005 Computerprogrammierung, Programme, Daten
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Risk-based authentication (RBA) aims to strengthen password-based authentication rather than replacing it. RBA does this by monitoring and recording additional features during the login process. If feature values at login time differ significantly from those observed before, RBA requests an additional proof of identification. Although RBA is recommended in the NIST digital identity guidelines, it has so far been used almost exclusively by major online services. This is partly due to a lack of open knowledge and implementations that would allow any service provider to roll out RBA protection to its users.
To close this gap, we provide a first in-depth analysis of RBA characteristics in a practical deployment. We observed N=780 users with 247 unique features on a real-world online service for over 1.8 years. Based on our collected data set, we provide (i) a behavior analysis of two RBA implementations that were apparently used by major online services in the wild, (ii) a benchmark of the features to extract a subset that is most suitable for RBA use, (iii) a new feature that has not been used in RBA before, and (iv) factors which have a significant effect on RBA performance. Our results show that RBA needs to be carefully tailored to each online service, as even small configuration adjustments can greatly impact RBA's security and usability properties. We provide insights on the selection of features, their weightings, and the risk classification in order to benefit from RBA after a minimum number of login attempts.
The documentation requirements of data published in long term archives have significantly grown over the last decade. At WDCC the data publishing process is assisted by “Atarrabi”, a web-based workflow system for reviewing and editing metadata information by the data authors and the publication agent. The system ensures high metadata quality for long-term use of the data with persistent identifiers (DOI/URN). By these well-defined references (DOI) credit can properly be given to the data producers in any publication.
Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user’s posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers.
Within qualitative interviews we examine attitudes towards driverless cars in order to investigate new mobility services and explore the impact of such services on everyday mobility. We identified three main issues that we would like to discuss in the workshop: (I) Designing beyond a driver-centric approach; (II) Developing mobility services for cars which drive themselves; and (III) Exploring self-driving practices.
Application developers constitute an important part of a digital platform’s ecosystem. Knowledge about psychological processes that drive developer behavior in platform ecosystems is scarce. We build on the lead userness construct which comprises two dimensions, trend leadership and high expected benefits from a solution, to explain how developers’ innovative work behavior (IWB) is stimulated. We employ an efficiencyoriented and a social-political perspective to investigate the relationship between lead userness and IWB. The efficiency-oriented view resonates well with the expected benefit dimension of lead userness, while the social-political view might be interpreted as a reflection of trend leadership. Using structural equation modeling, we test our model with a sample of over 400 developers from three platform ecosystems. We find that lead userness is indirectly associated with IWB and the performance-enhancing view to be the stronger predictor of IWB. Finally, we unravel differences between paid and unpaid app developers in platform ecosystems.
This paper addresses the urgent need for international standardization of Context Metadata for e-Learning environments. In particular, E-Learning when distributed over the Internet, can synchronously and asynchronously reach a huge number of learners but also has to deal with a variety of different cultures and societies and the related complications. A lot of the differences strongly demand adaptation processes in which especially the contents are being modified to fit the needs in the targeted contexts. In our approach solving this task, we determined a list of around 160 significant possible differences and defined those as context metadata. In this paper, we show the results of our research regarding to the determination of context related influence factors as well as approaches to deal with them and present a first specification of the representing context-metadata.
In this paper, we present a solution how to test cultural influences on E-Learning in a global context. Based on a metadata approach, we show how specifically cultural influence factors can be determined to transfer and adapt learning environments. We present a method how those influence factors can be validated for both, to improve the dynamical meta-data specification and to be used in the development of (international) E-Learning scenarios.
Beyond HCI and CSCW: Challenges and Useful Practices Towards a Human-Centred Vision of AI and IA
(2019)
Data emerged as a central success factor for companies to benefit from digitization. However, the skills in successfully creating value from data – especially at the management level – are not always profound. To address this problem, several canvas models have already been designed. Canvas models are usually created to write down an idea in a structured way to promote transparency and traceability. However, some existing data science canvas models mainly address developers and are thus unsuitable for decision-makers and communication within interdisciplinary teams. Based on a literature review, we identified influencing factors that are essential for the success of data science projects. With the information gained, the Data Science Canvas was developed in an expert workshop and finally evaluated by practitioners to find out whether such an instrument could support data-driven value creation.
