{"id":"W2806148690","doi":"10.51611/iars.irj.v8i1.2018.84","title":"Cryptanalysis of Mono-Alphabetic Substitution Ciphers using Genetic Algorithms and Simulated Annealing","year":2018,"lang":"en","type":"article","venue":"IARS International Research Journal","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cryptanalysis; Ciphertext; Plaintext; Simulated annealing; Computer science; Cipher; Algorithm; Genetic algorithm; Substitution (logic); Theoretical computer science; Cryptography; Encryption; Machine learning; Computer security; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.002129474,0.00009205341,0.0001305286,0.0008498434,0.0004023901,0.0003299555,0.000856005,0.00005316338,0.00009454949],"category_scores_gemma":[0.0003590588,0.00008546262,0.0000952086,0.0008473005,0.0004346401,0.0004497174,0.0002413133,0.0003397283,0.000005896946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000080242,"about_ca_system_score_gemma":0.0001296537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003963945,"about_ca_topic_score_gemma":0.000003459198,"domain_scores_codex":[0.9978395,0.0002857803,0.000341722,0.0002429084,0.0009877224,0.0003024296],"domain_scores_gemma":[0.9979783,0.0002373315,0.0001412827,0.0002063079,0.001278307,0.0001585117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006376918,0.0005672898,0.05730044,0.0000618427,0.002306037,0.0005404207,0.006652192,0.07334703,0.1158469,0.2836356,0.0004574068,0.4586472],"study_design_scores_gemma":[0.0004862665,0.0002137092,0.006889947,0.0001216028,0.00001862805,0.0002239929,0.0001369746,0.9355313,0.004483155,0.05115014,0.0006106966,0.0001336434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.61315,0.0002445769,0.3856851,0.0001831716,0.0002942519,0.00004151559,0.000001640594,0.00001230749,0.0003873855],"genre_scores_gemma":[0.9575596,0.0001312701,0.04199897,0.00002417957,0.0002606542,6.615235e-7,5.572181e-7,0.000006054562,0.00001802659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8621842,"threshold_uncertainty_score":0.3485064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08168138884846182,"score_gpt":0.3965612098387054,"score_spread":0.3148798209902436,"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."}}