{"id":"W2579067088","doi":"10.1109/ism.2016.0079","title":"Posture Selection Based on Two-Layer AP with Application to Human Action Recognition Using HMM","year":2016,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Hidden Markov model; Initialization; Computer science; Pattern recognition (psychology); Cluster analysis; Artificial intelligence; Frame (networking); Layer (electronics); Selection (genetic algorithm); Sequence (biology); Action recognition; Action (physics); Speech recognition","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":[0.0001578788,0.000156307,0.0001008835,0.0003020048,0.0003043869,0.0001225798,0.0001480095,0.00007200916,0.0001462725],"category_scores_gemma":[0.00000946765,0.0001066973,0.00003875188,0.0004703544,0.00001296374,0.0007436408,0.00001886259,0.00009284932,0.0004388351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000190426,"about_ca_system_score_gemma":0.00003868847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006843971,"about_ca_topic_score_gemma":0.000155694,"domain_scores_codex":[0.9987893,0.0000676987,0.0001729622,0.0004767496,0.0002840911,0.0002091749],"domain_scores_gemma":[0.9992632,0.0000411971,0.0001091735,0.0002601861,0.000225039,0.0001011561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008772767,0.0001781669,0.0003651391,0.00000999334,0.00001351302,0.000001306063,0.00005647916,0.0005223023,0.6696109,0.001244021,0.0005229861,0.3273875],"study_design_scores_gemma":[0.002979952,0.001615665,0.01028509,0.0003125387,0.00004995399,0.00006113646,0.00004656313,0.1328442,0.8419727,0.005233189,0.003627233,0.0009717954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2674258,3.51464e-7,0.7289202,0.0007431598,0.0000850127,0.0003096224,0.000002036436,0.0002874549,0.00222635],"genre_scores_gemma":[0.9775064,5.757373e-7,0.02052646,0.001407074,0.0002165022,0.0000675017,0.00001551849,0.00001611744,0.0002438228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7100806,"threshold_uncertainty_score":0.5640483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04698285809244866,"score_gpt":0.3039921134936428,"score_spread":0.2570092554011941,"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."}}