Naturally Together: A Systematic Approach for Multi-User Interaction With Natural Interfaces
Bibliographic record
Abstract
New technology is moving towards intuitive and natural interaction techniques that are increasingly embedded in human space (e.g., home and office environment) and aims to support multiple users, yet their interfaces do not cover it to the full. Imagine that you have a multi-user device, should it act differently in different situations, people, and group settings? Current Multi-User Interfaces address each of the users as an individual that works independently from others, and there is a lack of understanding of the mechanisms that impact shared usage of these products. Thus we have linked environmental (external) and user-centered (internal) factors to the way users interact with multi-user devices. We analyzed 124 papers that involve multi-user interfaces and created a classification model out of 8 factors. Both the model and factors were validated by a large-scale online study. Our model defines the factors affecting multi-user usage with a single device and leads to a decision on the most important ones in different situations. This paper is the first to identify these factors and to create a set of practical guidelines for designing Multi-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 | 0.051 | 0.078 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".