meeting of the minds 2012

C A R N E G I E M E L L O N U N I V E R S I T Y I N Q A T A R

Meetingofthe Minds

Meeting of the minds is an annual symposium at Carnegie Mellon

University that gives students an opportunity to present their research

and project work to a wide audience of faculty, fellow students, family

members, industry representatives and the larger community. Students

use posters, videos and other visual aides to present their work in a

manner that can be easily understood by both experts and non experts.

Through this experience, students learn how to brindege the gap between

conducting research and presenting it to a wider audience. A review

committee consisting of industry experts and faculty members from other

universities will review the presentations and choose the best projects and

posters. Awards and certificates are presented to the winners.

Table of Contents

POSTER # TITLE PAGE

Business Administration Poster Q1 Islamic Finance Meets Wall Street 1

Computer Science Posters Q2 Building a Virtual Computer from the Ground Up 3

Q3 DevelopingScenariosforaQatar-specificRoadSafetySimulator 5

Q4 EvaluationoftheAbilityofaRobottoEmbodyDifferentCulturalTraits 7

Q5 EvaluationofVariationsinGivingDirectionsAcrossCultures 9

Q6 Image Processing on the Cloud: Characterizing Edge Detection on Biomedical Images 11

Q7 MalwareInc.–FacebookandGoogleAppEngine 13

Q8 MalwareInc-WebBrowsers 15

Q9 Multi-RobotSimulation 17

Q10 ProjectingNamedEntityBoundariesfromEnglishtoArabic 19

Q11 SCOUT:ExtendingtheReachofSocial-BasedContext-AwareUbiquitousSystem 21

Information Systems Posters Q12 A3 (A-Cubed) 23

Q13 EZIntern:InternshipTrackingSystem 25

Q14 Lost&Found 27

Q15 MoltaQatartans:TartansForumSystem 29

Q16 UsingMobileTechnologyforEnhancingYoungQatariHealthBehavior 31

Humanities and Social Sciences Q17 Gettin’theFlow;Makin’GoodGrades 33

Q18 ServiceLearningatCMU-Q:Motivations,Gains,andChallenges 35

Post-Graduate Posters QG1 ChallengesinMobileOpportunisticNetworks 37

QG2 CharacterizationofHadoopMapReduceApplications 39

QG3 CoGRS:ACenter-of-GravityReduceTaskScheduleforMapReduce 41

QG4 GreenLoc:EnergyEfficientWi-Fi-basedIndoorLocalization 43

QG5 Hala2.0:ConsiderationsforDevelopingaTestBedforMulti-Lingual,

Cross-CulturalHumanRobotInteraction 45

QG6 PerformancePredictionofMapReduceApplicationsinElasticComputeClouds 47

QG7 SmartReader:Anaturallanguageprocessing-basedactiveandinteractivesystem

foraccessingEnglishlanguagecontentandadvancedlanguagelearning 49

QG8 VOtus:AFlexibleandScalableMonitoringFrameworkforVirtualizedClusters 51

Islamic Finance Meets Wall Street

AuthorEdmond Abi Saleh (BA 2011)

Faculty AdvisorPatrick Sileo, Ph.D

CategoryBusiness Administration

AbstractThis research explores the feasibility of establishing Islamic banking and finance within the capitalist

Americanfinancialsystem.This isa relevantandpressing topicseeingasMuslimcommunitieshave, for

centuries, slowlyandsurelyestablished themselveswithinAmericansociety. It is thus interesting to look

at theirfinancialparticipation in theAmericanmarketespecially in thewakeof theeconomiccrisis.More

precisely,howtheAmericaneconomyversustraditional Islamicfinancehasfacedthecrisisandhowthis

potentiallyaffectscapitalismintheUnitedStates.Inthatregard,theresearchwillarguethat:Islamicfinance

(throughcanonicaltextsandtheirapplicationwithinMuslimcommunities)isonlypartlyinaccordancewith

theAmericancapitalisteconomicpractices,whichmeansthatitcannotbeimplementedintheUnitedStates

onalargescaleorinapreciseandcorrectmanner(meticulouslyfollowingIslamiclaw).Theresearchwillput

forththisargumentbyfirstexamininghowMuslimcommunitiescametoAmericaandhowwell-established

orinfluentialtheyare.Second,itwillanalyzewhatthesespecificcommunities’religiousvaluessayaboutthe

economicsphereandhowitshouldoperate.Finally,allthisinformationwillbebroughttogethertolookat

presentdayIslamicfinanceintheUnitedStates.Theobjectiveistoassesshowcompatibleitcanbewith

theAmericancapitalistvaluesarguingthatthosetwofinancialmodelsaretoodivergentintheiressencesto

extensivelyco-existatpresent.Inasecondpart,theresearchwillanalyzetheIMXL,whichistheDowJones

Islamicmarketindex.TheanalysiswilltrytolookforsimilaritiesorgapsbetweenIslamicmarketsandregular

markets. Thisquantitative analysiswill reveal that there is little or nodifferencebetween theDowJones

IslamicmarketindexandtheregularDowJonesIndex.Theanalysiswillalsosuggesthypothesesastothe

behavioroftheIslamicindexinthemarket.

1

Building a Virtual Computer from the Ground Up

AuthorsKenrick Fernandes (CS 2014)Jyda Moussa (CS 2014)

Faculty AdvisorKemal Oflazer, Ph.D.

CategoryComputer Science

Abstract:Inthiswork,wepresentourexplorationofunderstandingandbuildingafull-fledgedcomputersystemfrom

scratch–fromthegatelevelallthewaytothesoftwarelevels.Weimplementedthiscomputerusingthetools

containedintheElementsofComputingSystemsframework.Forthisindependentstudy,weweremotivated

toobtainabetterunderstandingofhowamoderncomputerfunctions,fromthehardwarelevelrightupto

theinterfacesthatusersworkwith.Buildingavirtualcomputerfromthegatelevelrequiredustobuildthe

individualgatecomponents,followedbyanArithmeticLogicUnit(ALU)andfinallyaCentralProcessingUnit

(CPU).Wethenimplementedanassemblertotranslateassemblylanguageprogramstomachinelanguage

thatwouldrunonthecomputerwebuilt.Ourposteraimstodescribeourexperienceinthis independent

studyaswellastheindividualprojectswehaveimplementedtodate.

3

Developing Scenarios for a Qatar-specific Road Safety Simulator

AuthorRaggi al Hammouri (CS 2012)

Faculty AdvisorBrett Browning, Ph.D.


Abstract:Driving simulators are increasingly being utilized as training devices to complement traditional in-vehicledriver training.Byallowingscenario-basedcoachingand tailoring the instructivecontent to thestudent’slearningneeds,drivingsimulatorscanplayasignificantroletowardsimprovingroadsafety.

WilliamsTechnologyCentreinQatarhasstartedaprojecttodesignandbuildacustomizeddrivingsimulationplatformfortheadvancedtrainingofdriversinQatar.Thisworkfocusesonthecreationoftheprogramsthatspecifythescenario,andcontrolthebehaviorofthesimulatedvehiclesintheWilliams’drivingsimulator.WeaimtodeveloparichenvironmentwheresubjectdriversencounterrealisticmodelingofdrivingbehaviorasfoundinQatar,andcontrolledpresentationofeventsandsituations.

