Proceedings of the 2013 ACM MobiCom workshop on Lowest cost denominator networking for universal access
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2013 ACM MobiCom Workshop on Lowest Cost Denominator Networking -- LCDNet 2013. Internet has crossed new frontiers with access getting faster and cheaper. The Internet Society's recent global Internet survey reveals that the Internet should be considered as a basic human birthright. On one end, we have the developed world where access is getting faster and services being developed to utilize faster access. On the other end, there are people who do not have access to the Internet at all. Some may not be able to get it due to lack of infrastructure support (which accounts to the notion of digital divide problem faced by most people in developed countries). There have been significant initiatives to solve the problem of affordable infrastructure. Crucially, most of these approaches address infrastructural barriers without addressing economic ones. Leaving connectivity for all to be governed by market economics is a major impediment to achieving the full benefits of the Internet, and that basic Internet access should be made freely available to all due to its societal benefits. The current Internet access model governed by market economics makes it practically infeasible for enabling universal access especially for those with socio-economic barriers. There are both research and policy challenges to the realization of a future Internet capability that will offer appropriate access to all parts of society. The goal of the ACM LCDNet 2013 workshop is to address a range of research challenges (feasibility, scalability, security, new privacy challenges, robustness, resource allocation, sustainability, performance etc.) that are needed to overcome to enable universal Internet access. The call for papers attracted several submissions on a wide range of networking topics. The papers were peer reviewed by a minimum of three reviewers and were extremely selective. The program committee finally accepted 9 papers (including posters). We hope that these proceedings will serve as a valuable reference for both academic and industry researchers. We also encourage attendees to attend the keynote presentations. These valuable and insightful talks can and will guide us to a better understanding of the challenges in enabling Universal Internet access: The Challenge of Deployment, Prof Srinivasan Keshav (University of Waterloo, Canada)Nanosatellites for Universal Network Access, Scott Burleigh (JPL, NASA)
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.019 |
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".