Secure Location Validation with Wi-Fi Geo-fencing and NFC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With rapid growth in technology and increase use of mobile devices, many enterprises have started to move away from fixed workstations to wireless mobile workstations. Moreover, some of the mobile applications may require access to sensitive data with the permissions being determined by the current location of a user. Typical access control systems are expensive and hard to maintain, especially systems that include location based access control. The complications arise from the purchase of additional equipment and time to integrate and secure a solution into the existing system. In such systems, one of the biggest problems is to figure out a cost effective and easy to implement solution to a problem of access permissions being determined by the current location of a user. In this paper we propose a simple scheme that uses near field communication technology along with Wi-Fi access point to securely validate the location of a user. Our scheme is efficient, secure and can be incorporated with other existing access control systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it