{"id":"W2604490975","doi":"10.1609/aaai.v31i2.19093","title":"Real-Time Indoor Localization in Smart Homes Using Semi-Supervised Learning","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Train; Real-time computing; Scope (computer science); Supervised learning; Task (project management); Home automation; Deep learning; Artificial intelligence; Human–computer interaction; Telecommunications; Artificial neural network; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002003647,0.0006902819,0.001015782,0.000604308,0.0003927383,0.0007531655,0.002039519,0.0008168649,0.0008750424],"category_scores_gemma":[0.004437855,0.0005157364,0.0007048451,0.0005056551,0.0008752838,0.001396324,0.001290303,0.001039276,0.00062999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006644191,"about_ca_system_score_gemma":0.0009384642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004661291,"about_ca_topic_score_gemma":0.00593963,"domain_scores_codex":[0.9989034,0.0003650795,0.00006848614,0.0003341527,0.000225204,0.0001036921],"domain_scores_gemma":[0.9963136,0.001868235,0.0004508418,0.0004950065,0.0007237883,0.000148482],"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":[0.0003833089,0.000394791,0.005031257,0.0001635055,0.000121913,0.0001573643,0.0003187363,0.7107326,0.008635631,0.00146551,0.002106298,0.2704891],"study_design_scores_gemma":[0.000006020173,0.00002355629,0.0003075115,0.000002814646,0.000002711339,0.00001084754,0.00001142896,0.9977365,0.001163359,0.0006261196,0.0001049098,0.000004379259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05243099,0.0001007069,0.9445431,0.00009315438,0.00001939253,0.00005984467,0.0000668885,0.002111869,0.0005740277],"genre_scores_gemma":[0.7973803,0.00008035183,0.200448,0.00009965208,0.00005093367,0.0001489566,0.0003804358,0.000120493,0.001290959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004661291,"threshold_uncertainty_score":0.01059639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04826180824294481,"score_gpt":0.2768741957613913,"score_spread":0.2286123875184465,"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."}}