國際會議發(fā)言稿

國際會議發(fā)言稿

篇一：國際學(xué)術(shù)會議發(fā)言稿

1.Prologue

Thankyou,Mr.Chairman,foryourgraciousintroduction.Iamhonoredtohavethechancetoaddressyouonthisspecialoccasion.Thetopicofmypaperis“TransactionCostandFarmers’ChoiceofAgriculturalProductsSelling”.Theoutlineofmytalkasfollows.ThefirstpartIwanttointroducethebackgroundofthisresearch.Thesecondpartsuggestsasimplehouseholdchoicemodel.Thethirdpartcoversthedatausedinthisresearch.Andthen,weintroducetheempiricalresults.Finally,asimpleconclusionisgiven.

2.Introduction

Well,let’smoveonthefirstpartofthistopic.Themotivationofthisworklikethis.Institutionaleconomicspositsthatagentsmakingdecisionsondifferenttypesoftransactionsdosoinacostlyway.Forexample,farmersdecidingsellaparticularcroptowhombasetheirdecisionsnotonlyonthepricetheyexpecttoreceiveineachmarketchoicebutalsoonadditionalcostsrelatedtotransactinginthesemarkets.

Iwanttouseapicturetoillustrateit.Forexample,givensomemarketchannels,farmers’choicescanberegardedasequilibriumbetweenthesurplusandtheadditionalcoststhatrelatedtotransacting.Especiallyindevelopingcountries,high-valuecropproducersfullyparticipateinthemarketandthetransactioncosthasbeenthehardconstrainttofarmers.Furthermore,Farmers’marketchoicescanbetakenasachoicedilemmaoftransactioncostandproductionsurplus.Consequently,thescientificquestionofthisresearchishowtransactioncostaffectsplanters’choices.

3.Methodology

Let’smovetothetheoreticalmodelofourresearch.Considerahouseholdmodelinonerotation.Instage1,famerηneedstoallocatetheinputfactors.ThisprocesscanqbesetintoafunctionlikethisQ??Q,Qηmeanstheoutputfarmersdecideqtoproduce.pimpliestheOutputpriceWimpliesInputPriceand.z:?isfixedinput.Onceproducewhatandproducehowmanyaredecided,nextquestiontobeconsideredishowmuchproductstobetransactedinmarket.HereweusethreeccCηmeanshowfunctionstodescribethisquestion.Thefirstequation,c??p,z?

muchagriculturalproductsusedbyfamersthemselves.pimpliesthepricethecagriculturalproduct,z?suggeststhefluctuationofCη.Thesecondequationq??Q??c?,qηmeanstheamountofagriculturalproductstransactedin

q?n?market.Thethirdequationi?q?impliestheamountexchangedinnthtime.

InStage3,farmerswilldecidetoselltheproductstowhom.Chanelj’smarketpriceis

bdecidedbyanexogenesispriceandfarmers’negotiatingpower.pij?p*

j?BBesidesthis,weuseamatrixtoshowthenetprofitofChaneljXik?ik,???ik?

andthenfarmers’choicecanbeexpressedinatypicalchoicemodel

expPr?1exp?k?1

Basedonthechoicemodel,anotherimportantconceptisfamers’channelchoice.Here,wesetfivetypes.Theyrankbythemarketbarriers.Accordingly,wesetagroupdiscretenumbertoexpressthem.Y:dependentvariableY=5,meansfarmerchoose

brokers.Y=1,farmerssellproductstoconsumersdirectly.

4.Dataandestimationprocedures

Here,weillustratethedatadistributionwiththismap.AccordingtotheAgriculturalregionalizationfromDepartmentofAgriculture,TheapplespecializationareasinChinacontaintwoparts:BoSeaareaandLoessPlateau.BoSeaAreainredcolor,containsHebei,ShandongandLiaoning3provinces.AndLoessPlateauingreencolor,containsShanxi,Henan,ShaanxiandGansu4provinces.Firstly,weusePPSmethodtogetthefirststagesamplingunit14countiesin7provinces.Thenuserandomsamplemethodtogetvillageandhousehold.Theyareoursampledistribution.

