CAEDISI: a cellular automata editor and simulator for network decontamination
Bibliographic record
Abstract
We consider the problem of decontaminating a network where all nodes are infected by a virus [1]. The decontamination strategy is performed using a Cellular Automata model [2]. Each node of the network is represented by the automata cell and thus, network host status is also mapped to cell state (contaminated, decontaminating, decontaminated). Each cell state is synchronously updated according to a set of local rules. Our goal is to design the set of rules that will accomplish the decontamination in an optimal way. To support our theoretical study, a research tool has been implemented. As we have refined the concept of neighbourhood in a Cellular Automata with the addition of new dimensions, it contains crucial simulation functionalities that is not found in existing Cellular Automata application. Moreover, the simulation component has been supplemented with additional modules that makes the software an editor, a validation tool and more importantly, a decontamination rules generator. With respect to its implementation, caedisi is a rich internet application (RIA) which uses the latest trend in technology in order to offer great usability and collaboration. Furthermore, simulation results are stored in database and can be studied as a data mining project in a subsequent phase.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".