Pengenalan Captcha dengan Multivalued Image Decomposition dan Vector Space Image Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Completely Automated Public Turing Tests to Tell Computers and Humans Apart (CAPTCHA) merupakan program untuk meningkatkan keamanan web. Pengenalan CAPTCHA menggunakan aplikasi sering mengalami kegagalan karena posisi dari simbol yang terlalu rapat, juga karena sulitnya melatih simbol baru jika gagal dikenali. Metode Naive Pattern Recognition Algorithm salah satu metode yang belum memberikan hasil yang maksimal karena kesalahan pada proses pengenalan simbol tidak dapat dilatih kembali sehingga aplikasi tetap tidak akan mengenali simbol tersebut. Metode Multivalued Image Decomposition dan Vector Space Image Recognition dapat memberikan hasil yang lebih maksimal dengan menggunakan Training Set, dimana simbol yang tidak dikenali akan dilatih/training agar proses pengenalan simbol selanjutnya lebih akurat. Pengujian dilakukan terhadap CAPTCHA dengan berbagai warna background, CAPTCHA dengan simbol yang saling berdekatan (menyatu) dan kombinasi warna simbol dengan background yang berbeda. Untuk CAPTCHA dengan simbol berukuran berbeda dan saling terhubung, tidak dapat dikenali. Dengan threshold 0.90, hasil pengujian dengan training set yang dilakukan terhadap dengan algoritma ini menunjukkan akurasi tingkat keberhasilan sebesar 87%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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