{"id":"https://openalex.org/W4414760883","doi":"https://doi.org/10.1145/3711875.3729123","title":"Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data","display_name":"Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data","publication_year":2025,"publication_date":"2025-06-23","ids":{"openalex":"https://openalex.org/W4414760883","doi":"https://doi.org/10.1145/3711875.3729123"},"language":"en","primary_location":{"id":"doi:10.1145/3711875.3729123","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711875.3729123","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711875.3729123","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd Annual International Conference on Mobile Systems, Applications and Services","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711875.3729123","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102023088","display_name":"Chen Gong","orcid":"https://orcid.org/0000-0002-2888-2370"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Gong","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2888-2370","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017720328","display_name":"Bo Liang","orcid":"https://orcid.org/0000-0001-7226-8178"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Liang","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7226-8178","affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006292758","display_name":"Wei Gao","orcid":"https://orcid.org/0000-0003-2144-6960"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Gao","raw_affiliation_strings":["University of Pittsburgh, Pittsburgh, Pennsylvania, USA"],"raw_orcid":"https://orcid.org/0000-0003-2144-6960","affiliations":[{"raw_affiliation_string":"University of Pittsburgh, Pittsburgh, Pennsylvania, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003999919","display_name":"Chenren Xu","orcid":"https://orcid.org/0000-0001-9171-2596"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210128818","display_name":"Institute of Software","ror":"https://ror.org/033dfsn42","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128818"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenren Xu","raw_affiliation_strings":["Key Laboratory of High Confidence Software Technologies, Ministry of Education (PKU), Beijing, China","School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9171-2596","affiliations":[{"raw_affiliation_string":"Key Laboratory of High Confidence Software Technologies, Ministry of Education (PKU), Beijing, China","institution_ids":["https://openalex.org/I4210128818"]},{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"209","last_page":"222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9966999888420105,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9966999888420105,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.8428000211715698},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.676800012588501},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.5302000045776367},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5271000266075134},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.5171999931335449},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.454800009727478}],"concepts":[{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.8428000211715698},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7667999863624573},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.676800012588501},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.5302000045776367},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5271000266075134},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.5171999931335449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4616999924182892},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45829999446868896},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.454800009727478},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3862000107765198},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.33489999175071716},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2667999863624573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711875.3729123","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711875.3729123","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711875.3729123","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd Annual International Conference on Mobile Systems, Applications and Services","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711875.3729123","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711875.3729123","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711875.3729123","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd Annual International Conference on Mobile Systems, Applications and Services","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3420942567","display_name":null,"funder_award_id":"62061146001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8888994136","display_name":null,"funder_award_id":"62272010","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4414760883.pdf"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W2014161185","https://openalex.org/W2056716515","https://openalex.org/W2194775991","https://openalex.org/W2566079294","https://openalex.org/W2567079332","https://openalex.org/W2626807326","https://openalex.org/W2761292292","https://openalex.org/W2782599016","https://openalex.org/W2798333393","https://openalex.org/W2884805522","https://openalex.org/W2950821050","https://openalex.org/W2963185411","https://openalex.org/W2984128863","https://openalex.org/W3001100660","https://openalex.org/W3009116081","https://openalex.org/W3012910746","https://openalex.org/W3018508273","https://openalex.org/W3018805456","https://openalex.org/W3109645351","https://openalex.org/W3113714919","https://openalex.org/W3166254754","https://openalex.org/W4214671648","https://openalex.org/W4289520498","https://openalex.org/W4307922140","https://openalex.org/W4318256669","https://openalex.org/W4320893548","https://openalex.org/W4376167392","https://openalex.org/W4385764165","https://openalex.org/W4387212628","https://openalex.org/W4390873552","https://openalex.org/W4390873596","https://openalex.org/W4392152951","https://openalex.org/W4395685900","https://openalex.org/W4396919034","https://openalex.org/W4399324069","https://openalex.org/W4399856208"],"related_works":[],"abstract_inverted_index":{"Generative":[0],"models":[1],"have":[2],"gained":[3],"significant":[4],"attention":[5],"for":[6],"their":[7],"ability":[8],"to":[9,64,85,97],"produce":[10],"realistic":[11],"synthetic":[12,34,42,69,82,118,124,141],"data":[13,35,43,87,119,125],"that":[14,122,134],"supplements":[15],"the":[16,39,47,94,150],"quantity":[17],"of":[18,41,68,101,103,140],"real-world":[19],"datasets.":[20],"While":[21],"recent":[22],"studies":[23],"show":[24],"performance":[25,49,146,154],"improvements":[26],"in":[27,79],"wireless":[28,81],"sensing":[29],"tasks":[30],"by":[31,155],"incorporating":[32],"all":[33],"into":[36],"training":[37],"sets,":[38],"quality":[40,66,95,126],"remains":[44],"unpredictable":[45],"and":[46,61,71,88,106,143],"resulting":[48],"gains":[50],"are":[51],"not":[52],"guaranteed.":[53],"To":[54,109],"address":[55],"this":[56],"gap,":[57],"we":[58,113],"propose":[59],"tractable":[60],"generalizable":[62],"metrics":[63],"quantify":[65],"attributes":[67],"data\u2014affinity":[70],"diversity.":[72],"Our":[73,131],"assessment":[74],"reveals":[75],"prevalent":[76],"affinity":[77],"limitation":[78,96],"current":[80],"data,":[83,142],"leading":[84],"mislabeled":[86],"degraded":[89],"task":[90,128],"performance.":[91],"We":[92],"attribute":[93],"generative":[98],"models'":[99],"lack":[100],"awareness":[102],"untrained":[104],"conditions":[105],"domain-specific":[107],"processing.":[108],"mitigate":[110],"these":[111],"issues,":[112],"introduce":[114],"SynCheck,":[115],"a":[116],"quality-guided":[117],"utilization":[120,139,152],"scheme":[121],"refines":[123],"during":[127],"model":[129],"training.":[130],"evaluation":[132],"demonstrates":[133],"SynCheck":[135],"consistently":[136],"outperforms":[137],"quality-oblivious":[138],"achieves":[144],"4.3%":[145],"improvement":[147],"even":[148],"when":[149],"previous":[151],"degrades":[153],"13.4%.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
