{"id":"W4401597494","doi":"10.1109/icasspw62465.2024.10627018","title":"Towards Collaborative Multimodal Federated Learning for Human Activity Recognition in Smart Workplace Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Toronto","funders":"","keywords":"Computer science; Activity recognition; Human–computer interaction; Knowledge management; Collaborative learning; Artificial intelligence","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":[0.001720895,0.0008750779,0.001189227,0.0006434323,0.0004528465,0.0008079974,0.001714626,0.001041578,0.001219167],"category_scores_gemma":[0.003901655,0.0003064726,0.000729456,0.0006514016,0.0006220636,0.002317609,0.002700709,0.001509174,0.0007749245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005339908,"about_ca_system_score_gemma":0.0006958984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002426433,"about_ca_topic_score_gemma":0.003153029,"domain_scores_codex":[0.9987507,0.0004050552,0.00005304182,0.0004222578,0.0002111826,0.0001577239],"domain_scores_gemma":[0.9986306,0.0004922123,0.0001179586,0.0004204491,0.0002393676,0.00009929136],"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":[0.0005288696,0.0007953739,0.005082258,0.00009572576,0.0001451223,0.0002436146,0.000369258,0.3096797,0.01782568,0.005317018,0.003439568,0.6564778],"study_design_scores_gemma":[0.00001053441,0.00007892855,0.0004435869,0.000005980442,0.00001085851,0.00006533162,0.00005803072,0.9875202,0.005363811,0.005742991,0.0006884977,0.00001116014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01813588,0.0001045493,0.9798789,0.0001009241,0.00001973584,0.00002710639,0.00004658027,0.001215369,0.0004709387],"genre_scores_gemma":[0.7216498,0.0001175108,0.2750436,0.0003037563,0.00004701438,0.0001328653,0.0003666545,0.0001023776,0.002236337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002426433,"threshold_uncertainty_score":0.009101033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536492444722671,"score_gpt":0.2945081338223574,"score_spread":0.2591432093751307,"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."}}