{"id":"https://openalex.org/W2008812481","doi":"https://doi.org/10.1145/1719970.1720006","title":"Towards maximizing the accuracy of human-labeled sensor data","display_name":"Towards maximizing the accuracy of human-labeled sensor data","publication_year":2010,"publication_date":"2010-02-07","ids":{"openalex":"https://openalex.org/W2008812481","doi":"https://doi.org/10.1145/1719970.1720006","mag":"2008812481"},"language":"en","primary_location":{"id":"doi:10.1145/1719970.1720006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1719970.1720006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th international conference on Intelligent user interfaces","raw_type":"proceedings-article"},"type":"article","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/A5086237721","display_name":"Stephanie Rosenthal","orcid":"https://orcid.org/0000-0002-7583-4590"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Stephanie L. Rosenthal","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032134965","display_name":"Anind K. Dey","orcid":"https://orcid.org/0000-0002-3004-0770"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anind K. Dey","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5086237721"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.3233,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.5824214,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"259","last_page":"268"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994000196456909,"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":0.9994000196456909,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T12607","display_name":"Personal Information Management and User Behavior","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8769655227661133},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6721765995025635},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6547390818595886},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.560575008392334},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5184999704360962},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4915948808193207},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44979360699653625},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4455740749835968},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.4440707862377167},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43437063694000244},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4221895933151245}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8769655227661133},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6721765995025635},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6547390818595886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.560575008392334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5184999704360962},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4915948808193207},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44979360699653625},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4455740749835968},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.4440707862377167},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43437063694000244},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4221895933151245},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1719970.1720006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1719970.1720006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th international conference on Intelligent user interfaces","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.161.7852","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.161.7852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~srosenth/papers/Rosenthal_IUI10.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W154197214","https://openalex.org/W178903071","https://openalex.org/W1588556181","https://openalex.org/W1602360307","https://openalex.org/W1970188685","https://openalex.org/W1971500380","https://openalex.org/W2000672666","https://openalex.org/W2005219594","https://openalex.org/W2022710553","https://openalex.org/W2029778954","https://openalex.org/W2039618015","https://openalex.org/W2041545805","https://openalex.org/W2059216172","https://openalex.org/W2067091994","https://openalex.org/W2067760738","https://openalex.org/W2101498401","https://openalex.org/W2114683127","https://openalex.org/W2116144139","https://openalex.org/W2117733879","https://openalex.org/W2123026288","https://openalex.org/W2130451161","https://openalex.org/W2130626845","https://openalex.org/W2141282920","https://openalex.org/W2151023586","https://openalex.org/W2156221064","https://openalex.org/W2166353350","https://openalex.org/W2187606973","https://openalex.org/W2795245714","https://openalex.org/W3005822403","https://openalex.org/W6738852829","https://openalex.org/W7067123713"],"related_works":["https://openalex.org/W2124823771","https://openalex.org/W2161052216","https://openalex.org/W4317548404","https://openalex.org/W2786391746","https://openalex.org/W3022007134","https://openalex.org/W2130553454","https://openalex.org/W2087783760","https://openalex.org/W3132346564","https://openalex.org/W2991483587","https://openalex.org/W2610740816"],"abstract_inverted_index":{"We":[0,80,141,157,188],"present":[1],"two":[2],"studies":[3],"that":[4,22,82,94,168,179,197],"evaluate":[5],"the":[6,30,35,44,59,96,119,122,143,146,149,153,180,195,200],"accuracy":[7,97,107,144],"of":[8,54,69,72,87,98,108,110,145,162],"human":[9,27],"responses":[10,147,173],"to":[11,43,128,148,170,184],"an":[12,51,73,111],"intelligent":[13],"agent's":[14],"data":[15,36,89,117],"classification":[16],"questions.":[17],"Prior":[18],"work":[19,137],"has":[20],"shown":[21],"agents":[23,181],"can":[24],"elicit":[25],"accurate":[26,172],"responses,":[28],"but":[29],"applications":[31],"vary":[32],"widely":[33],"in":[34],"features":[37,90],"and":[38,67,75,91,135,166],"prediction":[39,71,92],"information":[40,93,163,196],"they":[41],"provide":[42],"labelers":[45,199],"when":[46],"asking":[47],"for":[48,77,192],"help.":[49],"In":[50,101,121],"initial":[52],"analysis":[53],"this":[55],"work,":[56],"we":[57,105],"found":[58,158],"five":[60,155],"most":[61],"popular":[62],"features,":[63],"namely":[64],"uncertainty,":[65],"amount":[66],"level":[68],"context,":[70],"answer,":[74],"request":[76],"user":[78],"feedback.":[79],"propose":[81],"there":[83],"is":[84],"a":[85,130],"set":[86],"these":[88,190],"maximizes":[95],"labeler":[99],"responses.":[100],"our":[102],"first":[103],"study,":[104,124],"compare":[106],"users":[109,165],"activity":[112],"recognizer":[113],"labeling":[114],"their":[115,186],"own":[116],"across":[118,152],"dimensions.":[120,156],"second":[123],"participants":[125],"were":[126],"asked":[127],"classify":[129],"stranger's":[131],"emails":[132],"into":[133,194],"folders":[134],"strangers'":[136],"activities":[138],"by":[139],"interruptibility.":[140],"compared":[142],"users'":[150],"self-reports":[151],"same":[154],"very":[159,171],"similar":[160],"combinations":[161],"(for":[164],"strangers)":[167],"led":[169],"as":[174,176],"well":[175],"more":[177],"feedback":[178],"could":[182],"use":[183,189],"refine":[185],"predictions.":[187],"results":[191],"insight":[193],"help":[198],"most.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
