Abstract
Innovations in virtual reality have boosted their use for business purposes and personal consumption. The main objective of this technology is to transport the user to virtual environments. The use of avatars to replace the user's body in these environments increases the levels of presence and embodiment of the user. Through the inverse kinematics it is possible to animate the avatars according to the data obtained in sensors scattered around the user's body, replicating the movements in a avatar. These sensors can vary in number offering different levels of fidelity in the tracking of the human body. In this paper, we study the impact of the number of sensors used in the presence of the user and in the embodiment of the avatar, using three, five and six tracking points. The results show that there is no statistically significant differences in presence nor in any of its sub-scale, however one can observe a positive trend in the 6-points conditions. As for embodiment, the results show that there is statistically signifficant differences in some of presence sub-scales, namelly tactile sensation, response and in the embodiment as a whole.

Abstract
Every year, thousands of new students begin their Masters in STEM related topics. Despite being regarded as a common occurrence by the faculty, it represents the culmination of years of studying and preparation for their professional life. Notwithstanding, these students face well-known recurrent problems: how to choose a topic, how to choose an advisor, how to start researching, and how to deal with all the unknowns associated with academic research. Although there are several books on how to write a thesis, most of them avoid prescriptive recommendations on topics beyond research per se or focus on doctoral students, for which the duration and motivation are significantly different. In this paper, we draft a pattern language comprised of thirty patterns that we have observed from supervising over a hundred masters students within the last decade.

Abstract
As with every software, Internet-of-Things (IoT) systems have their own life-cycle, from conception to construction, deployment, and operation. However, the testing requirements from these systems are slightly different due to their inherent coupling with hardware and human factors. Hence, the procedure of delivering new software versions in a continuous integration/delivery fashion must be adopted. Based on existent solutions (and inspired in other closely-related domains), we describe two common strategies that developers can use for testing IoT systems, (1) Testbed and (2) Simulation-based Testing, as well as one recurrent solution for its deployment (3) Middleman Update.

Abstract
The herein proposed research project brings together the area of the multi-agent recommender systems and the IoT and aims to study the extent to which a context-based multi-agent recommender system can contribute to improving efficiency in the evacuation of buildings under a fire emergency, recommending the most adequate and efficient evacuation routes in real time. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
Modern search is heavily powered by knowledge bases, but users still query using keywords or natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We have previously proposed the graph-of-entity as a purely graph-based representation and retrieval model, however this model would scale poorly. We tackle the scalability issue by adapting the model so that it can be represented as a hypergraph. This enables a significant reduction of the number of (hyper)edges, in regard to the number of nodes, while nearly capturing the same amount of information. Moreover, such a higher-order data structure, presents the ability to capture richer types of relations, including nary connections such as synonymy, or subsumption. We present the hypergraph-of-entity as the next step in the graph-of-entity model, where we explore a ranking approach based on biased random walks. We evaluate the approaches using a subset of the INEX 2009 Wikipedia Collection. While performance is still below the state of the art, we were, in part, able to achieve a MAP score similar to TF-IDF and greatly improve indexing efficiency over the graph-of-entity.