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When discussing interpretable machine learning results, researchers need to compare them and check for reliability, especially for health-related data. The reason is the negative impact of wrong results on a person, such as in wrong prediction of cancer, incorrect assessment of the COVID-19 pandemic situation, or missing early screening of dyslexia. Often only small data exists for these complex interdisciplinary research projects. Hence, it is essential that this type of research understands different methodologies and mindsets such as the Design Science Methodology, Human-Centered Design or Data Science approaches to ensure interpretable and reliable results. Therefore, we present various recommendations and design considerations for experiments that help to avoid over-fitting and biased interpretation of results when having small imbalanced data related to health. We also present two very different use cases: early screening of dyslexia and event prediction in multiple sclerosis.

Social computing is ubiquitous and intensifying in the 21st Century. Originally used to reference computational augmentation of social interaction through collaborative filtering, social media, wikis, and crowdsourcing, here I propose to expand the concept to cover the complete dynamic interface between social interaction and computation, including computationally enhanced sociality and social science, socially enhanced computing and computer science, and their increasingly complex combination for mutual enhancement. This recommends that we reimagine Computational Social Science as Social Computing, not merely using computational tools to make sense of the contemporary explosion of social data, but also recognizing societies as emergent computers of more or less collective intelligence, innovation and flourishing. It further proposes we imagine a socially inspired computer science that takes these insights into account as we build machines not merely to substitute for human cognition, but radically complement it. This leads to a vision of social computing as an extreme form of human computer interaction, whereby machines and persons recursively combine to augment one another in generating collective intelligence, enhanced knowledge, and other social goods unattainable without each other. Using the example of science and technology, I illustrate how progress in each of these areas unleash advances in the others and the beneficial relationship between the technology and science of social computing, which reveals limits of sociality and computation, and stimulates our imagination about how they can reach past those limits together.

International audience; The area of Engineering Interactive Computing Systems (EICS) is defined as the intersection between Software Engineering (SE) and Human-Computer Interaction (HCI). In this paper, we provide an overview of what EICS is and how it is positioned with respect to other venues in HCI, such as CHI, UIST, and IUI, highlighting its legacy and paying homage to past scientific events from which EICS emerged. We also take this opportunity to deliver a comparative analysis of the past, present, and perhaps future research and development questions raised in EICS by analyzing the EICS papers published from its first edition to this one, based on global and chronological word clouds, phrase nets, and topic modelling.

Touch-surface multi-stroke gestures, as well as freehand drawings, are typically acquired by devices and sensors as a suite of timestamped points on a plane. This Cartesian coordinate system, although useful for computation like complexity analysis, gesture classification and recognition, becomes complex and inefficient when gestures need to be visualized and directly manipulated for editing. To address these challenges, a new mathematical representation of these gestures via a Bézier curve is defined to initiate a model-based approach for gesture direct manipulation (e.g., cut, copy, paste, translate, scale, rotate, deform, crop, compose, decompose). SketchADoodle, an Android-based mobile application for drawing, gesturing, demonstrates how the pseudo-code of the Bézier-based operations are engineered for real-time direct manipulation. We release the programming code for further development of gesture-based user interfaces based on Bézier curves.

We investigate professional greenhouse growers’ user experience (UX) when using climate-management systems in their daily work. We build on the literature on UX, in particular UX at work, and extend it to ordinary UX at work . In a 10-day diary study, we collected data with a general UX instrument (AttrakDiff), a domain-specific instrument, and interviews. We find that AttrakDiff is valid at work; its three-factor structure of pragmatic quality (PQ), hedonic identification quality, and hedonic stimulation quality is recognizable in the growers’ responses. In this article, UX at work is understood as interactions among technology, tasks, structure, and actors. Our data support the recent proposal for the ordinariness of UX at work. We find that during continued use, UX at work is middle-of-the-scale, remains largely constant over time, and varies little across use situations. For example, the largest slope of the four AttrakDiff constructs when regressed over the 10 days was as small as 0.04. The findings contrast existing assumptions and findings in UX research, which is mainly about extraordinary and positive experiences. In this way, the present study contributes to UX research by calling attention to the mundane, unremarkable, and ordinary UXs at work.

