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Record W4253237288 · doi:10.32920/ryerson.14663832

Video and Audio Fusion for Streaming Applications

2021· preprint· en· W4253237288 on OpenAlexaboutno aff
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Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMultimedia Communication and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMicrophoneThe InternetJava appletMultimediaSynchronization (alternating current)Dialog boxRobotJavaReal-time computingComputer graphics (images)Operating systemTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

This thesis explores a technique for the fusion of streamed audio and video services for real-time applications. It discusses certain novel techniques used to overcome the problems with video and audio synchronization over the Internet of a tele-bot. We have developed a demonstration called the WAX, at the Network-Centric Applied Research Team (N-CART) laboratory located within the School of Computer Science at Ryerson University. WAX is equipped with an onboard camera and a microphone, as well as a 2.4 GHz wireless transceiver for transmitting video and audio feeds, and at the same time receiving commands from the WAX robot server. By launching a web browser and loading the Java client applets, a user can see as well as hear what is around WAX in near real-time, while being able to move the robot around its environment.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.370
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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