{"id":"https://openalex.org/W2474507027","doi":"https://doi.org/10.1145/2935651.2935653","title":"AnnoTainted","display_name":"AnnoTainted","publication_year":2016,"publication_date":"2016-06-26","ids":{"openalex":"https://openalex.org/W2474507027","doi":"https://doi.org/10.1145/2935651.2935653","mag":"2474507027"},"language":"en","primary_location":{"id":"doi:10.1145/2935651.2935653","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2935651.2935653","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International on Workshop on Physical Analytics","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/A5033417348","display_name":"Rahul Majethia","orcid":"https://orcid.org/0000-0003-4698-0545"},"institutions":[{"id":"https://openalex.org/I26604189","display_name":"Shiv Nadar University","ror":"https://ror.org/05aqahr97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26604189"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Rahul Majethia","raw_affiliation_strings":["Shiv Nadar University, Greater Noida, India"],"affiliations":[{"raw_affiliation_string":"Shiv Nadar University, Greater Noida, India","institution_ids":["https://openalex.org/I26604189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015646328","display_name":"Akshit Singhal","orcid":null},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Akshit Singhal","raw_affiliation_strings":["University of Texas, Arlington, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas, Arlington, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031166080","display_name":"Lakshmi Manasa K","orcid":null},"institutions":[{"id":"https://openalex.org/I26604189","display_name":"Shiv Nadar University","ror":"https://ror.org/05aqahr97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26604189"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Lakshmi Manasa K","raw_affiliation_strings":["Shiv Nadar University, Greater Noida, India"],"affiliations":[{"raw_affiliation_string":"Shiv Nadar University, Greater Noida, India","institution_ids":["https://openalex.org/I26604189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074857642","display_name":"Sahiti Kunchay","orcid":"https://orcid.org/0000-0001-8468-8794"},"institutions":[{"id":"https://openalex.org/I26604189","display_name":"Shiv Nadar University","ror":"https://ror.org/05aqahr97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26604189"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kunchay Sahiti","raw_affiliation_strings":["Shiv Nadar University, Greater Noida, India"],"affiliations":[{"raw_affiliation_string":"Shiv Nadar University, Greater Noida, India","institution_ids":["https://openalex.org/I26604189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049834669","display_name":"Shubhangi Kishore","orcid":null},"institutions":[{"id":"https://openalex.org/I26604189","display_name":"Shiv Nadar University","ror":"https://ror.org/05aqahr97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26604189"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shubhangi Kishore","raw_affiliation_strings":["Shiv Nadar University, Greater Noida, India"],"affiliations":[{"raw_affiliation_string":"Shiv Nadar University, Greater Noida, India","institution_ids":["https://openalex.org/I26604189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080888193","display_name":"Vijay Nandwani","orcid":null},"institutions":[{"id":"https://openalex.org/I26604189","display_name":"Shiv Nadar University","ror":"https://ror.org/05aqahr97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26604189"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vijay Nandwani","raw_affiliation_strings":["Shiv Nadar University, Greater Noida, India"],"affiliations":[{"raw_affiliation_string":"Shiv Nadar University, Greater Noida, India","institution_ids":["https://openalex.org/I26604189"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5033417348"],"corresponding_institution_ids":["https://openalex.org/I26604189"],"apc_list":null,"apc_paid":null,"fwci":1.6579,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.88515457,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"13","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9990000128746033,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9983999729156494,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social 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":0.8229800462722778},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7318210601806641},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6947645545005798},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6002833247184753},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.5397658348083496},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5313758254051208},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5270241498947144},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5131826996803284},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.45529675483703613},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.4513416290283203},{"id":"https://openalex.org/keywords/crowdsensing","display_name":"Crowdsensing","score":0.4237821698188782},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.1355690062046051}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8229800462722778},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7318210601806641},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6947645545005798},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6002833247184753},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.5397658348083496},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5313758254051208},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5270241498947144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5131826996803284},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.45529675483703613},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.4513416290283203},{"id":"https://openalex.org/C2780821482","wikidata":"https://www.wikidata.org/wiki/Q25381721","display_name":"Crowdsensing","level":2,"score":0.4237821698188782},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.1355690062046051},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2935651.2935653","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2935651.2935653","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International on Workshop on Physical Analytics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1178673422","https://openalex.org/W1494922272","https://openalex.org/W1496056137","https://openalex.org/W2017353533","https://openalex.org/W2017634428","https://openalex.org/W2061491173","https://openalex.org/W2097906225","https://openalex.org/W2101276256","https://openalex.org/W2102319615","https://openalex.org/W2104933073","https://openalex.org/W2105046342","https://openalex.org/W2106981652","https://openalex.org/W2108467170","https://openalex.org/W2122005484","https://openalex.org/W2131573934","https://openalex.org/W2132791018","https://openalex.org/W2141868140","https://openalex.org/W2148143831","https://openalex.org/W2156744053","https://openalex.org/W2604721075","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W3032998312","https://openalex.org/W135177976","https://openalex.org/W4384486036","https://openalex.org/W1503094549","https://openalex.org/W2337920774","https://openalex.org/W4286908577","https://openalex.org/W2886410948","https://openalex.org/W2025875869","https://openalex.org/W2896200027","https://openalex.org/W2904716865"],"abstract_inverted_index":{"In":[0,55],"this":[1,83],"work,":[2],"we":[3],"provide":[4],"motivation":[5],"for":[6,15,23,36,52,82],"a":[7,65,75],"zero-effort":[8],"crowdsensing":[9],"task:":[10],"auto-annotated":[11],"ground":[12,71],"truth":[13],"collection":[14,86],"physical":[16],"activity":[17,40,106],"recognition.":[18],"Data":[19,85],"obtained":[20],"through":[21],"Smartphones":[22],"classification":[24],"of":[25,105,133],"human":[26],"activities":[27,135],"is":[28,62],"prone":[29],"to":[30,47,126],"discrepancies,":[31],"which":[32,90,120],"reiterates":[33],"the":[34,56,93,142],"need":[35],"better":[37],"and":[38,87,146],"larger":[39],"datasets.":[41],"Artificial":[42],"data":[43,61,99,139],"generation":[44],"algorithms":[45],"fail":[46],"efficiently":[48],"generate":[49],"quality":[50,98],"instances":[51],"minority":[53],"data.":[54],"proposed":[57,115,143],"model,":[58],"crowd-sourced":[59],"sensor":[60,138],"classified":[63,134],"by":[64,69,124],"robust":[66],"classifier":[67,144],"built":[68],"researchers":[70],"up.":[72],"We":[73],"nominate":[74],"Generic":[76],"Classifier":[77],"with":[78,102],"\u2265":[79],"95%":[80],"accuracy":[81],"purpose.":[84],"distribution":[88],"models":[89],"ensure":[91],"that":[92],"crowd":[94],"client":[95],"receives":[96],"non-skewed,":[97],"from":[100],"locations":[101],"higher":[103],"degree":[104],"occurrence":[107],"are":[108,117],"elucidated":[109],"upon.":[110],"Also":[111],"integrated":[112],"within":[113],"our":[114],"model":[116],"Location-Specific":[118],"Classifiers,":[119],"can":[121],"be":[122],"utilized":[123],"developers":[125],"optimize":[127],"on":[128],"location-specific":[129],"tasks.":[130],"Effective":[131],"validation":[132],"using":[136],"diverse":[137],"streams":[140],"improves":[141],"systems":[145],"boosts":[147],"ground-truth":[148],"accuracy.":[149]},"counts_by_year":[{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2016-07-22T00:00:00"}