Gamification is the use of game design elements to enhance the teaching-learning process and turn a regular, non-game activity into a fun, engaging game. Simultaneously, serious games are proposed... Show More

Gamification is the use of game design elements to enhance the teaching-learning process and turn a regular, non-game activity into a fun, engaging game. Simultaneously, serious games are proposed as an efficient and enjoyable way of conducting cognitive assessment, as they combine a serious intention with game rules and targets. In this scenario, game engines have emerged as information technologies for serious games and educational games development; however, this development has usually been performed without a guide to identifying game attributes to be present in the game. To address this gap, we present an analysis of the most used game engines to identify game and learning attributes supported for serious and educational games development. Findings from this analysis provide a guide of the most popular game engines that offer the largest support for game attributes, which were also classified by game categories.

In a Smart City is required computational platforms, which allow environments with multiple interconnected and embedded systems, where the technology is integrated with the people, and can respond... Show More

In a Smart City is required computational platforms, which allow environments with multiple interconnected and embedded systems, where the technology is integrated with the people, and can respond to unpredictable situations. One of the biggest challenges in developing Smart City is how to describe and dispose of enormous and multiple sources of information, and how to share and merge it into a single infrastructure. In previous works, we have proposed an Autonomic Reflective Middleware with emerging and ubiquitous capabilities, which is based on intelligent agents that can be adapted to the existing dynamism in a city for, ubiquitously, respond to the requirements of citizens, using emerging ontologies that allow the adaptation to the context. In this work, we extend this middleware using the fog computing paradigm, to solve this problem. The fog extends the cloud to be closer to the things that produce and act on the smart city. In this paper, we present the extension to the middleware, and examples of utilization in different situations in a smart city.

There are several cities and countries whose population depends on agriculture. Crops demand close monitoring regarding diseases because these ones can affect significantly both production and... Show More

There are several cities and countries whose population depends on agriculture. Crops demand close monitoring regarding diseases because these ones can affect significantly both production and post-harvest life. The identification of disease symptoms plays a crucial role in the successful cultivation of crops. The diagnosis of diseases is a challenging task since many symptoms should be considered, which makes a proper diagnosis becomes a knowledge handling problem. This paper specifies an ontology-based decision support system that promotes the knowledge of experts for the plant disease diagnosis to farmers. This system takes advantage of ontologies in two ways, to exploit the knowledge contained in the ontology for decision support purposes, in this case, the diagnosis of diseases, and to provide a standard vocabulary for integrating phytopathology data sources. The system was evaluated for the diagnosis of diseases presented in short-cycle and perennial crops achieving promising results based on the F-measure metric.

Ontologies are used to represent knowledge and they have become very important in the Semantic Web era. Ontologies evolve continuously during their life cycle to adapt to new requirements and needs... Show More

Ontologies are used to represent knowledge and they have become very important in the Semantic Web era. Ontologies evolve continuously during their life cycle to adapt to new requirements and needs, especially in the biomedical field, where the number of ontologies and their complexity have increased during the last years. On the other hand, a vast amount of clinical knowledge resides in natural language texts. For these reasons, building and maintaining biomedical ontologies from natural language texts is a relevant and challenging issue. In order to provide a general solution and to minimize the experts' participation during the ontology enriching process, a methodology for extracting terms and relations from natural language texts is proposed in this work. This framework is based on linguistic and statistical methods and semantic role labeling technologies, having been validated in the domain of diabetes, where they have obtained encouraging results with an F-measure of 82.1% and 79.9% for concepts and relations, respectively.