Table Of ContentStudies in Computational Intelligence 443
Editor-in-Chief
Prof.JanuszKacprzyk
SystemsResearchInstitute
PolishAcademyofSciences
ul.Newelska6
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Forfurthervolumes:
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Roger Lee (Ed.)
Software Engineering,
Artificial Intelligence,
Networking and
Parallel/Distributed
Computing 2012
ABC
Editor
Prof.Dr.RogerLee
SoftwareEngineering&InformationTechnologyInstitute
CentralMichiganUniversity
Mt.Pleasant,Michigan48859
U.S.A.
E-mail:[email protected]
ISSN1860-949X e-ISSN1860-9503
ISBN978-3-642-32171-9 e-ISBN978-3-642-32172-6
DOI10.1007/978-3-642-32172-6
SpringerHeidelbergNewYorkDordrechtLondon
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Preface
The purpose of the 13thInternational Conference on Computer and Information
Science(SNPD2012)heldonAugust8–10,2012inKyoto,Japanwastobringto-
getherresearchersandscientists,businessmenandentrepreneurs,teachersandstu-
dents to discuss the numerous fields of computer science, and to share ideas and
informationin a meaningfulway. Ourconferenceofficersselected the best17 pa-
persfromthosepapersacceptedforpresentationattheconferenceinordertopub-
lishtheminthisvolume.Thepaperswerechosenbasedonreviewscoressubmitted
by membersof the programcommittee,and underwentfurtherroundsof rigorous
review.
InChapter1,KenjiKawamotoetal.Inthispaper,weevaluatedthefeaturesused
in previous studies while taking into account secular changes to classify normal
trafficintothenormalcategoryandanomaloustrafficintotheanomalouscategory
correctly. A secular change in this study is a difference in a feature between the
datethetrainingdatawerecapturedandthedatethetestdatawerecapturedinthe
samecircumstance.TheevaluationisbasedontheEuclideandistancebetweenthe
normalcodebookoranomalouscodebookmadebyvectorquantizationandthetest
data.Wereportonwhatcausesthesesecularchangesandwhichfeatureswithlittle
ornosecularchangeareeffectiveformalwaredetection.
InChapter2,HideoHiroseetal.Inthispaper,wefurtherinvestigatethepredic-
tion accuracy of the tree-GA by comparing the trade-off curve obtained by using
thetree-GAwiththatobtainedbyusingthePRIM(PatientRuleInductionMethod)
proposedbyFriedmanandFisher.Wehavefoundthatthetree-GArevealsthesupe-
riorityoverthePRIMinsomecases.
InChapter3,TomohikoTakagietal.Thispaperproposesanovelback-to-back
testing framework in which a SVM (support vector machine) classifies its results
automatically.
InChapter4,BiplobR.Ray.Inthispaper,weproposealightweightstenographic-
based approach to ensure RFID data confidentiality and integrity as well as the
recoveryoftamperedRFIDdata.
In Chapter 5, Nazia Zaman et al. In this paper, we propose a novel approach,
whichallowsaquickincreaseofthroughputbyusingexplicitfeedbackfromrouters.
VI Preface
In Chapter 6, GongzhuHu et al. In this paper,we build multivariateregression
modelsofhomepricesusingadatasetcomposedof81homes.Wethenappliedthe
maximuminformationcoefficient(MIC)statisticstotheobservedhomevalues(Y)
andthepredictedvalues(X)asanevaluationoftheregressionmodels.Theresults
showedveryhighstrengthoftherelationshipbetweenthetwovariablesXandY.
InChapter7,TsuyoshiMiyazakietal.Inthispaper,wedescribeanimprovement
ofthemethodthatdetectsdistinctivemouthshapesfromJapaneseutteranceimage
sequence.
In Chapter 8, Chung-HungHsieh et al. In thisstudy,we proposea totally non-
contactimage-to-patientregistrationtechniqueusingkinectsensorandanICP-based
(IterativeClosestPoint-based)registrationalgorithmwhichisnamedWAP-ICP.
InChapter9,QimingChenetal.InthispaperWeproposethepage-flowapproach
characterized by extending and externalizing the database buffer pool to DCP to
allowtheproducerQEtoputqueryresultsasdatapages(blocks)totheDCPtobe
retrievedbytheconsumerQE.
