{"id":"W4390238639","doi":"10.1016/j.comnet.2023.110156","title":"Leveraging pervasive computing for ambient intelligence: A survey on recent advancements, applications and open challenges","year":2023,"lang":"en","type":"article","venue":"Computer Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Ambient intelligence; Leverage (statistics); Data science; Ubiquitous computing; Open research; Field (mathematics); Analytics; Context (archaeology); Context-aware pervasive systems; Human–computer interaction; Artificial intelligence; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001283272,0.000225385,0.0003388422,0.0001511865,0.0003842126,0.0005472027,0.001198991,0.00007259589,0.000002758001],"category_scores_gemma":[0.00002789295,0.0002347799,0.00005331096,0.0006245932,0.00003650164,0.000347801,0.001580889,0.0001805885,0.00003942186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008031647,"about_ca_system_score_gemma":0.00004324764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003252074,"about_ca_topic_score_gemma":0.00004613735,"domain_scores_codex":[0.9978664,0.0002348019,0.0003725447,0.0008763788,0.0002084081,0.0004414586],"domain_scores_gemma":[0.9973828,0.001412431,0.0001940739,0.0005958244,0.0002787331,0.0001361094],"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":[0.00001179778,0.00006173294,0.0004318671,0.00002409069,0.00004021794,0.000002562973,0.0007491155,0.008873796,0.000001364266,0.001836648,0.002064776,0.985902],"study_design_scores_gemma":[0.0004107186,0.0001778703,0.01723347,0.0001946444,0.000005236308,0.000009340812,0.00009714421,0.9418712,0.0000264394,0.001254186,0.03838307,0.0003367175],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001390647,0.0007130242,0.9936614,0.001108397,0.0009325948,0.001759289,0.000009545002,0.0002902466,0.0001348579],"genre_scores_gemma":[0.967346,0.002947712,0.02671058,0.001276483,0.0007397743,0.0006692138,0.0001532024,0.00004753142,0.0001095399],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9855653,"threshold_uncertainty_score":0.9574045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1642765570846529,"score_gpt":0.3475898213126941,"score_spread":0.1833132642280411,"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."}}