{"id":"W2162798455","doi":"10.1109/hpcc.2011.30","title":"True Random Number Generator Using GPUs and Histogram Equalization Techniques","year":2011,"lang":"en","type":"article","venue":"","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Random number generation; Computer science; Convolution random number generator; Pseudorandom number generator; Histogram; Encryption; Random seed; Entropy (arrow of time); Random function; Algorithm; Theoretical computer science; Random field; Mathematics; Artificial intelligence; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00109418,0.00036201,0.0004015358,0.0006142263,0.0002132695,0.0006807535,0.0006281306,0.0005243457,0.002358664],"category_scores_gemma":[0.004630861,0.0002243483,0.0003067539,0.0005250313,0.0005223588,0.001170856,0.0005476562,0.0006299699,0.0006814363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004683261,"about_ca_system_score_gemma":0.0004160362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005209422,"about_ca_topic_score_gemma":0.0006380703,"domain_scores_codex":[0.9990929,0.0003531626,0.00004637766,0.00009504396,0.0003633584,0.00004917657],"domain_scores_gemma":[0.9983814,0.0008703154,0.0001408068,0.0003319002,0.0002395214,0.00003608098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007485661,0.0001394574,0.002859291,0.0002059365,0.0001000692,0.0003712135,0.0002214593,0.2993886,0.07922164,0.08791988,0.004188642,0.5246353],"study_design_scores_gemma":[0.00004327788,0.000120875,0.000421175,0.00001121924,0.0000129957,0.0002180258,0.00001168969,0.94864,0.0356473,0.01055932,0.004288202,0.00002589221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0120446,0.0001049658,0.9851939,0.00008555585,0.00003392166,0.00005918487,0.00002643878,0.001178172,0.001273241],"genre_scores_gemma":[0.3532799,0.0001491236,0.643317,0.00009552031,0.00003702292,0.0001440608,0.00009461531,0.0001788295,0.002703847],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002358664,"threshold_uncertainty_score":0.007890582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04428948473826359,"score_gpt":0.2693835634002423,"score_spread":0.2250940786619788,"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."}}