[D69] End-user vibration customization tools: Parameters and examples
Bibliographic record
Abstract
Summary form only given, as follows. Touch feedback (e.g., vibrations) can add to the expressiveness and utility of electronic devices, but users have a broad range of preferences as to their content and deployment. Rather than requiring of designers the nearly impossible task of pleasing everyone, we aim to empower users with easy-to-use tools that balance control with effort-of-use, for a desired degree of customizability. We focus in particular on affective qualities. In this demo, in the context of several application scenarios, we propose five parameters that can describe vibration customization tools, and demonstrate them with three tool concepts. Respectively, these follow themes of Choice (fast and convenient: choose individual stimuli), Filter (moderate control: modify base parameters of individual stimuli) and Block (high control: compose stimuli by arranging their component parts). Our aim is to open a discussion on end-user customization and tools, and learn of more contexts that could benefit from such an approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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".