{"id":"W2998180866","doi":"10.1109/iccad45719.2019.8942176","title":"Strengthening PUFs using Composition","year":2019,"lang":"en","type":"article","venue":"","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Arbiter; Computer science; Composition (language); Layer (electronics); Theoretical computer science; Resilience (materials science); Work (physics); Engineering; Parallel computing; Linguistics","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":[0.00008995401,0.00009442241,0.0001168518,0.00006466087,0.0001234215,0.0001367524,0.0003357796,0.00003520102,0.0001610026],"category_scores_gemma":[0.000004255544,0.00008410998,0.00006452629,0.000296097,0.00001443923,0.0007039832,0.0001785006,0.0001133378,0.0004322717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004681096,"about_ca_system_score_gemma":0.00003636608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006890651,"about_ca_topic_score_gemma":0.000002825308,"domain_scores_codex":[0.9991523,0.0000324858,0.0001300139,0.0002769923,0.0001971287,0.0002110908],"domain_scores_gemma":[0.9993981,0.00005185635,0.00004129131,0.0003783115,0.00006177988,0.00006866331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001170755,0.0002654041,0.001589623,0.00002810733,0.00003765463,0.000006728133,0.0004893549,0.003187125,0.06972215,0.9076595,0.0008797304,0.01612296],"study_design_scores_gemma":[0.0005215629,0.0001234212,0.001736079,0.00004046001,0.00001103955,0.00002093324,0.00004889462,0.9556249,0.01545232,0.01694642,0.009083117,0.0003908789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4325618,0.00001488521,0.5443631,0.0001794453,0.000493635,0.0001005182,0.000001507808,0.0002427619,0.02204237],"genre_scores_gemma":[0.972553,0.00000110409,0.02670527,0.0002468487,0.00009068439,0.000001725525,0.000004238791,0.000005083651,0.0003920602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9524378,"threshold_uncertainty_score":0.5556121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01309271318733242,"score_gpt":0.2293495055348272,"score_spread":0.2162567923474948,"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."}}