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Proceedings in Adaptation, Learning and Optimization 16 Kaj-Mikael Björk Editor Proceedings of ELM 2021 Theory, Algorithms and Applications Proceedings in Adaptation, Learning and Optimization Volume 16 SeriesEditor Meng-HiotLim,NanyangTechnologicalUniversity,Singapore,Singapore Theroleofadaptation,learningandoptimizationarebecomingincreasinglyessential andintertwined.Thecapabilityofasystemtoadapteitherthroughmodificationofits physiologicalstructureorviasomerevalidationprocessofinternalmechanismsthat directlydictatetheresponseorbehavioriscrucialinmanyrealworldapplications. Optimizationliesattheheartofmostmachinelearningapproacheswhilelearning andoptimizationaretwoprimarymeanstoeffectadaptationinvariousforms.They usuallyinvolvecomputationalprocessesincorporatedwithinthesystemthattrigger parametricupdatingandknowledgeormodelenhancement,givingrisetoprogressive improvement. This book series serves as a channel to consolidate work related to topics linked to adaptation, learning and optimization in systems and structures. Topicscoveredunderthisseriesinclude: (cid:129) complex adaptive systems including evolutionary computation, memetic computing, swarm intelligence, neural networks, fuzzy systems, tabu search, simulatedannealing,etc. (cid:129) machinelearning,datamining&mathematicalprogramming (cid:129) hybridizationoftechniquesthatspanacrossartificialintelligenceandcomputa- tionalintelligenceforsynergisticallianceofstrategiesforproblem-solving. (cid:129) aspectsofadaptationinrobotics (cid:129) agent-basedcomputing (cid:129) autonomic/pervasivecomputing (cid:129) dynamicoptimization/learninginnoisyanduncertainenvironment (cid:129) systemicallianceofstochasticandconventionalsearchtechniques (cid:129) allaspectsofadaptationsinman-machinesystems. Thisbookseriesbridgesthedichotomyofmodernandconventionalmathematical andheuristic/meta-heuristicsapproachestobringabouteffectiveadaptation,learning andoptimization.Itpropelsthemaximthattheoldandthenewcancometogether andbecombinedsynergisticallytoscalenewheightsinproblem-solving.Toreach suchalevel,numerousresearchissueswillemergeandresearcherswillfindthebook seriesaconvenientmediumtotracktheprogressesmade. IndexedbyINSPEC,zbMATH. AllbookspublishedintheseriesaresubmittedforconsiderationinWebofScience. Kaj-Mikael Björk Editor Proceedings of ELM 2021 Theory, Algorithms and Applications Editor Kaj-MikaelBjörk GraduateSchoolandResearch ArcadaUniversityofAppliedSciences Helsinki,Finland ISSN 2363-6084 ISSN 2363-6092 (electronic) ProceedingsinAdaptation,LearningandOptimization ISBN 978-3-031-21677-0 ISBN 978-3-031-21678-7 (eBook) https://doi.org/10.1007/978-3-031-21678-7 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2023 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Organizers Kaj-MikaelBjörk GraduateSchoolandResearch,Arcada UniversityofAppliedSciences,Helsinki, Finland ProfessorJiuwenCao InstituteofInformationandControl, HangzhouDianziUniversity,Xiasha Hangzhou,Zhejiang,310018,China AssociateProfessorChi-ManVong DepartmentofComputerandInformation Science,UniversityofMacau,Macau, 999078,China Dr.YoanMiche BellLabs,Nokia,Espoo,Finland; DepartmentofInformationand ComputerScience,SchoolofScience, AaltoUniversity,FI-00076,Finland v Contents PretrainedE-commerceKnowledgeGraphModelforProduct Classification ...................................................... 1 Chi-ManWong,Chi-ManVong,andYiminZhou ANovelMethodologyforObjectDetectioninHighlyCluttered Images ........................................................... 10 KallinCarolusKhan, EdwardRatner, ChristopherDouglas, andAmauryLendasse Extreme Learning Machines for Offline Forged Signature Identification ...................................................... 24 ZhenLi, LeonardoEspinosa-Leal, AmauryLendasse, andKaj-MikaelBjörk RandomizedModelStructureSelectionApproachforExtreme LearningMachineAppliedtoAcidSulfateSoilDetection ............. 32 AntonAkusok,Kaj-MikaelBjörk,VirginiaEstévez,andAntonBoman Online label distribution learning based on kernel extreme learningmachine .................................................. 41 JintaoHuangandChi-ManVong ANewEnergyVehicleThermalRunawayDataProcessingModel BasedonMachineLearningAlgorithm .............................. 52 LiangXiaoming,FuZhen,LiuXiangchao,PengKai,ChangWei, andLiYuan Marital Stability and Divorce Prediction Among Couples: AMachineLearningApproach ..................................... 68 SadeqFallahtafti, AlirezaFallahtafti, GaryR.Weckman, andHamideMohammadinasab vii viii Contents PredictingtheLoadingParametersofaSquarePanelUponLinear Deflection ......................................................... 84 LeonardoEspinosa-Leal,SilasGebrehiwot,andHeikkiRemes OntheIntensiveCare UnitAdmissionDuring theCOVID-19 PandemicintheRegionofLleida,Spain:AMachineLearning Study ............................................................. 92 DidacFlorensa, JordiMateo, FrancescSolsona, PereGodoy, andLeonardoEspinosa-Leal VerificationofStaticSignaturesusingDynamicTimeWarping onFeaturesfromHighPressurePoints .............................. 104 RubenAcosta-Velasquez, LeonardoEspinosa-Leal, AlexanderGarcia-Perez,andKaj-MikaelBjörk ExtremeLearningMachine-BasedOperationalStateRecognition: AFeasibilityStudywithMechanicalVibrationData .................. 114 JukkaJunttila,VilleS.Lämsä,andLeonardoEspinosa-Leal UnsupervisedHandwrittenSignatureVerificationwithExtreme LearningMachines ................................................ 124 AntonAkusok, LeonardoEspinosa-Leal, AmauryLendasse, andKaj-MikaelBjörk ELMShip:AnEfficientShipClassifierUsingExtremeLearning Machines ......................................................... 