Proceedings of the 8th international conference on Mobile systems, applications, and services
Bibliographic record
Abstract
Welcome to the 8th Annual ACM International Conference on Mobile Systems, Applications and Services -- MobiSys'10 -- in beautiful San Francisco. MobiSys provides a highly-selective forum for cutting-edge research that takes a broad systems perspective of mobile computing. Papers appearing in this year's conference cover a range of topics: from smartphones and energy-efficient localization to collaborative sensing and automotive applications. Notably, there is an increasing emphasis on smartphones, which reflects their growing momentum as powerful, programmable mobile systems. Continuing the tradition of past conferences, this year's conference is preceded by a day of workshops and a PhD forum. Prof. Rosalind W. Picard and Prof. Deborah Estrin are delivering the keynote speeches this year, discussing their research in affective computing and participatory sensing. The demonstration and poster chair, Romit Roy Choudhary, has put together an exciting session that exhibits the best of the current work in our field. MobiSys'10 received 126 submissions, which is roughly the same as the last three years. The papers were reviewed in two rounds. Each paper received at least three reviews during the first round. Based on these reviews, 86 papers were selected to be reviewed in the second round, with each paper receiving up to two additional reviews. All papers discussed at the PC meeting received at least five reviews, almost all of them from PC members. In addition, every review was read and vetted for quality by the PC chairs. All told, our diligent reviewers produced 527 reviews. We discussed 59 papers at an all-day PC meeting, which followed the HotMobile workshop in Annapolis, MD. At the meeting, 25 papers were accepted to appear in the conference program. All accepted papers were shepherded by a PC member.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.162 | 0.117 |
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".