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.

Abstract