Bibliographic record
Abstract
With the advent of mobile devices with large displays, it is intuitive and natural for users to interact with an application on a mobile device using multi-touch gestures. In this paper, we propose that these multi-touch gestures can be streamed on-the-fly among multiple participating users, making it possible to engage users in a collaborative or competitive experience. Such multi-touch streams, featuring very low streaming bit rates, can be rendered on receivers to precisely reconstruct the states of an application. We present the challenges, system framework, embedded algorithm design, and real-world evaluation of TouchTime, a new system that has been designed from scratch to facilitate the streaming of multi-touch gestures among multiple users. By seamlessly combining local computation on mobile devices and services from the "cloud," we explore the design space of suitable mechanisms to represent and packetize multi-touch gestures, and of practical protocols to transport concurrent live multi-touch streams over the Internet. Specifically, we propose an auction-based reflector selection algorithm to achieve the minimal end-to-end delay in a live multi-touch streaming session. To demonstrate TouchTime, we have developed a new real-world music composition application --- called MusicScore --- using the Apple iPad Programming SDK, and used it as our running example and experimental testbed to evaluate our design choices and implementation of TouchTime.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".