Abstract

Abstract
In this work, we propose a Dynamic Difficulty Adjustment methodology to achieve automatic video game balance. The balance task is modeled as a meta game, a game where actions change the rules of another base game. Based on the model of Reinforcement Learning (RL), an agent assumes the role of a game master and learns its optimal policy by playing the meta game. In this new methodology we extend traditional RL by adding the existence of a meta environment whose state transition depends on the evolution of a base environment. In addition, we propose a Multi Agent System training model for the game master agent, where it plays against multiple agent opponents, each with a distinct behavior and proficiency level while playing the base game. Our experiment is conducted on an adaptive grid-world environment in singleplayer and multiplayer scenarios. Our results are expressed in twofold: (i) the resulting decision making by the game master through gameplay, which must comply in accordance to an established balance objective by the game designer; (ii) the initial conception of a framework for automatic game balance, where the balance task design is reduced to the modulation of a reward function (balance reward), an action space (balance strategies) and the definition of a balance space state.

Abstract
With the penetration of smart homes in distribution systems, and due to the effect of their schedulable load on reducing the peak load of the network as well as their comfort index, microgrid?s scheduling in the presence of smart homes has become an important issue. In this regard, this paper presents a tri-objective optimization framework for energy management of microgrids in the presence of smart homes and demand response (DR) program. The model is implemented on an 83-bus distribution system with 11 microgrids. The uncertainties of renewable energy resources (RESs) output power and load demand have been taken into account and the objective function is modeled in the form of bi-objective and tri-objective models using the max-min fuzzy method. The objectives include the operating cost, emissions, and peak-to-average ratio (PAR). The results indicate that an increase in DR penetration reduces the PAR and operating costs and leads to a decrease in the customers? comfort. Besides, the simulation results show that the best results are obtained from the tri-objective model, and in this model, three goals, including the operating costs, emissions, and PAR index are close to their optimal values, while the customers? comfort index is also satisfactory. Finally, the results show that considering smart homes in the network reduces the operation cost and emission by about 16 % and 17 %, respectively.

Abstract
Based in the current growth rate of metropolitan areas, providing infrastructures and services to allow the safe, quick and sustainable mobility of people and goods, is increasingly challenging. The European Union has been promoting diverse initiatives towards sustainable transport development and environment protection by setting targets for changes in the sector, as those proposed in the 2011 White Paper on transport. Under this context, this study aims at evaluating the environmental performance of the transport sector in the 28 European Union countries, from 2015 to 2017, towards the policy agenda established in strategic documents. The assessment of the transport environmental performance was made through the aggregation of seven sub-indicators into a composite indicator using a Data Envelopment Analysis approach. The model used to determine the weights to aggregate the sub-indicators is based on a variant of the Benefit of the Doubt model with virtual proportional weights restrictions. The results indicate that, overall, the European Union countries had almost no variation on its transport environmental performance during the time span under analysis. The inefficient countries can improve the transport sustainability mainly by drastically reducing the greenhouse gas emissions from fossil fuels combustion, increasing the share of freight transport that uses rail and waterways and also the share of transport energy from renewable sources. © 2021, Springer Nature Switzerland AG.

Abstract
Folk music is a fundamental immaterial heritage that promotes cultural identity. However, it lacks a substantial body of open access materials, and its promotion has been disconnected from the education curriculum. In this context, facilitated access to annotated high-quality folk music content can promote better educational tools and enhance cultural heritage literacy. Based on this, we advance and detail three main contributions: 1) a standardized model to musically annotate Iberian folk music; 2) a new database, named I-Folk, with annotated files following the proposed model; and 3) tools for navigating and retrieving folk music contents from the database. A particular emphasis is given to the educational application of the proposed model, contents, and tools in education. Ultimately, we strive for the promotion of Iberian folk music to the educators' community.

Abstract
One of the principal complications of patients that suffer from Diabetes Mellitus (DM) and that can lead to ulceration is the Diabetic foot. As tissue inflammation causes temperature variation, several studies show that thermography can be used to detect complications in diabetic foot and help predicting the risk of ulceration. It is known that, although healthy individuals present characteristic plantar temperature variation patterns, the same does not happen with diabetic patients, for which a particular pattern can not be found; thus, making the measurement of the temperature variation more difficult. Given that, it is important to research in this field in order to obtain methods that can detect atypical variations of the temperature in the sole of the foot. With this in mind, the objective of this work is to present a methodology to analyze the distribution of temperature in thermograms of the foot's plant and classify it as belonging to a DM individual with risk of ulceration or a healthy individual. After foot partitioning with a clustering algorithm, basic statistical descriptors are computed for each cluster. A binary classifier to predict the risk of ulceration in the diabetic foot was evaluated with the different descriptors; both a quantitative temperature index and a classification threshold are calculated for each descriptor. To evaluate the performance of the classifier, experiments were conducted using a public dataset (containing 45 thermograms of healthy individuals and 122 images of DM ones); the following metrics were obtained: Accuracy = 78%, AUC = 86% and F-measure = 84%, with the best descriptor.