ePAD: Engaging Platform for Art Development
Bibliographic record
Abstract
We present a class of devices for use by art thera-pists working with older adults with a progressive illness such as Alzheimer’s disease. We call these devices ePADs. An ePAD combines a touch-screen interface with intelligent user modeling and sens-ing through cameras using computer vision. Us-ing a probabilistic model, an ePAD monitors the behaviours of a user as well as aspects of their af-fective or internal state, including their responsive-ness and engagement with the device. The ePAD then uses decision theoretic planning to enable sit-uated, adaptive strategies for interaction with a hu-man user. In this paper, we discuss results and anal-ysis of a survey of arts therapists, and of one-on-one interviews. We then give details of the ePAD class, framed as a partially observable Markov de-cision process, or POMDP. A key element of this class is that instantiations can be easily made for a wide range of customisable devices and interface applications for art-making moderation. We show examples of particular instances of this model on three devices and with three different interfaces. We give laboratory demonstrations of the function-ality of the devices, and we present and discuss our next steps, including end user testing. 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".