{"id":"W4313343372","doi":"10.1007/978-3-031-22295-5_19","title":"VinciDecoder: Automatically Interpreting Provenance Graphs into Textual Forensic Reports with Application to OpenStack","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Computer science; Cloud computing; Provenance; Sentence; Tree (set theory); Testbed; Data science; Natural language processing; Artificial intelligence; World Wide Web; Information retrieval","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":[0.001097561,0.001865666,0.0008090189,0.003907835,0.0006621674,0.002680938,0.001718048,0.001062059,0.011688],"category_scores_gemma":[0.005503869,0.0008437836,0.001195116,0.001535911,0.0008866654,0.002651558,0.002914952,0.001211324,0.005992937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006348435,"about_ca_system_score_gemma":0.001426681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004220909,"about_ca_topic_score_gemma":0.005404949,"domain_scores_codex":[0.9991961,0.00008565179,0.00006996658,0.0001832849,0.0004136551,0.00005131431],"domain_scores_gemma":[0.9975213,0.001166983,0.0002281148,0.0005436169,0.0004449873,0.00009497105],"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":[0.0006136061,0.0002534376,0.004091302,0.001582957,0.000233146,0.001593374,0.001572182,0.02169232,0.05400768,0.01473071,0.1471872,0.7524419],"study_design_scores_gemma":[0.0002426738,0.0002286764,0.00521963,0.0005580174,0.000220172,0.001889816,0.0009346201,0.5292091,0.1800835,0.05713559,0.2239276,0.0003506537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01216954,0.0003242153,0.5643557,0.0002129025,0.0002392726,0.0004076658,0.006792804,0.4111847,0.004313216],"genre_scores_gemma":[0.1281048,0.0007043524,0.7766798,0.0002318282,0.000120477,0.0005112845,0.02903167,0.05149257,0.01312325],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.011688,"threshold_uncertainty_score":0.03910023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005546182153218648,"score_gpt":0.2194010435745475,"score_spread":0.2138548614213288,"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."}}