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Record W2795520152 · doi:10.1145/3173574.3174208

D-SWIME

2018· preprint· en· W2795520152 on OpenAlex
Gaganpreet Singh, William Delamare, Pourang Irani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Manitoba
FundersCanada Research Chairs
KeywordsPanning (audio)SmartwatchComputer scienceZoomHuman–computer interactionPaceMobile deviceMultimediaEmbedded systemWearable computerWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

Smartwatches enable rapid access to information anytime and anywhere. However, current smartwatch content navigation techniques, for panning and zooming, were directly adopted from those used on smartphones. These techniques are cumbersome when performed on small smartwatch screens and have not been evaluated for their support in mobility and encumbrance contexts (when the user's hands are busy). We studied the effect of mobility and encumbrance on common content navigation techniques and found a significant decrease in performance as the pace of mobility increases or when the user was encumbered with busy hands. Based on these initial findings, we proposed a design space which would improve efficiency when navigation techniques, such as panning and zooming, are employed in mobility contexts. Our results reveal that our design space can effectively be used to create novel interaction techniques that improve smartwatch content navigation in mobility and encumbrance contexts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.284
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations32
Published2018
Admission routes2
Has abstractyes

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