Abstract
Nowadays and in the contemporary age, the reality of an all-autonomous traffic seems closer and closer. However, this transition period casts a lot of cards onto the table. Although technology can be replacing people at the driver seat, it has not as yet gained our full trust in what concerns communication in real time and safety. Humans interact on a daily basis in their various activities, and traffic is no exception. Most actions performed on the road rely on our perception of others’ awareness and potential reactions. For instance, pedestrians seek for an eye contact before crossing the road, drivers seek for a gesture before starting a manoeuvre, and so forth. Thus, the question remaining is what happens when someone is seeking such a communication interaction and the car has no driver, nor has it someone who even knows what the car is doing. Moreover, people seating in the car might be performing any other activities but driving. Other questions also arise such whether people will accept the idea of trusting self-driving vehicles, or whether will they feel safe when walking amongst such machines. In this paper we pursue a rather social perspective and will raise questions, covering the literature so as to understand what practitioners, researchers and the industry have been doing to overcome the lack of confidence in self-driving cars and improve their trustworthiness towards more efficient and smarter mobility, as well as to identify trends and approaches to answer these emerging questions. Copyright

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 widespread use of internet is changing the way supply chain echelons interact with each other in order to respond to increasing customer requests of personalized products and services. Companies acquainted with the concept of industry 4.0 (i4.0) embrace the use of internet to improve their internal and external processes, delivering the dynamic and flexible response customers want. This chapter aims to discuss how supply chains may benefit from the adoption of i4.0 technologies by their partners and highlights some of its implementation challenges. Eight technologies cover most of i4.0 applications: additive manufacturing; big data & analytics; cloud computing; cyber-physical systems; cyber security; internet of things; collaborative robotics; and visual computing. At individual level, technologies such as additive manufacturing, collaborative robots, visual computing and cyber-physical systems establish the connectivity of a certain company. However, the integration of the whole supply chain, based on the principles of i4.0, demands that information provided by each company (Big Data) is shared through a collaborative system based on Cloud Computing and Internet of Things technologies. To safely share useful information, Cyber Security techniques must be implemented in individual systems and cloud solutions. Summing up, even though the adoption of i4.0 demands an individual initiative, it will only raise the supply chain’s competitive advantage if all companies adapt their manufacturing and supply chain processes. The main advantage foreseen here is based on an improved communication system of the whole supply chain, bringing consumers closer to the production process. © 2018, Springer International Publishing AG, part of Springer Nature.

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
In data imputation problems, researchers typically use several techniques, individually or in combination, in order to find the one that presents the best performance over all the features comprised in the dataset. This strategy, however, neglects the nature of data (data distribution) and makes impractical the generalisation of the findings, since for new datasets, a huge number of new, time consuming experiments need to be performed. To overcome this issue, this work aims to understand the relationship between data distribution and the performance of standard imputation techniques, providing a heuristic on the choice of proper imputation methods and avoiding the needs to test a large set of methods. To this end, several datasets were selected considering different sample sizes, number of features, distributions and contexts and missing values were inserted at different percentages and scenarios. Then, different imputation methods were evaluated in terms of predictive and distributional accuracy. Our findings show that there is a relationship between features’ distribution and algorithms’ performance, and that their performance seems to be affected by the combination of missing rate and scenario at state and also other less obvious factors such as sample size, goodness-of-fit of features and the ratio between the number of features and the different distributions comprised in the dataset.

Abstract