In this extended poster, we present a model that aims to provide developers with an extensive and extensible set of context-aware interaction techniques, greatly facilitating the creation of meaningful AR-based user experiences. To provide a complete view of the model, we detail the different aspects that form its theoretical foundations, while also discussing several considerations for its correct implementation.

Procedural terrain generators are widely used in computer games, simulators and other virtual environments. Traditionally, these methods have been focused on generating realistic landscapes and their elements, like mountains or rivers. In contrast, there is a movement in the industry which employs virtual worlds using volumetric terrains. This movement uses its owns methods to generate terrains, but they lack of customization features and are completely centered around their specific problem domains. In this paper, we propose an alternative approach to generate procedural volumetric terrains specially suited for shared worlds. Our approach generates completely customizable volumetric terrains with layered materials and other features (such as mineral veins, material mixtures and underground material flow). We enable the designer to specify the characteristics of the terrain and its materials with intuitive parameters. Moreover, our method uses a specific representation for the terrain based on stacked material structures, reducing the memory size and making it easier to share.

As social networks and always-connected mobile devices grow in popularity, the control over personal information weakens. This is especially true for teenagers between 15 and 18 years old, one of the population groups that shares more information online, but also the most unaware of the risks associated with this activity. For this reason, many institutions have developed programs to educate the students in the correct use of the new communication mediums. However, the concepts about information security require a lot of expert knowledge and are very difficult to explain appropriately. In this paper, we present a serious game designed to enhance an information security presentation aimed at high school students. This is achieved through the use of augmented reality to give shape and form to the intangible cybersecurity concepts and allow the students to interact with them using the same rule set that was explained during the presentation.

Mechanical properties are the attributes of a metal to withstand several forces and tensions. Specifically, ultimate tensile strength is the force a material can resist until it breaks. The only way to examine this mechanical property is the employment of destructive inspections that renders the casting invalid with the subsequent cost increment. In a previous work we showed that modelling the foundry process as a probabilistic constellation of interrelated variables allows Bayesian networks to infer causal relationships. In other words, they may guess the value of a variable (for instance, the value of ultimate tensile strength). Against this background, we present here the first ultimate tensile strength prediction system that, upon the basis of a Bayesian network, is able to foresee the values of this property in order to correct it before the casting is made. Further, we have tested the accuracy and error rate of the system with data of a real foundry.