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explainability in the field of visual analytics.

People are becoming increasingly sophisticated in their ability to navigate information spaces using search, hyperlinks, and visualization. But, mobile phones preclude the use of multiple coordinated views that have proven effective in the desktop environment (e.g., for business intelligence or visual analytics). In this work, we propose to model information as multivariate heterogeneous networks to enable greater analytic expression for a range of sensemaking tasks while suggesting a new, list-based paradigm with gestural navigation of structured information spaces on mobile phones. We also present a mobile application, called Orchard, which combines ideas from both faceted search and interactive network exploration in a visual query language to allow users to collect facets of interest during exploratory navigation. Our study showed that users could collect and combine these facets with Orchard, specifying network queries and projections that would only have been possible previously using complex data tools or custom data science.

Multiple description coding (MDC) provides a favorable solution for human-centered image communication, which takes into account people’s varying watching situations as well as people’s demand for real-time image display. As an effective technique for MDC, three-description lattice vector quantization (3D-LVQ) is considered for image coding in this paper. Based on intra- and inter-correlation in the 3D-LVQ index assignment as well as wavelet intra-subband correlation, a novel predictive decoding method for 3D-LVQ-based image coding is proposed to enhance side decoding performance, which attempts to predict lost descriptions (sublattice points) in a good way for better reconstructions of wavelet vectors (fine lattice points) in the side decoding. Experimental results validate effectiveness of the proposed decoding scheme in terms of rate-distortion performance.

Emoji are widely used in computer-mediated communication to express concepts and emotions. Skin tone modifiers were added in 2015 with the hope of better representing user diversity, and, indeed, recent work has shown that these modifiers are especially popular among darker-skinned users, who are a minority on Twitter. Previous work also showed that the vast majority of tone-modified emoji have a tone similar to the user’s own skin tone, suggesting that self-representation is a major factor in tone use. In this article, we first show that the basic finding (users mainly choose a tone that is similar to their own skin tone) generalizes to different sub-populations of users, including users from majority-Black regions. We then extend the analysis of tone use to quantify and examine cases where users modulate their tones—that is, for a particular emoji, they choose either to use a different tone than their usual one or no tone at all (after having previously used one). We show that even though these uses constitute only a small proportion of emoji usage, many instances are readily classifiable as ways of representing other people. The evidence we present is therefore crucial in working toward a broader understanding of the connection between emoji and identity expression online. We also offer explanations for why the darkest emoji skin tones are not used, by examining aspects of their design, which make them less suited to self-representational usage. This highlights the need for careful consideration of both design and human diversity when creating emoji. Moreover, despite early fears in the media, we find little evidence of negative usage, even when tones are used in a non-self-representational manner. In sum, our findings lend even more support to the highly positive role that emoji and skin tone modifiers play in identity and expression in computer-mediated communication.

Timely detection of an individual’s stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer’s stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F 1-score of up to 0.87 in the lab and 0.66 in the field, on par with clinical-grade sensors.

The research reported in this paper is part of a large study that investigates collaborative information seeking (CIS) and decision making in the context of tourism. The aim of the present paper was to identify gaps and gap-bridging techniques in situated tourism CIS and technology use across the travel stages of trip planning and during the trip. Interview and diary data were collected from 20 free-trip groups of 63 Chinese tourists who traveled from mainland China to Australia during 2018-2019. We identified 8 types of gaps as well as 8 gap-bridging strategies. The results show that tourists demonstrated adaptive CIS behavior over the trip as they solved or compromised the gaps encountered. Suggestions on system design are also discussed to better support tourist information and collaborative practices. Refereed/Peer-reviewed

Grounded theory (GT) is a research methodology that entails a systematic workflow for theory generation grounded on emergent data. In this paper, we juxtapose GT workflows with typical workflows in visualization and visual analytics, shortly VIS, and underline the characteristics shared by these workflows. We explore the research landscape of VIS to observe where GT has been applied to generate VIS theories, explicitly as well as implicitly. We propose a "why" typology for characterizing aspects in VIS where GT can potentially play a significant role. We outline a "how" methodology for conducting GT research in VIS, which addresses the need for theoretical advancement in VIS while benefitting from other methods and techniques in VIS. We exemplify this "how" methodology by adopting GT approaches in studying the messages posted on VisGuides - an Open Discourse Forum for discussing visualization guidelines.