WehavecreatedaroadandanumberoftrafficmodelsforDoha,aswellasspecifictrainingscenarios.WedescribethearchitectureofthescenariosubsystemofWilliams’drivingsimulator,focusingontherepresen-tationofthevirtualworldandthecontrolofvehiclebehavior.Wepresentsomeexamplescenariosandshowtheresultsofthesescenariosinpractice.

5

Evaluation of the Ability of a Robot to Embody Different Cultural Traits

AuthorAmna AlZeyara (CS 2014)

Faculty AdvisorsMajd F. Sakr, Ph.D.Micheline Ziadee

Abstract:Ourfocusisthedevelopmentofeffectivemulti-lingual,cross-culturalHuman-Robot-Interaction.Inthiswork,

weattempttounderstandthedifferentvisualaccentsinArabicandAmericanfacialexpressionsandcreate

culture-specificfacialexpressionsforafemalemulti-lingual,cross-culturalrobottestbed.

Ourworkistwofold:theidentificationoftheexistenceofaccentvariationinfacialexpressionsacrosscul-

turesandthevalidationofhumanrecognitionofthesesaccents.Facialexpressionsareembodiedinculture

andarecrucialforeffectivecommunication;hencetheyplayanimportantroleinmulti-lingualcross-cultural

Human-Robot-Interaction.ElfenbeinandAmbadyfoundthattherearedifferentaccentsinfacialexpressions

whichareculture-specificand that thedifferences inexpressionsbetweenculturescancreatemisunder-

standings.SeveralstudiescomparedAmericanexpressionswithexpressionsfromotherculturesbutnoneof

themincludedArabicfacialexpressions.

Inthiswork,weutilizetwosetsoffacialexpressions;onespecifictotheArabiccultureandtheotherspe-

cifictotheAmericanculture.WebasetheAmericanexpressionsonsamplesfromMMIFacialExpression

Database,aweb-baseddatabaseforfacialexpressionanalysis.However,nosuchdatabaseexistsforArabic

facialexpressions.Consequently,werecordedvideosofyoungArabwomennarratingstoriesthatexpress

sixemotions:happiness,sadness,surprise, fear,disgust,anddisappointment.Thesevideosareanalyzed

to extract Arabic accents in facial expressions. Accents frombothArab andAmerican cultures are then

implementedona3Dmodel.Wevalidatetheexistenceoftheseaccentsbyinvestigatingwhetherhumans

candetectdifferencesbetweenArabicandAmericanfacialexpressions.Ourinitialobservationshaveshown

differencesinangerexpressions.Arabicexpressionofangerischaracterizedbyraisedeyebrowswhilethe

Americanexpressionofanger,asdescribedbyPaulEkman,ishighlightedbylowered,drawn-togethereye-

brows.Ournextstepwillinvolvecreatingculture-specificlinguisticcontentandpairingitwithculture-specific

expressions.

7

Evaluation of Variations in Giving Directions Across Cultures

AuthorsHuda Gedawy (CS 2012)Micheline Ziadee (Research Assistant)

AdvisorMajd F. Sakr, Ph.D.


AbstractThisworkexploresthedifferencesindirection-givingstrategiesbetweentwogroups,nativeArabicandnativeEnglishspeakers.Thisstudywillhelpinfluencedesigndecisionsformulti-lingual,cross-culturalhumanrobotinteractions.

Thereareclearculturalinfluencesonmodesofcommunication.Previousresearchstudiesfoundthatdirection-givingtechniquesandstrategiesvarybetweendifferentculturalgroups.BurhanudeencomparedJapaneseandEnglishnativespeakersandfoundthatlocatorremarksaremorefrequentlyusedbyJapanesenatives,whiletheuseofdirectivesismorecommonwithnativeEnglishspeakers.

Inthiswork,weexaminethediscoursefornavigationinstructionsofmembersoftwotargetgroups,ArabicnativespeakersandEnglishnativespeakers.Weaddressthefollowingquestions:Howdoeslanguageandstrategiesusedforprovidingdirectionsvarybetweenthesetwogroups?Whatarethedifferencesandwhatarethesimilarities?Arethereanypossiblegender-relateddifferencesingivingdirections?

Werecorded56participantsgivingoraldirectioninstructionsfor3specificlocationsintheCarnegieMellonQatarcampus.Wetranscribedtheoralrecordingsandannotatedthetextsbycategorizingthedirectionalexpressions into: landmarks, units (time and distance), cardinals, movement commands, conditionalmovement commands, advisory commands and positional commands. Non-directional commands, likeintermediatedetails,werecategorizedasfillers.Ouranalysisalsoincludedthenumberofpauses,repetitionanderrorcorrection.

Ourresultsshowedthatwhengivingdirections,EnglishnativespeakersuseaconsiderablyhighernumberofwordsthanArabicnativespeakers.Also,thedifferenceinnumberofwordsbetweenmalesandfemalesinthefirstgroupismuchhigherthanthatinthesecondgroup.Englishnativespeakershadahigherfrequencyofusingcardinals,intermediateinformation,andfilledpauses. Ontheotherhand,ArabicnativespeakersdifferedgreatlyfromEnglishnativespeakersintheiruseofunitsofdistanceandrepeatedinformation.Basedontheseresults,weconcludethatculture,language,andgender

influenceaspeakersdiscourseandstrategyforgivingdirections.

9

Image Processing on the Cloud: Characterizing Edge Detection on Biomedical Images

AuthorManoj Reddy (CS 2013)

Faculty AdvisorsMohammad HammoudMajd F. Sakr, Ph.D.


AbstractInordertoanalyzeanddeducevaluableinformationfrombigimagedata,wehavedevelopedaframeworkfordistributedlarge-scaleimageprocessinginHadoopMapReduce.Avastamountofscientificdataisnowrepresentedintheformofimagesfromsourcesincludingcameras,medicaltomographyandastronomicalremotesensing.Applyingalgorithmsontheseimageshasbeencontinuallylimitedbytheprocessingcapacityofasinglemachine.MapReducecreatedbyGooglepresentsapotential solution.MapReduceefficientlyparallelizescomputationbydistributingtasksanddataacrossmultiplemachines.Hadoop,anopensourceimplementationofMapReduce,isgainingawidespreadpopularityduetofeaturessuchasscalability,faulttoleranceandability tousecommodityclusters.Hadoop isprimarilyusedwith text-based inputdata. Incontrast,itsabilitytoprocessimageshasnotbeenfullyexplored.WeproposeaframeworkthatefficientlyenablesimageprocessingonHadoop.

Existingapproachesindistributedimageprocessingsufferfromtwomainproblems:(1)inputimagesneedtobeconvertedtoacustomfileformatand(2)imageprocessingalgorithmsrequireadherencetoaspecificAPI thatmight imposesome restrictionsonapplyingsomealgorithms toHadoop.Our frameworkavoidstheseproblemsby:(1)bundlingallsmallimagesintoonelargefilethatcanbeseamlesslyparsedbyHadoopand(2)relaxinganyrestrictionbyallowingadirectportingofanyimageprocessingalgorithmtoHadoop.AReduce-lessjobisthenlaunchedwherethecodeforprocessingimagesandamechanismtowritethembackseparatedtoHDFSare includedinMappers.Wehavetestedtheframeworkwithastate-of-the-art imageprocessingalgorithm,EdgeDetection,ona largedataset,3760,ofbiomedical images.WeobservedthatusingHadoopSequenceFilestobundlealargenumberofsmallimagesintoalargesequencefilemightnotbethemostefficientsolution,asweobservedastorageoverheadofupto3x.Furthermore,andtoexamineHadoop’sbehaviorwithimageprocessing,wearecharacterizingEdgeDetectionalongseveraldimensions,suchasdegreeofparallelismandnetworktrafficpatterns.Ourcharacterizationstudyhasshownthatvaryingthenumberofmap taskshasasignificant impactonHadoop’sperformance.Thebestperformancewasobtainedwhenthenumberofmaptasksequals thenumberofavailableslots,as longas theapplicationresource demand is satisfied. Additionally, we observed a speedup of 2.1X as compared to the defaultHadoopconfiguration.

