Cross-device transfer in a collaborative multi-surface environment without user identification
Bibliographic record
Abstract
Combining large interactive surface computers (e.g., digital walls and tables) with smaller, multi-touch surface devices (e.g., smartphones and tablets) provides groups of users with both private and shared workspaces during collaborative (or competitive) activities. Such multi-surface environments introduce the need for effective interaction techniques that enable the transfer of digital content from one device to another, commonly known as cross-device transfer. Utilizing popular existing cross-transfer methods, such as Pick-and-Drop, in a multi-user multi-surface environment, however, require systems that can distinguish between users in order for the environment to accurately know who is transferring what content to what device. Yet, most commercially available digital tabletop systems are not capable of distinguishing between different users. Therefore, existing cross-device transfer methods must be adapted to work in such a user-information limited context. This paper presents a user study comparing the effectiveness of two adapted transfer methods in the context of a strategic digital tabletop card game task. The two transfer methods included a virtual portals-style method, called Bridges, and an adapted Pick-and-Drop method (A-PND). The studied transfer methods both supported the high-levels of card-transfer between private (tablet) and tabletop surfaces required by the game task. Also, participants' reported preferences were equally divided between the two techniques. An in-depth qualitative analysis of the study data revealed that each transfer method provided unique advantages and disadvantages for the game task, which aligned better or worse with different players' personal task goals.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".