Social on the road: enabling secure and efficient social networking on highways
Bibliographic record
Abstract
This article presents SOR, a vehicular social network to enable social communications and interactions among users on the road during their highway travels. Motivated by the limited connection to Internet contents and services, the essential goal of SOR is to encourage distributed users on the road to spontaneously contribute as the information producer, assembler, and distributer in order to provide timely and localized infotainments to each other through low-cost inter-vehicle communications. To be specific, SOR enables individual users to maintain a personal blog, similar to one on Facebook and Twitter, over which users can create and share personal content information to the public such as travel blogs with pictures and videos. By accessing each other's SOR blogs and commenting on interesting topics, passengers can exchange messages and initiate social interactions. In the specific highway environment, SOR addresses two challenges in the context of vehicular social communications. First, vehicular social communications tend to be frequently interrupted by diverse vehicle mobility and intermittent intervehicle connections, which is annoying to users. To address this issue, SOR adopts a proactive mechanism by estimating the connection time between peer vehicles, and recommending vehicles with relatively long-lasting and stable intervehicle connections for social communications. Second, as users on the road are typically strangers to each other, they are reluctant to disclose personal information to others. This makes it challenging to identify users of shared interests and accordingly restricts the scale of users' social interactions. To remedy that, SOR provides a secured solution to protect sensitive user information during social communications. Lastly, we use simulations to verify the performance of SOR.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".