5.EmpiricalResults

6.Conclusions

篇二：國際會議作報告英語發(fā)言稿

Thankyou,prof.….Mynameis…..I’mfrom…..Iamverypleasedtobeheretojointhisforum.Thetopicofmypresentationispropertiesofrapidconstructionmaterialsforsoilpavementoffieldairfield.Asisshowninthepicture,themainpartsofmyresearchareaboutsoilpavement.

Mypresentationwillincludethesefourparts:

First,somebackgroundinformationaboutthisresearch;second,themainworkwehavedone;third,someconclusionswehavegotandthelast:innovationandpresentationofourpublishedpapers.

WhyIchoosethisitem?Ithinkitcanbeillustratedfromthefollowingfourparts.First,theexistingquantityofairfieldsisstillnotsufficientandtheairfieldshavemanyshortcomingsespeciallyinwartime.Second,thecomplementaryfacilities,suchashighwayrunwaysarefarlessthanairfields,however,havemoreweakness.Third,acertainamountoffieldairfieldisquitenecessaryconsideringsomeemergenciessuchasrescueanddisasterrelief.Forth,thefieldairfieldcanfillthevoidofairfieldandtheycan

becombinedtobeairfieldnetwork.

Themeaningandaimofthisresearchcontainsthreeparts.Fast,convenientandvalidity,fastmeansthefieldairfieldmustbeconstructed

asfastaspossible,convenientmeanstheconstructionshouldneedtheminimumequipment,laborandmaterialsconsideringtheactualconstructioncondition,validitymeanstheconstructedairfieldisabletosupporttheoperationofgivenaircraftinspecificallytime.

Justlikemanyotherterritories,thesituationoftheresearchisthattheArmytakesadvancedline.TheArmydeclaresthattheycanreachtoanywhereontheearthin96hours,themostimportantmethodforforceprojectionisthoughaircraft,thusrapidconstructionofpavementisthekeyproblemforrapidforcetransportation.

Themainworkwehavedonecanbesummarizedasfourparts,materialschoosing,schememaking,mechanicalpropertiesresearchandwater-stablepropertiesresearch.

Wechoosetwokindsofsoils,whicharegotfromXi’an,ShanxiprovinceandJiuquan,Gansuprovinceseparately.ThesandfromBaRiverwasconsideratetoinvestigatetheinfluenceofsandtothepropertiesofstabilizedsoil.Thechosenthreekindsofpowdersarecement,limeandnew-typestabilizerdevelopedbyChang’anUniversity.Theprinciplesinconsideringthefunctionof4kingsoffibersarereferringdifferentlength,typeandmixingthem.

Onaccountofthetime,Iwillmakeabriefdescriptionabouttheexperimentscheme.Insummary,threepartswereproposedtodistinguishtheaffectingfactorsinmakingexperimentscheme.Theyarepowdercontrol,fibercontrolandotherfactors.Takingpowdercontrolforexample,thedosageofcementisrespectively6%,8%and10%whenthesoilisstabilizedonlybycement,whilethedosageofcementdecreaseto3%,5%and7%whenthelimeisaddictedtostabilizedsoil.Thefollowingtwofactorsarestabilizerandsand.

Sixkindsofexperimentswereperformedtoinvestigatetheinfluenceofabovefactorstothemechanicalpropertiesofstabilizedsoil.Theaimofcompactiontestistofindthemaximumdrydensityandoptimummoisturecontent.Theaimofcompressionstrengthtestistodeterminetheoptimumdosageofcement,lime,powderstabilizerandfiber,meanwhileevaluatingtheperformanceofstabilizedsoil.Theaimofsplittingtensionstrengthtestissimilartocompressionstrengthtest,theleftpictureissamplestabilizedbycement,whiletherightpictureisthesamplestabilizedbyfiberandcement.Thedirectsheerisanotherimportantparameteringeotechnicalengineering.Itinfluencesthefoundationbearingcapacityandmanyotherpropertiesespeciallyforsoilbaseandbasecourse.Theleftpictureshowsthecourseofmaking

sampleandtherightpictureshowsthetestprocess.

TheCBRtestandreboundmodulustestarereferencedfromhighwaytestspecificationtoevaluatingthecomprehensivecapacitiesofeachstructurelevelofthepavement.Forboththetwotests,theleftpictureshowsthecourseofmakingsampleandtherightpictureshowsthetestprocess.Whatshouldbenotedisthatthenumberofsampleisatleast6,thelastresultistheaveragevalueofthesedategotfromtestaftereliminatingthebadresults.

