Perspective cursor
Bibliographic record
Abstract
Multi-display environments and smart meeting rooms are now becoming more common. These environments build a shared display space from variety of devices: tablets, projected surfaces, tabletops, and traditional monitors. Since the different display surfaces are usually not organized in a single plane, traditional schemes for stitching the displays together can cause problems for interaction. However, there is a more natural way to compose display space -- using perspective. In this paper, we develop interaction techniques for multi-display environments that are based on the user's perspective on the room. We designed the Perspective Cursor, a mapping of cursor to display space that appears natural and logical from wherever the user is located. We conducted an experiment to compare two perspective-based techniques, the Perspective Cursor and a beam-based technique, with traditional stitched displays. We found that both perspective techniques were significantly faster for targeting tasks than the traditional technique, and that Perspective Cursor was the most preferred method. Our results show that integrating perspective into the design of multi-display environments can substantially improve performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".