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Katharina Morik, Jörg Rahnenführer, Christian Wietfeld (Eds.) Machine Learning under Resource Constraints · Applications Also of interest Volume 1 Machine Learning under Resource Constraints. Fundamentals Morik, Marwedel (Eds.), 2023 ISBN 978-3-11-078593-7, e-ISBN 978-3-11-078594-4 Volume 2 Machine Learning under Resource Constraints. Discovery in Physics Morik, Rohde (Eds.), 2023 ISBN 978-3-11-078595-1, e-ISBN 978-3-11-078596-8 Machine Learning under Resource Constraints Final Report of CRC 876 Editor in Chief Katharina Morik Volume 3/3 Machine Learning under Resource Constraints Applications Edited by Katharina Morik, Jörg Rahnenführer and Christian Wietfeld Editors Prof. Dr. Katharina Morik Prof. Dr. Christian Wietfeld TU Dortmund University TU Dortmund University Department of Computer Sciences Department of Electrical Engineering and Chair for Artificial Intelligence Information Technology Computer Science 8 Chair for Communication Networks Otto-Hahn-Str. 12 Otto-Hahn-Straße 6 44221 Dortmund 44221 Dortmund Germany Germany Prof. Dr. Jörg Rahnenführer TU Dortmund University Department of Statistics Statistical Methods in Genetics and Chemometrics Vogelpothsweg 87 44227 Dortmund Germany ISBN 978-3-11-078597-5 e-ISBN (PDF) 978-3-11-078598-2 e-ISBN (EPUB) 978-3-11-078614-9 DOI https://doi.org/10.1515/9783110785982 This work is licensed under the Creative Commons Attribution 4.0 International License. For details go to https://creativecommons.org/licenses/by/4.0/. Creative Commons license terms for re-use do not apply to any content (such as graphs, figures, photos, excerpts, etc.) not original to the Open Access publication and further permission may be required from the rights holder. The obligation to research and clear permission lies solely with the party re-using the material. Library of Congress Control Number: 2022949226 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2023 with the author(s), editing © 2023 Katharina Morik, Jörg Rahnenführer and Christian Wietfeld, published by Walter de Gruyter GmbH, Berlin/Boston This book is published open access at www.degruyter.com. Cover image: Collaborative Research Center 876 Printing and binding: CPI books GmbH, Leck www.degruyter.com Contents 1 Editorial 1 | 2 Health/Medicine 3 | 2.1 MachineLearninginMedicine CatherineJutzeler,KarstenBorgwardt|3 2.2 VirusDetection RolandHergenröder,FrankWeichert,KonstantinWüstefeld, VictoriaShpacovitch|21 2.3 CancerDiagnosticsandTherapyfromMolecularData SvenRahmann,AlexanderSchramm,JohannesKöster|43 2.4 BayesianAnalysisforDimensionalityandComplexityReduction ZeyuDing,KatjaIckstadt,AlexanderMunteanu|58 2.5 SurvivalPredictionandModelSelection JörgRahnenführer,MichelLang,JakobRichter|71 2.6 ProteinComplexSimilarity BiancaKStöcker,TillSchäfer,PetraMutzel,JohannesKöster, NilsKriege,SvenRahmann|85 3 Industry4.0 103 | 3.1 KeynoteonIndustry4.0 MichaeltenHompel,MoritzRoidl|103 3.2 QualityAssuranceinInterlinkedManufacturingProcesses JochenDeuse,KatharinaMorik,AmalSaadallah,JanBüscher, ThorbenPanusch|114 3.3 LabelProportionLearning MarcoStolpe,KatharinaMorik|136 3.4 SimulationandMachineLearning PetraWiederkehr,KatharinaMorik,AmalSaadallah, FelixFinkeldey|157 3.5 High-PrecisionWirelessLocalization JanisTiemann|180 3.6 IndoorPhotovoltaicEnergyHarvesting MojtabaMasoudinejad|195 3.7 Micro-UAVSwarmTestbedforIndoorApplications NilsGramse,MoritzRoidl,ShrutarvAwasthi, ChristopherReining|212 VIII | Contents 4 SmartCityandTraffic 225 | 4.1 Inner-CityTrafficFlowPredictionwithSparseSensors ThomasLiebig|225 4.2 Privacy-PreservingDetectionofPersonsandClassificationofVehicle Flows MarcusHaferkamp,BenjaminSliwa,ChristianWietfeld|241 4.3 GreenNetworkingandResourceConstrainedClientsforSmartCities PascalJörke,ChristianWietfeld|261 4.4 VehicletoVehicleCommunications:MachineLearning-Enabled PredictiveRouting CedrikKrieger,BenjaminSliwa,ChristianWietfeld|272 4.5 ModellingofHybridVehicularTrafficwithExtendedCellularAutomata MichaelSchreckenberg,TimVranken|285 4.6 EmbeddedCrowdsensingforPavementMonitoringanditsIncentive Mechanisms MaximillianMachado,RanRan,LiangCheng|297 5 CommunicationNetworks 313 | 5.1 CapacityAnalysisofIoTNetworksinUnlicensedSpectrum StefanBöcker,ChristianArendt,ChristianWietfeld|313 5.2 Resource-EfficientVehicle-to-CloudCommunications BenjaminSliwa|331 5.3 Mobile-DataNetworkAnalyticsofHighlyReliableNetworks RobertFalkenberg,KarstenHeimann,BenjaminSliwa|342 5.4 MachineLearning-Enabled5GNetworkSlicing CanerBektas,DennisOverbeck,FabianKurtz,ChristianWietfeld|354 5.5 PotentialofMillimeterWaveCommunications KarstenHeimann,SimonHäger,ChristianWietfeld|375 6 Privacy 391 | 6.1 Keynote:ConstructionofInference-ProofAgentInteractions