Using cursor prediction to smooth telepointer jitter
Bibliographic record
Abstract
Telepointers are an important type of embodiment in real-time distributed groupware. Telepointers can increase the presence of remote participants and can provide considerable awareness information about people's locations and activities. However, the motion of a telepointer is often disrupted by network jitter. Although some strategies exist for dealing with jitter, none of these techniques are able to restore the immediacy and smoothness of a real cursor. In this paper we investigate the use of prediction - commonly used in networked simulations and games - to reduce the effects of jitter on telepointer motion. To determine whether prediction can be effective for improving telepointers, we carried out two experiments that tested the effects of different prediction schemes (some real and some artificial) on people's ability to interpret telepointer gestures. These studies show that although cursor prediction is still a difficult problem, there are both potential performance improvements, and definite preference advantages. Our studies suggest that telepointer prediction should be routinely used to increase the immediacy and naturalness of remote interaction, and suggest that prediction can also improve interpretation in certain situations.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".