{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007522714,0.0005903376,0.0004489631,0.0002626572,0.0001803981,0.0003996428,0.0008217546,0.0005704307,0.001070246],"category_scores_gemma":[0.001584903,0.0001945215,0.0005491069,0.0001592189,0.0008200301,0.0006237226,0.0008024027,0.0009645678,0.0003145396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005673841,"about_ca_system_score_gemma":0.000358237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0020713,"about_ca_topic_score_gemma":0.001655984,"domain_scores_codex":[0.9994234,0.0001997832,0.00001645944,0.0001400254,0.0001456486,0.00007485411],"domain_scores_gemma":[0.9994193,0.0002467625,0.00007344502,0.0001279781,0.0001029363,0.00002966621],"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":[0.00019098,0.00007136128,0.002081903,0.00005935023,0.00005084401,0.0001967348,0.0001060354,0.8487231,0.01614019,0.008520647,0.002491933,0.1213669],"study_design_scores_gemma":[0.000002406166,0.00002167176,0.0002065321,0.000003182155,0.000003466591,0.00003751607,0.00000561185,0.9958026,0.002277009,0.001339403,0.0002966723,0.000003918577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05898523,0.0002902363,0.9350427,0.0003438619,0.00006234913,0.00006362878,0.00007642921,0.0009815834,0.004153924],"genre_scores_gemma":[0.9451806,0.0001117591,0.05042168,0.0002214534,0.00001840679,0.00005803639,0.0001392433,0.00004659514,0.00380235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0020713,"threshold_uncertainty_score":0.004118502,"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."}}