11

Malware Inc. – Facebook and Google AppEngine

AuthorsTalal Al-Haddad (CS 2013)Manoj Reddy (CS 2013)

Faculty AdvisorThierry Sans, Ph.D.


AbstractAnewgenerationofsoftwarehasemergedwiththeincreasingpopularityofwebandcloud-basedapplications.

CloudcomputingplatformssuchasFacebookandGoogleAppEngineallowthirdpartydeveloperstobuild

applications forusers that runona remote infrastructure.Thesenewplatformsgive rise tonewsecurity

threatsandanewgenerationofmalware.Malware,short formalicioussoftware, issoftwaredesigned to

disruptcomputeroperation,gathersensitiveinformation,orgainunauthorizedaccesstocomputersystems.

Inthiswork,weaimtoevaluatethesecurityriskofexposuretomalwareonthetwopopularcloudapplication

ecosystems:FacebookandGoogleAppEngine.

Google AppEngine applications are hosted on Google infrastructure and can access Google services and also

user’spersonaldatasuchGmailandGoogleCalendar.Asaproofofconcept,wehavedevelopedamalware

thatpretendstodousefulstatisticsontheuser’smailboxbutinfact,thisapplicationsearchesthroughemails

forlogins,passwordsandcreditcardinformation.Whenfound,thesedataaresenttoaremotewebserver

controlledbytheattacker.Thisoperationcannotbedetectedbytheuser.

Facebookallowsdeveloperstowriteapplicationsthatcanperformawidevarietyoftaskssuchascollaborative

gaming and access user information for social statistics.Contrary toGoogle, these applications are not

storedonFacebookserversbutonserverscontrolledbythedeveloper.Asaconsequence,whenusersuse

suchapplications, theirprofiledatacanbestolenandsent toaremotewebserversimilar to thecaseof

GoogleAppEngine.

Themalwareapplicationsdemonstratethat it iseasytodeploymaliciousapplicationsoncloudplatforms

andthisisahugecauseforconcernsinceeverincreasingamountofsensitivedataisstoredonline.Future

workincludesdevelopingtechniquesforautomatedprofilingofapplicationstosensemaliciousactivityina

sandboxedenvironment.

13

Malware Inc - Web Browsers

AuthorsBaljit Singh (CS 2014) Fahim Dalvi (CS 2014)

Faculty AdvisorThierry Sans, Ph.D.


AbstractAnextensionisanapplicationthatenhancesthefunctionalityofabrowser.Thedifferenttypesofextensions

available today range from includingacalculator inabrowser tostoringpasswords in thebrowser inan

encrypted form.Thegoal of theMalware Incprojectwas toevaluate the riskof exposure tomalware in

popularbrowserssuchasGoogleChromeandMozillaFirefox.AMalwareisanysoftwarethatmaliciously

triestodisruptcomputeroperations,stealsensitiveinformationandsoon.Wewereabletocreateseveral

malwareintheformofextensions,twoofwhicharedescribedbelow.

InGoogleChrome,wecreatedanextensionthatcouldrecordthepasswordsofuserswhentheytrytologin

usinge-bankingmode.Whentheuserentersdatausinganon-screenkeyboard,ascreenshotofthekeyboard

wascapturedwithemphasisonthebuttonthatwaspressed.Usingtheseriesofscreenshotsreceived,the

developercouldthendeducethedataenteredeasily.

OnMozillaFirefox,wewereabletocreateanextensionthatcouldremotelydownloadandexecuteexternal

programs(whichloggedallthedatathatyoutypedonyourkeyboard),andthenreadfiles(thelogs)fromthe

user’ssystemandtransmitthembacktothedeveloperwithoutanyextrapermissions.

Theantivirusisunabletotrackthiscommunication,asitisentirelythroughthebrowser,oneofthetrusted

softwareonthemachine.Fromtheinformationwehavegatheredthroughthisproject,ouraimistocreate

newtechnologiesthatcandetectandstopmaliciousextensions.Oneofourideasistohaveanextension

thatcankeeptrackofallthecommunicationinitiatedbyotherextensions,essentiallycreatingan“antivirus”

forthebrowser.

15

Multi-Robot Simulation

AuthorsSidra Alam (CS 2013)Hanan Alshikhabobakr (CS 2013) Shailja Relwani (CS 2014)

Faculty AdvisorsM. Bernardine Dias, Ph.D. Brett Browning, Ph.D.


AbstractMulti-robot research can be very challenging due to the difficulty of operating several complex vehicles

simultaneously. Simulation can be a powerful tool to speed up software development if the simulator is

sufficientlyaccurate.Theprojectbuildsfromanexistingrobotsimulationtool–theUrbanSearchandRescue

Simulator(USARSim).ThefocusoftheresearchwastocustomizeandenhancetheUSARSIMmulti-robot

environment to enablehighconfidenceof capturing thecomplexitiesof real robot execution, specifically

for the robots in theQri8 robotics lab atCarnegieMellonUniversity inQatar. In terms of an application

domain, our focuswas on disaster response scenarios. The primary task for the robots in this scenario

wasexploration.For the simulation testing,wecreatedmaps for thedisaster response scenario andwe

addedphysicalconfigurationmodelsfortherobots.WeusedgenericrobotcontrolsoftwarecalledPlayerto

controltherobotsinthesimulator.Tovalidatetheexperiment,weranatestwithrealrobotsandanidentical

simulationtestandcomparedtheexecutedbehaviors.

17

Projecting Named Entity Boundaries from English to Arabic

AuthorNehal Elkady Hussein (CS 2013)

Faculty AdvisorBehrang Mohit, Ph.D.


Abstract: NamedEntityRecognition(NER)istheprocessofidentifyingspansoftextthatconstitutepropernamesandthenclassifyingthemaccordingtotheirtype(e.g.namesofpersons,locations,organizations,etc.).Englishisoneofthefewlanguageswhichhavedecentamountsofhuman-annotateddatafortrainingNERsystems.Incontrast,languageslikeArabicdonothaveenoughannotateddataandrobustNERsystems.ToovercomethisresourceshortageofArabic,weimplementedsoftwarethatautomaticallytagsArabictextwithnamedentityinformationusingacross-lingualprojection.Namedentityprojectionisaframeworktousewordalign-mentstotransferthenamedentityknowledgefromaresourcerichlanguage(e.g.English)toaresource-poorlanguage(e.g.Arabic).

Aword-alignedparallelcorpuscontainssentencesinEnglish,theirtranslationinArabic,andtheword-levelalignmentinformationbetweentheparallelsentences.OurnamedentityprojectionsoftwareusesthewordalignmentstofindtheproximityofthenamedentitiesontheArabicsentence.OutputoftheprojectionsystemistheannotationofthenamedentitiesontheArabicsideoftheparallelcorpus.Majorchallengesinourpro-jectioncomefromthedivergenceofthelanguagesandthenoiseinthewordalignments.Also,sinceArabicwordscouldbealignedtomorethanoneEnglishword,andviceversa,weobservecaseswhereoneArabicwordgetsmappedtotwoEnglishentities.

