Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Bibliographic record
Abstract
It is our great pleasure to welcome you all to the Fifth ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE 2006), which is held in conjunction with ACM SIGMOD/PODS 2006. This workshop brings together researchers in databases, networking, and mobile computing with the aim of connecting the data management and mobile computing communities. It provides opportunities for a full day of exciting discussions on the topic of data management in mobile and wireless environments. The workshop serves as a forum for researchers and technologists to discuss the state-of-the-art, present their contributions, generate novel ideas, and set new directions for data management for mobile and wireless access.MobiDE 2006 is the fifth in a series of workshops. The previous workshops took place in Seattle, WA, in conjunction with MobiCom 1999; in Santa Barbara, CA, together with SIGMOD/PODS 2001; in San Diego, CA, in conjunction with MobiCom 2003; and in Baltimore, MD, in conjunction with SIGMOD/PODS 2005.The workshop program has been put together with the aim of presenting new and controversial research ideas so as to foster interaction among researchers from around the world. The call for papers attracted 31 submissions from 14 countries: Brazil, Canada, China, Cyprus, France, Germany, Greece, Japan, Korea, Norway, Portugal, Tunis, U.K., and U.S.A. Due in parts to MobiDE now being an established outlet for research results and to the co-location with ACM SIGMOD/PODS, many submissions were of very high quality, making the selection process quite competitive. All submissions were reviewed by at least 3 members of the Program Committee. This was followed by a discussion phase, where the reviewers of each submission had the opportunity to discuss the submission and its reviews. As a result, 10 submissions were accepted as full papers, and 2 submissions that reported on promising works in progress were accepted as short papers. In addition, a very interesting invited talk, A Data Architecture for Consumer RFID Applications, by Gaetano Borriello from the University of Washington is included in the program. This talk probes into the consumer side of the mobile environment.The program reflects the depth and breadth of the field, with sessions covering important aspects of mobility, location-based processing, and the introduction of sensor technology into the real world. Thus, these proceedings provide an excellent point of reference on the increasingly important area of data engineering for mobile and wireless computing.
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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 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.038 |
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".