Abstract
As the usage of head-mounted displays (HMD) increases, it is important to establish best usage practices to ensure the appropriate use of Virtual Reality (VR) equipment. Among the factors that can contribute to a better user experience are exposure time, the content type and the gender of the user. This study evaluates the impact of these variables on users' Sense of Presence and Cybersickness when visualising 360 content using HMDs. Two types of 360 content (captured video vs. virtual environment) were evaluated across four different exposure times (1, 3, 5 and 7 min). Regarding Sense of Presence, the results revealed a statistically significant difference for Content Type, Gender, and Content Type x Gender. Regarding Cybersickness, no statistically significant results were found for any of the independent variables. Overall, the results encourage the use of synthesized environments for a female audience; for non-interactive environments, captured environments are more effective than synthesized environments; and exposure time is not a concern for experiences lasting between 1 and 7 min.

Abstract
Current real-time embedded systems development frameworks lack support for the verification of properties using explicit time where counting time (i.e., durations) may play an important role in the development process. Focusing on the real-time constraints inherent to these systems, we present a framework that addresses the specification of duration properties for runtime verification by employing a fragment of metric temporal logic with durations. We also provide an overview of the framework, the synthesis tools, and the library to support monitoring properties for real-time systems developed in C++11. The results obtained provide clear evidence of the feasibility and advantages of employing a duration-sensitive formalism to increase the dependability of avionic controller systems such as the PX4 and the Ardupilot flight stacks.

Abstract
Research datasets include all kinds of objects, from web pages to sensor data, and originate in every domain. Concerns with data generated in large projects and well-funded research areas are centered on their exploration and analysis. For data in the long tail, the main issues are still how to get data visible, satisfactorily described, preserved, and searchable. Our work aims to promote data publication in research institutions, considering that researchers are the core stakeholders and need straightforward workflows, and that multi-disciplinary tools can be designed and adapted to specific areas with a reasonable effort. For small groups with interesting datasets but not much time or funding for data curation, we have to focus on engaging researchers in the process of preparing data for publication, while providing them with measurable outputs. In larger groups, solutions have to be customized to satisfy the requirements of more specific research contexts. We describe our experience at the University of Porto in two lines of enquiry. For the work with long-tail groups we propose general-purpose tools for data description and the interface to multi-disciplinary data repositories. For areas with larger projects and more specific requirements, namely wind infrastructure, sensor data from concrete structures and marine data, we define specialized workflows. In both cases, we present a preliminary evaluation of results and an estimate of the kind of effort required to keep the proposed infrastructures running.  The tools available to researchers can be decisive for their commitment. We focus on data preparation, namely on dataset organization and metadata creation. For groups in the long tail, we propose Dendro, an open-source research data management platform, and explore automatic metadata creation with LabTablet, an electronic laboratory notebook. For groups demanding a domain-specific approach, our analysis has resulted in the development of models and applications to organize the data and support some of their use cases. Overall, we have adopted ontologies for metadata modeling, keeping in sight metadata dissemination as Linked Open Data.

Abstract
In this paper, we present YAKE!, a novel feature-based system for multi-lingual keyword extraction from single documents, which supports texts of different sizes, domains or languages. Unlike most systems, YAKE! does not rely on dictionaries or thesauri, neither it is trained against any corpora. Instead, we follow an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in many different languages without the need for external knowledge. This can be beneficial for a large number of tasks and a plethora of situations where the access to training corpora is either limited or restricted. In this demo, we offer an easy to use, interactive session, where users from both academia and industry can try our system, either by using a sample document or by introducing their own text. As an add-on, we compare our extracted keywords against the output produced by the IBM Natural Language Understanding (IBM NLU) and Rake system. YAKE! demo is available at http://bit.ly/YakeDemoECIR2018. A python implementation of YAKE! is also available at PyPi repository (https://pypi.python.org/pypi/yake/).

Abstract
We study hybrid automata from a coalgebraic point of view. We show that such a perspective supports a generic theory of hybrid automata with a rich palette of definitions and results. This includes, among other things, notions of bisimulation and behaviour, state minimisation techniques, and regular expression languages.

Abstract
The moment immediately before the "add to cart" decision is very critical in online shopping. Drawing on theories of transfer, spreading activation and human-computer interaction, the superiority of markerless Augmented Reality (AR) and Marker-based augmented reality (M) over Conventional Interactive (CI) is hypothesized. Although those multimedia tools are not part of the product/brand motivating the consumer interest they interfere in the interactive performance of the ecommerce. 150 consumers in a lab experiment showed higher emotional response, interactive response and brand evaluation in M and AR than CI. Contrary to what was expected the usability results were the inverse. That is, usability of CI outperforms M and AR. Considering only AR and M interfaces their effect on psychological variables was not statistically significant. A sophisticated or a simple interface had no impact on intention to buy the target brand, but the brand recommendation improved from M to AR. The differing effect of those three interface systems was mediated by brand familiarity, perceived risk, opinion leadership and positive emotional traits.