{"id":"https://openalex.org/W4319753190","doi":"https://doi.org/10.3390/bdcc7010030","title":"A Reasonable Effectiveness of Features in Modeling Visual Perception of User Interfaces","display_name":"A Reasonable Effectiveness of Features in Modeling Visual Perception of User Interfaces","publication_year":2023,"publication_date":"2023-02-08","ids":{"openalex":"https://openalex.org/W4319753190","doi":"https://doi.org/10.3390/bdcc7010030"},"language":"en","primary_location":{"id":"doi:10.3390/bdcc7010030","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc7010030","pdf_url":"https://www.mdpi.com/2504-2289/7/1/30/pdf?version=1676971119","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-2289/7/1/30/pdf?version=1676971119","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081881960","display_name":"Maxim Bakaev","orcid":"https://orcid.org/0000-0002-1889-0692"},"institutions":[{"id":"https://openalex.org/I4210128424","display_name":"Siberian Independent Institute","ror":"https://ror.org/03g1eje15","country_code":"RU","type":"education","lineage":["https://openalex.org/I1313323035","https://openalex.org/I4210127387","https://openalex.org/I4210128424"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Maxim Bakaev","raw_affiliation_strings":["Independent UX Consultant, Novosibirsk 630132, Russia"],"affiliations":[{"raw_affiliation_string":"Independent UX Consultant, Novosibirsk 630132, Russia","institution_ids":["https://openalex.org/I4210128424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056374262","display_name":"Sebastian Heil","orcid":"https://orcid.org/0000-0003-2761-9009"},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sebastian Heil","raw_affiliation_strings":["Department of Computer Science, Technische Universitat Chemnitz, 09111 Chemnitz, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Technische Universitat Chemnitz, 09111 Chemnitz, Germany","institution_ids":["https://openalex.org/I2610724"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087069237","display_name":"Martin Gaedke","orcid":"https://orcid.org/0000-0002-6729-2912"},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Gaedke","raw_affiliation_strings":["Department of Computer Science, Technische Universitat Chemnitz, 09111 Chemnitz, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Technische Universitat Chemnitz, 09111 Chemnitz, Germany","institution_ids":["https://openalex.org/I2610724"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5081881960"],"corresponding_institution_ids":["https://openalex.org/I4210128424"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.6149,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.67603356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"7","issue":"1","first_page":"30","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9980999827384949,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9980999827384949,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9872999787330627,"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/T11094","display_name":"Face Recognition and Perception","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7826839089393616},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6572765707969666},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.641276478767395},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.6384241580963135},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6192542910575867},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5637396574020386},{"id":"https://openalex.org/keywords/orderliness","display_name":"Orderliness","score":0.5565118193626404},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47708162665367126},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.44362112879753113},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41251859068870544},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40841132402420044},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3973456621170044},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10472899675369263},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09299346804618835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7826839089393616},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6572765707969666},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.641276478767395},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.6384241580963135},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6192542910575867},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5637396574020386},{"id":"https://openalex.org/C2775920722","wikidata":"https://www.wikidata.org/wiki/Q7100719","display_name":"Orderliness","level":2,"score":0.5565118193626404},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47708162665367126},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.44362112879753113},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41251859068870544},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40841132402420044},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3973456621170044},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10472899675369263},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09299346804618835},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/bdcc7010030","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc7010030","pdf_url":"https://www.mdpi.com/2504-2289/7/1/30/pdf?version=1676971119","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5296ed1b36134a99904140d1336bb30d","is_oa":true,"landing_page_url":"https://doaj.org/article/5296ed1b36134a99904140d1336bb30d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data