IMPReSS: Improved Multi-Touch Progressive Refinement Selection Strategy
Bibliographic record
Abstract
We developed a progressive refinement technique for VR object selection using a smartphone as a controller. Our technique, IMPReSS, combines conventional progressive refinement selection with the marking menu-based CountMarks. CountMarks uses multi-finger touch gestures to “short-circuit” multi-item marking menus, allowing users to indicate a specific item in a sub-menu by pressing a specific number of fingers on the screen while swiping in the direction of the desired menu. IMPReSS uses this idea to reduce the number of refinements necessary during progressive refinement selection. We compared our technique with SQUAD and a multi-touch technique in terms of search time, selection time, and accuracy. The results showed that IMPReSS was both the fastest and most accurate of the techniques, likely due to a combination of tactile feedback from the smartphone screen and the advantage of fewer refinement steps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".