategies.

Abstract