{"id":"W2109589711","doi":"10.1145/2457450.2457453","title":"Identity verification based on handwritten signatures with haptic information using genetic programming","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Computer science; Naive Bayes classifier; Support vector machine; Artificial intelligence; Haptic technology; Identity (music); Pattern recognition (psychology); Signature (topology); Genetic programming; Random forest; Machine learning; Relevance (law); Data mining; 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","sts"],"consensus_categories":[],"category_scores_codex":[0.0002000052,0.0002674783,0.000191745,0.0003589164,0.001805053,0.0005396242,0.001784842,0.0001175684,0.00001431779],"category_scores_gemma":[0.00002233834,0.0002546269,0.00007215962,0.00111087,0.0002666176,0.001271662,0.00008756753,0.0004434086,0.0001038944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032675,"about_ca_system_score_gemma":0.0001151859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002132075,"about_ca_topic_score_gemma":0.00001513807,"domain_scores_codex":[0.9983017,0.0001089127,0.000475257,0.0004439379,0.0003565591,0.0003135947],"domain_scores_gemma":[0.9957123,0.0006028913,0.000252869,0.002869454,0.0003790497,0.0001834555],"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":[0.000007087205,0.0008786711,0.0002096787,0.00003715294,0.00005364113,2.404469e-7,0.0005483052,0.06438303,0.0007019531,0.01098457,0.00003611112,0.9221596],"study_design_scores_gemma":[0.0004953597,0.0001099254,0.006235599,0.00006102032,0.00003778954,0.000009957545,0.0001524212,0.9871209,0.00008612655,0.001379285,0.004004453,0.0003071109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002742769,0.00009787962,0.9918343,0.003115076,0.00003592094,0.001647553,0.00001764059,0.0003628148,0.0001460413],"genre_scores_gemma":[0.4784654,0.00004610767,0.5203616,0.0002675871,0.00002311452,0.0007702861,0.00004619576,0.00001228456,0.000007424669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9227379,"threshold_uncertainty_score":0.9999906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01616722248062151,"score_gpt":0.2584575447652899,"score_spread":0.2422903222846684,"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."}}