Attentive user interfaces: the surveillance and sousveillance of gaze-aware objects
Bibliographic record
Abstract
Attentive user interfaces are user interfaces that aim to support users' attentional capacities. By sensing users' attention for objects and people in their everyday environment and by treating user attention as a limited resource, these interfaces avoid today's ubiquitous patterns of interruption. Focusing upon attention as a central interaction channel allows development of more sociable methods of communication and repair with ubiquitous devices. Our methods are analogous to human turn-taking in group communication. Turn-taking improves the user's ability to conduct foreground processing of conversations. Attentive user interfaces bridge the gap between foreground and periphery of user activity in a similar fashion, allowing users to move smoothly in between. The authors present a framework for augmenting user attention through attentive user interfaces. We propose 5 key properties of attentive systems: to (1) sense attention, (2) reason about attention, (3) regulate interactions, (4) communicate attention and (5) augment attention. We conclude with a discussion of privacy considerations of attentive user interfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".