VeDi: A vehicular crowd-sourced video social network for VANETs
Bibliographic record
Abstract
As one of the important members of Internet of Things (IoT), vehicles have seen steep advancement in communication technology.With the advent of Vehicular Ad-Hoc Networks (VANETs), vehicles now can evolve into social interactions to share safety, efficiency, and comfort related messages with other vehicles. In this paper, we study vehicular social network from Social Internet of Things (SIoT) perspective and propose VeDi, a vehicular crowd-sourced video social network for VANETs.When a user shares a video in the VeDi, it can be accessed by other surrounding vehicles. Any social interaction (e.g. view, comment, like) with the video on the roadway are stored in the social network cloud along with the video itself. In VeDi, every vehicle maintains a list of video related metadata (e.g. blur and shakiness) of available videos which are used to selectively retrieve quality videos by surrounding vehicles. We also present a method to determine representative quality scores for an entire video clip using blur and shakiness values. The prototype implementations and experimental results denote that the proposed system can be a viable option to create video social networks such as youtube, vine, and vimeo by employing vehicular crowd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".