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).

Abstract
Current times are denoting unprecedented indicators of scientific data production, and the involvement of the wider public (the crowd) on research has attracted increasing attention. Drawing on review of extant literature, this paper outlines some ways in which crowdsourcing and mass collaboration can leverage the design of intelligent systems to keep pace with the rapid transformation of scientific work. A systematic literature review was performed following the guidelines of evidence-based software engineering and a total of 148 papers were identified as primary after querying digital libraries. From our review, a lack of methodological frameworks and algorithms for enhancing interactive intelligent systems by combining machine and crowd intelligence is clearly manifested and we will need more technical support in the future. We lay out a vision for a cyberinfrastructure that comprises crowd behavior, task features, platform facilities, and integration of human inputs into AI systems. © Springer Nature Switzerland AG 2018.

Abstract
Technological evolution impacts the research and development of new solutions, as well as consumers’ expectations and behaviors. With the advent of the new millennium, collaboration systems and technologies were introduced to support ordinary cooperative work and inter-dependent, socially and culturally mediated practices as integral units of everyday life settings. Nevertheless, existing classification systems are limited in scope to analyze technological developments and capture the intellectual structure of a field, understood as an abstraction of the collective knowledge of its researchers and their socially mediated activities. Ten years after the introduction of Mittleman et al.’s taxonomy, we build upon earlier work and adopt this classification scheme to provide a descriptive, taxonomy-based analysis of four distinct venues focused on collaborative computing research: ACM CSCW, ACM GROUP, ECSCW, and CRIWG. The proposal consists of achieving evidence on technical attributes and impacts towards characterizing the evolution of socio-technical systems via (and for) taxonomic modeling. This study can also constitute an important step towards the emergence of new, potentially more valid and robust evaluation studies combining Grounded Theory with alternative methods and techniques. © Springer Nature Switzerland AG 2018.