{"id":"https://openalex.org/W4416350498","doi":"https://doi.org/10.1109/ton.2026.3705395","title":"DK-Root: A Joint Data-and-Knowledge-Driven Framework for Root Cause Analysis of QoE Degradations in Mobile Networks","display_name":"DK-Root: A Joint Data-and-Knowledge-Driven Framework for Root Cause Analysis of QoE Degradations in Mobile Networks","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4416350498","doi":"https://doi.org/10.1109/ton.2026.3705395"},"language":null,"primary_location":{"id":"doi:10.1109/ton.2026.3705395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ton.2026.3705395","pdf_url":null,"source":{"id":"https://openalex.org/S5407042750","display_name":"IEEE Transactions on Networking","issn_l":"2998-4157","issn":["2998-4157"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Networking","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.11737","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101913497","display_name":"Q. Li","orcid":"https://orcid.org/0009-0002-8803-352X"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qizhe Li","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-4046-8620","affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052612257","display_name":"H. Chen","orcid":"https://orcid.org/0009-0005-6482-517X"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haolong Chen","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0009-3138-8320","affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100631199","display_name":"Jiansheng Li","orcid":"https://orcid.org/0000-0002-3708-3677"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiansheng Li","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0000-7136-6577","affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shuqi Chai","orcid":"https://orcid.org/0000-0002-8250-7782"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuqi Chai","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-8250-7782","affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025138760","display_name":"Xuan Li","orcid":"https://orcid.org/0009-0009-3505-0589"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Li","raw_affiliation_strings":["Experience Laboratory, Huawei Technologies Company Ltd., Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9159-2333","affiliations":[{"raw_affiliation_string":"Experience Laboratory, Huawei Technologies Company Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059824798","display_name":"Y. Thomas Hou","orcid":"https://orcid.org/0000-0003-3716-5768"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuzhou Hou","raw_affiliation_strings":["Experience Laboratory, Huawei Technologies Company Ltd., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Experience Laboratory, Huawei Technologies Company Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinhua Shao","orcid":"https://orcid.org/0009-0009-8369-5955"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinhua Shao","raw_affiliation_strings":["China Telecommunications Group Company Ltd., Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-8369-5955","affiliations":[{"raw_affiliation_string":"China Telecommunications Group Company Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210136246"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404522","display_name":"Fangfang Li","orcid":"https://orcid.org/0000-0002-7392-586X"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangfang Li","raw_affiliation_strings":["China Telecommunications Group Company Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Telecommunications Group Company Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210136246"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043358532","display_name":"Kaifeng Han","orcid":"https://orcid.org/0000-0001-6940-073X"},"institutions":[{"id":"https://openalex.org/I4210130112","display_name":"China Academy of Information and Communications Technology","ror":"https://ror.org/038dte259","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210130112","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaifeng Han","raw_affiliation_strings":["China Academy of Information and Communications Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6940-073X","affiliations":[{"raw_affiliation_string":"China Academy of Information and Communications Technology, Beijing, China","institution_ids":["https://openalex.org/I4210130112"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004583148","display_name":"Guangxu Zhu","orcid":"https://orcid.org/0000-0001-9532-9201"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangxu Zhu","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-9532-9201","affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01928258,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":null,"first_page":"5879","last_page":"5894"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.7429999709129333,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.7429999709129333,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.02410000003874302,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.023099999874830246,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5580999851226807},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.5263000130653381},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5236999988555908},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5162000060081482},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.45489999651908875},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.39730000495910645},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.3725000023841858},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.35989999771118164},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3547999858856201},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.35280001163482666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7705000042915344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