Robot Secured Wireless Authentication Using Look-Up Table Based Coding
Bibliographic record
Abstract
Securing restricted areas is a major concern for sensitive institutes, like military or research labs. Most security measures rely on having some of the biometrics that human beings possess to identify the user and grant access permissions. Yet, with the wide spread of robotic systems and avatars, these entities need to be authenticated as well, since they might require access to such facilities, hence, the need to provide security measures that can handle non-biological entities with high levels of security and that is not vulnerable to hacking is becoming essential.In this research we propose a novel and unique system for encoding the passcode that is known only to the authorized user using a specially designed look-up table. The system’s hardware requires reconfiguring doors’ security modules to have Near Field Communication (NFC) initialized inside them, and have the Robot initialized with the same hardware, and a wireless controller for the NFC will be given to the operator. The hardware has low-cost, and is easy to use since it is considered as plug and play module. XBEE was adopted as a wireless communication module between the operator and the robot in order to wirelessly connect to an NFC chip that is installed (as plug and play) on any Robotic system.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.002 |
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".