Fourkindsofexperimentswereperformedtoinvestigatetheinfluenceofabovefactorstothewater-stablepropertiesofstabilizedsoil.Thescouringtestisnotthestatedexperimentincurrentspecification.Itisperformedbyusthroughlookinguplargequantityofinterrelatedliterature,andtwodifferentwaystocarryout.Theleftpictureshowsthemethodofvibrationtableandtherightpictureshowsthemethodoffatiguetestinstrument.Penetranttestreferstotheexperimentinrelatingconcretespecification.Theleftpictureshowstheprocessofsaturation,therightpictureshowsthetestprocess.

Cantabriatestandothertestsarealloriginalexperiments;theyareusedinstabilizedsoilforfirsttime,hereIwillnotdevelopmynarrative.

Asregardstheinnovation,Ithinkitthroughoutthewholeresearch,includingmaterialschoosing,schememaking,mechanicalandwater-stableexperiments.Ithinkitcanbedrawledfromthefollowingkeywords,suchassoilchoosing,sand,powders,fibers,andsoon.Threemainpartscanbesummarized.First,selectingtwokindsofsoils,threekindsofpowders,severalcombinations;second,severalkindsoffibers,differentlengthandadmixture;third,comprehensiveexperiments,testmethodandtestinstrument.

篇三：模擬國際會議演講稿

Recsplorer:RecommendationAlgorithmsBasedonPrecedenceMining

1.Introduction

Thankyouverymuch,Dr.Li,foryourkindintroduction.Ladiesandgentlemen,Goodmorning!Iamhonoredtohavebeeninvitedtospeakatthisconference.BeforeIstartmyspeech,letmeaskaquestion.Doyouthinkrecomemdationsfromothersareusefulforyourinternetshopping?Thankyou.Itisobviousthatrecommendationsplayanimportantroleinourdailyconsumptiondecisions.

Today,mytopicisaboutRecommendationAlgorithmsBasedonPrecedenceMining.Iwanttoshareourinterestingresearchresultonrecommendationalgorithmswithyou.Thecontentofthispresentationisdividedinto5parts:insession1,Iwillintruducethetradictionalrecommendationandournewstrategy;insession2,IwillgivetheformaldefinitionofPrecedenceMining;insession3,Iwilltalkaboutthenovelrecommendationalgorithms;experimentalresultwillbeshowedinsession4;andfinally,Iwillmakeaconclusion.

2.Body

Session1:Introduction

Thepictureonthisslideisaninstanceofrecommemdationapplicationonamazon.

Recommendersystemsprovideadviceonproducts,movies,webpages,andmanyothertopics,andhavebecomepopularinmanysites,suchasAmazon.Manysystemsusecollaborativefilteringmethods.ThemainprocessofCFisorganizedasfollow:first,identifyuserssimilartotargetuser;second,recommenditemsbasedonthesimilarusers.Unfortunately,theorderofconsumeditemsisneglect.Inourpaper,weconsideranewrecommendationstrategybasedonprecedencepatterns.Thesepatternsmayencompassuserpreferences,encodesomelogicalorderofoptionsandcapturehowinterestsevolve.

Precedenceminingmodelestimatetheprobabilityofuserfutureconsumptionbasedonpastbehavior.Andtheseprobabilitiesareusedtomakerecommendations.Throughourexperiment,precedenceminingcansignificantlyimproverecommendationperformance.Futhermore,itdoesnotsufferfromthesparsityofratingsproblemandexploitpatternsacrossallusers,notjustsimilarusers.

Thisslidedemonstratesthedifferencesbetweencollaborativefilteringandprecedencemining.Supposethatthescenarioisaboutcourseselection.Eachquarter/semesterastudentchoosesacourse,andratesitfrom1to5.Figurea)showsfivetranscripts,atranscriptmeansalistofcourse.Uisourtargetstudentwhoneedrecommendations.Figureb)illustrateshowCFwork.Assumesimilarusersshareatleasttwocommoncoursesandhavesimilarrating,thenu3andu4aresimilartou,andtheircommoncoursehwillbearecommendationtou.Figurec)presentshowprecedenceminingwork.Forthisexample,weconsiderpatternswhereonecoursefollowsanother.Supposepatternsoccouratleasttwotranscripsarerecognizedassignificant,then,andarefoundout.Andd,h,andfarerecommendationtouwhohastakena,gande.