This paper presents Shape Aware Haptic Retargeting, an extension of "state-of-the-art" haptic retargeting that is the first to support retargeted interaction between any part of the user's hand and any part of the target object. In previous haptic retargeting algorithms, the maximum retargeting is applied only when the hand position aligns with the target position. Shape Aware Haptic Retargeting generalizes the distance computation process to instead consider the hand and target geometry. The shortest hand-target distance is then used to calculate the applied retargeting offset. This ensures the full amount of haptic retargeting is applied at the point of contact with the passive haptic regardless of contact position on the hand or target. We leverage existing geometry algorithms to implement three distance computation methods: Multi-Point, Primitive and Mesh Geometry, in addition to conventional single position approaches. These are evaluated through a set of simulated interactions instead of the single position representation used in previous haptic retargeting systems. The evaluation demonstrated all three approaches can provide improved interaction accuracy over a Point distance computation method, with Mesh Geometry being the most accurate and Primitive being the preferred method for combined performance and interaction accuracy. Refereed/Peer-reviewed

ispartof: pages:167-175 ispartof: Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces pages:167-175 ispartof: IEEE Conference on Virtual Reality and 3D User Interfaces location:Christchurch, New Zealand date:12 Mar - 16 Mar 2022 status: accepted

International audience; In the context of the IEEE VR 2022 3DUI Contest entitled “Arts,Science, Information, and Knowledge - Visualized and Interacted”, this paper presents a VR application to highlight the usage of the rubber slider metaphor. The rubber slider is an augmentation of usual 2 D slider controls where users can bend the slider axis in order to control an additional degree of freedom value in the application. This demonstration immerses users in a Virtual Environment where they will be able to explore a database of art pieces geolocated on a 3D earth model and their corresponding art movements displayed on a timeline interface

Considering the importance of getting the most out of virtual reality (VR) training, we evaluated the use of breaks to reduce virtual reality aftereffects in head-mounted displays. In a crossover design, we compared a 50-minute no-break exposure with an interrupted 50-minute exposure with four 10-minute breaks. Cybersickness, re-action times, workload and spatial learning were measured. Despite device manufacturers recommending breaks to reduce sickness, we found no evidence that breaks can mitigate aftereffects. Introducing breaks may also impact performance and logistical costs for training. Refereed/Peer-reviewed

International audience; We propose TimeTables, a novel prototype system that aims to support data exploration, using embodiment with space-time cubes in virtual reality. TimeTables uses multiple space-time cubes on virtual tabletops, which users can manipulate by extracting time layers or individual buildings to create new tabletop views. The surrounding environment includes a large space for multiple linked tabletops and a storage wall. TimeTables presents information at different time scales by stretching layers to drill down in time. Users can also jump into tabletops to inspect data from an egocentric perspective. We present a use case scenario of energy consumption displayed on a university campus to demonstrate how our system could support data exploration and analysis over space and time. From our experience and analysis we believe the system has a high potential in assisting spatio-temporal data exploration and analysis.

Recently, chatbots have been deployed in health care in various ways such as providing educational information, and monitoring and triaging symptoms. However, they can be ineffective when they are designed without a careful consideration of the cultural context of the users, especially for marginalized groups. Chatbots designed without cultural understanding may result in loss of trust and disengagement of the user. In this paper, through an interview study, we attempt to understand how chatbots can be better designed for Black American communities within the context of COVID-19. Along with the interviews, we performed design activities with 18 Black Americans that allowed them to envision and design their own chatbot to address their needs and challenges during the pandemic. We report our findings on our participants��� needs for chatbots��� roles and features, and their challenges in using chatbots. We then present design implications for future chatbot design for the Black American population.

