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Record W4243644268 · doi:10.22215/etd/2019-13614

CountMarks: Multi-Finger Marking Menus for Mobile Interaction with Head-Mounted Displays

2019· dissertation· en· W4243644268 on OpenAlexaff
Jordan R. Pollock

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsCarleton University
Fundersnot available
KeywordsGestureHuman–computer interactionComputer scienceMobile deviceMobile interactionMobile phoneInteraction techniqueHead (geology)PhoneInterface (matter)Computer visionWorld Wide Web

Abstract

fetched live from OpenAlex

Head-mounted displays (HMDs) are becoming thinner, lighter and wireless. Soon we may see these displays used in public in devices like smart glasses. In this thesis, we designed, implemented and evaluated a novel multi-touch marking menu technique for use with HMDs. CountMarks extends conventional marking menus (gesture-based radial menus) by using multi-finger input on a mobile phone screen. This supports selecting items from each of four menus (one for each finger) with a single swipe, reducing the need for deeper menu hierarchies. We discuss the design of two variations of CountMarks, exploring selection efficiency, public acceptability, and ergonomic comfort.

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 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 categoriesMeta-epidemiology (narrow)
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 score0.837
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.336
Teacher spread0.320 · 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 teacher head, not a consensus.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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