Abstract
Teenagers have been identified as an audience group that is often excluded from museum curatorial strategies. One strategy to counteract this problem is to involve cultural heritage professionals (CHPs) in the design process of museum based digital experiences targeted at teens. In this paper, 12 CHPs from a local natural history museum took part in a co-design activity over 20 hours, aiming to create and deploy digital tours for teenagers aged between 16-19. We present the three prototypes that derived from these design sessions. These were then tested by both 12 CHPs and 12 teenagers separately, and we report on lessons learned from the evaluation of these prototypes by both groups. © Dupré et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK

Abstract

Abstract
Museums promote cultural experiences through exhibits and the stories behind them. Nevertheless, museums are not always designed to engage and interest young audiences, especially teenagers. Throughout this paper, we discuss teenagers as an important group to be considered within the Children-Computer Interaction field, and we report some techniques on designing with teens, in particular, arguing that participatory design methods can involve teenagers in the design process of technology for museums. For this purpose, we conceptualized, designed and deployed a co-design activity for teenagers (aged 15–17), where teenagers together with a researcher jointly created and designed a medium fidelity prototype. For this case study, participants were divided into groups and invited to think and create games and story plots for a selected museum. All the prototypes were made by the participants with the support and guidance of the researcher and the Aurasma software, an augmented reality tool. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
This article presents a medical simulation solution. This solution allows a physician to train a clinical scenario by interacting with a graphical interface. The aim is to instigate the learning and internalization of clinical procedures. Currently these resources have been intensifying in the most diverse areas, being our focus, Medicine. Within this area, the focus is on medical simulation. There are numerous biomedical simulation centers, whose main objective is to create realistic simulations to aid health professionals. Thus, it is intended to optimize its performance, to meet the needs detected and to anticipate unexpected situations (critical or complex events). However, current simulation systems face some limitations, since they have enough difficulties in the development of new scenarios, since they are restricted to the level of modularity and the number of simulated situations. The training of these professionals is limited to simulation centers. The goal is to create a platform to simulate real scenarios and develop serious games that simulate various clinical situations, in order to facilitate access to this type of training and training.

Abstract
Immersion is a state of altered consciousness-not the prim suspension of disbelief, but its joyous capsizing. Since approximately 2012, a new ecosystem of immersive virtual reality technologies and experiments has emerged. In this emerging ecosystem, journalism is still a minor component. Nevertheless, media outlets such as The New York Times, BBC or ABC News have been producing virtual reality news stories. This led to the advent of immersive journalism, not only as a media phenomenon, but also as an academic concept. Drawing on some notions and concepts like the definitions of immersive journalism, immersion and presence, as well as some examples of the relation between journalism practices and visual media, we analyse, reflect and provide a general overview about the main concepts, uses, opportunities and limits of immersive journalism. Thus, the main goal of this article is to provide a theoretical and conceptual gateway that serves as a starting point for immersive journalism future academic and industry endeavours.

Abstract
Technology driven interventions provide us with an increasing amount of fine-grained data about the patient. This data includes regular ecological momentary assessments (EMA) but also response times to EMA questions by a user. When observing this data, we see a huge variation between the patterns exhibited by different patients. Some are more stable while others vary a lot over time. This poses a challenging problem for the domain of artificial intelligence and makes on wondering whether it is possible to predict the future mental state of a patient using the data that is available. In the end, these predictions could potentially contribute to interventions that tailor the feedback to the user on a daily basis, for example by warning a user that a fall-back might be expected during the next days, or by applying a strategy to prevent the fall-back from occurring in the first place. In this work, we focus on short term mood prediction by considering the adherence and usage data as an additional predictor. We apply recurrent neural networks to handle the temporal aspects best and try to explore whether individual, group level, or one single predictive model provides the highest predictive performance (measured using the root mean squared error (RMSE)). We use data collected from patients from five countries who used the ICT4Depression/MoodBuster platform in the context of the EU E-COMPARED project. In total, we used the data from 143 patients (with between 9 and 425 days of EMA data) who were diagnosed with a major depressive disorder according to DSM-IV. Results show that we can make predictions of short term mood change quite accurate (ranging between 0.065 and 0.11). The past EMA mood ratings proved to be the most influential while adherence and usage data did not improve prediction accuracy. In general, group level predictions proved to be the most promising, however differences were not significant. Short term mood prediction remains a difficult task, but from this research we can conclude that sophisticated machine learning algorithms/setups can result in accurate performance. For future work, we want to use more data from the mobile phone to improve predictive performance of short term mood.