In Chapter 10, Tomas Kucˇera et al. In this paper, we present an algorithm for
automateddeploymentplanningofhierarchicalcomponentsystems.Thealgorithm
incorporatescomponentdemandsandmachineresourcesinordertomaximizeper-
formanceofdeployedapplications.Wealsopresentanimplementationofthealgo-
rithmfortheSOFA2componentframework.
InChapter11,KanuBokuetal.Inthispaper,weproposeacase-basedmethodfor
generatingemotionalsyntheticspeechbyexploitingthecharacteristicsofthemaxi-
mumamplitudeandtheutterancetimeofvowels,andthefundamentalfrequencyof
emotionalspeech.
InChapter12,YucongDuanetal.Inthispaper,weexploredatacleaningofvery
largedatabasewithfocusonsemanticrichdataandlinkeddata,fromaknowledge
managementperspective.
In Chapter 13, Masahide Nakamura et al. This paper presents a novel service
creation environment,called SensorService Binder (SSB),which providesa user-
friendlyinterfaceforcreatingcontext-awareserviceswithintheHNS.Builtontop
of the service-orientedHNS, the SSB allows non-expertusers to register contexts
using the sensors, and to bind the registered context to any operation of the net-
workedappliances.
In Chapter 14, Masateru Tsunoda et al. In this paper, we focused risk factors
whichhavestrongandstablerelationshipstocostoverrun,andanalyzedthemusing
the Sharpe ratio based index. As a result, we identified some risk factors which
haverelativelystrongandstablerelationshipstocostoverrun.Aftertheanalysis,we
experimentallypredictedcostoverrunprojectsby collaborativefiltering,usingthe
riskfactorsasindependentvariables.Theresultsuggestedthatcostoverrunprojects
canbepredictedbytheriskfactors.
In Chapter15,KazunoriIwataetal. Inthisstudy,we establish errorprediction
modelsatvariousstagesofembeddedsoftwaredevelopmentusinghybridmethods
ofself-organizingmaps(SOMs)andmultipleregressionanalyses(MRAs). SOMs
areatypeofartificialneuralnetworksthatreliesonunsupervisedlearning.
Preface VII
InChapter16,TomokazuArita etal.Inthispaper,weintroduceattributegraph
grammarsforlabeledgridgraphs,andproposetheirapplicationto generatingtab-
ular forms representing program specification forms with grid structures, such as
two–dimensionalarrays.
In Chapter 17, Seiichi Serikawa et al. In this paper, we proposed a new fusion
ruleformultimodalmedicalimagesbasedonMSFLCT.
Itisoursincerehopethatthisvolumeprovidesstimulationandinspiration,and
thatitwillbeusedasafoundationforworksyettocome.
August2012 TeruhisaHochin
NobuhiroInuzuka
TokuroMatsuo
Contents
EvaluationofSecularChangesinStatisticalFeaturesofTrafficforthe
PurposeofMalwareDetection.................................... 1
KenjiKawamoto,MasatsuguIchino,MitsuhiroHatada,YusukeOtsuki,
HiroshiYoshiura,JiroKatto
AComparativeStudyintheBumpHuntingbetweentheTree-GAand
thePRIM ..................................................... 13
HideoHirose,GenkiKoga
Back-to-Back Testing Framework Using a Machine Learning
Method....................................................... 27
TomohikoTakagi,TakeshiUtsumi,ZengoFurukawa
StenoCiphertoProvideDataConfidentialityandTamperedData
RecoveryforRFIDTag.......................................... 37
BiplobR.Ray,MorshedChowdhury,JemalAbawajy
Multimedia StreamRateControloverMANETBasedonRouter
Feedback ..................................................... 53
NaziaZaman,MorshedChowdhury
MultivariateRegressionModelingforHomeValueEstimateswith
EvaluationUsingMaximumInformationCoefficient................. 69
GongzhuHu,JinpingWang,WenyingFeng
AnImprovementofBasicMouthShapeDetectionRatefromJapanese
UtteranceImageSequenceUsingOpticalFlow...................... 83
TsuyoshiMiyazaki,ToyoshiroNakashima,NaohiroIshii
ANon-contactImage-to-PatientRegistrationMethodUsingKinect
SensorandWAP-ICP ........................................... 95
Chung-HungHsieh,Chung-HsianHuang,Jiann-DerLee
