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Record W2903242599 · doi:10.22215/etd/2018-13200

2D Selection in Virtual Reality with Head Mounted Displays

2018· dissertation· en· W2903242599 on OpenAlexaff
Adrian Ramcharitar

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsCarleton University
Fundersnot available
KeywordsJoystickCursor (databases)Computer scienceVirtual realityOptical head-mounted displayAccelerationComputer graphics (images)Transfer functionInput deviceSimulationComputer visionArtificial intelligenceHuman–computer interactionComputer hardwareEngineeringPhysics

Abstract

fetched live from OpenAlex

We present an evaluation of a new selection technique for virtual reality (VR) systems presented on head-mounted displays. The technique, dubbed EZCursorVR, presents a 2D cursor that moves in a head-fixed plane, simulating a 2D desktop-like cursor for VR. The cursor can be controlled by any 2 degree of freedom (DOF) and 3/6DOF input device. We conducted two experiments based on ISO 9241-9. In the first study, we compared the effectiveness of EZCursorVR using six different controllers. Results indicate that the mouse offered the best performance, while the position-control joystick performed the worst. In the second study we evaluate EZCursorVR using three different transfer functions using the mouse with different degrees of cursor acceleration. Results indicate that, despite previous research, constant acceleration performed better than the other two transfer functions. We believe that future evaluation needs to be conducted to evaluate different acceleration curve steepnesses using the same transfer function.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
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
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.315
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2018
Admission routes1
Has abstractyes

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