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.

Abstract
Introduction: Clinical trials of blended Internet-based treatments deliver a wealth of data from various sources, such as self-report questionnaires, diagnostic interviews, treatment platform log files and Ecological Momentary Assessments (EMA). Mining these complex data for clinically relevant patterns is a daunting task for which no definitive best method exists. In this paper, we explore the expressive power of the multi-relational Inductive Logic Programming (ILP) data mining approach, using combined trial data of the EU E-COMPARED depression trial. Methods: We explored the capability of ILP to handle and combine (implicit) multiple relationships in the E-COMPARED data. This data set has the following features that favor ILP analysis: 1) Time reasoning is involved; 2) there is a reasonable amount of explicit useful relations to be analyzed; 3) ILP is capable of building comprehensible models that might be perceived as putative explanations by domain experts; 4) both numerical and statistical models may coexist within ILP models if necessary. In our analyses, we focused on scores of the PHQ-8 self-report questionnaire (which taps depressive symptom severity), and on EMA of mood and various other clinically relevant factors. Both measures were administered during treatment, which lasted between 9 to 16 weeks. Results: E-COMPARED trial data revealed different individual improvement patterns: PHQ-8 scores suggested that some individuals improved quickly during the first weeks of the treatment, while others improved at a (much) slower pace, or not at all. Combining self-reported Ecological Momentary Assessments (EMA), PHQ-8 scores and log data about the usage of the ICT4D platform in the context of blended care, we set out to unveil possible causes for these different trajectories. Discussion: This work complements other studies into alternative data mining approaches to E-COMPARED trial data analysis, which are all aimed to identify clinically meaningful predictors of system use and treatment outcome. Strengths and limitations of the ILP approach given this objective will be discussed.

Abstract
The development of a single window system for collaborative Multimodal Logistics services with offer for optimized and integrated door-to-door services is a complex endeavor. The concept was previously worked on by a set of European projects whose results were taken as best practices and lessons learned from the logistics sector. In this current paper we present a background and the major problems faced by the logistics sector and how IT can address them, and the existing expectations for collaborative real time door-to-door logistics Services. Next, we present the main results, consisting of an innovative system addressing these expectations - the Logistics Single Window. Finally we draw the conclusions from the results and present how the results innovate when compared with current state of the art. © 2017 IEEE.

Abstract
Over the last decades, CSCW research has undergone significant structural changes and has grown steadily with manifested differences from other fields in terms of theory building, methodology, and socio-technicality. This paper provides a quantitative assessment of the scientific literature for mapping the intellectual structure of CSCW research and its scientific development over a 15-year period (2001-2015). A total of 1713 publications were subjected to examination in order to draw statistics and depict dynamic changes to shed new light upon the growth, spread, and collaboration of CSCW devoted outlets. Overall, our study characterizes top (cited and downloaded) papers, citation patterns, prominent authors and institutions, demographics, collaboration patterns, most frequent topic clusters and keywords, and social mentions by country, discipline, and professional status. The results highlight some areas of improvement for the field and a lot of well-established topics which are changing gradually with impact on citations and downloads. Statistical models reveal that the field is predominantly influenced by fundamental and highly recognized scientists and papers. A small number of papers without citations, the growth of the number of papers by year, and an average number of more than 39 citations per paper in all venues ensure the field a healthy and evolving nature. We discuss the implications of these findings in terms of the influence of CSCW on the larger field of HCI.

Abstract
The importance of entrepreneurship education from elementary school through college is now recognized as an important aspect of children's education. At the level of basic education, the development of entrepreneurial activities using Information and Communication Technologies, specifically three-dimensional virtual worlds, is seen as an area with potential for exploration. The research presented herein is a model that allows the development of entrepreneurial activities in virtual worlds with children attending primary education. This model allows the preparation, monitoring and development of entrepreneurship education activities in virtual worlds, including safe interaction in virtual worlds between the children and the community. For this, we identified a set of requirements that would allow the teaching and learning of entrepreneurship in virtual worlds, from which a technological model was implemented through an application, EMVKids (after the Portuguese expression "Empreendedorismo em Mundos Virtuais com Criancas", entrepreneurship with children in virtual worlds).