{"id":"W3123776038","doi":"10.1109/access.2022.3149890","title":"Privacy Assured Recovery of Compressively Sensed ECG Signals","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Encryption; Compressed sensing; Key (lock); SIGNAL (programming language); Wearable computer; Wearable technology; Real-time computing; Computer hardware; Embedded system; Computer security; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001107308,0.0001445206,0.0002607394,0.000140246,0.00008844649,0.00004529133,0.0005505231,0.00004356724,0.0001812153],"category_scores_gemma":[0.00001567092,0.0001602556,0.00008777319,0.0002519955,0.00002665728,0.000195259,0.0001766242,0.0002114221,0.000004395389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004768098,"about_ca_system_score_gemma":0.00002133038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006393369,"about_ca_topic_score_gemma":0.000001973843,"domain_scores_codex":[0.9990272,0.00007434897,0.0002619512,0.0001716975,0.0002611988,0.0002035956],"domain_scores_gemma":[0.9993203,0.0001078486,0.00009803394,0.0003705886,0.00006236768,0.00004087377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007532989,0.00009296552,0.0008003015,0.00005812893,0.0001945099,0.00008577584,0.000254588,0.3198955,0.5722674,0.00003914068,0.09630486,0.009931514],"study_design_scores_gemma":[0.0003497656,0.00008259632,0.001939426,0.0000533536,0.00003383395,0.00002115818,0.0000239167,0.04393973,0.9398951,0.002585432,0.01074441,0.000331262],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678044,0.000396459,0.02439581,0.0000475247,0.0009003617,0.0002780301,0.00005838496,0.0008185213,0.005300516],"genre_scores_gemma":[0.9986277,0.00003130394,0.001002095,0.0001133105,0.00006637818,0.00003199913,0.000009382597,0.00004020998,0.00007768361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3676278,"threshold_uncertainty_score":0.6535035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03603107974754857,"score_gpt":0.2743393094338231,"score_spread":0.2383082296862745,"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."}}