Proceedings of the First ACM workshop on Sensor and actor networks
Bibliographic record
Abstract
Welcome to SANET 2007, the 1st ACM Workshop on Sensor Actor Networks -- SANET 2007. The advent of nano-technology and advances in communications has made it technologically feasible and economically viable to develop low-power devices that integrate general-purpose computing with multi-purpose sensing and wireless communications capabilities. It is expected that sensor networks will have a significant impact on a wide array of applications ranging from military, to scientific, to industrial, to health-care, to domestic, to environmental, establishing ubiquitous wireless sensor networks that will pervade society redefining the way in which we live and work. Recently, in an attempt to integrate sensor networks in the fabric of human activities it has been recognized that it would be beneficial to augment sensor networks by either actuators or actors. Actuators are simple devices programmed to take immediate, one-shot, action in response to sensory input. Actors are more sophisticated entities that, in addition to actuating can provide a meaningful, long-term, interaction with the environment. This long-term interaction presupposes intelligent coordination with both the sensory data but also with anticipated changes in the environment. The resulting augmented version of sensor networks is commonly referred to as Sensor Actor Networks (SANET). In response to the Call for Papers, fifteen papers from USA, Europe, Asia and Canada had been submitted. Based on three review reports per paper, they were classified as accept, reject or discuss. For those papers in the discuss list, additional opinions were sought. As a result, a total of six papers were selected for presentation at SANET 2007. The workshop could not be successful without the help of many organizations and individuals. First, we would like to thank the workshop general chairs, Symeon Symeon Papavassiliou and Ivan Stojmenovic, for their support and guidance. Next, we wish to thanks the program committee (PC) members, and the PC members and external reviewers for evaluating the assigned papers in a timely and professional manner. Last, but not the least, we thank all the authors for their submissions.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.018 |
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".