{"id":"W2550615967","doi":"10.1145/2910674.2910707","title":"Towards User Activity Recognition Through Energy Usage Analysis And Complex Event Processing","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Complex event processing; Event (particle physics); Energy (signal processing); Human–computer interaction; Artificial intelligence; Programming language","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":[0.0003464637,0.0001922466,0.0003369342,0.000217715,0.000185057,0.0003314344,0.000302374,0.00007984984,0.0002073197],"category_scores_gemma":[0.00004669022,0.0001338482,0.0001338078,0.0009141688,0.0000650462,0.002963294,0.0002484097,0.00005587979,0.00003048435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008633438,"about_ca_system_score_gemma":0.00007639065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008929326,"about_ca_topic_score_gemma":0.0007551622,"domain_scores_codex":[0.9982439,0.0002288893,0.0002594291,0.0006270832,0.0003588785,0.0002818176],"domain_scores_gemma":[0.998907,0.0001889092,0.0001932256,0.0003965371,0.0002070956,0.0001072058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000006094151,0.00006948506,0.00115563,0.00001075766,0.0001216497,0.000003849563,0.0001953859,5.684798e-7,0.006415096,0.0002793204,0.0001845974,0.9915575],"study_design_scores_gemma":[0.006545963,0.0006376051,0.436201,0.0007294776,0.001170404,0.0003566516,0.0005217692,0.1585326,0.2826579,0.03221143,0.07580557,0.004629669],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07407366,0.00002498861,0.919512,0.001520749,0.00009972417,0.0001005588,0.000008889697,0.0002336963,0.004425742],"genre_scores_gemma":[0.9867125,0.00002506541,0.01194661,0.0003563229,0.00005850989,0.00003706911,0.000004222507,0.000009617061,0.0008500595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9869279,"threshold_uncertainty_score":0.5458171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06398244635940045,"score_gpt":0.2938401323543601,"score_spread":0.2298576859949596,"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."}}