Reflectance Imaging Spectroscopy (RIS) is a hyperspectral imaging technique used for investigating the molecular composition of materials. It can help identify pigments used in a painting, which are relevant information for art conservation and history. For every scanned pixel, a reflectance spectrum is obtained and domain experts look for pure representative spectra, called endmembers, which could indicate the presence of particular pigments. However, the identification of endmembers can be a lengthy process, which requires domain experts to manually select pixels and visually inspect multiple spectra in order to find accurate endmembers that belong to the historical context of an investigated painting. We propose an integrated interactive visual-analysis workflow, that combines dimensionality reduction and linked visualizations to identify and inspect endmembers. Here, we present initial results, obtained in collaboration with domain experts., Session 5, Andra Popa, Francesca Gabrieli, Thomas Kroes, Anna Krekeler, Matthias Alfeld, Boudewijn Lelieveldt, Elmar Eisemann, and Thomas Höllt

Redirected Walking (RDW) shrinks large virtual environments to fit small physical tracking spaces while supporting natural locomotion. Particularly in predictive RDW, one of the core concepts of RDW, algorithms rely on predicting users’ future paths to adjust the induced redirection, which manipulates users’ perception to deviate their physical paths from the intended virtual paths. Current path predictions either assume drastic simplifications or build on complex human locomotion models, which are inappropriate for real-time planning and thus not usable for RDW. Further, adapting existing predictive RDW algorithms to unconstrained open space exponentially increases their computational complexity, so that they are not applicable in real-time. In this work-in-progress paper, we discuss the currently prevalent issues of path prediction in RDW and propose simple yet flexible path prediction models that support dynamic virtual open spaces. Our proposed prediction models consist of two shapes: a drop shape represented by the lemniscate of Bernoulli and a sector shape. They define an area, in which linear and clothoidic walking trajectories will be investigated., 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ISBN:978-1-6654-8402-2, ISBN:978-1-6654-8403-9

When using tangible props as proxies for virtual objects, it is im-portant that these haptic proxies are similar to and co-located with their virtual counterparts. This makes it challenging to scale virtual scenarios because more proxies are needed as scenarios grow more complex. Haptic retargeting, or virtual remapping, makes it possible to repurpose the same physical prop as a proxy for multiple virtual objects. This paper details a user study comparing two techniques for repurposing haptic proxies; namely haptic retargeting based on body warping and change blindness remapping. Participants performed a simple button-pressing task, and 24 virtual buttons were mapped onto four haptic proxies with varying degrees of misalignment. Body warping and change blindness remapping were used to realign the real and virtual buttons, and the results indicate that users failed to reliably detect realignment of up to 7.9 cm for body warping and up to 9.7 cm for change blindness remapping. Moreover, change blind-ness remapping yielded significantly higher self-reported agency, and marginally higher ownership. Taken together these results sug-gest that this less explored technique has potential when it comes to repurposing haptic proxies for virtual reality.

Researchers are daily engaged in bibliographic tasks concerning literature search and review, both in the role of authors of scientific papers and when they are reviewers or evaluators. Current indexing platforms poorly support the visual exploration and comparative metadata analysis coming from subsequent searches. To address these issues, we designed and realized VisualBib(va), an online visual analytics solution, where a visual environment includes analysis control, bibliography exploration, automatic metadata extraction, and metrics visualization for real-time scenarios. We introduce and discuss here the relevant functions that VisualBib(va) supports through one usage scenarios related to the creation of a bibliography., Posters, Antonina Dattolo, Marco Corbatto, and Marco Angelini

