{"id":"https://openalex.org/W3170168951","doi":"https://doi.org/10.1109/percom50583.2021.9439109","title":"Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels","display_name":"Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels","publication_year":2021,"publication_date":"2021-03-22","ids":{"openalex":"https://openalex.org/W3170168951","doi":"https://doi.org/10.1109/percom50583.2021.9439109","mag":"3170168951"},"language":"en","primary_location":{"id":"doi:10.1109/percom50583.2021.9439109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom50583.2021.9439109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051213779","display_name":"Suwen Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suwen Lin","raw_affiliation_strings":["University of Notre Dame, Indiana, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Indiana, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076011510","display_name":"Louis Faust","orcid":"https://orcid.org/0000-0002-9741-7894"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Louis Faust","raw_affiliation_strings":["University of Notre Dame, Indiana, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Indiana, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068157871","display_name":"Nitesh V. Chawla","orcid":"https://orcid.org/0000-0003-3932-5956"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nitesh V. Chawla","raw_affiliation_strings":["University of Notre Dame, Indiana, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Indiana, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965999722480774,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965999722480774,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9962999820709229,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7750008702278137},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6696918606758118},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6146808862686157},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5458526611328125},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5390118956565857},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5266525745391846},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4676840901374817},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.4284711480140686},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3338991105556488},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3303648829460144},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08993229269981384}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7750008702278137},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6696918606758118},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6146808862686157},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5458526611328125},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5390118956565857},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5266525745391846},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4676840901374817},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.4284711480140686},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3338991105556488},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3303648829460144},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08993229269981384},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/percom50583.2021.9439109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom50583.2021.9439109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332179","display_name":"Office of the Director","ror":"https://ror.org/04nseet23"},{"id":"https://openalex.org/F4320333051","display_name":"Intelligence Advanced Research Projects Activity","ror":"https://ror.org/01v3fsc55"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1866072925","https://openalex.org/W1921293667","https://openalex.org/W1968127217","https://openalex.org/W2028586471","https://openalex.org/W2110065044","https://openalex.org/W2127136816","https://openalex.org/W2158153309","https://openalex.org/W2169340948","https://openalex.org/W2517889164","https://openalex.org/W2519191729","https://openalex.org/W2530816535","https://openalex.org/W2557074642","https://openalex.org/W2577784528","https://openalex.org/W2592691248","https://openalex.org/W2605108175","https://openalex.org/W2744939564","https://openalex.org/W2752295241","https://openalex.org/W2752971446","https://openalex.org/W2772357980","https://openalex.org/W2785925437","https://openalex.org/W2795282075","https://openalex.org/W2800146313","https://openalex.org/W2803187616","https://openalex.org/W2804025582","https://openalex.org/W2809398771","https://openalex.org/W2885593519","https://openalex.org/W2889573943","https://openalex.org/W2899290839","https://openalex.org/W2903921973","https://openalex.org/W2905290040","https://openalex.org/W2942640105","https://openalex.org/W2951970475","https://openalex.org/W2953070460","https://openalex.org/W2954300939","https://openalex.org/W2963081269","https://openalex.org/W2963371670","https://openalex.org/W2963735582","https://openalex.org/W2963759070","https://openalex.org/W2964037754","https://openalex.org/W2964155802","https://openalex.org/W2964274690","https://openalex.org/W2964292098","https://openalex.org/W2967052791","https://openalex.org/W2972729724","https://openalex.org/W2978625989","https://openalex.org/W3024280677","https://openalex.org/W3121846313","https://openalex.org/W3137695714","https://openalex.org/W6639331287","https://openalex.org/W6640298173","https://openalex.org/W6733814495","https://openalex.org/W6743778314","https://openalex.org/W6743885473","https://openalex.org/W6746023024","https://openalex.org/W6750523955","https://openalex.org/W6751420435","https://openalex.org/W6751647823","https://openalex.org/W6753772092","https://openalex.org/W6758317311","https://openalex.org/W6763485134","https://openalex.org/W6764051988"],"related_works":["https://openalex.org/W3195649134","https://openalex.org/W2281498195","https://openalex.org/W4375867731","https://openalex.org/W2017526120","https://openalex.org/W2610664080","https://openalex.org/W2188304107","https://openalex.org/W2611989081","https://openalex.org/W2761510556","https://openalex.org/W2892259437","https://openalex.org/W2944566775"],"abstract_inverted_index":{"The":[0,165],"prevalence":[1],"of":[2,49,69,126,134,146],"mobile":[3],"sensors":[4],"makes":[5],"it":[6,74],"possible":[7],"for":[8,156],"researchers":[9],"to":[10,76,84,98,108,116,142,175,182,194],"collect":[11],"and":[12,26,39,57,162,180],"analyze":[13],"the":[14,31,47,67,70,77,86,101,110,127,139,144,169,177],"pervasive":[15],"sensed":[16],"human":[17,37],"activity":[18],"data":[19,34,119,128,161,174],"with":[20,100],"machine":[21],"learning":[22,81,157],"tools.":[23],"These":[24,62],"analyses":[25],"applications":[27],"heavily":[28],"rely":[29],"on":[30,188],"self-report":[32,51,103],"survey":[33,64,104],"that":[35,136],"reflect":[36],"physical":[38],"psychological":[40],"behaviors.":[41],"However,":[42],"many":[43],"factors":[44],"can":[45],"affect":[46],"reliability":[48,145],"such":[50,53],"surveys,":[52],"as":[54],"participants'":[55],"trusty":[56],"ambiguous":[58],"descriptions":[59],"in":[60,120,138],"questionnaires.":[61],"inaccurate":[63],"labels":[65],"challenge":[66],"development":[68],"models,":[71],"especially":[72],"when":[73],"comes":[75],"case":[78],"where":[79],"deep":[80],"approaches":[82],"tend":[83],"memorize":[85],"noisy":[87,102,163],"information":[88,171],"from":[89,129,158,172],"training":[90,179],"data.":[91],"In":[92],"this":[93],"paper,":[94],"we":[95,150],"set":[96],"out":[97],"deal":[99],"labels.":[105,164],"We":[106],"aim":[107],"address":[109],"following":[111],"challenges:":[112],"1)the":[113],"limited":[114],"access":[115],"clean":[117],"labeled":[118],"real-world":[121,190],"scenarios;":[122],"2)different":[123],"noise":[124,135,184],"ratio":[125],"different":[130],"participants;":[131],"3)various":[132],"types":[133],"appear":[137],"data;":[140],"4)how":[141],"evaluate":[143],"participants.":[147],"As":[148],"such,":[149],"propose":[151],"a":[152,189],"generalizable":[153],"model,":[154],"Lan,":[155],"multi-modal":[159],"sensory":[160,173],"proposed":[166],"model":[167,178,197],"incorporates":[168],"temporal":[170],"guide":[176],"thus":[181],"augment":[183],"tolerance.":[185],"Extensive":[186],"experiments":[187],"dataset":[191],"were":[192],"conducted":[193],"show":[195],"our":[196],"outperforms":[198],"state-of-the-art":[199],"benchmarks.":[200]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}