Appropriating Digital Fabrication Technologies — A comparative study of two 3D Printing Communities
(2015)
Digital fabrication technologies have a great potential for empowering consumers to produce their own creations. However, despite the growing availability of digital fabrication technologies in shared machine shops such as FabLabs or University Labs, they are often perceived as difficult to use, especially by users with limited technological aptitude. Hence, it is not yet clear if the potentials of the technology can be made accessible to a broader public, or if they will remain limited to some form of “maker elite”. In this paper, we study the appropriation of digital fabrication on the example of the use of 3D printers in two different communities. In doing so, we analyze how users conceptualize their use of the 3D printers, what kind of contextual understanding is necessary to work with the machines, and how users document and share their knowledge. Based on our empirical findings, we identify the potentials that the machines offer to the communities, and what kind of challenges have to be overcome in their appropriation of the technology.
3D Printers as Sociable Technologies: Taking Appropriation Infrastructures to the Internet of Things
(2017)
Quantum mechanical theories are used to search and optimized the conformations of proposed small molecule candidates for treatment of SARS-CoV-2. These candidate compounds are taken from what is reported in the news and in other pre-peer-reviewed literature (e.g. ChemRxiv, bioRxiv). The goal herein is to provided predicted structures and relative conformational stabilities for selected drug and ligand candidates, in the hopes that other research groups can make use of them for developing a treatment.
Eco-InfoVis at Work
(2020)
Designing consumption feedback to support sustainable behavior is an active research topic. In recent years, relevant work has suggested a variety of possible design strategies. Addressing the more recent developments in this field, this paper presents a structured literature review, providing an overview of current information design approaches and highlighting open research questions. We suggest a literature-based taxonomy of used strategies, data source and output media with a special focus on design. In particular, we analyze which visual forms are used in current research to reach the identified strategy goals. Our survey reveals that the trend is towards more complex and contextualized feedback and almost every design within sustainable HCI adopts common visualization forms. Furthermore, adopting more advanced visual forms and techniques from information visualization research is helpful when dealing with ever-increasing data sources at home. Yet so far, this combination has often been neglected in feedback design.
This paper aspires to develop a deeper understanding of the sharing/collaborative/platform economy, and in particular of the technical mechanisms upon which the digital platforms supporting it are built. In surveying the research literature, the paper identifies a gap between studies from economical, social or socio-technical angles, and presentations of detailed technical solutions. Most cases study larger, ‘monotechnological’ platforms, rather than local platforms that lend components from several technologies. Almost no literature takes a design perspective. Rooted in Sharing & Caring, an EU COST Action (network), the paper presents work to systematically map out functionalities across domains of the sharing economy. The 145 technical mechanisms we collected illustrate how most platforms are depending on a limited number of functionalities that lack in terms of holding communities together. The paper points to the necessity of a better terminology and concludes by discussing challenges and opportunities for the design of future and more inclusive platforms.
Trust is the lubricant of the sharing economy. This is true especially in peer-to-peer carsharing, in which one leaves a highly valuable good to a stranger in the hope of getting it back unscathed. Nowadays, ratings of other users are major mechanisms for establishing trust. To foster uptake of peer-to-peer carsharing, connected car technology opens new possibilities to support trust-building, e.g., by adding driving behavior statistics to users' profiles. However, collecting such data intrudes into rentees' privacy. To explore the tension between the need for trust and privacy demands, we conducted three focus group and eight individual interviews. Our results show that connected car technologies can increase trust for car owners and rentees not only before but also during and after rentals. The design of such systems must allow a differentiation between information in terms of type, the context, and the negotiability of information disclosure.