Weintroducedseveralheuristicsinourprojectionalgorithm.Twoimportantonesare:(1)Welabeledanamedentityifandonlyifithasagapoftwowordsorless,andthewordsformingthegaparenotalignedtootherwordswithinthesamesentence; (2) Ifanyoftheelementsconstitutinganamedentitywererepeatedweremovethemandthenlabelthenamedentity.

Theevaluationofourprojectionsystemwasbasedoncomparingitsoutputwiththegoldstandardanswersprovidedbyhumanannotation.Todothat,weselectedthirtyrandomArabicsentencesfromtheparallelcor-pusandannotatedthemwiththegoldstandardnamedentityboundaries.Wethencomparedtheresultsofourprojectionoutputagainstthisgold-standarddata.Thedisagreement(error)betweentheprojectionoutputandthegold-standarddatawasabout6.3%.Moreover,weusedthestandardprecisionandrecallmetricstoevaluatetheprojection.Precisionmeasurestheaccuracyoftheprocedureandrecallmeasuresthecoverageofit.Forourthirtysentencescorpus,weachievedaprecisionof81.4%andarecallof76%whicharepromis-ingbaselinesforcreatingnewnamedentityresourcesforArabic.Furthermore,weusedourprojecteddatatoenhanceabaselinenamedentitysystemandachievedasignificant4%averageimprovement.

19

SCOUT: Extending the Reach of Social-Based Context-Aware Ubiquitous System

AuthorDania Abed Rabbou (CS 2012)

Faculty AdvisorsAbderrahmen Mtibaa, Ph.D. Khaled Harras, Ph.D.


AbstractTheproliferationofsocial-networks,localizationsystems,andhigh-endmobiledeviceshascreatedafertile

groundforthedevelopmentofsystemsthatareawareofinterestsandadaptivetolocation.Withtheburgeon-

ingofthedomainofsocial-basedcontext-awaresystems,numerouschallengesarebecomingofincreased

importance.Onesuchchallenge,notaddressedsofarbytheresearchcommunity,isend-to-endcommuni-

cationbetweenubiquitoussystemsandtheirusers.Communicationinexistingsystemsiseithercentralized

ordistributed.Centralizedsystemsrequireuserstobeconnectedtoaserveratalltimesandthusassume

theavailabilityofinternetconnectivityeverywhere.Inreality,internetconnectivitymaybeabsent,charged,

energy-consuming,heterogeneous,andover-loaded.Asanalternative,distributedcommunicationenables

userstoobtaininformationfromneighboringdevicesbuttheavailabilityofinformationandtheextentofits

disseminationaredictatedsolelybyusermobilityandcontacts.

Werealize theneedforanewhybridmodethat leveragesthecentralizedanddistributedcommunication

modes.Usingthehybridmode,aubiquitoussystemdisseminatesamessagetointerestedconnectedand

unconnectedusersbyfirstselectingasubsetofconnecteduserssufficienttoreachtheunconnectedones

andsecondlybyforwardingthemessagefromtheseuserstoneighboringusersmostlikelytomeettheuncon-

nectedones.Weimplementedthishybridcommunicationmodeonasocial-basedcontext-awareubiquitous

system,SCOUT,whichwebuiltasatestingplatform.Weplantoevaluateourproposedsolutionagainstthe

existingarchitecturesbasedonthreemetricsimportanttotheperformanceofubiquitoussystems,namely:

(i) success rate or the ratio of the number of users reached to the total number of interested users, (ii) end-to-

enddelayortheaveragetimedelayincurredtoreachallinterestedunconnectedusers,and(iii)costortotal

numberofmessageforwardsrequiredtoreachalltheinterestedunconnectedusers.

21

A3 (A-Cubed)

AuthorsLakshmi Prakash (CS 2015)Sabih Bin Wasi (CS 2015)Tamim Jabban (CS 2015)

Faculty AdvisorSelma Limam Mansar, Ph.D.

CategoryInformation Systems

AbstractAstheyentertheworldofundergraduateeducation,studentsallaroundtheglobediscoveranewworld.

Universitiesusevariousmodelstoadvisethemtomakeinformeddecisionsabouttheireducation.Indeed,

atmultiplestagesduringtheiracademiclife,studentsencounteroneormoreofthefollowingproblemsin

choosing courses:

1. Itisnotalwaysobvioustoanticipatetheimpactofachangetoastudyplan,suchaspostponing,drop-

pingorchanginga‘corecourse’–especiallyinanacademicsystemwheremostcoursesareinterde-

pendent.

2. Ifvisionaroundplanningisobstructed,studentsfinditriskytoswitchmajorsinbetweentheiracademic

career.

3. Whenitcomestochoosingcourses,themerecoursedescriptionorcoursetitlemaynotbeenoughto

makegoodchoices.

A3wasinitiatedasanindependentstudyprojectinInformationSystem,andattemptstosolvesucha

demandingproblem.Basedonpatternsformajordegreeprograms,A3advisesastudentwithaplanthatis

well-fittedforastudentpursuingacareerwithvaryinginterests.Additionally,A3makesiteasiertochoose

freeelectives.A3letsusersvisualizetheirfutureundergraduateacademicplans.Itenablesstudentsto

maximizesatisfactionfromtheirundergraduatedegree,makingitmoremeaningfulfortheirprofessionallife.

Withanextensivedatabaseofcoursesofferedoncampus,A3attemptstoprovidecomprehensiveinforma-

tionabouteachcoursethatwouldaidstudentstopicktherightcoursesbeforehand.Italsoaimstobring

sociallifetotheapplicationbyintegratingpeersuggestion,rankingandadvisingfeaturesforeachcourseor

anentirecustomizedcourseplan.

A3currentlycanbeusedtoitsfullpotentialforCMU-Qstudentswhohavebeenofferedachoiceof5

majorsandvariousminors.Inthelongerrun,A¬3aimstoactasamuch-neededadvisingtoolforstudents

atanystage.Iftheycanvisualize,bendandenhancetheircourseplanwiselyandtimely,itcanunarguably

benefittheirfutureacademicandprofessionallife.

23

EZ Intern: Internship Tracking System

AuthorsHissa Al-Bahr (IS 2013)Reham Al-Tamime (IS 2013) Nabeeha Haque (IS 2013) Abhay Valiyaveettil (IS 2013)

Faculty AdvosorIan Lacey, Ph.D.


AbstractThemainobjectiveofthisresearchistodesignasystemfortheOfficeofProfessionalDevelopmentinorder

toprovidestudentswithanattractiveanduserfriendlywebsitethatcontainsalltheopportunitiesforintern-

shipsandjobsavailablelocallyforCMUQstudentsandtoallowthemtoapplyonline.

Students at CMUQ receive several important and interesting emails from time to time about job/internship

opportunitiesfromtheOfficeofProfessionalDevelopmentatCMUQ.Eachofthoseemailshasadifferent

subjectandisaimedatdifferentgroupsofstudents.SometimescompaniesemailtheOfficeofProfessional

DevelopmentofCMUQtoinformstudentsaboutaspecificopportunityandthentheOfficeofProfessional

DevelopmentofCMUQforwardsittostudents.

