Digital/electronic signature for lawyers/judges using serverless applications on AWS
Bibliographic record
Abstract
As the Internet is used more and more widely, the application of information technology has deepened in all industries including law. Today, the process for planning a trial in Quebec and Canada (i.e., using a case protocol form) requires original handwritten signatures, which is inefficient and would benefit from the use of e-signatures. As well, emerging cloud application architectures that use serverless technologies carry many advantages, for example, no server management is necessary as applications scale automatically. The law industry could make use of these technological advantages as it automates its processes in the future. \n \nA first step in this research is to understand the state of the art of the electronic signature process. The process of signing a case protocol requires each lawyer involved to individually sign the form, demonstrating their consent. In the case of an electronic signature, without the use of commercial software, each of these signatures could be lifted from the form and applied next to the others on the final document. This operation requires the use of image processing technologies to locate and transfer individual signatures. \n \nOnce the challenges of the signature process are studied and image processing technologies are investigated, the research activities aim to design and experiment with an image processing technique pipeline to propose a solution through a case study. This experiment is then implemented in a trial protocol processing prototype to validate its robustness.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".