Special Issue on Human Accuracy in Mobile Data Collection at IJHCS
Web: https://www.journals.elsevier.com/international-journal-of-human-computer-studies/call-for-papers/special-issue-on-human-accuracy-in-mobile-data-collection <https://www.journals.elsevier.com/international-journal-of-human-computer-studies/call-for-papers/special-issue-on-human-accuracy-in-mobile-data-collection>.
Deadline for paper submission: November 15th, 2018
The widespread availability of mobile devices and an aspiration to study humans ‘in situ’ has led many researchers to rely on mobile-based human data contributions. While the ubiquitous nature and advanced sensor technologies of smartphones and other mobile devices makes them highly suitable for in-the-wild research, the accuracy of human labelled submission remains an underexplored area. As researchers largely rely on human contributions, ensuring a sufficient accuracy of data submissions is essential to produce valid study results. While it is well-known that human accuracy is subject to fluctuation over time and across contexts, common current practice is to consider all mobile human data submissions as both accurate and equal to one another. Many methodological questions, best practices, and evaluation techniques regarding the quality of human contributions remain unanswered.
This Special Issue aims to present a set of high-quality, high-impact, original research results reporting the current state of the art of human accuracy in mobile sensing. We are interested in submissions utilising a variety of methodological perspectives, including but not limited to mobile crowdsourcing, citizen science, and self-report methods such as experience sampling. Manuscripts must be original, but significant revisions of papers recently presented at conferences and workshops will be considered (at least 50% new material). We encourage contributions in the following key areas and considering a mobile human-labelled data contributions context:
- Evaluation Techniques: methods and methodologies to assess the accuracy of individual responses and/or respondents in lieu of ground truth data.
- Contextual Intelligence: applying contextual information, obtained through e.g. hardware or software sensors, to determine human data accuracy or appropriate moments for data collection.
- Intrinsic and Extrinsic Motivation: understanding human participant motivation and incentives and its effect on the quality of human contributions.
- Viewpoints: discussion on potential and pitfalls of human-labelled data collection.
- Survey papers: focused, comprehensive, and analytical summary of the current state of the art which transcends beyond one single methodology.
Final manuscripts are due 15th of November 2018, but early submissions are encouraged. All contributions will be rigorously peer reviewed to the usual exacting standards of the IJHCS journal. Further information, including submission procedures and advice on formatting and preparing your manuscript, can be found at: https://www.elsevier.com/journals/international-journal-of-human-computer-studies/1071-5819/guide-for-authors <https://www.elsevier.com/journals/international-journal-of-human-computer-studies/1071-5819/guide-for-authors>.
Manuscripts are submitted via the Elsevier Evise : https://www.evise.com/profile/api/navigate/IJHCS <https://www.evise.com/profile/api/navigate/IJHCS>. Authors must select “VSI: Mobile Human Accuracy” when they reach the "Article Type" step in the submission process. To discuss a possible contribution, please contact the Special Issue editors at: [log in to unmask] <mailto:[log in to unmask]>.
- Manuscript submission due: November 15, 2018
- First round notification: January 30, 2019
- Revised manuscript due: March 1, 2019
- Final decision made: March 15, 2019
- Final revision due: April 15, 2019
- Expected publication date: July 15, 2019
Niels van Berkel (The University of Melbourne)
Jorge Goncalves (The University of Melbourne)
Katarzyna Wac (University of Copenhagen, University of Geneva)
Simo Hosio (University of Oulu)
Anna L. Cox (University College London)
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