Thebestpartaboutthissystemisthatitenablesthestudentstoapplyonlineandcanstoretheirrésumé

onlinesothattheydonothavetouploaditeachtimetheyapply.Thisideacameaboutwhenthegroup

memberswerethinkingofasystemthatcouldbeimplementedatCMU-Qandthemembersreceiveda

seriesofe-mailsregardinginternships.Thisinspiredtheteamtodevelopasystemthatcouldeliminatethe

unnecessarystackofe-mails.ThegroupgotintouchwiththeOfficeofProfessionalDevelopmentinorder

toconductsomemoreresearchandtounderstandtheprocessmoreclosely.Thisprojectisreallyimportant

sinceitcanresolveapressingissuethatCMU-Qfacesatthemoment.Withinternshipopportunitiesonthe

rise,wereallywanttohaveanorganizedsystemforstudentstoapply.

25

Lost & Found

AuthorsShivani Arora (BA 2013)Nur Aysha Anggraini (IS 2013)Fatema Akbar (IS 2013)Maryam Yousuf (IS 2013)

Faculty AdvisorIan Lacey, Ph.D.


AbstractEducationCityisacommunitywithstudents,faculty,staff,andvisitorsacrosssixbranchcampuses.These

membersbelongingtodifferentcampusesinteractwitheachotheronadailybasisformeetings,cross-regis-

teredcourses.Withsomuchmovingaround,often,thesecommunitymemberstendtoloseitemswithintheir

buildingorothers.Currently,searchingfortheirlostitemsisaverytediousandtiresomeprocess–a“loser”

hastophysicallyapproachthesecuritydesksatoneormorebuildingsandaskthemaboutthelostitem.And

iftheitemisavailable,the“loser”hastofilloutseveralformstoclaimtheitem.If,ontheotherhand,theitem

hasnotbeenfoundbythesecurityguards,the“loser”mayhavetoapproachthesecuritydeskseveraltimes

againlatertofindtheiritem.

TheLost&Foundsystemautomatesthispaper-based,time-consumingprocess,makingitsimpleandconve-

nientfortheECcommunitymembers.Itservesasanonlineweb-application,whereECmemberscancheck

iftheirlostitemhasbeenfoundbyanysecuritydesksinanyoftheECbuildings,andiftheitemhasnotbeen

found,theycanreporttheirlostitem.Iftheiritemisfoundinthefuture,theywillbenotified.TheLost&Found

systemultimatelyconnectsthetwosidesofthelostandfoundprocessinanefficient,user-friendlymanner.

27

MoltaQatartans: Tartans Forum System

AuthorsWadha Alajmy (IS 2013) Mariem Fekih (IS 2013) Tasneem Jahan (IS 2013) Abhinav Vemuri (IS 2013)

Faculty AdvisorIan Lacey, Ph.D.


AbstractTheposterisaboutasystemtheteamisworkingonaspartoftheJuniorProjectISclass.Theteamwaslookingforawaytolinkalumniwithcurrentstudents.AftercontactingKhadraDualehfromtheprofessionaldevelopment office and Feras Villanueva from themarketing department, the team concluded that theredoesn’texitaCMUQwebsite that joinsallmembersof thecommunity together.Since there isalreadyawebsitefortheAlumniAssociationinPittsburgh,theteamrealizedthatitwouldbeinterestingiftheQatarcampushadasimilarsystemforstudentsandalumniwhostudiedinQatar. AmongthewaysthatgraduatesstayconnectedtoCMUQare:(1)ACMUQemail,wherealumnisendemailstoKhadraorprofessors inorder tostay informedabout theupcomingevents, talks,andopportunities togiveback.(2)SocialnetworkingwebsiteslikeFacebookorTwitter,wherealumnicancommunicatewitheachotherandcurrentstudents.ThroughthesesocialwebsitestheycanstayupdatedabouttheirfriendsandalsocansharetheirexperiencesaftergraduationwithstudentsatCMUQ.

However,thesemeansremaininefficientandrepresentseveralproblems:

• ThealumnimayhavedisabledtheirCMUQemailsornolongerusethemandcheckthem. • Thealumnidon’treceiveorreplytoanyemailcomingfromCMUQorfromtheprofessional

developmentoffice.

The“TartansForumSystem”isawebsitethatwillbringinthedifferentpartsoftheCMUQcommunitytothesamepage.Itwillhelpto:

• Enhancethebondsbetweenalumniandcurrentstudentsbyenablingthemtoexchangeideas,thoughts,advice,andevenjobopportunities.

• EnablethealumnitostayintouchwithCMUQfacultyandstaff.

• KeeptheAlumniupdatedaboutupcomingeventsoncampusorevenorganizethemandinvitepeopletoattend.

29

Using Mobile Technology for Enhancing Young Qatari Health Behavior: An Experiment Design

AuthorsAysha Anggraini (IS 2013) Nawal Behih (IS 2014)Maahd Shahzad (IS 2014)

Faculty AdvisorSelma Limam Mansar, Ph.D.


AbstractThis project introduces the design of an experiment that tests the effectiveness of mobile applications

foraweight lossprogram.Amobileapplicationwasdeveloped,combiningthreemodules:a tailoredtext

messagingservice,goalsettingandprogressmonitoringsupport,andasocialnetworksetup.Theintentof

theprojectistotestitseffectivenessinachievingweightmanagementgoals.Anelementoflocalizationis

addedtotheapplicationdesign,addressingtheneedsofQatar’spopulation.Whenthemobileapplication

developmentiscompletedaninterventionstudyisperformedwithparticipantsbecausetheapplicationhas

tobetested.Thisprojectdescribesausabilitystudydesign:verbalfeedbackiscollectedfromparticipants

ofthelocalcommunitywhoareinteractingwiththetechnology.Eachparticipantisaskedtocompletealist

oftasks,structuredaccordingtothethreeinterfaces.Thefeedbackallowsthedeveloperstoenhancethe

mobileapplication.ThisMoMsubmissionsummarizesprogressonanawardedUREPentitled“UsingMobile

TechnologyforEnhancingYoungQatariHealthBehavior”.

Ourcontributionhassofarbeenondevelopingsomepartsoftheapplicationanddesigningtheusabilitytesting

experiment.Wemeetweeklyasaresearchteamanddiscussprogressaccordingtoatimeline.WeusetheIS

labresources(aserver,Androidphones,Androiddevelopertools,andvoicerecorderstorecordparticipants’

feedback).ThisresearchprojectisimportantbecauseitisinlinewithQatar’sgoalstoreduceobesityand

relatedhealthproblems.Itshowsushowmoderntechnologycanbeusedtosolvehealthproblems.

31

Gettin’ the Flow; Makin’ Good Grades

AuthorsMaha Al-Moghany (CS 2012)Sara Kawas

Faculty AdvisorDavid Emmanuel Gray, Ph.D.

CategoryHumanities and Social Sciences

AbstractCsikszentmihalyi’sflowtheorydescribesastateofmindinwhichapersonbecomesfully“immersed”andengaged inanactivity.According toCsikszentmihalyi, apersoncanachieveflowevenwhendoingwhatmightbethoughtthedullestofactivities,aslongasthatperson’sskillsareonparwiththechallengeoftheactivity.Inourstudy,weexplorethisphenomenoninsidetheclassroom,determiningtheextenttowhichflowinfluencesstudentperformanceandtheirperceivedlearning.Somepreviousstudieshavefocusedonflowinlearningenvironments,butourstudyfocusesonasingleuniversity-levelcourseoveranentiresemester,andcomparesthebalanceofstudentskillsandcourseworkchallengetogrades.

