A new interface for cloning objects in drawing systems
Bibliographic record
Abstract
Cloning objects is a common operation in graphical user interfaces. One example is calendar systems, where users commonly create and modify recurring events, i.e. repeated clones of a single event. Inspired by the calendar paradigm, we introduce a new cloning technique for 2D drawing programs. This technique allows users to clone objects by first selecting them and then dragging them to create clones along the dragged path. Moreover, it allows editing the generated sequences of clones similar to the editing of calendar events. Novel approaches for the generation of clones of clones are also presented. We compared our new clone creation technique with generic duplication via copy-and-paste, smart duplication, and a dialog driven technique on a standard desktop system. The results show that the new cloning method is always faster than dialogs and smart duplication for most conditions. We also compared our clone editing method against rectangular selection. The results show that our method is better in general. In situations where rectangle selection is effective, our method is still competitive. Participants preferred the new techniques overall, too.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".