Evaluation of blockchain techniques to ensure secure access on remote FPGA laboratories
Bibliographic record
Abstract
Laboratories are part of the learning process of students from the most diverse areas of knowledge. However, many problems related to physical accessibility and resources become a challenge for teachers and educational institutions. These disadvantages make remote labs, especially computing-oriented labs such as FPGA labs, become very popular in the current scenario. It happens because these labs provide a faster way to guarantee quality results equal to the physical model, reducing costs and offering access to students who did not have access to the labs. However, the security system must be implemented and improved continuously, following the advance of technology. This work aims to develop a security and access control system for remote labs using blockchain techniques to standardize and keep the security process up-to-date with new security techniques. The primary authentication processes, authorization and second verification are discussed and adapted for the remote experimentation scenario. This research is part of an ongoing project to create a security system for remote FPGA labs. At the end of the project, we hope to deliver a functional FPGA platform with standardized security processes following blockchain techniques. The project has researchers from the Laboratory at Distance (L@d) of the TÉLUQ University in Montreal, Canada and the Remote Experimentation Laboratory (RExLab) of the Federal University of Santa Catarina (UFSC) Brazil collaborating.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".