StudentswhotookajointCMU-Q/NU-Qcourseinlogicalreasoning(cross-listedas80-206atCMU-QandPHIL242-70atNU-Q)weresurveyedthroughoutthesemesteraftereachquizaboutthequiz’sperceivedchallengeandabouttheirownperceivedlevelofskillinsolvingthatquiz’sproblems.Whenweanalyzedtheresults,weclusteredthestudentsintofourgroups,usingamethodproposedbyMassiminiandCarli.First,apatheticstudentsarethosewhoreportlowerthanaverageonbothchallengeandskillforaquiz.Second,anxiousstudentsarethosewhoreporthigherthanaveragechallengebut lowerthanaverageskill.Third,boredstudentsarethosewhoreportlowerthanaveragechallengebuthigherthanaverageskill.Fourthandfinally,studentsinflowarethosewhoreporthigherthanaverageonbothchallengeandskill.Attheendofthesemester,studentswereaskedtocomparetheirlogicabilitiesatthebeginningofthesemesterwithwheretheybelievedtheirabilitieswerecurrently.Wethenusedthistocalculatetheirperceivedlearningoverthesemester.

Fromouranalysis,wefoundthatstudentsinflowscoredahigheraveragegradeonquizzesthanboredorapatheticstudents.Yet,anxiousstudentsdidscoreahigheraveragegradethanstudentsinflow.However,studentsinflowreportedthehighestperceivedlearningdistanceamongallfourgroupsofstudents.Ourre-sultsindicateadifferencebetweengradesandperceivedlearningamongthefourgroups,whichfuturestud-iesoughttoexploreinordertodiscoveramoredetailedcausalrelationshipamongthesevariables.Further-more,ourresultsmayencouragefurtherworkintodiscoveringtheextenttowhichinstructorscanencourageflowintheirstudents,andwhetherthismightleadstudentstogethighergrades.

33

Service Learning at CMU-Q: Motivations, Gains, and Challenges

AuthorsShivani Arora (BA 2013)Firas Bata (BA 2013)

Faculty AdvisorSilvia Pessoa, Ph.D.

CategoryHumanities and Social Sciences

AbstractThisstudyexaminesthemotivation,gains,andchallengesexperiencedby27undergraduatestudentsat

CarnegieMellonUniversityinQatar(CMU-Q)whoparticipatedasvolunteerteachersinanadultliteracy

programformigrantworkersinQatar.Atotalof27studentreflectionswereanalyzedforrecurrentthemes,

22studentscompletedasurveyabouttheirparticipationintheprogram,andsevenstudentswereperson-

allyinterviewed.

Thefindingsindicatethatinitially,studentsinhighereducationneedincentivessuchasobtainingacademic

credittoenrollincommunityserviceprograms.Inadditiontoacademiccredit,duringtheirexperience,the

participatingstudentsgainedseveraltangibleandintangiblebenefitsincludingdevelopingseveralnew

skillssuchascommunicationandteachingskills,andanappreciationforthedifferencesinthecommunities

surroundingthem.AlthoughtheinitialmotivationforthestudentstoparticipateinLanguageBridgesmay

havebeenacademiccredit,thisinitialexperiencemaylaterleadtostudentsparticipatinginsimilarpro-

gramsinthefuturefortrulyhumanitarianandaltruistreasons.Basedonthesefindings,wediscussrecom-

mendationsforcurricularandmetacurricularpracticesatCMU-Q.

35

Challenges in Mobile Opportunistic Networks

AuthorAbderrahmen Mtibaa

Faculty AdvisorKhaled Harras, Ph.D.

CategoryPostgraduate

AbstractMobile devices such as smart-phones and tablets are becoming ubiquitous, with ever increasingcommunication capabilities. In situations where the necessary infrastructure is unavailable, costly, oroverloaded, opportunistically connecting these devices becomes a challenging area of research. Data isdisseminatedusingnodes that store-carry-and-forwardmessagesacross thenetwork. In suchnetworks,nodecooperationisfundamentalforthemessagedeliveryprocess.Therefore,thelackofnodecooperation(e.g.,anodemayrefusetoactasarelayandsettleforsendingandreceivingitsowndata)causesconsiderabledegradation in the network. In order to ensure node cooperation in such networks,we investigate threemain challenges: (i) ensuring fair resource utilization among participating mobile devices, (ii) enabling trustful communicationbetweenusers,and(iii)guaranteeingscalablesolutionsforlargenumberofdevices.

(i) Fairness isparticularly important formobileopportunisticnetworkssince itactsasamajor incentivefornodecooperation.WeproposeandevaluateFOG-areal-timedistributedframeworkthatensuresefficiency-fairnesstrade-offforusersparticipatingintheopportunisticnetwork.

(ii) Since usersmay not accept to forwardmessages in opportunistic networks without incentives, we

introduceasetoftrust-basedfilterstoprovidetheuserwithanoptionofchoosingtrustworthynodesincoordinationwithpersonalpreferences,locationpriorities,contextualinformation,orencounter-basedkeys.

(iii) Mobile opportunistic solutions should scale to large networks. Our hypothesis is that in large scalenetworks,mobile-to-mobilecommunicationhasitslimitations.WethereforeintroduceCAF,aCommunityAwareForwardingframework,whichcaneasilybeintegratedwithmoststate-of-the-artalgorithms,inordertoimprovetheirperformanceinlarge-scalenetworks.CAFusessocialinformationtobreakdownthenetworkintosub-communities,andforwardmessageswithinandacrosssub-communities.

Inthethreecontributionsweproposeabove,weadoptareal-tracedrivenapproachtostudy,analyze,andvalidate our algorithms and frameworks.Our analysis is based on differentmobility traces including theSanFranciscotaxicabtrace,tracescollectedfromconferencessuchasInfocom\’06andCoNext\’07,andDartmouthcampuswirelessdataset.

37

Characterization of Hadoop MapReduce Applications

AuthorsMohammad HammoudM. Suhail Rehman

Faculty AdvisorMajd F. Sakr, Ph.D.


AbstractHadoopisanopensourceimplementationofMapReduce,oneofthemostsuccessfulrealizationsoflarge-

scaledata-intensivecloudcomputingplatforms.HadoopMapReduce isbecomingaubiquitousprogram-

mingmodelfindingapplicationsonmanyfieldsincludingweb,medicineandastronomy,amongothers.Asa

result,HadoopMapReduceisfacedwithanenormousspaceofapplicationdomainsandsystemparameters

thatmightimpactMapReduceemploymentsandstudies.Assuch,characterizingHadoopbecomescrucial

for:(1)fosteringtheunderstandingoftheMapReducemodelsoastoenablepracticalapplicationsofMapRe-

duceprograms,(2)gleaninginsights intotheframework’sbottlenecksandpotentially identifyingdesirable

optimizations,(3)establishingaquantitativefoundationforevaluatingMapReduceapplications,and(4)sup-

portingresearchandcomparisonsacrossmultiplerelatedstudies.Weproposeandapplyacharacterization

methodologythatservesinmeetingtheseobjectives.

