{"id":"W2145131854","doi":"10.1109/iri.2012.6303015","title":"Recognition of fuzzy contexts from temporal data under uncertainty case study: Activity recognition in smart homes","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Normality; Intelligent decision support system; Context (archaeology); Artificial intelligence; Realization (probability); Computational intelligence; Fuzzy logic; Machine learning; Fuzzy control system; Mathematics","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":[0.001133159,0.0004024175,0.0005713181,0.001078282,0.0006597139,0.0008700316,0.0006676452,0.001284509,0.0008799616],"category_scores_gemma":[0.004799222,0.0001903064,0.0005369265,0.0009977891,0.0008047236,0.001290525,0.0006630819,0.000607062,0.0001103962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007565013,"about_ca_system_score_gemma":0.0004993722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008232959,"about_ca_topic_score_gemma":0.008562568,"domain_scores_codex":[0.9992399,0.0001555102,0.00009271683,0.00016281,0.0002610007,0.00008814947],"domain_scores_gemma":[0.9973538,0.00187217,0.000218285,0.0001681549,0.0002964104,0.00009117854],"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":[0.00129889,0.000351729,0.03845501,0.000510302,0.0001286691,0.01114895,0.002302785,0.6994314,0.02762244,0.02458346,0.001630021,0.1925364],"study_design_scores_gemma":[0.00002301911,0.0001055917,0.005965404,0.00002716365,0.00003187015,0.0008672599,0.0007235493,0.9679447,0.01435879,0.008615965,0.001292774,0.00004384524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.635088,0.0004908021,0.3598449,0.0005567003,0.00003608379,0.000169881,0.0005869372,0.0003876164,0.002838988],"genre_scores_gemma":[0.9433907,0.0001061345,0.05588591,0.00001744921,0.00001296809,0.00003390836,0.0001317369,0.000007696252,0.0004135185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008232959,"threshold_uncertainty_score":0.01637006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1487422228858467,"score_gpt":0.3298542391241626,"score_spread":0.1811120162383159,"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."}}