HCI meets Material Science
Bibliographic record
Abstract
With the proliferation of flexible displays and the advances in smart materials, it is now possible to create interactive devices that are not only flexible but can reconfigure into any shape on demand. Several Human Computer Interaction (HCI) and robotics researchers have started designing, prototyping and evaluating shape-changing devices, realising, however, that this vision still requires many engineering challenges to be addressed. On the material science front, we need breakthroughs in stable and accessible materials to create novel, proof-of-concept devices. On the interactive devices side, we require a deeper appreciation for the material properties and an understanding of how exploiting material properties can provide affordances that unleash the human interactive potential. While these challenges are interesting for the respective research fields, we believe that the true power of shape-changing devices can be magnified by bringing together these communities. In this paper we therefore present a review of advances made in shape-changing materials and discuss their applications within an HCI context.
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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) | 0.001 | 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".