TNT: improved rotation and translation on digital tables
Bibliographic record
Abstract
Digital tabletop systems allow users to work on computational objects in a flexible and natural setting. Since users can easily move to different positions around a table, systems must allow people to orient artifacts to their current position. However, it is only recently that rotation and translation techniques have been specifically designed for tabletops, and existing techniques still do not feel as simple and efficient as their real-world counterparts. To address this problem, we studied the ways that people move and reorient sheets of paper on real-world tabletops. We found that in almost all cases, rotation and translation are carried out simultaneously, and that an open-palm hand position was the most common way to carry out the motion. Based on our observations, we designed a new set of reorientation techniques that more closely parallel real-world motions. The new techniques, collectively called TNT, use three-degree-of-freedom (3DOF) input to allow simultaneous rotation and translation. A user study showed that all three variants of TNT were faster than a recent technique called RNT; in addition, participants strongly preferred TNT.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
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".