Bibliographic record
Abstract
Multi-touch mobile devices (e.g. iPhone and iPad) and motion-sensing game controllers (e.g. Kinect for Xbox 360) share one common feature: users interact with computing devices in non-conventional gesture-intensive ways, be they multi-touch gestures on the iPad or body motion gestures with the Kinect. As a new way to interact with computing devices, gestures have been proven to be intuitive and natural, with very minimal learning curve. They can be used in applications beyond games, such as those that allow the creation of artistic and musical content in a collaborative fashion. In order for multiple users to collaborate or compete in real time, however, such gestures need to be streamed in multiple broadcast sessions with an “all-to-all” broadcast nature, with each session corresponding to one of users as a source of a gesture stream. These streams of gestures typically incur low yet bursty bit rates, but have unique requirements with respect to delay and loss. In this paper, we present the design of GestureFlow, a gesture broadcast protocol designed specifically for concurrent gesture streams in multiple broadcast sessions. We motivate the effectiveness and practicality of using inter-session network coding, and address challenges introduced by linear dependence, discovered in our extensive experiments involving a new gesture-intensive iPad application that we developed from scratch.
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 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".