Report on Networking and Programming Languages 2017
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The third workshop on Networking and Programming Languages, NetPL 2017, was held in conjunction with SIGCOMM 2017. The workshop series attracts invited speakers from academia and industry and a selection of contributed abstracts for short presentations. NetPL brings together researchers from the networking community and researchers from the programming languages and verification communities. The workshop series is a timely forum for exciting trends, technological and scientific advances in the intersection of these communities. We describe some of the highlights from the invited talks through the lens of three trends: Advances in network machine architectures, network programming abstractions, and network verification. NetPL included five invited speakers, four from academia, and one from industry. The program contained six contributed papers out of eight submitted for presentation. The workshop organizers reviewed the abstracts for quality and scope. A total of 42 registrations were received and the attendance occupied the lecture room to the brink. Slides and abstracts from all talks are available from the workshop home page: http://conferences.sigcomm.org/sigcomm/2017/workshop-netpl.html. Videos of the presentations are available in the NetPL YouTube channel: https://www.youtube.com/channel/UCqU8E2n4MHthZUVb1xK2nRQ.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it