Performance Evaluation of Warped Virtual Surfaces in Virtual Reality
Bibliographic record
Abstract
This thesis proposes a novel surface warping (scaling) technique similar to applyingControl-Display (CD) gain to the traditional mouse cursor on a screen with a 1:1 input device in Virtual Reality (VR).We call the technique Warped Virtual Surfaces (WVS).WVS solution is a promising way of providing large tactile surfaces in VR while using small physical surfaces with little impact on user performance.We utilized a stylus with VR head-mounted displays (HMDs), enabling users to interact with arbitrarily large virtual panels in VR while their real physical movement is within a fixed-sized real panel area.WVS on a tablet or a physical panel would entail users interacting with larger virtual panels while getting the benefits of haptic feedback from a smaller real physical panel or tablet.We evaluated the WVS method in two separate experiments.Experiment results from Fitts' law reciprocal tapping task comparing different scale factors (SFs) indicated there was a significant difference in movement time for large scale factors in both experiments.We first evaluated user performance on a digital drawing tablet and stylus with 2D tracking with WVS under different SFs.In the case of throughput and error rate, the analysis did not find a significant difference between scale factors in our first study.Non-inferiority statistical testing revealed that performance in terms of throughput and error rate for large scale factors was no worse than a 1-to-1 mapping.This indicates that warping had minimal impact on performance.For our second study, we investigated how WVS affected user performance with 3D stylus tracking and without a tablet or physical panel (i.e.in-air selection).We found similar results for error rate as our first study, i.e. performance in terms of error rate for different scale factors, was no worse than a 1-to-1 mapping.However, unlike our first study, our analysis found a significant difference in throughput.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".