XSDGuide - Automated Generation of Web Interfaces from XML Schemas: A Case Study for Suspicious Activity Reporting
Bibliographic record
Abstract
This article presents XSDGuide , a software prototype aimed at facilitating the creation of user interfaces consistent with a data model expressed as a set of XML schemas. XSDGuide was developed while researching intelligent user interfaces for data entry associated with the production of Suspicious Activity Reports (SARs) conforming to NIEM-SAR, an XML-based information-dissemination framework. These SARs communicate potentially suspicious or unlawful incidents to the appropriate authorities. The XSD schemas defining a specific SAR are fed to XSDGuide, which then automatically creates user interface guides, rendered on a web page. The user can interact with this application to populate the report’s fields, validate the SAR being created and save the report as a valid XML instance. Validation is a two-step process, where a JavaScript ruleset created from the schema pre-validates the document in the browser before it is sent for full validation to the back end, which relies on a traditional full-fledged validator. Despite the prototype’s limitations, the HTML interfaces that are generated allow users to inspect and become familiar with complex schemas and also to produce validated XML instance documents for the purposes of experimentation and testing.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".