In this paper, we present a 360 degrees panoramic Mixed Reality (MR) system that visualises shared gaze cues using contextual speech input to improve task coordination. We conducted two studies to evaluate the design of the MR gaze-speech interface exploring the combinations of visualisation style and context control level. Findings from the first study suggest that an explicit visual form that directly connects the collaborators' shared gaze to the contextual conversation is preferred. The second study indicates that the gaze-speech modality shortens the coordination time to attend to the shared interest, making the communication more natural and the collaboration more effective. Qualitative feedback also suggest that having a constant joint gaze indicator provides a consistent bi-directional view while establishing a sense of co-presence during task collaboration. We discuss the implications for the design of collaborative MR systems and directions for future research. Refereed/Peer-reviewed

A recent literature review on personal space in human-robot interaction identified a research gap for the influence of contextual factors. At the same time, psychological research on interpersonal distancing and theoretical considerations based on compensatory control models suggest the importance of considering these factors in robot path planning. To address this gap, we tested the effect of room size and working memory load on participants’ comfort distance toward an approaching robot. In a preregistered 3 × 2 within-subject design, N = 72 participants were approached by a mobile manufacturing robot in a corridor with varying room size and with and without a cognitive secondary task. As dependent variables, comfort distance, arousal, and perceived control were measured. While room size and working memory load had no significant direct effect on comfort distance, participants felt higher arousal and lower control in smaller rooms and in conditions with high working memory load, which in turn caused larger comfort distances (indirect effect). With experience, comfort distances decreased. Based on the indirect effects, future studies should test the effect of more extreme manipulations on comfort distances. Robots should adapt their path planning by keeping larger distances toward human workers in stressful environments to avoid discomfort. Version of record

Design probes, an essential research tool during the COVID-19 pandemic, are ancillary "personal" data gathering tools that enable researchers to enter the private world of research participants. This paper compares two case studies of design probes used during the pandemic for radical placemaking in hybrid digital-physical environments: Digital Art Summer School in Northrock, Ireland, with eleven participants, and Chatty Bench Project in Brisbane, Australia, with sixteen participants. The paper further expands on the design methodology of the probes and their deployment during the online radical placemaking projects. From the participant responses to the probes’ activities and interviews, both studies demonstrated that the probes fostered placemaking in digital environments during the pandemic. The paper concludes with three lessons on the potential of probes as a critical research instrument to enable creativity, build social capital and create bonds between people and places during uncertain and turbulent times.

Since early 2020 the COVID-19 pandemic has had a considerable impact on many aspects of daily life. A range of different measures have been implemented worldwide to reduce the rate of new infections and to manage the pressure on national health services. A primary strategy has been to reduce gatherings and the potential for transmission through the prioritisation of remote working and education. Enhanced hand hygiene and the use of facial masks have decreased the spread of pathogens when gatherings are unavoidable. These particular measures present challenges for reliable biometric recognition, e.g. for facial-, voice- and hand-based biometrics. At the same time, new challenges create new opportunities and research directions, e.g. renewed interest in non-constrained iris or periocular recognition, touch-less fingerprint- and vein-based authentication and the use of biometric characteristics for disease detection. This article presents an overview of the research carried out to address those challenges and emerging opportunities., Comment: New section on Societal impact, updates on recent references

Dashboards are used ubiquitously to gain and present insights into data by means of interactive visualizations. To bridge the gap between non-expert dashboard users and potentially complex datasets and/or visualizations, a variety of onboarding strategies are employed, including videos, narration, and interactive tutorials. We propose a process model for dashboard onboarding that formalizes and unifies such diverse onboarding strategies. Our model introduces the onboarding loop alongside the dashboard usage loop. Unpacking the onboarding loop reveals how each onboarding strategy combines selected building blocks of the dashboard with an onboarding narrative. Specific means are applied to this narration sequence for onboarding, which results in onboarding artifacts that are presented to the user via an interface. We concretize these concepts by showing how our process model can be used to describe a selection of real-world onboarding examples. Finally, we discuss how our model can serve as an actionable blueprint for developing new onboarding systems. Osterreichische Forschungsforderungsgesellschaft 881844 Version of record

