GestureFlow: QoE-Aware Streaming of Multi-TouchGestures in Interactive Multimedia Applications
Bibliographic record
Abstract
With the proliferation of multi-touch mobile devices, such as smartphones and tablets, users interact with devices in non-conventional gesture-intensive ways. As a new way to interact with mobile devices, gestures have been proven to be intuitive and natural with a minimal learning curve, and can be used in interactive multimedia applications. In order for multiple users to collaborate in an interactive manner, we propose that gestures can be streamed in multiple broadcast sessions, with each session corresponding to one of the users as the source of a gesture stream. During the interactive session, the Quality of Experience (QoE) of mobile users hinges upon delays from when gestures are entered by the source to when they are recognized by each of the receivers, which we refer to as gesture recognizing delays. In this paper, we present the design of GestureFlow, a gesture broadcast protocol designed specifically for concurrent gesture streams in multiple broadcast sessions, such that the gesture recognizing delay in each session is minimized. We motivate the effectiveness and practicality of using inter-session network coding, and address challenges introduced by the linear dependence of coded packets. We evaluate our protocol design using an extensive array of real-world experiments on mobile devices, involving a new gesture-intensive interactive multimedia application, called MusicScore, that we developed from scratch.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".