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dc.contributor.authorHong, Yu-Lunen_US
dc.contributor.authorWatson, Benjaminen_US
dc.contributor.authorThompson, Kennethen_US
dc.contributor.authorDavis, Paulen_US
dc.contributor.editorVrotsou, Katerina and Bernard, Jürgenen_US
dc.date.accessioned2021-06-12T11:22:04Z
dc.date.available2021-06-12T11:22:04Z
dc.date.issued2021
dc.identifier.isbn978-3-03868-150-2
dc.identifier.urihttps://doi.org/10.2312/eurova.20211090
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20211090
dc.description.abstractAnalysts now often use machine learning (ML) assistants, but find them difficult to use, since most have little ML expertise. Talk2Hand improves the usability of ML assistants by supporting interaction with them using knowledge boards, which intuitively show association, visually aid human recall, and offer natural interaction that eases improvement of displayed associations and addition of new data into emerging models. Knowledge boards are familiar to most and studied by analytics researchers, but not in wide use, because of their large size and the challenges of using them for several projects simultaneously. Talk2Hand uses augmented reality to address these shortcomings, overlaying large but virtual knowledge boards onto typical analyst offices, and enabling analysts to switch easily between different knowledge boards. This paper describes our Talk2Hand prototype.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectMixed / augmented reality
dc.subjectTheory of computation
dc.subjectSemi
dc.subjectsupervised learning
dc.titleTalk2Hand: Knowledge Board Interaction in Augmented Reality Easing Analysis with Machine Learning Assistantsen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.description.sectionheadersImmersive Analytics and Interaction
dc.identifier.doi10.2312/eurova.20211090
dc.identifier.pages1-5


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