Abstract: In this article we propose the possibility to increase the level of security during football matches due to analysis of data that are placed on the social networks of these events visitors. We considered different ways to recognize emotions from photographs and evaluate the tone of texts to trace the changes in the level of emotions in the photos depending on, took these pictures during the game with fights in the stands or during normal games. We tested this assumption and our hypothesis is partially confirmed. The software solution for emotion recognition from Microsoft Oxford showed that the level of emotion anger is almost 5 times higher in the photographs taken during the match with fights. In addition, other curious results were obtained, including after an analysis of the key of the comments left by events visitors’ photos.

Abstract: We consider steering of Dynamic Data-Drive Application Systems from two sources, firstly from dynamic data and secondly via human intervention to change parameters of the system. We propose an architecture for such hybrid steering and identify a Time Manager as an essential component. We perform experiments on an actual realisation of such a system, modelling a wa- ter distribution network, to show how the parameters of the Time Manager can be determined.

Abstract: In order to use environmental models effectively for management and decision-making, it is vital establish an appropriate level of confidence in their performance. There are different ways and different methodologies to establish the confidence of the models. For this reason an adequate error formula is a very important thing, because the results of the model can vary substantially. In this paper, we focus on the forest fire spread prediction. Several models have been developed to determine the forest fire propagation. Simulators implementing such models require diverse input parameters to deliver predictions about fire propagation. However, the data describing the actual scenario where the fire is taking place are usually subject to high levels of uncertainty. In order to minimize the impact of the input-data uncertainty a two-stage methodology was developed to calibrate the input parameters in an adjustment stage so that the calibrated parameters are used in the prediction stage to improve the quality of the predictions. Is in the adjustment stage where the error formula plays a crucial role, because different formulas implies different adjustments and, in consequence, different spread predictions. In this paper, different formulas are compared to show the impact in terms of prediction quality in DDDAS for forest fire spread prediction. These formulas have been tested using a real forest fire that took place in Arkadia (Greece) in 2011.

Abstract: Large wildfires can cover hundreds of thousands of acres and continue for months, varying in intensity as they encounter different environmental conditions, which may vary dramatically in time and space during a single fire. They can produce extreme behaviors such as fire whirls, blow-ups, bursts of flame along the surface, and winds ten times stronger than ambient conditions, all of which result from the interactions between a fire and its atmospheric environment and are beyond the capabilities of current operational tools. Coupled weather-wildland fire models tie numerical weather prediction models to wildland fire behavior modules to simulate the impact of a fire on the atmosphere and the subsequent feedback of these fire-induced winds on fire behavior, i.e. how a fire “creates it’s own weather”. The methodology uses one such coupled model, the Coupled Atmosphere-Wildland Fire Environment (CAWFETM) Model, which contains two-way coupling between two components: (1) a numerical weather prediction model formulated for and with numerical methods optimized for simulating airflow at 100s of m in very complex terrain, and (2) a wildland fire component that is based upon semi-empirical relationships for surface fire rate of spread, post-frontal heat release, and a canopy fire model. The fire behavior is coupled to the atmospheric model such that low level winds drive the spread of the surface fire, which in turn release sensible heat, latent heat, and smoke fluxes into the lower atmosphere, in turn feeding back to affect the winds directing the fire. CAWFE been used to explain basic examples of fire behavior and, in retrospective simulations, to reproduce large wildland fire events. Over a wide range of conditions, model results show rough agreement in area, shape, and direction of spread at periods for which fire location data is available; additional events unique to each fire such as locations of sudden acceleration, flank runs up canyons, and bifurcations of a fire into two heads; and locations favorable to formation of phenomena such as fire whirls and horizontal roll vortices. The duration of such events poses a prediction challenge, as meteorological models lose skill over time after initialization, firefighting may impact the fire, and processes such as spotting, in which burning embers are lofted ahead of the fire, are not readily represented with deterministic models. Moreover, validation data for such models is limited and fire mapping and monitoring has been done piecemeal with infrared imaging sensors producing 12-hourly maps of active fires with nominal 1 km pixels, complemented by sub-hourly observations from geostationary satellites at coarser resolution and other valuable but non-routine tools such as airborne infrared mapping. Thus, in recent work, CAWFE has been integrated with with spatially refined (375 m) satellite active fire data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), which is used for initialization of a wildfire already in progress in the model and evaluation of its simulated progression at the time of the next pass. This work develops and applies Dynamic Data System techniques to create innovative approaches to wildfire growth forecasting based on a more symbiotic data-model system.

Abstract: The emergence of applications that demand to handle efficiently growing amounts of data has stimulated the development of new computing architectures with several Processing Units (PUs), such as CPUs core, graphics processing units (GPUs) and Intel Xeon Phi (MIC). Aiming to better exploit these architectures, recent works focus on proposing novel runtime environments that offer a variety of methods for scheduling tasks dynamically on different PUs. A main limitation of such proposals refers to the constrained system configurations, usually adopted to tune and test the proposals, since setting more complete and diversified evaluation environments is costly. In this context, we present D-STHARk, a GUI tool for evaluating Dynamic Scheduling of Tasks in Hybrid Simulated ARchitectures. D-STHARk provides a complete simulated execution environment that allows evaluating dynamic scheduling strategies on simulated applications and hybrid architectures. We evaluate our tool by simulating the dynamic scheduling strategies presented in~\cite{sbac2014}, using the same architecture and application. {\it D-STHARk} was able to achieve the same conclusions originally reported by the authors. Moreover, we performed an experiment varying the number of coprocessors, which was not previously verified due to lack of real architectures, showing that we may reduce the energy consumption, while keeping the same performance.