ALIDA: Using machine learning for intent discernment in visual analytics interfaces
Bibliographic record
Abstract
In this paper, we introduce ALIDA, an Active Learning Intent Discerning Agent for visual analytics interfaces. As users interact with and explore data in a visual analytics environment they are each developing their own unique analytic process. The goal of ALIDA is to observe and record the human-computer interactions and utilize these observations as a means of supporting user exploration; ALIDA does this by using interaction to make decision about user interest. As such, ALIDA is designed to track the decision history (interactions) of a user. This history is then utilized to enhance the user's decision-making process by allowing the user to return to previously visited search states, as well as providing suggestions of other search states that may be of interest based on past exploration modalities. The agent passes these suggestions (or decisions) back to an interactive visualization prototype, and these suggestions are used to guide the user, either by suggesting searches or changes to the visualization view. Current work has tested ALIDA under the exploration of homonyms for users wishing to explore word linkages within a dictionary. Ongoing work includes using ALIDA to guide users in transfer function design for volume rendering within scientific gateways.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".