{"id":"W2566880782","doi":"10.1145/2967103","title":"Efficient Elliptic Curve Cryptography for Embedded Devices","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Embedded Computing Systems","topic":"Cryptography and Residue Arithmetic","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Elliptic curve cryptography; Scalar multiplication; Curve25519; Elliptic curve; Elliptic Curve Digital Signature Algorithm; Encryption; Public-key cryptography; Operand; Cryptography; Parallel computing; Arithmetic; Algorithm; Computer hardware; Operating system; Mathematics","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":[0.0009096232,0.0003983163,0.0004399879,0.0006332949,0.0006159132,0.0003450807,0.001601011,0.0001630767,0.000009645215],"category_scores_gemma":[0.00006041021,0.0002840865,0.000501278,0.00105785,0.0001168089,0.0001491105,0.00003660216,0.0002105091,0.00008722311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006478229,"about_ca_system_score_gemma":0.00007114372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002771866,"about_ca_topic_score_gemma":0.0000068068,"domain_scores_codex":[0.9968682,0.0003026843,0.0006635186,0.000933621,0.0005146337,0.0007173449],"domain_scores_gemma":[0.9957567,0.001918741,0.0002663373,0.001590882,0.0002520542,0.0002152351],"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":[0.0002490097,0.002501924,0.0005642452,0.0009233043,0.001483952,0.00004451981,0.01044994,0.4152104,0.00837428,0.2555694,0.001314531,0.3033145],"study_design_scores_gemma":[0.01039379,0.002328724,0.002813425,0.003701557,0.0003711302,0.0003527384,0.002176263,0.9356648,0.01014785,0.01252547,0.01581058,0.00371372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03130978,0.0003722828,0.9631879,0.001079215,0.00218862,0.0008690812,0.00002077651,0.0007187913,0.0002535584],"genre_scores_gemma":[0.9125534,0.00001682621,0.08688828,0.0002220308,0.0001126165,0.0001050591,0.000001545728,0.00003697781,0.00006325152],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8812436,"threshold_uncertainty_score":0.9999611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01831077850967127,"score_gpt":0.2590323599359768,"score_spread":0.2407215814263056,"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."}}