5907999873161316},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5662999749183655},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5580999851226807},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5236999988555908},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5162000060081482},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.45489999651908875},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44670000672340393},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.39730000495910645},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3725000023841858},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3547999858856201},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3398999869823456},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.30799999833106995},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3050999939441681},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C84945661","wikidata":"https://www.wikidata.org/wiki/Q7366567","display_name":"Root cause","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C130963320","wikidata":"https://www.wikidata.org/wiki/Q1401207","display_name":"Root cause analysis","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C2780505938","wikidata":"https://www.wikidata.org/wiki/Q17093282","display_name":"Unavailability","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C95491727","wikidata":"https://www.wikidata.org/wiki/Q992968","display_name":"Mobile telephony","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C135510737","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance indicator","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ton.2026.3705395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ton.2026.3705395","pdf_url":null,"source":{"id":"https://openalex.org/S5407042750","display_name":"IEEE Transactions on Networking","issn_l":"2998-4157","issn":["2998-4157"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Networking","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2511.11737","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.11737","pdf_url":"https://arxiv.org/pdf/2511.11737","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2511.11737","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.11737","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.11737","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.11737","pdf_url":"https://arxiv.org/pdf/2511.11737","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G283351877","display_name":null,"funder_award_id":"JCYJ20241202124934046","funder_id":"https://openalex.org/F4320336606","funder_display_name":"National Transgenic Science and Technology Program"},{"id":"https://openalex.org/G5047231983","display_name":null,"funder_award_id":"U25A20394","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7586278852","display_name":null,"funder_award_id":"RCBS20231211090816034","funder_id":"https://openalex.org/F4320336606","funder_display_name":"National Transgenic Science and Technology Program"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336606","display_name":"National Transgenic Science and Technology Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diagnosing":[0],"the":[1,27,47,137,140,176,179,184],"root":[2],"causes":[3],"of":[4,6,29,49,178],"Quality":[5],"Experience":[7],"(QoE)":[8],"degradations":[9],"in":[10],"operational":[11],"mobile":[12],"networks":[13],"is":[14],"challenging":[15],"due":[16],"to":[17,150],"complex":[18],"cross-layer":[19],"interactions":[20],"among":[21],"kernel":[22],"performance":[23],"indicators":[24],"(KPIs)":[25],"and":[26,44,117,126,133,139,169,183,191],"scarcity":[28],"reliable":[30],"expert":[31,70],"annotations.":[32],"Although":[33],"rule-based":[34,87],"heuristics":[35],"can":[36],"generate":[37],"labels":[38,88,149],"at":[39],"scale,":[40],"they":[41],"are":[42,143],"noisy":[43],"coarse-grained,":[45],"limiting":[46],"accuracy":[48],"purely":[50],"data-driven":[51],"approaches.":[52],"To":[53,99],"address":[54],"this,":[55],"we":[56,104],"propose":[57],"DK-Root,":[58],"a":[59,95,106,157],"joint":[60],"data-and-knowledge-driven":[61],"framework":[62],"that":[63,110,129],"unifies":[64],"scalable":[65],"weak":[66,125],"supervision":[67],"with":[68,146,164],"precise":[69],"guidance":[71],"for":[72],"robust":[73],"root-cause":[74,115],"analysis.":[75],"DK-Root":[76,165],"first":[77],"pretrains":[78],"an":[79],"encoder":[80,138],"via":[81],"contrastive":[82,97],"representation":[83,189],"learning":[84],"using":[85],"abundant":[86],"while":[89],"explicitly":[90],"denoising":[91],"their":[92],"noise":[93],"through":[94],"supervised":[96],"objective.":[98],"supply":[100],"task-faithful":[101],"data":[102],"augmentation,":[103],"introduce":[105],"class-conditional":[107],"diffusion":[108,121,181],"model":[109],"generates":[111],"KPIs":[112],"sequences":[113],"preserving":[114],"semantics,":[116],"by":[118],"controlling":[119],"reverse":[120],"steps,":[122],"it":[123],"produces":[124],"strong":[127],"augmentations":[128],"improve":[130],"intra-class":[131],"compactness":[132],"inter-class":[134],"separability.":[135],"Finally,":[136],"lightweight":[141],"classifier":[142],"jointly":[144],"fine-tuned":[145],"scarce":[147],"expert-verified":[148],"sharpen":[151],"decision":[152],"boundaries.":[153],"Extensive":[154],"experiments":[155],"on":[156],"real-world,":[158],"operator-grade":[159],"dataset":[160],"demonstrate":[161],"state-of-the-art":[162],"accuracy,":[163],"surpassing":[166],"traditional":[167],"ML":[168],"recent":[170],"semi-supervised":[171],"time-series":[172],"methods.":[173],"Ablations":[174],"confirm":[175],"necessity":[177],"conditional":[180],"augmentation":[182],"pretrain-finetune":[185],"design,":[186],"validating":[187],"both":[188],"quality":[190],"classification":[192],"gains.":[193]},"counts_by_year":[],"updated_date":"2026-07-04T06:09:54.619538","created_date":"2025-11-19T00:00:00"}