NowIwillaprobabilisticframeworktosolvetheprecedenceminingproblems.Ourtargetuserhasselectedcoursea,wewanttocomputetheprobabilitycoursexwillfollow,,Pr[x|a].

﹁howerve,whatwereallyneedtocalculateisPr[x|aX]ratherthanPr[x|a].Becauseinourcontext,

wearedecidingifxisagoodrecommendationforthetargetuserthathastakena.Thusweknowthatourtargetuser’stranscriptdoesnothavexbeforea.Forinstance,thetranscriptno.5willbeomitted.Inmorecommonsituation,ourtargetuserhastakenalistofcourses,T={a,b,c,…}not

﹁justa.Thus,whatreallyneedisPr[x|TX].Thequestionishowtofigureoutthisprobability.Iwill

answeritlater.

Session2:PrecedenceMining

WeconsiderasetDofdistinctcourses.WeuselowercaseletterstorefertocoursesinD.AtranscriptTisasequenceofcourses,,a->b->c->d.ThenthedefinitionofTop-kRecommendationProblemisasfollows.GivenasettranscriptsoverDfornusers,theextratranscriptTofatargetuser,andadesirednumberofrecommendationsk,ourgoalisto:

1.Assignascorescoretoeverycoursex∈Dthatreflectshowlikelyitisthetargetstudentwillbeinterestedintakingx.Ifx∈T,thenscore=0.

2.Usingthescorefunction,selectthetopkcoursestorecommendtothetargetuser.

Tocomputescores,weproposetousethefollowingstatistics,wherex,y∈D:

f:thenumberoftranscriptsthatcontainx.

g:thenumberoftranscriptsinwhichxprecedescoursey.

Thisslideshowsthecalculationresultoffandg.Forexample,fromthetable,weknowthatfis10andgis3.

WeproposeaprecedenceminingmodeltosolvetheTop-kRecommendationProblem.Hereare

﹁somenotation:xy,whichwehavememtionedinsession1,referstotranscriptwherexoccurs

withoutaprecedingy;x﹁yreferstotranscriptwherexoccurswithoutyfollowingit.Weusequantitiesfandgtocompteprobabilitiesthatencodetheprecedenceinformation.Forinstance,fromformular1to7.Iwouldnottellthedetailofallformulars.Wejustpayattentionto

﹁formular5,notethatthisquantityaboveisthesameas:Pr[x﹁y|yx]whichwillbeusedto

computescore.

Asweknow,thetargetuserusuallyhastakenalistofcoursesratherthanacourse,soweneedto

﹁extentourprobabilitycalculationformulars.Forexample,supposeT={a,b},Pr[xT]the

probabilityxoccurswithouteitheranaorbprecedingit;Pr[x﹁T]theprobabilityxoccurswithouteitheranaorbfollowingit.Thisprobabilitycanbecalculatedexactly.Sohowtocalculateit?

Session3:RecommendationAlgorithms

Let’sreviewsession2.Themaingoaloftherecommendationalgorithmsistocalculatethescore,andthenselectthetopkcoursesbasedonthesescores.TraditionalrecommendationalgorithmscomputearecommendationscoreforacoursexinDonlybasedonitsfrequencyofoccurence.Itdoesnottakeintoaccountthecoursestakenbythetargetuser.

OurrecommendationalgorithmscalledSingleMCconquertheshortcomingofthetraditionalones.Itcomputesthescoreusingtheformular5.Thedetailisasfollows:astudentwithatranscripToftakencourses,forthecoursey∈T,ifyandxappeartogetherintranscriptssatisfiesthe

﹁thresholdθ,thencomputethePr[x﹁y|yx],reflectingthelikelihoodthestudentwilltakecoursex

﹁andignoringtheeffectoftheothercoursesinT;finallythemaximumofPr[x﹁y|yx]ischoosenas

thescore.

HereisthecalculationformularofscoreofSignleMC.Forexample,withthehigerscore,dwillberecommended.

AnothernewrecommendationalgorithmnamedJointProbabilities