Proceedings of the 4th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
Bibliographic record
Abstract
On behalf of the program committee we are both pleased to welcome you to the fourth edition of the ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS) being held in San Jose, CA. ANCS has become one of the premier research conferences in networking, and is dedicated to the design of the hardware and software components used to create communication networks. This year, we continued the effort to maintain a high-quality program by addressing the most important research areas. The program includes several papers in areas such as multi-core processors and hardware implementations as they apply to network systems, while also addressing topics in switches, routers, packet classification and network I/O components. We were fortunate to have an outstanding program committee of 32 members who graciously contributed their time and expertise towards the success of the conference. Every one of them was dedicated and thoroughly reviewed the papers they were assigned, and we are sure the authors will be greatly helped by the feedback provided in the reviews. The quality of any conference is derived from the quality of the submitted papers and the quality of the paper selection process. This conference is indebted to all authors who submitted their best work and to the program committee made of experts in the field. We received 67 submissions from all over the world. About 60% of the submitted papers were from the U.S. and Canada, about 23% from Asia/Pacific, and about 17% from Europe, Middle East and Africa. Qualified papers received at least three reviews mostly from program committee members. The review process was double-blind. Authors' identities were kept anonymous throughout the process. At the end the program committee accepted 17 papers which will be presented in a single track over the two days of the conference. We also chose seven papers from the submitted papers to be presented as posters, with an associated two page abstract that is included in the Proceedings. The program will also have two excellent Keynote Talks and a Panel. We hope that all of you will find the program enjoyable, stimulating and useful to your research, for this would be the best reward for all the work that went into its preparation.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.093 | 0.064 |
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".