{"id":"W4385624263","doi":"10.1109/eurocon56442.2023.10199099","title":"Optimization of Witness Selection for Ensuring the Integrity of Data in a Blockchain-Assisted Health-IoT System","year":2023,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Correctness; Computer science; Server; Network packet; Blockchain; Witness; Data integrity; Wearable computer; Scheme (mathematics); Internet of Things; Wearable technology; Computer network; Distributed computing; Computer security; 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":[0.002147131,0.0008303512,0.001219666,0.0004165979,0.0008441962,0.001202933,0.001225977,0.001137099,0.003651604],"category_scores_gemma":[0.004257901,0.000286091,0.0003266905,0.0005383039,0.001068358,0.001905269,0.001700998,0.0007497765,0.0002703593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000983595,"about_ca_system_score_gemma":0.001612216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001850598,"about_ca_topic_score_gemma":0.001549403,"domain_scores_codex":[0.9988464,0.0004199096,0.00006387228,0.0002202855,0.0001588525,0.0002907605],"domain_scores_gemma":[0.9965689,0.00196147,0.0004458289,0.0002384646,0.0004133764,0.0003718018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006773885,0.0001076136,0.001666228,0.0001574134,0.00004469204,0.0003190789,0.0001544239,0.9424996,0.008431427,0.01412901,0.00101248,0.03080062],"study_design_scores_gemma":[0.00003381039,0.0001155475,0.000190446,0.000008476662,0.00001158198,0.00004842025,0.00003946116,0.9933903,0.001402735,0.00451063,0.0002398505,0.000008756087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2535927,0.0005701944,0.7389009,0.0007060067,0.00005500604,0.0002611082,0.000153615,0.0002952173,0.00546533],"genre_scores_gemma":[0.9818301,0.0001018685,0.01684845,0.00003911887,0.000009390194,0.00005060287,0.00004571436,0.00001340878,0.001061415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003651604,"threshold_uncertainty_score":0.01221579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09534556479885975,"score_gpt":0.3250036493382462,"score_spread":0.2296580845393865,"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."}}