Abstract
A multiplicity of innovative applications has been developed based on the mobile crowdsourcing (MCS) paradigm. One group of applications addresses the creation of accessibility maps in large cities. In this context, a conceptual model of a system for the detection and the timely notification of the existence of temporary obstacles and other dangers in the urban environment is proposed in "Pervasive Crowd Mapping for Dynamic Environments". This concept (PCM4DE) encompasses, among other technologies, the use of crowdsourcing. The system will be particularly useful to people with disabilities and elderly people. An exploratory literature review showed that data quality and the motivation strategies fur participating in the systems based on MCS remain two of the key challenges to the effectiveness of those systems. This paper aims to contribute to the implementation of the PCM4DE concept by proposing the development of a mechanism that should improve the data quality through motivation forms which will enable a positive personal user experience, i.e., an experience which meets the participant's objectives, needs and preferences.

Abstract
Teaching complex assembly and maintenance skills to human operators usually requires extensive reading and the help of tutors. In order to reduce the training period and avoid the need for human supervision, an immersive teaching system using spatial augmented reality was developed for guiding inexperienced operators. The system provides textual and video instructions for each task while also allowing the operator to navigate between the teaching steps and control the video playback using a bare hands natural interaction interface that is projected into the workspace. Moreover, for helping the operator during the final validation and inspection phase, the system projects the expected 3D outline of the final product. The proposed teaching system was tested with the assembly of a starter motor and proved to be more intuitive than reading the traditional user manuals. This proof of concept use case served to validate the fundamental technologies and approaches that were proposed to achieve an intuitive and accurate augmented reality teaching application. Among the main challenges were the proper modeling and calibration of the sensing and projection hardware along with the 6 DoF pose estimation of objects for achieving precise overlap between the 3D rendered content and the physical world. On the other hand, the conceptualization of the information flow and how it can be conveyed on-demand to the operator was also of critical importance for ensuring a smooth and intuitive experience for the operator.

Abstract
In this paper, we show how stream fusion, a program transformation technique used in functional programming, can be adapted for an Object-Oriented setting. This makes it possible to have more Stream operators than the ones currently provided by the Java Stream API. The addition of more operators allows for a greater deal of expressiveness. To this extent, we show how these operators are incorporated in the stream setting. Furthermore, we also demonstrate how a specific set of optimizations eliminates overheads and produces equivalent code in the form of for loops. In this way, programmers are relieved from the burden of writing code in such a cumbersome style, thus allowing for a more declarative and intuitive programming approach.

Abstract
Recent initiatives in data management recognize that involving the researchers is one of the more problematic issues and that taking into account the practices of each domain can ease this process. We describe here an experiment in the adoption of data description by researchers in the biomedical domain. We started with a generic lightweight ontology based on the Minimum Information for Biological and Biomedical Investigations (MIBBI) standard and presented it to researchers from the Institute of Innovation and Investigation in Health (I3S) in Porto. This resulted in seven interviews and four data description sessions using a RDM platform. The feedback from researchers shows that this intentionally restricted ontology favours an easy entry point into RDM but does not prevent them from identifying the limitations of the model and pinpointing their specific domain requirements. To complete the experiment, we collected the extra descriptors suggested by the researchers and compared them to the full MIBBI. Part of these new descriptors can be obtained from the standard, reinforcing the importance of common metadata models for broad domains such as biomedical research. © 2019, Springer Nature Switzerland AG.

Abstract
Computer programming is a difficult subject that can only be mastered with lots of practice. It is therefore of primary importance to rise and retain students' engagement during a programming course, a task in which gamification has been proven as a competent method. Even though there are numerous reports on applying gamification to programming courses, there are no available open resources or dedicated platforms that could be used by programming teachers to gamily their courses, meeting both the requirements of being easy to adopt and leaving the decisions on the scope of the course and the level of gamification to the teachers themselves. In order to fulfill this gap, a consortium of four European institutions initiated a common project to develop open gamified programming exercises and interactive course materials for popular programming languages. In this paper, we report the results of the first stage of this work, which defined the range of gamification concepts to be covered within the developed framework and its evaluation by students.

Abstract
Pedestrian behaviour modelling and simulation play a fundamental role in reducing traffic risks and new policies implementation costs. However, representing human behaviour in this dynamic environment is not a trivial task and such models require an accurate representation of pedestrian behaviour. Virtual environments have been gaining notoriety as a behaviour elicitation tool, but it is still necessary to understand the validity of this technique in the context of pedestrian studies, as well as to create guidelines for its use. This work proposes a proper methodology for pedestrian behaviour elicitation using virtual reality environments in conjunction with surveys or questionnaires. The methodology focuses on gathering data about the subject, the context, and the action taken, as well as on analyzing the collected data to finally output a behavioural model. The resulting model can be used as a feedback signal to improve environment conditions for experiment iterations. A concrete implementation was built based on this methodology, serving as an example for future studies. A virtual reality traffic environment and two surveys were used as data sources for pedestrian crossing experiments. The subjects controlled a virtual avatar using an HTC Vive and were asked to traverse the distance between two points in a city. The data collected during the experiment was analyzed and used as input to a machine learning model capable of predicting pedestrian speed, taking into account their actions and perceptions. The proposed methodology allowed for the successful data gathering and its use to predict pedestrian behaviour with fairly acceptable accuracy. © 2019 IEEE.