X Contents
Page-FlowinQueryEngineGrid ................................. 103
QimingChen,MeichunHsu,RenWu
AutomatedDeploymentofHierarchicalComponents................. 117
TomasKucˇera,PetrHneˇtynka,JanKofronˇ
SpeechSynthesisofEmotionsUsingVowelFeatures ................. 129
KanuBoku,TaroAsada,YasunariYoshitomi,MasayoshiTabuse
KnowledgeManagementforModelDrivenDataCleaningofVery
LargeDatabase ................................................ 143
YucongDuan,RogerLee
SupportingEnd-User DevelopmentofContext-AwareServicesin
HomeNetworkSystem.......................................... 159
MasahideNakamura,ShuheiMatsuo,ShinsukeMatsumoto
AnalyzingRiskFactorsAffectingProjectCostOverrun .............. 171
MasateruTsunoda,AkitoMonden,KenichiMatsumoto,RyosukeHatano,
ToshihikoNakano,YutakaFukuchi
ErrorPredictionMethodsfor Embedded SoftwareDevelopment
Using Hybrid Models of Self-Organizing Maps and Multiple
RegressionAnalyses ............................................ 185
KazunoriIwata,ToyoshiroNakashima,YoshiyukiAnan,NaohiroIshii
AnAttributeLabeledGridGraphGrammarandItsApplicationto
ProgramSpecificationForms .................................... 201
Tomokazu Arita, Tetsuro Nishino, Kimio Sugita, Kensei Tsuchida,
TakeoYaku
MultimodalMedicalImageFusioninExtendedContourletTransform
Domain....................................................... 215
SeiichiSerikawa, HuiminLu, Yujie Li, LifengZhang,ShiyuanYang,
AkiraYamawaki,ShotaNakashima,YuhkiKitazono
AuthorIndex ..................................................... 227
List of Contributors
JemalAbawajy WenyingFeng
DeakinUniversity,Australia TrentUniversity,Canada
[email protected] [email protected]
YoshiyukiAnan YutakaFukuchi
OmronSoftwareCo.,Ltd.,Japan Hitachi,Ltd.,Japan
[email protected] [email protected]
TomokazuArita ZengoFurukawa
J.F.OberlinUniversity,Japan KagawaUniversity,Japan
[email protected] [email protected]
TaroAsada MitsuhiroHatada
KyotoPrefecturalUniversity,Japan NTTCommunicationsCorporation,
t [email protected] Japan
[email protected]
KanuBoku
KyotoPrefecturalUniversity,Japan RyosukeHatano
[email protected] Hitachi,Ltd.,Japan
QimingChen HideoHirose
HPLabs,USA KyushuInstituteofTechnology,Japan
[email protected] [email protected]
MorshedChowdhury PetrHneˇtynka
DeakinUniversity,Australia CharlesUniversity,CzechRepublic
[email protected] [email protected]
YucongDuan Chung-HungHsieh
UniversityofMilano-Bicocca,Italy ChangGungUniversity,Taiwan
[email protected] [email protected]
XII ListofContributors
MeichunHsu TomasKucˇera
HPLabs,USA CharlesUniversity,CzechRepublic
[email protected]
Jiann-DerLee
GongzhuHu ChangGungUniversity,Taiwan
CentralMichiganUniversity,USA [email protected]
[email protected]
RogerLee
Chung-HsianHuang CentralMichiganUniversity,USA
ChangGungUniversity,Taiwan [email protected]
[email protected]
YujieLi
MasatsuguIchino KyushuInstituteofTechnology
UniversityofElectoro-Communications,
Japan HuiminLu
KyushuInstituteofTechnology
NaohiroIshii [email protected]
AichiInstituteofTechnology,Japan
[email protected] KenichiMatsumoto
NaraInstituteofScienceand
KazunoriIwata Technology,Japan
AichiUniversity,Japan [email protected]
[email protected]
ShinsukeMatsumoto
JiroKatto KobeUniversity,Japan
WasedaUniversity,Japan [email protected]
[email protected]
ShuheiMatsuo
KenjiKawamoto KobeUniversity,Japan
WasedaUniversity,Japan [email protected]
[email protected]
TsuyoshiMiyazaki
YuhkiKitazono KanagawaInstituteofTechnology,
KitakyushuNationalCollegeof Japan
Technology [email protected]
[email protected]
AkitoMonden
JanKofronˇ NaraInstituteofScienceand
CharlesUniversity,CzechRepublic Technology
[email protected] [email protected]
GenkiKoga MasahideNakamura
NomuraResearchInstitute,Ltd., KobeUniversity,Japan
Japan [email protected]
