MEETVERSE: A new way of Interaction on Online Meeting Platforms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Online meeting platforms are used widely in today’s era of Digital India. These meetingplatforms are used in providing online education, online dating and online business meetings, etc. Duringthe last decade, there is quite a development in online meeting methods. At present the meetingapplications solve almost everything be it sharing screen, muting mic, disabling your camera, andchanging the background but still they sometimes become boring. This article presents ways to makemeeting applications more interesting using Avatar formation, interacting using Avatar, and providing handgesture controls to increase and decrease the volume of the meeting platform.Different deep learning techniques are required to make different avatars according to different people.Different Machine learning and Computer Vision techniques are used such as face recognition forextracting the features from the face to directly apply them to the Avatar. These methods and features arean add-on to the existing Meeting Applications, which makes them more interactive.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it