and Cognitive Computing, Vol 7, Iss 1, p 30 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-2289/7/1/30/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/bdcc7010030","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/bdcc7010030","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc7010030","pdf_url":"https://www.mdpi.com/2504-2289/7/1/30/pdf?version=1676971119","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4319753190.pdf"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1915872631","https://openalex.org/W1964380033","https://openalex.org/W1980665706","https://openalex.org/W1999404243","https://openalex.org/W2090921891","https://openalex.org/W2166261011","https://openalex.org/W2400730403","https://openalex.org/W2532756262","https://openalex.org/W2562706347","https://openalex.org/W2754213847","https://openalex.org/W2765874585","https://openalex.org/W2803949131","https://openalex.org/W2894747149","https://openalex.org/W2896533325","https://openalex.org/W2902900188","https://openalex.org/W2910121883","https://openalex.org/W2921488597","https://openalex.org/W2943183446","https://openalex.org/W2962843773","https://openalex.org/W3031454183","https://openalex.org/W3035563919","https://openalex.org/W3048516901","https://openalex.org/W3080211177","https://openalex.org/W3089144988","https://openalex.org/W3093501041","https://openalex.org/W3100314869","https://openalex.org/W3100321043","https://openalex.org/W3103635814","https://openalex.org/W3122964075","https://openalex.org/W3160201490","https://openalex.org/W3160762091","https://openalex.org/W3171661055","https://openalex.org/W3175996217","https://openalex.org/W3195736779","https://openalex.org/W4214907589","https://openalex.org/W4285111453","https://openalex.org/W4285138146"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W2965782936","https://openalex.org/W4377697116","https://openalex.org/W2906722931","https://openalex.org/W4289083005"],"abstract_inverted_index":{"Training":[0],"data":[1,157],"for":[2,17],"user":[3,49],"behavior":[4],"models":[5,183],"that":[6,89,152],"predict":[7],"subjective":[8,101],"dimensions":[9,102],"of":[10,36,47,60,64,73,76,103,155,202],"visual":[11,45],"perception":[12],"are":[13,40],"often":[14],"too":[15],"scarce":[16],"deep":[18],"learning":[19],"methods":[20],"to":[21,31,180,187],"be":[22],"applicable.":[23],"With":[24,108],"the":[25,57,61,71,74,79,100,109,128,136,141,146,153,159,165,181,185,193,200],"typical":[26],"datasets":[27,88],"in":[28,44],"HCI":[29],"limited":[30],"thousands":[32],"or":[33],"even":[34],"hundreds":[35],"records,":[37],"feature-based":[38,119],"approaches":[39],"still":[41],"widely":[42],"used":[43,86],"analysis":[46],"graphical":[48],"interfaces":[50],"(UIs).":[51],"In":[52],"our":[53],"paper,":[54],"we":[55,85,112],"benchmarked":[56],"predictive":[58],"accuracy":[59,138],"two":[62,87],"types":[63],"neural":[65],"network":[66],"(NN)":[67],"models,":[68],"and":[69,78,106,114],"explored":[70],"effects":[72],"number":[75],"features,":[77],"dataset":[80,143,171],"volume.":[81],"To":[82],"this":[83,198],"end,":[84],"comprised":[90],"over":[91,173],"4000":[92],"webpage":[93],"screenshots,":[94],"assessed":[95],"by":[96,190],"233":[97],"subjects":[98],"per":[99],"Complexity,":[104],"Aesthetics":[105],"Orderliness.":[107],"experimental":[110],"data,":[111],"constructed":[113],"trained":[115],"1908":[116],"models.":[117],"The":[118],"NNs":[120,130],"demonstrated":[121],"16.2%-better":[122],"mean":[123],"squared":[124],"error":[125,161],"(MSE)":[126],"than":[127],"convolutional":[129],"(a":[131],"modified":[132],"GoogLeNet":[133],"architecture);":[134],"however,":[135],"CNNs\u2019":[137],"improved":[139],"with":[140],"larger":[142],"volume,":[144],"whereas":[145],"ANNs\u2019":[147],"did":[148],"not:":[149],"therefore,":[150],"provided":[151],"effect":[154],"more":[156,178],"on":[158],"models\u2019":[160],"improvement":[162],"is":[163],"linear,":[164],"CNNs":[166],"should":[167],"become":[168],"superior":[169],"at":[170],"sizes":[172],"3000":[174],"UIs.":[175],"Unexpectedly,":[176],"adding":[177],"features":[179],"NN":[182],"caused":[184],"MSE":[186],"somehow":[188],"increase":[189],"1.23%:":[191],"although":[192],"difference":[194],"was":[195],"not":[196],"significant,":[197],"confirmed":[199],"importance":[201],"careful":[203],"feature":[204],"engineering.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}