Proceedings of the 2014 ACM SIGCOMM workshop on Distributed cloud computing
Bibliographic record
Abstract
It is our great pleasure to welcome you to the ACM SIGCOMM Workshop on Distributed Cloud Computing (DCC 2014)! This interdisciplinary workshop is an opportunity for academic and industrial scientists working in the different fields of networking, cloud computing, and distributed systems, to meet and exchange their vision and expertise on how to plan and manage future research on distributed cloud. Indeed, after a very successful DCC 2013 workshop, co-located with IEEE/ACM Utility and Cloud Computing (UCC) in Dresden, Germany, we are happy to hold this workshop this year together with the premier networking conference SIGCOMM. The great interest in distributed cloud computing is also reflected in the number of submissions we have received. This year, the program committee accepted 10 out of 36 papers (acceptance ratio 27%), which cover a variety of topics related to the distributed cloud. In addition, the program committee accepted 5 papers for the poster session as well as a short pitch. The program is complemented with a keynote by Randall Sobie as well as an invited paper by Jacobus Van der Merwe. Moreover, we would like to encourage the workshop attendees to actively participate in the panel session which will take place at the end of the workshop.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".