{"id":"W40150383","doi":"","title":"Progressive user interfaces for regressive analysis: making tracks with large, low-level systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Concordia University; University of Victoria","funders":"","keywords":"Computer science; Exploit; Control flow; Human–computer interaction; Visualization; Leverage (statistics); Malware; Call graph; Usability; Control flow graph; User interface; Program comprehension; Malware analysis; Data visualization; Software; Software system; Data mining; Artificial intelligence; Theoretical computer science; Programming language; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002123795,0.0002191529,0.0003209107,0.0003324174,0.0001466388,0.0001622355,0.0007631025,0.00008791957,0.00002452721],"category_scores_gemma":[0.00003509068,0.0001542766,0.0001014436,0.0008044935,0.00005241355,0.0009239471,0.0001666483,0.000122718,0.000006746633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005074069,"about_ca_system_score_gemma":0.00003661138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001719848,"about_ca_topic_score_gemma":0.00005455282,"domain_scores_codex":[0.9984581,0.00004828869,0.0002992219,0.000581762,0.0002344976,0.0003781376],"domain_scores_gemma":[0.9984461,0.0000682089,0.0003534581,0.0006510951,0.0004143983,0.0000667119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002085592,0.002930978,0.01910716,0.001616316,0.01155694,0.001267384,0.03539856,0.003635782,0.003264675,0.7406577,0.00859384,0.1698851],"study_design_scores_gemma":[0.003214212,0.003671299,0.005974113,0.001632629,0.001054802,0.0004058742,0.00500731,0.1951653,0.76233,0.01180467,0.00647089,0.00326891],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004657709,0.0001638987,0.9920319,0.00002709235,0.0001374263,0.0007706817,0.00001377155,0.0009082955,0.00128926],"genre_scores_gemma":[0.6730602,0.000001463011,0.3258188,0.00005200602,0.00002137023,0.0004028657,0.000001554464,0.0000141974,0.0006275507],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7590653,"threshold_uncertainty_score":0.6291218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04603759736148835,"score_gpt":0.2957127822275336,"score_spread":0.2496751848660452,"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."}}