Proceedings of the 5th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 5th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN'08) that takes place in Vancouver, Canada on October 27-28, 2008. This year's ACM PE-WASUM symposium brings together scientists, engineers, and practitioners to share and exchange their experiences, discuss challenges, and report state-of-the-art and in-progress research on all aspects of wireless ad hoc, sensor, and ubiquitous networks with a specific emphasis on their performance evaluation and analysis. The mission of the symposium is to share novel approaches for monitoring, measuring, modeling, simulating, analyzing, optimizing and case-studying the characteristics of ad hoc, sensor, pervasive and ubiquitous networks, as well as exploring and developing new ad hoc networking protocols and tools. ACM PE-WASUN gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of performance evaluation. The call for papers attracted 42 submissions from Europe, Asia, the North and South America and the Middle East. The technical program committee accepted 10 long papers and 6 short papers that cover a variety of topics, including experimentations, tools for performance evaluation, models, performance evaluation of routing and MAC protocols. We hope that these proceedings will serve as valuable references for researchers and developers in the field of wireless ad hoc, sensor and ubiquitous networks.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".