{"id":"W4385584185","doi":"10.1049/cvi2.12231","title":"Visual privacy behaviour recognition for social robots based on an improved generative adversarial network","year":2023,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Petroleum Technology Research Centre; National Natural Science Foundation of China","keywords":"Computer science; Discriminator; Robot; Artificial intelligence; Feature extraction; Machine learning; Layer (electronics); Feature (linguistics); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008776253,0.0003252561,0.0003228217,0.0002226549,0.0008183164,0.0003808817,0.0008285451,0.0002195567,0.00001187492],"category_scores_gemma":[0.0000605151,0.0003292013,0.0001938262,0.0007160543,0.00004298031,0.0008052916,0.0004803485,0.0003444588,0.00006447336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009657111,"about_ca_system_score_gemma":0.0001123405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001293347,"about_ca_topic_score_gemma":0.00000473627,"domain_scores_codex":[0.9971971,0.0004162429,0.0003858362,0.0009377177,0.0004482834,0.000614809],"domain_scores_gemma":[0.9986188,0.0003782532,0.0002394349,0.0004186082,0.0002040383,0.0001408333],"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":[0.0004325558,0.0003002871,0.0003060093,0.00002547713,0.00003001292,0.00003335061,0.001069804,0.7095161,0.0006559206,0.0006317284,0.01120449,0.2757942],"study_design_scores_gemma":[0.002025241,0.001670674,0.006752232,0.00004809904,0.00001805091,0.000001895579,0.000007169184,0.986987,0.0001683118,0.001448721,0.0004733047,0.0003993159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0346785,0.000001806764,0.959211,0.001069264,0.003461506,0.0007224227,0.000010654,0.0008165581,0.00002827657],"genre_scores_gemma":[0.4959432,0.000001548588,0.4964337,0.001030698,0.005937155,0.00008700243,0.0004680185,0.00006357684,0.00003505713],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4627773,"threshold_uncertainty_score":0.999916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03257990366712037,"score_gpt":0.3363090467282953,"score_spread":0.3037291430611749,"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."}}