Proceedings of the 6th international conference on Mobile systems, applications, and services
Bibliographic record
Abstract
We welcome you to the Sixth International Conference on Mobile Systems, Applications, and Services, sponsored by ACM SIGMOBILE and USENIX, held in beautiful Breckenridge, Colorado June 17-20, 2008. We are grateful for the generous financial support provided to MobiSys 2008 by the National Science Foundation, Intel, Microsoft Research, Nokia, and IBM Research. We thank all of the faculty, staff, and students who have contributed to making MobiSys 2008 a success. In particular, we'd like to profusely thank our Program Committee Chairs Eyal de Lara and Carla Ellis and all the PC members, our local arrangements chair Mike Colagrosso, our workshops and tutorials chair Katie Siek, our posters and demonstrations chair Landon Cox, our publications chair Anmol Sheth, and our publicity chair Alex Varshavsky. In addition, we'd like to thank SIGMOBILE, our steering committee, and especially our steering committee chair Victor Bahl for all his advice and guidance during the preparations for MobiSys 2008. Mobile computing and communications have soared in popularity in recent years, and research interest has been intense and growing. With the advent of smartphone and wireless technology such as the iPhone and WiFi/WiMax, we have seen an explosion in innovation in mobile computing. The MobiSys conference captures this excitement by gathering a vibrant community of world-class researchers together to share cutting-edge ideas in mobile systems, applications, and services. We are excited to present the MobiSys 2008 program. The main conference program presents 22 sterling technical papers, a keynote speech by the renowned Roy Want of Intel, a panel discussion on key policy issues in mobile computing, and an invigorating session of 19 posters and demonstrations. In addition, we are pleased to present the Second International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments (HealthNet), the first Workshop on Virtualization in Mobile Computing (MobiVirt), a tutorial on Thick Mobile Client Programming Technologies: Python for S60 and J2ME, a tutorial on Vehicular Ad Hoc Networks, and a PhD Forum. We effusively thank all the organizers of the workshops, tutorials and PhD Forum, including HealthNet's General Co-Chairs Gaetano Borriello and Chiara Petrioli and TPC Co-Chairs Maria Ebling and Robin Kravets, MobiVirt's organizers Ramon Caceres and Landon Cox, the Nokia thick client tutorial's instructors Rich Hankins and Vidya Setlur, the VANET tutorial's instructors Hannes Hartenstein and Ken Laberteaux, and the PhD Forum's organizers Arta Doci and Alan Marchiori.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.163 | 0.137 |
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".