VANET '13:proceedings of the tenth ACM International Workshop on Vehicular Inter-NETworking, Systems, and Applications, June 25 , 2013, Taipei, Taiwan
Bibliographic record
Abstract
This year marks a very important date for the ACM international workshop on Vehicular inter-networking, systems, and applications as ACM VANET celebrates now its 10th edition. Starting in 2004 as "ACM international workshop on Vehicular ad hoc networks" already the change in title indicates that research on vehicular networking changed significantly throughout the past decade. Back in 2004, vehicular networking was primarily seen as a mobile networking challenge and consequently ACM VANET was co-located with ACM Mobicom. From the nine presentations and seven posters, many focused on radio- and data dissemination-related issues. Security was a concern that justified its own session even back then. Together with Mobicom, ACM VANET traveled to Philadelphia, Cologne, Los Angeles, Montreal, San Francisco, Beijing, Chicago, and finally Las Vegas. While communication protocols and security remained important topics, the scope broadened significantly including topics like simulation and performance aspects, but also more and more applications and real-world experiments. Consequently, in 2008 the workshop title was changed and the acronym VANET was re-interpreted as "VehiculAr Inter-NETworking". Finally, in 2012 ACM VANET joined ACM MobiSys in Ambleside (Lake District), UK and renamed again to its current name to give full consideration to this broadened and more system focused scope of vehicular networking. What has not changed over the years are the quality and the originality of papers submitted. With the strong support of our TPC members, the TPC chairs finally selected 8 full papers and 5 poster papers that are again covering a broad spectrum of topics. The papers are going to be presented in the workshop together with a panel that discusses how the ever-increasing usage of smartphones will become a game-changer for vehicular networking and a keynote talk.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".