Fingerprint Tarot Fortune Teller Game Utilizing Hénon Map-Based Pseudorandom Number Generator
Bibliographic record
Abstract
The project is about integrating biometric security, hardware-based randomness, and symbolic visualization through tarot for a unique user experience. It utilizes a field-programmable gate array (FPGA) for biometric authentication using fingerprint input to enhance security. Sensitive data is encrypted within the FPGA, ensuring tamper resistance and mitigating threats such as replay attacks. The system utilizes the entropy from the fingerprint as a seed for a pseudorandom number generator (PRNG) to select a tarot card displayed on a Raspberry Pi and a narrative. The project explores the fusion of digital security and human meaning by combining secure biometrics, hardware-accelerated cryptography, and symbolic storytelling. It aims to provide personalized authentication, interactive installations, and secure entertainment interfaces for users. A custom enclosure CAD design was developed for the FPGAintegrated biometric system to improve user-friendliness. The design focused on touch-based interaction using a touchscreen and fingerprint scanner to simplify the user interface and enhance user enjoyment. This approach allowed the team to concentrate on perfecting the PRNG code rather than dealing with moving parts and manual updates for user instructions. The Hénon Map-based PRNG is implemented in the FPGA for real-time applications.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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".