Abstract
Energy management in buildings can provide massive benefits in financial and energy saving terms. It is possible to optimize energy usage with smart grid techniques, where the benefits are enhanced when the energy consumer can trade the energy on energy markets, since it forces energy providers to compete with each other on the energy price. However, two hurdles oppose this approach: the devices providing control over appliances do not interoperate with each other; and energy markets limit trading activities to large quantities of energy, thus impeding access for small consumers. This work considers using the FlexOffer (FO) concept to allow the consumer to express its energy needs, and FO-related mechanisms to aggregate energy requests into quantities relevant for energy markets. Moreover, the presented system, named FlexHousing, is based on the Arrowhead Framework - a framework that simplifies design and implementation of distributed applications by means of normalizing communication via services - and exploits its Service Oriented mechanisms to provide device interoperability. The implemented FlexHousing system uses multi-level FO aggregation to empower either the final user, for example the owner of an apartment, to manage its own energy by defining their flexibilities, or to offload this responsibility to an energy manager who takes care of all the apartments in a building or set of buildings.

Abstract
Neural networks have been applied as a machine learning tool in many different areas. Recently, they have gained increased attention with what is now called deep learning. Neural networks algorithms have several parameters that need to be tuned in order to maximize performance. The definition of these parameters can be a difficult, extensive and time consuming task, even for expert users. One approach that has been successfully used for algorithm and parameter selection is metalearning. Metalearning consists in using machine learning algorithm on (meta)data from machine learning experiments to map the characteristics of the data with the performance of the algorithms. In this paper we study how a metalearning approach can be used to obtain a good set of parameters to learn a neural network for a given new dataset. Our results indicate that with metalearning we can successfully learn classifiers from past learning tasks that are able to define appropriate parameters.

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Abstract
In this work, we propose a lightweight approach for keyword extraction and ranking based on an unsupervised methodology to select the most important keywords of a single document. To understand the merits of our proposal, we compare it against RAKE, TextRank and SingleRank methods (three well-known unsupervised approaches) and the baseline TF. IDF, over four different collections to illustrate the generality of our approach. The experimental results suggest that extracting keywords from documents using our method results in a superior effectiveness when compared to similar approaches.

Abstract
Gesture recognition is very important for Human-Robot Interfaces. In this paper, we present a novel depth based method for gesture recognition to improve the interaction of a service robot autonomous shopping cart, mostly used by reduced mobility people. In the proposed solution, the identification of the user is already implemented by the software present on the robot where a bounding box focusing on the user is extracted. Based on the analysis of the depth histogram, the distance from the user to the robot is calculated and the user is segmented using from the background. Then, a region growing algorithm is applied to delete all other objects in the image. We apply again a threshold technique to the original image, to obtain all the objects in front of the user. Intercepting the threshold based segmentation result with the region growing resulting image, we obtain candidate objects to be arms of the user. By applying a labelling algorithm to obtain each object individually, a Principal Component Analysis is computed to each one to obtain its center and orientation. Using that information, we intercept the silhouette of the arm with a line obtaining the upper point of the interception which indicates the hand position. A Kalman filter is then applied to track the hand and based on state machines to describe gestures (Start, Stop, Pause) we perform gesture recognition. We tested the proposed approach in a real case scenario with different users and we obtained an accuracy around 89,7%.

Abstract