Proceedings of the fifth international workshop on Foundations of mobile computing
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 5th ACM SIGACT-SIGOPS International Workshop on Foundations of Mobile Computing (DIALM-POMC). This year's workshop continues its tradition of being the premier forum for presentation of research results and experience reports on leading edge issues of in both the design and analysis of discrete and distributed algorithms and the system modeling in the context of mobile, wireless, ad-hoc, and sensor networks. DIALM-POMC gives researchers and practitioners a unique opportunity to share their perspectives with others interested in mobile computing, discrete and distributed algorithms. The call for papers attracted 35 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 10 papers that cover a variety of topics, scheduling and topology control in wireless networks, broadcasting, medium access control and random walks in sensor networks. In addition, the program includes a keynote speech by Andrzej Pelc on Algorithmic Aspects of Radio Communication as well as 3 invited talks by Roger Wattenhofer on Theory for Sensor Networks: What Is It Good For?, Shlomi Dolev on Self-Stabilizing and Self-Organizing Mobile Networks and Alessandro Panconesi on Gossiping (via mobile?) in Social Networks. We hope that these proceedings will serve as a valuable reference for mobile computing researchers and developers.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
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".