135 LeonardoEspinosa-LealandAminMajd AnExtremeLearningMachineModelforVenuePresenceDetection ... 144 WiqarKhan,AsifRaza,HeidiKuusniemi,MohammedElmusrati, andLeonardoEspinosa-Leal Edammo’s Extreme AutoML Technology – Benchmarks andAnalysis ...................................................... 152 BrandonWarner,EdwardRatner,andAmauryLendasse MeasuringResearchProductivityatScale ........................... 164 PeggyLindner AuthorIndex ...................................................... 171 Pretrained E-commerce Knowledge Graph Model for Product Classification B Chi-Man Wong1,2( ), Chi-Man Vong2, and Yimin Zhou3 1 Alibaba Group, Hangzhou, China [email protected] 2 University of Macao, Macao, China 3 Chinese Academy of Sciences, Beijing, China Abstract. ProductclassificationinE-commerce platformsaim toclas- sify a product into correct category, in order to obtain a better guiding service for customer to purchase. Language model is usually used to encode product title into product embedding, and then fed into a classifier for multi-class classification. However, the product title is usually ambiguousandnoisy,leadingtopoorpredictionperformance.Toaddress this issue, we propose a novel pretrained E-commerce knowledge graph (PEKG) model to learn the representation of product from EKG, and then used it for fine-tuning. We formulate the pretraining on EKG as a multi-view learning problem, where the EKG is divided into four views. From PEKG, user and product representation is proposed to learn via aggregationofitsneighborinformation,andthesemanticmeaningfrom the EKG is learned via translation-based method. We experimentally prove that our proposal significantly outperforms baseline,showing that the PEKG can learn useful representation of product. · · Keywords: Pretrained model Product classification E-commerce knowledge graph 1 Introduction E-commerce businesses [12] enable sellers using the Internet and the Web for business transactions of goods, which is affecting much of people’s daily life due to its convenience. People can browse and purchase goods from billions of product of all kinds easily, leading to the rapid growing transaction on e-commerce platformsinrecentyears.Duetothemassivescaleandabundantvarietyofprod- ucts, the organization [2] of the billion items is especially important for guiding customer to purchase, and hence all items should be well categorised. However, the categorization of products is very difficult for seller due to the complexity of the category tree. In real world e-commerce platform, such category tree is usually contained multiple levels which categorise a product from coarse-to-fine level, and each level can be ranged from hundreds to thousands. For example, in Taobao, the number of leaf category is more than 20 thousands. Therefore, (cid:2)c TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2023 K.-M.Björk(Ed.):ELM2021,PALO16,pp.1–9,2023. https://doi.org/10.1007/978-3-031-21678-7_1 2 C. -M. Wong et al. it is in need to find a compromising automatic approach to perform accurate product classification for sellers. Recent approach typically employs natural language processing (NLP) tech- nique [7,21] to learn the embedding representation of product title via language model [3,4], and then such representation is used as input to a neural classifier formulti-classclassification.Duetotherecentpowerfulstate-of-the-artlanguage model, e.g., BERT [4] (Bidirectional Encoder Representations from Transform- ers), the classification performance is promising. However, the product title is made up by sellers based on their understanding of the product and the target customers. Such title may not reflect well of the product’s category, and most informationofthetitleisusuallyusedtoattractcustomer’sattentiontoclickand purchase. For example, a title may be “Creative retro Ceramic Cup Mug sim- ple coffee cup with lid spoon personality frosted couple Japanese drinking cup”, whichisconfusingbecausetherearemultipleproducttypessuchas“cup”,“coffee”, and “spoon”. Therefore, the information of title may be too noisy to learn therealclassificationoftheproduct,andmoreinformationshouldbeconsidered to provide a complete picture of the product. Knowledge Graph (KGs) [19] has been used to organise facts as triples, such as(iPad,colorIs,red) inE-commerceplatform(e.g,AlibabaorAmazon’sKnowl- edgeGraph).Theadvantageisthatmultiplesourcescanbeeasilyfusedintothe sameKG.Forexample,theuserbehavior,theattributesofproduct,thecatego- rization of product and the relation between products can be easily integrated into one KG namely e-commerce knowledge graph (EKG). Such E-commerce knowledgegraphcontainsabundantinformationofproductfrommultipleviews. In Alibaba, the e-commerce KG contains 70+ billion triples. Therefore, in this work, we propose using a pretrained E-commerce knowledge graph (PEKG) model to learn a complete information of product as product embedding, and then together fuse with the title embedding from BERT for product classification. In summary, contributions of this work are as follows: – we propose a pretrain and fine-tune framework for product classification, which integrate multiple sources of product-related information; – a pretrained e-commerce knowledge graph model is proposed to learn the representation of product from the e-commerce knowledge graph, which can overcome the noisy and confusing title-based product classification; – We test PEKG on our billion-scale e-commerce knowledge graph and test it on product classification, showing that PEKG successfully improves the performance. 2 Related Work 2.1 Pretrained Language Model In recent years, more and more researchers pay attention to the development of the pretrained language models [3,4], because the pretrained language models