JoachimBiskup|391 Bibliography|413 Index|461 ListofContributors|467 1 Editorial KatharinaMorik JörgRahnenführer ChristianWietfeld ThisisthethirdbookofaseriesofbooksdedicatedtotheresultsoftheDFGCollabora- tiveResearchCenter876on“MachineLearningunderResourceConstraints”.Thefirst bookoftheseriesdiscussesfundamentalinnovationsinthetheoryandalgorithmsof machinelearning.Thesecondbookcoverstheuseofmachinelearninginphysics.This volumefocusesonapplicationsofthemachinelearningapproachespresentedinBook 1.Themainideaistodemonstratewithspecificexampleshowmachinelearninghasbe- comeessentialaswellaspracticalinsolvingreal-lifeproblemsfromdiverseapplication areas,rangingfrommedicineandroboticstoroadtrafficandcommunicationnetworks. Variousreal-lifeexampleapplicationsshowthesignificantimpactofusingtailored machinelearningmethodstoimprovetheperformanceoftherespectiveprocesses andsystems.Akeyboundaryconditionimposedbythereal-lifeenvironmentsisthat resources,suchasenergy,storage,computingpower,computingtime,etc.,areoften limitedandthatthepracticabilityoftheproposedmachinelearningsolutionsdepends ontheefficientuseofthoseresources.Therefore,thesuccessofthesolutionsdiscussed inthisbookmustnotonlybemeasuredintermsofperformancegainsbut,atthesame time,intermsoftheirresourceefficiencyandcorrespondingsustainability.Formany domainexperts,thesheermultitudeofmachinelearningapproachesmakesitdifficult tochoosethe“right”onesforaparticularproblem.Whiletheavailabilityofsoftware toolslowerstheentrybarriertousemachinelearningmethodsbynon-experts,the applicationexamplescontainedinthisbookdemonstratethattrulysignificantimpacts canoftenonlybeachievedbyaninterdisciplinarycombinationofdomainknowledge andtheappropriateusageofmachinelearningmethods.Accordingly,thisbookaims topromoteproficiencyintheuseofmachinelearningmethodsbeyondthequickwins ofarbitrarilyusingwhateverapproachhappenstobeinfashion.Theapplicationsde- scribedinthisbookwilltouchuponamultitudeofmachinelearningoptionscovering thecompleteprocesschainfromdataacquisition,featureextraction,modelselection viavariouslearningapproachestomodelverificationandmodelvalidation.Itwillshow, forexample,thatwhilethedeeplearningapproachespopulartodaycanbebeneficial formanyproblemsinsomeareas,alternativemethodssuchasensemblelearningwith randomforestsaremoreaccuratewithmuchlessresourceutilizationinotherareas. Thefirstpartofthebookaddressestheapplicationareaofhealthandmedicine.After anoverviewofmachinelearninginmedicineprovidedbytheinvitedauthorsCatherine JutzelerandKarstenBorgwardt,anumberofresultsfromtheCRC876arepresented, coveringvirusdetection,proteinanalysis,andcancerdiagnosticsandtherapy.The OpenAccess.©2023theauthor(s),publishedbyDeGruyter. Thisworkislicensedunderthe CreativeCommonsAttribution4.0InternationalLicense. https://doi.org/10.1515/9783110785982-001 2 | 1 Editorial secondpartofthebookisdedicatedtotheapplicationofmachinelearningforindustry usecases.Ontheonehand,machinelearningenablesproactivequalityassessments; ontheother,itsapplicationforpreciselocalization,energyharvesting,andswarm controldemonstratesthepotentialofembeddingmachinelearningmethodsinalmost anyelementofthemanufacturingandlogisticsprocessofpresentandfutureindustry environments.Inthethirdpartofthebook,variousexamplesillustratethesignificant potentialofmachinelearningforsmartcityandtrafficusecases,suchastheprediction oftrafficflows,theprivacy-preservingdetectionofvehicleflows,andresource-efficient crowdsensinginsmartcities. Thefourthpartofthebookisaboutimprovingtheperformanceofcommunication networksthroughmachinelearning.Thisincludesnewapproachesforhighlyresource- efficientvehicle-to-cloudcommunicationsaswellasmachinelearning-enabledmobile datanetworkanalyticsandproperdimensioningof5Gnetworkslices.Asmanyappli- cationsofmachinelearninginvolvepersonaldataandmayaffectprivacyconcerns, thisbookalsoincludesachapteronageneralmethodologytoclassifyandhandle privacyaspectsofdatamanagementaspartofthemachinelearningprocesschain. Thisfocusonthedatahandlingcomplementstheprivacy-preservingmachinelearning techniques.Withthisbroadspectrumofapplicationandpracticalimplementationex- amples,wehopethatthisbookwillservedomainexpertsfromdiverseapplicationareas asinspirationfortheuseofmachinelearningfortheirapplication-specificproblems. Tomaximizetheimpact,manyofthepresentedsolutionsareprovidedasopensource publishedtogetherwithopendatasets,allowingforreproducibilityandsustainable transfer.Atthesametime,machinelearningexpertsareexpectedtobemotivatedby theimpressiveimpactoftheirworkonreal-lifeproblemstofurtherexpandthemachine learningsolutionspaceintermsofaccuracyandresourceefficiency. Dortmund,14.10.2022 KatharinaMorik,JörgRahnenführerandChristianWietfeld