{"id":"W4399914382","doi":"10.1016/j.patrec.2024.06.017","title":"HARWE: A multi-modal large-scale dataset for context-aware human activity recognition in smart working environments","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Huawei Technologies (Canada); University of Toronto","funders":"Huawei Technologies","keywords":"Computer science; Activity recognition; Task (project management); Context (archaeology); Artificial intelligence; Process (computing); Scale (ratio); Modal; Artificial neural network; Modalities; SIGNAL (programming language); Machine learning; Pattern recognition (psychology); Deep neural networks; Speech recognition","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001039809,0.0004919777,0.0004971328,0.0006317273,0.0003090693,0.0006917272,0.0006024436,0.0002185582,0.0001815855],"category_scores_gemma":[0.00004531282,0.0005645275,0.0002579644,0.0004657558,0.00007673677,0.001780625,0.000268748,0.0005801598,0.0009813275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003838107,"about_ca_system_score_gemma":0.00004389323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003288589,"about_ca_topic_score_gemma":0.001820463,"domain_scores_codex":[0.9960999,0.0004424355,0.0006850095,0.001456689,0.0004884148,0.000827532],"domain_scores_gemma":[0.9983744,0.0005170087,0.0002525379,0.0006201087,0.000043604,0.0001923412],"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":[0.00004450492,0.0005446636,0.005211513,0.0002619203,0.0001283263,0.0001363519,0.001283409,0.000003709136,0.02023826,0.000001991141,0.005978553,0.9661668],"study_design_scores_gemma":[0.03492498,0.001229418,0.08756731,0.01336867,0.0006727825,0.0008918363,0.00235622,0.4447985,0.09709744,0.002133107,0.3035138,0.01144599],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.259118,0.00005366785,0.7309487,0.002373669,0.00118662,0.001363841,0.004633449,0.0002909633,0.00003106859],"genre_scores_gemma":[0.9870202,0.00001361023,0.001556933,0.004027102,0.0003123285,0.001108182,0.005837131,0.00007639723,0.0000481068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9547208,"threshold_uncertainty_score":0.9997965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07766638033158467,"score_gpt":0.2979543367091869,"score_spread":0.2202879563776022,"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."}}