factors include the way in which alignment of interests is achieved mechanisms for communication am.docx

factors include the way in which alignment of interests is achieved mechanisms for communication am.docx

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factorsincludethewayinwhichalignmentofinterestsisachievedmechanismsforcommunicationam

ComputersinHumanBehavior,Volume25,Issue5,September2009,Pages1129-1138

Fu-YunYu

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Abstract

Despitethefactthatthebenefitsofstudentquestiongenerationarewelldocumented,moststudentsdonottakepartinquestiongenerationexercisesduringtheirformalschoolingandarenotaccustomedtoauthoringquestions.Underthepremisethatstudentquestiongenerationactivitiesshouldbebettersupportedinatimely,flexibleandlogisticallyfeasiblefashion,acustomizableonlinelearningenvironmentthataccentuatesvariousscaffoldingtechniqueshasbeendesignedanddeveloped.Theframeworkguidingthedevelopmentofthesystem,anditsassociateddesigns,aredescribed.Toassessthevariousbuilt-inscaffoldsusedtosupportstudents’learningactivitiesbymeansofquestiongeneration,astudywasundertakentothatmeasuredstudents’perceivedusefulnessofeachmechanism,aswellastheeffectsoftheperceivedusefulnessofthescaffoldsonstudents’attitudestowardquestiongenerationlearningactivitiesingeneral.Thedatacollectedindicatedthat,byutilizingcomputersandnetworktechnologies,thedevelopedsystemprovidedasupportivelearningenvironmentforstudent’squestiongenerationlearningactivities.Supportfeaturesnotyetincludedinothersimilarsystems（includingaccesstogenericquestionstemswithsamplequestions,accesstomodelquestions,two-waycycliccommunicationbetweenauthorsofquestionandassessors,andtheabilitytoconcealone’srealidentitybyanonymityornickname,etc.）,wereconfirmedtoprovideahighlevelofsupport.Recommendationsforclassroomimplementationsandfuturestudiesareoffered.

ArticleOutline

1.Introduction

1.1.Underlyingmechanismandbenefitsofstudent-generatedquestionsforlearning

1.2.Thecurrentstateofstudent-generatedquestions

1.3.Thepurposeoftheproject

2.Overviewofthecustomizablescaffoldedstudentquestiongenerationonlinelearningenvironment

2.1.Typesofstudentquestiongenerationlearningactivities

2.2.Frameworkguidingthecustomizablescaffoldedonlinelearningsystemforstudents’questiongenerationactivitiesandassociateddesigns

2.2.1.Reflectivesocialdiscourse

2.2.2.Processprompts

2.2.3.Processdisplays

2.2.4.Processmodels

2.3.Customizabilityoftheonlinestudentquestiongenerationlearningsystem

3.Astudyofstudents’perceptionsoftheusefulnessofthevarioussystemsupportmechanismsanditsinfluenceontheirattitudestowardtheeducationalpotentialofstudent-generatedquestions

3.1.Participantsandinstructionalcontext

3.2.Researchdesignandimplementationprocedures

3.3.Instruments

3.4.Results

4.Discussionandconclusions

4.1.Limitationsandsuggestionsforfuturestudies

Acknowledgements

References

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177

MachinelearningforradioxenoneventclassificationfortheComprehensiveNuclear-Test-BanTreaty  OriginalResearchArticle

JournalofEnvironmentalRadioactivity,Volume101,Issue1,January2010,Pages68-74

TrevorJ.Stocki,GuichongLi,NathalieJapkowicz,R.KurtUngar

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Abstract

AmethodofweapondetectionfortheComprehensivenuclear-Test-Ban-Treaty（CTBT）consistsofmonitoringtheamountofradioxenonintheatmospherebymeasuringandsamplingtheactivityconcentrationof131mXe,133Xe,133mXe,and135Xebyradionuclidemonitoring.Severalexplosionsamplesweresimulatedbasedonrealdatasincethemeasureddataofthistypeisquiterare.Thesedatasetsconsistedofdifferentcircumstancesofanuclearexplosion,andareusedastrainingdatasetstoestablishaneffectiveclassificationmodelemployingstate-of-the-arttechnologiesinmachinelearning.AstudywasconductedinvolvingclassicinductionalgorithmsinmachinelearningincludingNaïveBayes,NeuralNetworks,DecisionTrees,k-NearestNeighbors,andSupportVectorMachines,thatrevealedthattheycansuccessfullybeusedinthispracticalapplication.Inparticular,ourstudiesshowthatmanyinductionalgorithmsinmachinelearningoutperformasimplelineardiscriminatorwhenasignalisfoundinahighradioxenonbackgroundenvironment.

