{"id":"W4362606933","doi":"10.36227/techrxiv.22340764.v1","title":"Generalized Inverse Matrix Construction for Code Based Cryptography","year":2023,"lang":"en","type":"preprint","venue":"","topic":"graph theory and CDMA systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"McEliece cryptosystem; Gaussian elimination; Inverse; Cryptosystem; Cryptography; Decoding methods; Mathematics; Matrix (chemical analysis); Generalized inverse; Square matrix; Moore–Penrose pseudoinverse; Binary number; Algorithm; S-box; Discrete mathematics; Theoretical computer science; Computer science; Algebra over a field; Arithmetic; Block cipher; Pure mathematics; Symmetric matrix; Gaussian; Eigenvalues and eigenvectors","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000280654,0.0002943506,0.0003687255,0.000379913,0.00006131124,0.00006806219,0.0002023728,0.000380088,0.0001261719],"category_scores_gemma":[0.00001396001,0.0003011455,0.0004228232,0.0002043411,0.00005463528,0.00003504879,0.00004877293,0.000241039,0.00006241085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003402443,"about_ca_system_score_gemma":0.00002670545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003570753,"about_ca_topic_score_gemma":0.00007012755,"domain_scores_codex":[0.9988821,0.00005028741,0.000363707,0.0003155238,0.0001256872,0.0002626555],"domain_scores_gemma":[0.9992737,0.00008248493,0.00006521046,0.0004341051,0.00005914781,0.0000853417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001107313,0.00002584328,0.0008618971,0.004431476,0.0007913053,0.000007970974,0.0001722271,0.6175833,0.005246765,0.3005342,0.06953546,0.0006988526],"study_design_scores_gemma":[0.00213031,0.00003854989,0.0001125185,0.0003457581,0.0002359588,0.000006697614,0.000313342,0.7911877,0.008719341,0.1628361,0.03285487,0.001218861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1343408,0.0002310538,0.8439279,0.0001035962,0.01094613,0.00179762,0.0007849929,0.004773977,0.003093932],"genre_scores_gemma":[0.6722993,0.0003029975,0.3165632,0.0002501133,0.00164857,0.00384018,0.002179658,0.0007065295,0.002209388],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5379585,"threshold_uncertainty_score":0.9999441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02789132392455706,"score_gpt":0.2536716368298101,"score_spread":0.225780312905253,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}