{"id":"W4400527587","doi":"10.1109/fg59268.2024.10581974","title":"SMCTL: Subcarrier Masking Contrastive Transfer Learning for Human Gesture Recognition with Passive Wi-Fi Sensing","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Masking (illustration); Gesture; Subcarrier; Transfer of learning; Speech recognition; Gesture recognition; Transfer (computing); Artificial intelligence; Telecommunications; Channel (broadcasting)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004936235,0.0007195545,0.000422378,0.0003168248,0.0002082978,0.0003301065,0.001026964,0.0005595051,0.0019838],"category_scores_gemma":[0.001427083,0.0001702137,0.0003636242,0.0003498769,0.0004884301,0.0006921118,0.0009360665,0.0009405611,0.0008428307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003790539,"about_ca_system_score_gemma":0.0005737246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002855541,"about_ca_topic_score_gemma":0.004384395,"domain_scores_codex":[0.9998259,0.00003630382,0.000007419236,0.0000569476,0.00004522377,0.00002833604],"domain_scores_gemma":[0.9997172,0.0001207623,0.00002792747,0.00004690577,0.00006637226,0.00002082841],"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":[0.0003304446,0.0003202133,0.002034724,0.000123285,0.00007390274,0.0001706596,0.0001136844,0.1410597,0.04021891,0.002488859,0.006103708,0.8069618],"study_design_scores_gemma":[0.00001092728,0.0001209866,0.0006710209,0.000008520182,0.000008583944,0.00004423778,0.00001789983,0.9846392,0.01196478,0.001569012,0.0009350351,0.000009849343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06840845,0.0005277569,0.9243896,0.0002346212,0.0001228041,0.00008714265,0.0001815886,0.003491706,0.002556303],"genre_scores_gemma":[0.8483455,0.0002438445,0.1406097,0.0003161586,0.00006623903,0.000186039,0.0007696862,0.0001316178,0.009331301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002855541,"threshold_uncertainty_score":0.006636441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0103536349986908,"score_gpt":0.2129421232069143,"score_spread":0.2025884882082235,"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."}}