On validation of XML streams using finite state machines
Bibliographic record
Abstract
We study validation of streamed XML documents by means of finite state machines. Previous work has shown that validation is in principle possible by finite state automata, but the construction was prohibitively expensive, giving an exponential-size nondeterministic automaton. Instead, we want to find deterministic automata for validating streamed documents: for them, the complexity of validation is constant per tag. We show that for a reading window of size one and nonrecursive DTDs with one-unambiguous content (i.e. conforming to the current XML standard) there is an algorithm producing a deterministic automaton that validates documents with respect to that DTD. The size of the automaton is at most exponential and we give matching lower bounds. To capture the possible advantages offered by reading windows of size k, we introduce k-unambiguity as a generalization of one-unambiguity, and study the validation against DTDs with k-unambiguous content. We also consider recursive DTDs and give conditions under which they can be validated against by using one-counter automata.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".