TheonlytwocurrentstudiesoncharacterizingHadoopfocusprimarilyonpromotingbenchmarksuitesand

overlookestablishingamethodologythroughwhichHadoopcanbeeffectivelycharacterized. Incontrast,

wefocusondevelopinganeffectivecharacterizationmethodologyandsuggestusingthatmethodologyata

laterstagetocreateastandardbenchmarksuite.WeutilizetworepresentativeandwidelyadoptedMapRe-

ducebenchmarksandcharacterizeHadoopalongsevendimensions:thedegreeofparallelism,thedataset

distribution,thejobsystemresourceutilization,thejobcommunicationtocomputationratio,theexecution

timelines,thedatasettypesandsizes,andthejobnetworktrafficpattern.Asaresult,weattainedmultiple

observations.Forinstance,werealizedthathavingthenumberofmappersequaltothenumberofavailable

mapslotsleadstothebestparallelism.Thisdoesnotonlyimproveperformance,butalsotacklesoneofthe

rootcausesofthecommonstragglersprobleminMapReduce.Asanotherexample,weobservedthatthe

networkbandwidthdissipationofthereducestageexceedsthatoftheshufflestagebyapproximatelyafac-

toroftwo.Thismightsuggestaddressingthenetworkbandwidthproblematthereducestage,especially

sinceallresearchefforthasbeenputsofarontheshufflestage.

39

CoGRS: A Center-of-Gravity Reduce Task Schedule for MapReduce

AuthorsMohammad HammoudM. Suhail Rehman



Abstract:MapReduceisoneofthemostsuccessfulrealizationsoflarge-scaledata-intensivecloudcomputingplat-

forms.Ascomparedtotraditionalprogrammingmodels,MapReduceautomaticallyandefficientlyparallelizes

computationbyrunningmultipleMapand/orReducetasksoverdistributeddataacrossmultiplemachines.

Hadoop,anopensourceimplementationofMapReduce,schedulesMaptasksinthevicinityoftheirinput

splitstherebyreducingnetworktraffic.However,forreducetasks,Hadoopneitherexploitsdatalocalitynor

addressesdatapartitioningskewinherentinmanyMapReduceapplications.Consequently,MapReduceex-

periencesaperformancepenaltyandnetworkcongestionasobservedinourexperimentalresults.

Inthisstudy,weintroducetheCenter-of-GravityReducetaskScheduler(CoGRS),apracticalstrategyforim-

provingMapReduceperformanceinclouds.CoGRSattemptstoscheduleeachreduceratitscenter-of-grav-

itynode.Itcontrollablyavoidsschedulingskew,asituationwheresomenodesreceivemorereducersthan

others,andpromoteseffectivepseudo-asynchronousMapandReducephasesresultinginearliercompletion

ofsubmittedjobs,diminishednetworktraffic,andbetterclusterutilization.

WeimplementedCoGRSinHadoop-0.20.2andconductedextensiveexperimentationstoevaluateitspoten-

tial.WefoundthatCoGRSoutperformsthenativeHadoopschedulerby11%,andbyupto26%inruntime

performanceforourbenchmarkstudies.Inaddition,wedeployedCoGRSonAmazonEC2andfoundthat

ourstrategyscaleswell,reducingnetworktrafficbyasmuchas38.6%,whichconsequentlytranslatesinto

anruntimeimprovementofupto23.8%.Fromthesestudies,webelievethatCoGRSisapplicabletoseveral

cloud computing environments and applications, including but not limited to, shared environments and sci-

entificapplications.LASARpavesthewayfortheseapplications,andothers,tobeportedtovariousclouds

inaneffectivemanner.

41

GreenLoc: Energy Efficient Wi-Fi-based Indoor Localization

AuthorMohamed Abdellatif

Faculty AdvisorKhaled Harras, Ph.D.


AbstractUser-localizationandpositioningsystemshavebeenacorechallengeinthedomainofcontext-aware

pervasivesystemsandapplications.GPShasbeenthede-factostandardforoutdoorlocalization;however,

geo-satellitesignalsuponwhichGPSrelies,areinaccurateinindoorenvironments.Therefore,variousin-

doorlocalizationtechniquesbasedontriangulation,sceneanalysis,orproximity,havebeenintroduced.The

mostprominenttechnologiesoverwhichthesetechniquesareappliedincludeWiFi,Bluetooth,RFID,Infra-

red,andUWB.Duetotheubiquitousdeploymentofaccesspoints,WiFi-basedlocalizationviatriangulation

hasemergedtobeamongthemostprominentindoorpositioningsolutions.Amajordeploymentobstacle

forsuchsystems,however,isthehighenergyconsumptionratesofWiFiadaptersinmobiledeviceswhere

energyisthemostvaluableresource.

WeproposeGreenLoc,anindoorgreenlocalizationsystemthatexploitssensorsprevalentintoday’ssmart-

phonesinordertodynamicallyadaptthefrequencyoflocationupdatesrequired.Significantenergygains

can,therefore,beacquiredwhenusersarenotmobile.Forexample,accelerometerscanaidindetect-

ingdifferentuserstatessuchaswalking,runningorstopping.Basedonthesestates,mobiledevicescan

dynamicallydecideupontheappropriateupdatefrequency.Weaccommodatevariousmotionspeedsby

estimatingthevelocityofthedeviceusingthelatesttwolocationcoordinates,andthetimeintervalbetween

thesetworecordedlocations.WehavetakenthefirststepstowardsimplementingGreenLoc,basedon

theinfamousEkahausystem.Wehavealsoconductedpreliminarytestsutilizingtheaccelerometer,gravity,

gyroscope,andlightsensorsresidingontheHTCNexusOneandIPhone4smart-phones.

Tofurthersaveenergyintypicalindoorenvironments,suchasmalls,schools,andairports,GreenLoc

exploitspeople’sproximitywhenmovingingroups.Deviceswithinshort-rangeofeachotherdonotneces-

sarilyrequirethattheyeachbeindividuallytracked.Therefore,GreenLocdetectsandclustersusersmoving

togetherandelectsareferencenode(RN)basedondeviceenergylevelsandneeds.TheelectedRNwill

thenbetrackedviatriangulationwhileothernodesinthegroupwillbetrackedbasedontheRN’slocation

usingBluetooth.Ourinitialanalysisdemonstratesverypromisingresultswiththissystem.

43

Hala 2.0: Considerations for Developing a Test Bed for Multi-Lingual, Cross-Cultural Human Robot Interaction

AuthorsMicheline ZiadeeImran FanaswalaMaxim MakatchevAmna Al ZeyaraHuda GedawyNawal Behih

Faculty AdvisorMajd Sakr, Ph.D.Reid Simons, Ph.D.


AbstractThis research focuses on developingmulti-lingual, cross-cultural human robot interaction by identifying,analyzing, and utilizing language and culture-related factors/variables that influence human interactions.Ourtestbedisabi-lingual,cross-culturalrobotreceptionist,Hala,deployedattheCarnegieMellonQatarreceptionthathelpsuscollectthenecessarydata.HalacanspeakArabicandEnglishandsheinteractswithavarietyofusers.

Halaasaplatformmodelsuserinteractions,invitesuserswithaSICKlaser,handlesfacialanimations(borrowedfromPaulEkman’smodels), text-to-speechand lipsynchronization (borrowedEnglishvisemes forArabicspeech),aswellaserrorandreporting,postdialogueanalysis,networking/interprocesscommunication,andarichclientinterface.

Resultsfromourpriorworkindicatedvariationsinuserinteractionswiththerobot.Forexample,around97%oftheinteractionswithHalawereinEnglishbuttheaveragedurationwashigherforinteractionsinArabicasopposedtointeractionsinEnglish(231.7secondsforArabic/184.4secondsforEnglish).Also,ArabicnativespeakersweretwiceaslikelytoacceptaninvitefromtherobotthanEnglishnativespeakers.UsersinQatar,ascomparedtothoseintheUS,thanktherobotlessfrequently.

