{"id":"W3021192525","doi":"10.1145/3388176.3388188","title":"Resolving XACML Rule Conflicts using Artificial Intelligence","year":2020,"lang":"en","type":"article","venue":"","topic":"Access Control and Trust","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"XACML; Computer science; Set (abstract data type); Variety (cybernetics); Point (geometry); Simple (philosophy); Data mining; Feature (linguistics); Access control; Artificial intelligence; Machine learning; Computer security; Programming language","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":[0.02086548,0.001357116,0.001410452,0.004540882,0.002325659,0.01155312,0.004436683,0.002369696,0.00661414],"category_scores_gemma":[0.05938198,0.001481672,0.002134794,0.002701239,0.002414739,0.009457113,0.006323239,0.003825879,0.002002482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003151963,"about_ca_system_score_gemma":0.004229279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006470318,"about_ca_topic_score_gemma":0.007454703,"domain_scores_codex":[0.9760849,0.008491993,0.00350537,0.002806763,0.007968061,0.001142821],"domain_scores_gemma":[0.9635054,0.02304164,0.003640728,0.005775537,0.003307134,0.0007295772],"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":[0.0009495491,0.0006511151,0.009166211,0.0009284754,0.000363792,0.001486924,0.003672344,0.1074717,0.009788571,0.2062963,0.02948553,0.6297395],"study_design_scores_gemma":[0.0001576932,0.00009371416,0.0006215394,0.0003094909,0.0001102889,0.0004466629,0.0007246287,0.7989004,0.01627808,0.1295389,0.05270019,0.0001184176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01986578,0.0003333212,0.949792,0.001743562,0.0001640477,0.0007531507,0.0005324728,0.01681955,0.009996136],"genre_scores_gemma":[0.1138813,0.0001403223,0.8810899,0.0004476524,0.00005689091,0.000323397,0.0008348417,0.0004995121,0.002726193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02086548,"threshold_uncertainty_score":0.1103485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1961334067489819,"score_gpt":0.374030027840652,"score_spread":0.1778966210916701,"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."}}