{"id":"W4400770832","doi":"10.1109/jiot.2024.3430297","title":"Graph-Enhanced Low-Resource ECG Representation Learning for Emotion Recognition Based on Wearable Internet of Things","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Science and Technology Planning Project of Guangdong Province","keywords":"Computer science; Wearable computer; Internet of Things; Graph; Representation (politics); Resource (disambiguation); Wearable technology; The Internet; Artificial intelligence; Human–computer interaction; Computer network; Theoretical computer science; World Wide Web; Embedded system","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":[0.0002283627,0.0005577164,0.000430202,0.00043932,0.0001426002,0.0003836045,0.0005494862,0.0004271443,0.001361455],"category_scores_gemma":[0.001064113,0.0001449762,0.0006776801,0.0005196725,0.0002020286,0.0006844873,0.0004616948,0.0006279976,0.000501657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002454364,"about_ca_system_score_gemma":0.0001947435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001318773,"about_ca_topic_score_gemma":0.002011839,"domain_scores_codex":[0.9998354,0.00003424769,0.000008380471,0.00006182158,0.00003791781,0.00002230987],"domain_scores_gemma":[0.9997858,0.00007619538,0.00003060384,0.00003722611,0.00005624132,0.0000138714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003524653,0.0002699326,0.002741667,0.0001226615,0.000151717,0.0003014079,0.000122569,0.1677183,0.07139604,0.005786164,0.006660634,0.7443764],"study_design_scores_gemma":[0.000007992438,0.00006200216,0.001294335,0.000004404189,0.00002495352,0.00007768437,0.00001941526,0.987513,0.006225158,0.003973304,0.0007885007,0.00000912248],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06232003,0.0002956191,0.9342557,0.0002794622,0.0000732561,0.00006077699,0.0001544764,0.001165581,0.001395051],"genre_scores_gemma":[0.7832693,0.0005363686,0.2100571,0.0003252864,0.00009813037,0.00009666743,0.001175489,0.0001570499,0.004284508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001361455,"threshold_uncertainty_score":0.00455451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03503989855520173,"score_gpt":0.3192136661154103,"score_spread":0.2841737675602086,"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."}}