Everyday life takes place in space and time, and spatial experience lies at the heart of our existence. Understanding how we conceive spatial relations, and how we solve spatio-temporal problems, is therefore key to understanding human cognition. Spatial cognition research has advanced considerably over the past decades, with major successes particularly in computational implementations of knowledge representation and reasoning methods. Still, a range of key issues continue to pose major challenges. The goal of this report is to discuss the various options for the formalisation, implementation and automated solution of spatial problems including the following issues: the identification and specification of relevant concepts as expressed in human language; modules for automated understanding of domain descriptions; the use of spatial structures and affordances for direct spatial problem solving; and, the development of efficient planning systems capable of providing feasible solutions to spatial problems. This report documents the program and the outcomes of Dagstuhl Seminar 21492 "Representing and Solving Spatial Problems"., DagRep, Volume 11, Issue 11, pages 1-55

In this paper, natural language processing (NLP) and machine learning methods and tools are applied to the task of topic (thematic or semantic) classification of Portuguese proverbs. This is a difficult task since proverbs are usually very short sentences. Such classification should allow an easier selection of the most relevant proverbs for a given situation, considering their context in discourse or within a text. For that, we used, on the one hand, a collection of +32,000 proverbial expressions organized "thematically" into a large set of previously attributed topics (+2,200) and, on the other hand, the Orange data mining toolkit, along with the NLP and machine learning tools it provides. Since the classification provided in the collection of proverbs is, for the most part, based only on a keyword in the body of the proverbs, 2 experiments were set up, to determine the feasibility of the task with a modicum of effort and the most promising configurations applicable. Different sample sizes, 100 and 50 proverbs randomly selected per topic, corresponding to Scenario 1 and 2, respectively, were contrasted; several preprocessing strategies were explored, and different data representation methods tested against several learning algorithms. Results show that Neural Networks is the best performing model, achieving the best classification accuracy of 70% and 61%, in the two different experimental scenarios, Scenario 1 and 2, respectively. Some of the inaccurate classification cases seem to indicate that the machine learning approach can sometimes do a better job than a human classifier, especially considering the manual attribution of the topics by the collection’s author, the sheer number of topics involved, and the very unbalanced distribution of proverbs per topic. Based on the results achieved, the paper presents some proposals for future work to cope with such difficulties., OASIcs, Vol. 104, 11th Symposium on Languages, Applications and Technologies (SLATE 2022), pages 2:1-2:8

There has been little research into whether a persona's picture should portray a happy or unhappy individual. We report a user experiment with 235 participants, testing the effects of happy and unhappy image styles on user perceptions, engagement, and personality traits attributed to personas using a mixed-methods analysis. Results indicate that the participant's perceptions of the persona's realism and pain point severity increase with the use of unhappy pictures. In contrast, personas with happy pictures are perceived as more extroverted, agreeable, open, conscientious, and emotionally stable. The participants’ proposed design ideas for the personas scored more lexical empathy scores for happy personas. There were also significant perception changes along with the gender and ethnic lines regarding both empathy and perceptions of pain points. Implications are the facial expression in the persona profile can affect the perceptions of those employing the personas. Therefore, persona designers should align facial expressions with the task for which the personas will be employed. Generally, unhappy images emphasize realism and pain point severity, and happy images invoke positive perceptions. info:eu-repo/semantics/acceptedVersion

This report documents the program and the outcomes of Dagstuhl Seminar 22031 "Bringing Graph Databases and Network Visualization Together". Due to the ongoing restrictions caused by the COVID-19 pandemic, this purely on-site seminar had a reduced number of participants. Twenty-two researchers and practitioners from the Network Visualization and Graph Database communities met to initiate collaborative work and exchange between the two communities. The seminar served to establish a common understanding of the state of the art and the terminology in both communities, and to connect participants to tackle joint research challenges. Survey talks on the first days laid the foundations for subsequent plenary discussions and working groups. Further lightining talks during the next days gave more detailed insight into specific research questions and practical challenges. The contributions of the seminar include bringing the communities together, the identification of the top areas of research interest, and the characterization of research challenges and research questions. As an outcome, a position paper is planned, and further collaborations and joint publications are on the way., DagRep, Volume 12, Issue 1, pages 67-82