简介：SomeresultsonRaRbtransformationofcompoundfiniteautomataoverfinitefieldaregeneralizedtothecaseofcommutativerings.PropertiesofRaRbtransformationarediscussedandappliedtotheinversionproblemforcompoundfiniteautomata.

简介：Adatastreamisamassiveunboundedsequenceofdataelementscontinuouslygeneratedatarapidrate.Duetothisreason,mostalgorithmsfordatastreamssacrificethecorrectnessoftheirresultsforfastprocessingtime.Theprocessingtimeisgreatlyinfluencedbytheamountofinformationthatshouldbemaintained.Thisissuebecomesmoreseriousinfindingfrequentitemsetsorfrequencycountingoveranonlinetransactionaldatastreamsincetherecanbealargenumberofitemsetstobemonitored.WehaveproposedamethodcalledtheestDecmethodforfindingfrequentitemsetsoveranonlinedatastream.Inordertoreducethenumberofmonitoreditemsetsinthismethod,monitoringthecountofanitemsetisdelayeduntilitssupportislargeenoughtobecomeafrequentitemsetinthenearfuture.Forthispurpose,thecountofanitemsetshouldbeestimated.Consequently,howtoestimatethecountofanitemsetisacriticalissueinminimizingmemoryusageaswellasprocessingtime.Inthispaper,theeffectsofvariouscountestimationmethodsforfindingfrequentitemsetsareanalyzedintermsofminingaccuracy,memoryusageandprocessingtime.

简介：Visitingtrianglesthatconformadigitalterrainmodelisacoreoperationinanumberoffieldslikeanimationandvideogamesorgeneratingprofiles,cross-sections,andcontoursincivilengineering.Perfornfingthevisitinanefficientmannerisanissuespeciallywhentheoutputofthetraversaldependsinsomewayonadditionalparametersorinformationchangingovertime,forexample,amovingpointofview.Inthisworkwereportasetofrulesthat,givenadigitalterrainmodeldefinedoveraregulargridandanarbitrarypointofviewoutsidetheterrain,defineatotalback-to-frontorderinthesetofdigitalterrainmodeltriangleswithrespecttothepoint.Thesetofrulesisminimal,completeandcorrect.Toassesshowtherulesperform,wehaveimplementedaCPU-basedalgorithmforrealisticallyrenderingheightfieldsdefinedoverregulargrids.Thealgorithmdoesnotmakeuseofthez-bufferorshadersfeaturedbyourgraphicscard.Weshowhowouralgorithmisimplementedandshowvisualresultsobtainedfromsyntheticandrealdata.Wefurtherdiscussthealgorithmperformancewithrespecttotwoalgorithms：anaivealgorithmthatvisitstrianglesaccordingtogridindicesanddoesnotsolvethehiddenlineproblem,andthez-bufferprovidedbythegraphicscardfeaturedbyourcomputer.Ouralgorithmallowsreal-timeinteractionwhenthepointofviewarbitrarilymovesin3Dspaceandweshowthatitsperformanceisasgoodasthatofthez-buffergraphicscard.

简介：一线性(q，，，m(n))局部地可译码的代码(LDC)C：\mathbbF{\mathbbF}n\mathbbF{\mathbbF}m(n)是从向量空间\mathbbF的线性转变{\mathbbF}n到空间\mathbbF{\mathbbF}每消息标志xi能与概率为被恢复的m(n)至少\frac1|\mathbbF|+e\frac{1}{{\left|\mathbb{F}\right|}}从由查询仅仅q的一个使随机化的算法的C(x)的+\varepsilonC放(x)，就算直到C的m(n)位置(x)被贿赂。在Dvir的一个最近的工作，为线性LDC降低界限的作者表演能为算术电路暗示更低的界限。他建议那证明界限更低因为在建筑群或真实的地上的LDC是为接近他的之一的一个好起点推测。我们的主要结果是m(n)=(n2)为在任何东西上的线性3质问LDC的更低的界限，可能无限，地。常数在(

简介：Cloudcomputingisatechnologythatprovidesuserswithalargestoragespaceandanenormouscomputingpower.However,theoutsourceddataareoftensensitiveandconfidential,andhencemustbeencryptedbeforebeingoutsourced.Consequently,classicalsearchapproacheshavebecomeobsoleteandnewapproachesthatarecompatiblewithencrypteddatahavebecomeanecessity.Forprivacyreasons,mostoftheseapproachesarebasedonthevectormodelwhichisatimeconsumingprocesssincetheentireindexmustbeloadedandexploitedduringthesearchprocessgiventhatthequeryvectormustbecomparedwitheachdocumentvector.Tosolvethisproblem,weproposeanewmethodforconstructingasecureinvertedindexusingtwokeytechniques,homomorphicencryptionandthedummydocumentstechnique.However,1)homomorphicencryptiongeneratesverylargeciphertextswhicharethousandsoftimeslargerthantheircorrespondingplaintexts,and2)thedummydocumentstechniquethatenhancestheindexsecurityproduceslotsoffalsepositivesinthesearchresults.Theproposedapproachexploitstheadvantagesofthesetwotechniquesbyproposingtwomethodscalledthecompressedtableofencryptedscoresandthedoublescoreformula.Moreover,weexploitasecondsecureinvertedindexinordertomanagetheusers'accessrightstothedata.Finally,inordertovalidateourapproach,weperformedanexperimentalstudyusingadatacollectionofonemilliondocuments.Theexperimentsshowthatourapproachismanytimesfasterthananyotherapproachbasedonthevectormodel.