ArticleOutline

1.Introduction

2.Datasetsthatwereusedandgenerated

2.1.Measureddata

2.2.Syntheticdata

3.Eventclassification

3.1.Thelineardiscriminator

3.2.Machinelearning

4.Results

5.Conclusion

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178

Network-awareschedulingforreal-timeexecutionsupportindata-intensiveopticalGrids  OriginalResearchArticle

FutureGenerationComputerSystems,Volume25,Issue7,July2009,Pages794-803

FrancescoPalmieri

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Abstract

HighperformancecommunicationsupportisacriticalaspectformodernGridenvironmentswherethedeploymentofdata-intensiveapplicationsoftenrequiresmovingPetabytesofdatabetweengeographicallydistantdatarepositoriesinspecifictimeranges.Thebest-effortdeliverysystemoftheInternet,oftenusedastheunderlyingtransportnetwork,imposessevereconstraintsonthetransferofmassiveamountsofdata,andthusrestrictsthedeploymentoftheaboveapplicationsonwide-areascales.Besidesthelackofbandwidth,theinabilitytoprovidededicatedlinksmakesthecurrentnetworktechnologynotwellsuitedforperformancecriticalGridcomputing.Asolutionisneededtoprovidededicatedend-to-endconnections,dynamicallyallocableon-demandorbyscheduledreservationtocriticaldata-intensiveapplications.Wavelengthroutingisatthestateoftheartthemostpromisingsolutiontorealizesuchconnections.Accordingly,weproposeanevolutionaryGridapproachbuiltoninter-domainend-to-endlightpathprovisioning,controlledbyameta-schedulinglogiconadvancereservationbasis,offeringtherequiredconnectivityservicestotheinvolvedGridnodesbyinfluencingjobschedulingdecisionswithnetwork-relatedinformation.WediscussopportunitiesandchallengesinthenetworkcontrolplaneandGridmiddlewaredesign,togetherwiththerequiredinterfaces,adaptersandalgorithmscriticaltotheprovisionofnetwork-assistedextensibleresourceschedulingservices.TheproposedsolutionwillresultinaflexibleGridarchitecturethatsupportscooperationbetweendifferentschedulingentities,basedonascalableframeworkfordynamicconfigurationandinterconnectionofmultipletypesofresourcesforhighperformanceGridapplicationsovergloballydistributedopticalnetworksystems.

ArticleOutline

1.Introduction

2.Backgroundconcepts

2.1.OpticaltransportnetworksforhighperformanceGrids

2.2.TheevolutionoftransportnetworkssuitableforGrids

3.Advancereservationandnetwork-awarescheduling

4.Managingthenetworkresources

4.1.Lightpathdiscoveryandestablishment

4.2.Networkserviceinterface

4.2.1.Webserviceimplementation

4.2.2.Networkcontrolplaneaccess

5.Themeta-schedulingservice

5.1.Network-awareresourcescheduling

6.Performanceevaluationandresultanalysis

7.Relatedwork

8.Conclusions

References

Vitae

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179

Amixedspectrummanagementframeworkforthefuturewirelessservicebasedontechno-economicanalysis:

TheKoreanspectrumpolicystudy  OriginalResearchArticle

TelecommunicationsPolicy,Volume33,Issue8,September2009,Pages407-421

JunseokHwang,HyenyoungYoon

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Abstract

Theevolutionofradiotechnologyandvariousserviceshasincreasedtheworld'sdependenceonwirelesscommunications.Thedemandforandvalueofspectrumresourcesthereforearealsoincreasing.Spectrumefficiencyisthemostimportantfactorinmanagingspectrumscarcity.However,underthecurrentspectrummanagementapproach,itisdifficulttoadoptinnovativetechnologiesthatimprovespectrumefficiencyandflexibleusageinthecurrentdynamicwirelessmarket.Recently,therehavebeenseveralapproachestoimproveefficientuseofspectrumresources,andeachapproachhasitsownadvantagesanddisadvantages.Therefore,thisresearchfirstdiscussescurrentissuesandanalyzesrelativesocialwelfarebasedonthedifferentcharacteristicsoftechnologyandmarketconditionstocomparevariousattributesofeachapproach.Basedonthetechno-economicsimulationresults,thispaperintroducesamixedspectrummanagementframeworkforthefuturewirelessserviceandsupportpolicymakers’decisionmaking.Furthermore,themixedspectrumpolicytospectrummanagementinKoreaisproposedtofindamorerealisticandefficientspectrummanagementpolicy.

ArticleOutline

1.Introduction

2.Spectrummanagementregime

2.1.Property-rightsapproachallowingsecondarytrading

2.2.Openapproach（unlicensed）

2.3.Commonapproach（spectrumsharing）

2.4.Internationaltrends

3.Modeldescription

3.1.SINR-basedutilityfunctionmodel

3.2.Welfaremodel

4.Experiment

4.1.Scenariodescription

4.2.Parameterdescriptions

5.Resultsandimplications

5.1.Resultsdependingonmarketconditions

5.2.Resultsdependingonservicecharacteristics

5.3.Decisioncriteria—mixedregime

6.Conclusion

Acknowledgements

References