Proceedings of the 2024 Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems
Bibliographic record
Abstract
Welcome to ApPLIED 2024, the workshop on Advanced tools, programming languages, and Platforms for Implementing and Evaluating algorithms for Distributed systems.ApPLIED aims to bring together distributed system designers and practitioners from academia and industry to share their experiences and perspectives in designing and building distributed systems.Our goal is to foster stimulating discussions at the intersection of the theory and practice of distributed systems and make ApPLIED the premier forum for such discussions.We have kept the workshop scope broad to encourage participation from academia and industry.We are delighted to have Christian Cachin (University of Bern, Switzerland) as a keynote speaker presenting on 'Consensus in Blockchains: Theory and Practice,' and an exciting technical program consisting of six invited papers, three accepted regular submissions and four short research statements.We received six regular papers and one short research statement, and the acceptance rate of regular submissions was 50%.Every submission and invited paper was reviewed by at least three reviewers.Accepted submissions and invited papers were included in the proceedings only after they gained support from the technical program committee.The six invited papers appear before the three peer-reviewed papers in the proceedings, while the four short research statements appear last.
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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.094 | 0.035 |
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".