{"id":"W3155581945","doi":"10.1109/tii.2021.3074152","title":"Federated Tensor Decomposition-Based Feature Extraction Approach for Industrial IoT","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Computer science; Dimension (graph theory); Data mining; Tensor (intrinsic definition); Decomposition; Feature extraction; Dimensionality reduction; Tensor decomposition; Big data; Data modeling; Machine learning; Artificial intelligence; Database; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.0003268493,0.0003774058,0.0004696543,0.0002689116,0.0008918339,0.00032264,0.0001795744,0.0008292368,0.0002356031],"category_scores_gemma":[0.00009766335,0.0003818792,0.0003643135,0.0007518281,0.00007288795,0.0002599275,0.000001996453,0.001083054,0.00004204425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002523701,"about_ca_system_score_gemma":0.0004381088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007089862,"about_ca_topic_score_gemma":0.00000767559,"domain_scores_codex":[0.9977651,0.0001210447,0.0009709152,0.0002833789,0.0004390312,0.0004205805],"domain_scores_gemma":[0.9975115,0.0009158465,0.000411024,0.0004145986,0.0005186744,0.0002283712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005590388,0.01777958,0.00007752005,0.00099958,0.002432785,0.0000318934,0.002461076,0.2514851,0.02529202,0.01574012,0.5148212,0.1632887],"study_design_scores_gemma":[0.02375956,0.0008255431,0.00001223267,0.0003696367,0.001186311,0.0002548074,0.003856602,0.4207576,0.4967642,0.002565591,0.04761909,0.002028793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02435933,0.000004056935,0.9690824,0.001301414,0.0009824883,0.001522704,0.0007118765,0.0003929359,0.001642849],"genre_scores_gemma":[0.7707147,0.000008281195,0.2215122,0.001438942,0.001072991,0.001302416,0.0008924669,0.0001322438,0.00292579],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7475702,"threshold_uncertainty_score":0.9998633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128814839262955,"score_gpt":0.3448710913636853,"score_spread":0.2319896074373898,"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."}}