Abstract
Hotspot detection aims at identifying sub-groups in the observations that are unexpected, with respect to some baseline information. For instance, in disease surveillance, the purpose is to detect sub-regions in spatiotemporal space, where the count of reported diseases (e.g. cancer) is higher than expected, with respect to the population. The state-of-the-art method for this kind of problem is the space-time scan statistics, which exhaustively search the whole space through a sliding window looking for significant spatiotemporal clusters. Space-time scan statistics makes some restrictive assumptions about the distribution of data, the shape of the hotspots and the quality of data, which can be unrealistic for some non-traditional data sources. A novel methodology called EigenSpot is proposed where instead of an exhaustive search over the space, it tracks the changes in a space-time occurrences structure. The new approach does not only present much more computational efficiency but also makes no assumption about the data distribution, hotspot shape or the data quality. The principal idea is that with the joint combination of abnormal elements in the principal spatial and the temporal singular vectors, the location of hotspots in the spatiotemporal space can be approximated. The experimental evaluation, both on simulated and real data sets, reveals the effectiveness of the proposed method.

Abstract
The number of available 3D digital objects has been increasing considerably. As such, searching in large collections has been subject of vast research. However, the main focus has been on algorithms and techniques for classification, indexing and retrieval. While some works have been done on query interfaces and results visualization, they do not explore natural interactions. The authors propose a speech interface for 3D object retrieval in immersive virtual environments. As a proof of concept, they developed the LS3D prototype, using the context of LEGO blocks to understand how people naturally describe such objects. Through a preliminary study, it was found that participants mainly resorted to verbal descriptions. Considering these descriptions and using a low cost visualization device, the authors developed their solution. They compared it with a commercial application through a user evaluation. Results suggest that LS3D can outperform its contestant, and ensures better performance and results perception than traditional approaches for 3D object retrieval.

Abstract
This paper presents the analysis of the importance of a set of explanatory (input) variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL). The available input variables include extensive hourly time series records of weather forecasts, previous prices, and regional aggregation of power generations and power demands. The paper presents the comparisons of the forecasting results achieved with a model which includes all these available input variables (EMPF model) with respect to those obtained by other forecasting models containing a reduced set of input variables. These comparisons identify the most important variables for forecasting purposes. In addition, a novel Reference Explanatory Model for Price Estimations (REMPE) that achieves hourly price estimations by using actual power generations and power demands of such day is described in the paper, which offers the lowest limit for the forecasting error of the EMPF model. All the models have been implemented using the same technique (artificial neural networks) and have been satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). The relative importance of each explanatory variable is identified for the day-ahead price forecasts in the MIBEL. The comparisons also allow outlining guidelines of the value of the different types of input information.

Abstract
ENDURE and BLUECOM+ are two EEA Grants projects that aim at enabling the monitoring of large and remote ocean areas by providing wireless communications and energy to humans and systems, namely unmanned vehicles, which are crucial for making large scale ocean monitoring cost-effective. ENDURE targets enabling autonomous underwater vehicles (AUVs) to remain in operation for longer periods of time than what is practical today, thus increasing the possibility of covering larger areas at lower costs. BLUECOM+ aims at connecting systems and humans in remote ocean areas by providing cost-effective, broadband, and reliable communications in alternative to satellite communications. Together these projects will bring up the enablers for data collection under the environmental monitoring programme, as well as to the achievement and sustainability of a GES in marine waters. © 2016 Taylor & Francis Group, London.

Abstract
Optimal operation of power systems with high integration of renewable power sources has become difficult as a consequence of the random nature of some sources like wind energy and photovoltaic energy. Nowadays this problem is solved using the Monte Carlo Simulation (MCS) approach, which allows the consideration of important statistical characteristics of wind and solar power production, such as the correlation between consecutive observations, the diurnal profile of the forecasted power production, and the forecasting error. In this paper, a new model of the unit scheduling of power systems with significant renewable power generation based on the scenario generation/reduction method combined with the priority list (PL) method is proposed that finds the probability distribution function (PDF) of a determined generator be committed or not. This approach allows the recognition of the role of each generation unit on the day-ahead unit commitment (UC) problem with a probabilistic point of view, which is important for acquiring a cost-effective and reliable solution. The capabilities and performance of the proposed approach are illustrated through the analysis of a study case, where the spinning reserve requirements are probabilistically verified with success.

Abstract
The recent advances made in human-computer interaction have allowed us to manipulate digital contents exploiting recognitionbased technologies. However, no work has been reported that evaluates how these interfaces influence the performance of different user groups. With the appearance of multiple sensors and controllers for hand gesture recognition, it becomes important to understand if these groups have similar performance levels concerning gestural interaction, and if some sensors could induce better results than others when dealing with users of different age brackets. In this respect, it could also be important to realize if the device's sensor accuracy in terms of hand / full body recognition influences interaction performance. We compare two gesturesensing devices (Microsoft Kinect and Leap Motion) using Fitts' law to evaluate target acquisition performances, with relation to users' age differences. In this article, we present the results of an experiment implemented to compare the groups' performance using each of the devices and also realize which one could yield better results. 60 subjects took part in this study and they were asked to select 50 targets on the screen as quickly and accurately as possible using one of the devices. Overall, there was a statistically significant difference in terms of performance between the groups in the selection task. On the other hand, users' performance showed to be rather consistent when comparing both devices side by side in each group of users, which may imply that the device itself does not influence performance but actually the type of group does. © 2015 ACM.