Concepture: a regular language based framework for recognizing gestures with varying and repetitive patterns
Bibliographic record
Abstract
We present Concepture, a framework based on regular language grammars for the authoring and recognition of sketched gestures with infinitely varying and repetitive patterns. Such gestures, while often seen in gesture based applications are currently hard-coded and not customizable. We endorse an example-based workflow, where users author gestures by sketching one or more example instances of the gesture. We de-construct these examples into perceptible stroke segments. Adjacent segment-pairs further capture local spatial relationships between segments and these segment-pairs form the alphabet of a regular language. We then initialize a grammar for our gesture by admitting strings that represent the user provided examples. Grammar refinement is user-friendly, in that we automatically generate new candidate gestures that are visually presented to the user for verification as instances of the gesture. We show Concepture to be effective in efficiently authoring a number of common, yet difficult to recognize gestures, and illustrate it using clip-art and image annotation applications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".