Proceedings of the 1st ACM international workshop on Foundations of wireless ad hoc and sensor networking and computing
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2nd ACM International Workshop on Foundations of Wireless Ad Hoc and Sensor Networking and Computing (ACM FOWANC'09), which takes place in New Orleans on May 18, 2009. With the success of ACM FOWANC'08 at Hong Kong last year, we are delighted to continue this workshop with ACM MobiHoc 2009 and hope it will become a regular ACM MobiHoc workshop in the following years. ACM FOWANC'09 is devoted to distributed algorithms and theoretical methods in the context of wireless ad hoc and sensor networking and computing. This workshop is intended to foster cooperation among researchers in wireless ad hoc and sensor networking and theoreticians in algorithm and theory, and push the theoretical research forward for a deeper understanding about ad hoc and sensor networking and computing. This year, ACM FOWANC received less submissions than last year, however, the overall quality of submissions was excellent. Most of the papers received three peer reviews from our technical program committee (TPC), comprising people from industry, national laboratories, and universities all over the world. After a thorough analysis of the reviews returned, we accepted 10 regular papers for the workshop. The final workshop program includes three technical sessions and one keynote address. We are honored to have Prof. Jie Wu from Florida Atlantic University to give an opening keynote address, titled as On Self-Organization in MANETs.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.019 |
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".