Artifact for the OOPSLA 2022 paper: SigVM: Enabling Event-Driven Execution for Truly Decentralized Smart Contracts
Bibliographic record
Abstract
This VM image is based on an Ubuntu-20.04-desktop-LTS image. It was tested using Oracle VM VirtualBox Manager Version 6.1 (and the default config: 4 processors and 8192 MB base memory). The admin password is `test`. The image includes all the tools (with their dependencies) used to carry out the experiments in the paper. All the installed tools that are related to the paper can be found in the folder `/home/test/Documents/sigvmrepo/`. The public GitHub repository SigChain-conflux (https://github.com/SigVM/SigChain-conflux) contains the source code of `SigChain` an implementation of SigVM client. SigChain is implemented as an extension to Conflux-rust (https://github.com/Conflux-Chain/conflux-rust), Conflux Client. Conflux-rust shares a similar architecutre with Openethereum (https://github.com/openethereum/openethereum), Openethereum is deprecated since May 2022, and Conflux-rust and Openethereum are both written in the same programming language, Rust programming language. Conflux-rust execution environment is based on the Ethereum virtual machine (EVM). The public GitHub repository SigSolid (https://github.com/SigVM/SigSolid) contains SigSolid an extension of Solidity compiler `solc` supporting event-driven smart contracts programming (SigVM). The benchmarks (and tool parameters) used to carry the experiments in the paper can be found in the folder `/home/test/Documents/sigvmrepo/conflux-signal-handler-case-study`. Also, the public GitHub repository conflux-signal-handler-case-study (https://github.com/SigVM/conflux-signal-handler-case-study) contains all the open sourced benchmarks and tool parameters used in our experiments.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.278 | 0.156 |
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".