{"id":"https://openalex.org/W2986669737","doi":"https://doi.org/10.1145/3357254.3357266","title":"Latent cognizance","display_name":"Latent cognizance","publication_year":2019,"publication_date":"2019-08-16","ids":{"openalex":"https://openalex.org/W2986669737","doi":"https://doi.org/10.1145/3357254.3357266","mag":"2986669737"},"language":"en","primary_location":{"id":"doi:10.1145/3357254.3357266","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357254.3357266","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition","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/A5088789995","display_name":"Pisit Nakjai","orcid":"https://orcid.org/0000-0001-8471-4390"},"institutions":[{"id":"https://openalex.org/I179193067","display_name":"Khon Kaen University","ror":"https://ror.org/03cq4gr50","country_code":"TH","type":"education","lineage":["https://openalex.org/I179193067"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Pisit Nakjai","raw_affiliation_strings":["Khon Kaen University, Khon Kaen, Thailand"],"affiliations":[{"raw_affiliation_string":"Khon Kaen University, Khon Kaen, Thailand","institution_ids":["https://openalex.org/I179193067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053858111","display_name":"Jiradej Ponsawat","orcid":null},"institutions":[{"id":"https://openalex.org/I179193067","display_name":"Khon Kaen University","ror":"https://ror.org/03cq4gr50","country_code":"TH","type":"education","lineage":["https://openalex.org/I179193067"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Jiradej Ponsawat","raw_affiliation_strings":["Khon Kaen University, Khon Kaen, Thailand"],"affiliations":[{"raw_affiliation_string":"Khon Kaen University, Khon Kaen, Thailand","institution_ids":["https://openalex.org/I179193067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038364223","display_name":"Tatpong Katanyukul","orcid":"https://orcid.org/0000-0003-3586-475X"},"institutions":[{"id":"https://openalex.org/I179193067","display_name":"Khon Kaen University","ror":"https://ror.org/03cq4gr50","country_code":"TH","type":"education","lineage":["https://openalex.org/I179193067"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Tatpong Katanyukul","raw_affiliation_strings":["Khon Kaen University, Khon Kaen, Thailand"],"affiliations":[{"raw_affiliation_string":"Khon Kaen University, Khon Kaen, Thailand","institution_ids":["https://openalex.org/I179193067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088789995"],"corresponding_institution_ids":["https://openalex.org/I179193067"],"apc_list":null,"apc_paid":null,"fwci":0.42,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.71878702,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"164","last_page":"169"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9993000030517578,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9993000030517578,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9972000122070312,"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/T10320","display_name":"Neural Networks and Applications","score":0.9919000267982483,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7860971689224243},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7509058713912964},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6845293045043945},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.5840332508087158},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5702345967292786},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5040711164474487},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4776236414909363},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.47606581449508667},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.45411160588264465},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.44356581568717957},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.440137654542923},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4380648136138916},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.39584988355636597},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35227078199386597},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3386576473712921},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.058675289154052734}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7860971689224243},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7509058713912964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6845293045043945},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.5840332508087158},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5702345967292786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5040711164474487},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4776236414909363},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.47606581449508667},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.45411160588264465},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.44356581568717957},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.440137654542923},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4380648136138916},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.39584988355636597},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35227078199386597},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3386576473712921},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.058675289154052734},{"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3357254.3357266","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357254.3357266","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1895577753","https://openalex.org/W2054781704","https://openalex.org/W2096733369","https://openalex.org/W2102605133","https://openalex.org/W2115627867","https://openalex.org/W2147768505","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2243397390","https://openalex.org/W2399033357","https://openalex.org/W2467604901","https://openalex.org/W2559085405","https://openalex.org/W2570343428","https://openalex.org/W2596142952","https://openalex.org/W2765434781","https://openalex.org/W2790808809","https://openalex.org/W2801991413","https://openalex.org/W2803756472","https://openalex.org/W2804086070","https://openalex.org/W2888987635","https://openalex.org/W2962985038","https://openalex.org/W2963149653","https://openalex.org/W2963499153","https://openalex.org/W3099206234","https://openalex.org/W4237686802"],"related_works":["https://openalex.org/W2372267530","https://openalex.org/W2969189870","https://openalex.org/W2965643117","https://openalex.org/W4303857162","https://openalex.org/W2407375987","https://openalex.org/W3049691116","https://openalex.org/W2505726097","https://openalex.org/W2010643158","https://openalex.org/W2106867672","https://openalex.org/W3081214562"],"abstract_inverted_index":{"Despite":[0],"overwhelming":[1],"achievements":[2],"in":[3,21],"recognition":[4,38,58,107],"accuracy,":[5],"extending":[6],"an":[7,49,52,69],"open-set":[8,111],"capability---ability":[9],"to":[10],"identify":[11],"when":[12],"the":[13],"question":[14],"is":[15],"out":[16],"of":[17,51,55,68,79,87],"scope---remains":[18],"greatly":[19],"challenging":[20],"a":[22,37,42,56,65,74,85,100,109],"scalable":[23,110],"machine":[24],"learning":[25],"inference.":[26],"A":[27],"recent":[28],"research":[29],"has":[30,81],"discovered":[31],"Latent":[32],"Cognizance":[33],"(LC)---an":[34],"insight":[35],"on":[36,41,73,84],"mechanism":[39],"based":[40],"new":[43,62],"probabilistic":[44,71],"interpretation,":[45],"Bayesian":[46],"theorem,":[47],"and":[48,94,103],"analysis":[50],"internal":[53],"structure":[54],"commonly-used":[57],"inference":[59,76],"structure.":[60],"The":[61],"interpretation":[63],"emphasizes":[64],"latent":[66],"assumption":[67],"overlooked":[70],"condition":[72],"learned":[75],"model.":[77],"Viability":[78],"LC":[80],"been":[82],"shown":[83],"task":[86],"sign":[88],"language":[89],"recognition,":[90],"but":[91],"its":[92],"potential":[93],"implication":[95],"can":[96,104],"reach":[97],"far":[98],"beyond":[99],"specific":[101],"domain":[102],"move":[105],"object":[106],"toward":[108],"recognition.":[112]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2019-11-22T00:00:00"}