MétaCan
Menu
Back to cohort
Record W3028804793 · doi:10.1145/3313831.3376233

BISHARE: Exploring Bidirectional Interactions Between Smartphones and Head-Mounted Augmented Reality

2020· article· en· W3028804793 on OpenAlexaff
Fengyuan Zhu, Tovi Grossman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHuman–computer interactionComputer scienceAugmented realityOptical head-mounted displaySpace (punctuation)Mobile deviceArtificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

In pursuit of a future where HMD devices can be used in tandem with smartphones and other smart devices, we present BISHARE, a design space of cross-device interactions between smartphones and ARHMDs. Our design space is unique in that it is bidirectional in nature, as it examines how both the HMD can be used to enhance smartphone tasks, and how the smartphone can be used to enhance HMD tasks. We then present an interactive prototype that enables cross-device interactions across the proposed design space. A 12-participant user study demonstrates the promise of the design space and provides insights, observations, and guidance for the future.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.172
GPT teacher head0.351
Teacher spread0.179 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations132
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicAugmented Reality ApplicationsFrench-language works237,207