{"id":"W4381746829","doi":"10.1109/percomworkshops56833.2023.10150397","title":"Learning Using Privileged Information for Wearable-based Human Activity Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Activity recognition; Wearable computer; Computer science; Set (abstract data type); Human–computer interaction; Machine learning; Artificial intelligence; Wearable technology; Embedded system","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":[0.002308074,0.0009155287,0.001400777,0.0009252129,0.0003905439,0.001007261,0.00143214,0.0007439854,0.001503675],"category_scores_gemma":[0.008861834,0.0004674869,0.0007519621,0.001090594,0.0009588649,0.002674133,0.001668957,0.001837704,0.0008995176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004980984,"about_ca_system_score_gemma":0.000626484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00158864,"about_ca_topic_score_gemma":0.002704702,"domain_scores_codex":[0.9986957,0.0004807959,0.00006558042,0.0004161941,0.0002205038,0.0001212342],"domain_scores_gemma":[0.9965332,0.0017364,0.0002839297,0.0009674304,0.0002857402,0.0001933881],"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":[0.0005260325,0.0007446795,0.00921703,0.0002431437,0.0001909252,0.000213748,0.0003994196,0.1620808,0.01443778,0.006124956,0.00408576,0.8017356],"study_design_scores_gemma":[0.0000205632,0.0003357703,0.00345471,0.00005228671,0.0000366627,0.0001871849,0.000110523,0.9631575,0.007015855,0.02272911,0.002856656,0.00004307424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0523618,0.001835544,0.9415023,0.0004821725,0.0001090058,0.00009115227,0.0002956966,0.00180121,0.00152107],"genre_scores_gemma":[0.855134,0.001154412,0.1398734,0.0004606131,0.0002358382,0.0001793929,0.0009022225,0.00009122858,0.001968776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002308074,"threshold_uncertainty_score":0.01220638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09675949243133791,"score_gpt":0.3095530176750168,"score_spread":0.2127935252436788,"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."}}