In this work, we discuss Hala 2.0, which has more Arabic features in expression and interaction. WeconstructedHala’spersonalitytakingintoaccountthesocio-culturalcontextinwhichherinteractionswilltakeplace.WehaveexpandedHala’sstatelesscontent,inEnglishandinArabic,toincludepreviouslyunansweredqueriesthatwegatheredfromHala’slogs.Wehaveintroducednaturallanguageprocessingwithsemanticandsyntacticparsingandcreatednewstate-fullcontentwherebyHalaiscapableofcarryingonameaningfulconversation. WedevelopedArabicvisemestoreplacetheborrowedEnglishvisemesthatwereusedforArabicspeech.Inadditiontothat,wedevelopedmorefacialanimationsthataddtoHala’sabilitytoexpressemotions;ourlateststudyfocusedonunderstandingthedifferencesinaccentsoffacialexpressionsthatvarybetweencultures–inthiscase,ArabicandAmerican.Inanotherstudy,weinvestigatedthestrategiesusedbyEnglishandArabicnativespeakerswhengivingdirections.ResultsfromthesestudieswillinfluencethedesignofHala2.0.

45

Performance Prediction of MapReduce Applications in Elastic Compute Clouds

AuthorFan Zhang



AbstractTheMapReduceprogrammingmodelisawidelyacceptedsolutiontoaddresstherapidgrowthoftheso-

calledbig-dataprocessingdemands.Variousapplicationswithavery largevolumeof inputdatacan run

on an elastic compute cloud composed ofmany distributed computing instances. This elastic compute

cloudisbestrepresentedbyavirtualcluster,suchasAmazonEC2.PerformancepredictionofMapReduce

applicationsischallengingduetothecomplexinteractionoftheMapReduceframeworkrunningonhighly

parameterizeddistributedvirtualizedresources.

Inthisstudy,wehavecharacterizedaseriesofMapReduceapplicationsonAmazonEC2,andidentifiedhow

inputdatasizeandclustersizeaffecttheexecutiontime.Westudiedthescalingcurveofeachapplication,

andprovidedanumberofdatacleaning,miningandpredictionmethodstodiscoverthedatapattern.These

MapReduceapplicationsspanacrossdata-intensive, compute-intensive, and iterativebenchmarks. Initial

observationssuggestanearpositivepower-lawdistributionofexecutiontimeagainsttheinputdatasizeand

anegativepower-lawdistributionagainsttheclustersize.

Basedonalltheseobservations,wehaveestimatedthepredictionerrorandsuggestedmethodstoreduce

the estimation error. Five regression and predictionmethods, linearly incremental regression, polynomial

regression, exponential regression, moving average regression and power regression are thoroughly

investigatedandcomparedforestimatingpredictionerrors.Weconcludethatthepowerregressionperforms

bestatperformancepredictioncomparedwiththeothermethodsevaluated.

Our observations andperformancepredictionmethodswill aid users in choosing appropriate computing

resources, both virtual and physical, from small-scale experimental test runs. This approachwill predict

performancespeedupsor slowdowns forMapReduceapplicationswhenscaling the infrastructureor the

inputdatasets.

47

SmartReader: A natural language processing-based active and interactive system for accessing English language content and advanced language learning

AuthorsKemal OflazerTeruko MitamuraHideki ShimaJun ArakiAhmed Salama

Faculty AdvisorKemal Oflazer, Ph.D


Abstract:SmartReader is ageneral-purpose “readingappliance”being implementedatCarnegieMellonUniversity(QatarandPittsburgh)–buildinguponanearlierprototypeversion.Itisanartificialintelligencesystemthatemploysadvancedlanguageprocessingtechnologiesandcaninteractwiththereaderandrespondtoque-riesabout thecontent,wordsandsentences inatext.Weexpect it tobeusedbystudents inQatarandelsewheretohelpimprovetheircomprehensionofEnglishtext.

SmartReader ismotivatedby theobservation that text isstill thepredominantmediumfor learningespe-ciallyattheadvancedlevelandthattext,being``bland,’’ishardlyaconduciveandmotivatingmediumforlearning.Thisisespeciallytruewhenonedoesnothaveaccesstotoolsthatenableonegetoverlanguageroadblocks,rangingfromunknownwordstounrecognizedandforgottennames,tohard-to-understandsen-tences.SmartReaderstrivestomakereading(English)textualmaterial,an“active”andan“interactive”pro-cesswiththeuserinteractingwiththetextusinganytime-anywherecontextually-guidedquerymechanismbased-oncontextualuserintentrecognition.WithSmartReader,ausercan:

• Inquireaboutthecontextuallycorrectmeaningorsynonymsofawordoridiomaticandmulti-wordcon-structions.

• Selectaperson’sname,andthengetanimmediate `flashback’’tothefirst(orthelast)timethepersonwasencounteredintexttoremindherselfthedetailsoftheperson.

• Extractasummaryofasectiontorememberimportantaspectsofthecontentatthepointsheleftoff,andcontinuereadingwithasignificantlyrefreshedcontext.

• SelectasentencethatshemaynotbeabletounderstandfullyandaskSmartReadertobreakitdown,simplifyorparaphrasetocomprehenditbetter.

• Testhercomprehensionofthetextinapageorachapter,byaskingSmartReadertodynamicallygeneratequizzesandansweringthem.

• Askquestionsaboutthecontentofthetextandgetanswersinadditiontomanyotherfunctions.

SmartReaderisbeingimplementedasamulti-platform(tablet/PC)client-serversystemusingHTML5tech-nology,withUnstructuredInformationManagementArchitecture–UIMAtechnology(usedrecentlyinIBM’sWatsonQ/AsystemintheJeopardyChallenge)astheunderlyinglanguageprocessingframework.

49

VOtus: A Flexible and Scalable Monitoring Framework for Virtualized Clusters

AuthorsSuhail RehmanMohammad Hammoud

Faculty AdvisorMajd Sakr, Ph.D.


AbstractCloudcomputingrevolutionizesthewaybigdataisprocessedandoffersacompellingparadigmtoorga-

nizations.Data-intensivescientificapplicationsarebeingportedtocloudenvironmentssuchasvirtualized

clusters;however,thisprocessposesitsownsetofchallenges.

Given thecomplexityof theapplicationexecutionenvironmentaswellas the infrastructure, routine tasks

suchasmonitoring,performanceanalysisanddebuggingofapplicationsdeployedon thecloudbecome

cumbersomeandcomplex.Thesetasksoftenrequirecloseinteractionandinspectionofmultiplelayersin

theapplicationsoftwarestack.Forexample,whenanalyzingadistributedapplicationthathasbeenprovi-

sionedonaclusterofvirtualmachines,aresearchermightneedtolookatthevirtualresourceusage(e.g.,

virtualCPUandvirtualmemory)andthecorrespondingphysicalresourceusage(physicalCPUandphysical

memory)ofthecluster.Thiswouldrequiretwodifferentsetsoftoolstocollectandanalyzeperformancemet-

ricsfromeachlevel.OnesuchtoolisOtus,whichcurrentlyreportsonlythevirtualresourceutilizationandnot

thephysicalresourceutilizationonvirtualizedclusters.

ThroughVOtus,wehaveextendedOtustoincludephysicalresourcemetricsthatcanbecollectedfromthe

hypervisor.Forexample,aresearchercannowviewthevirtualresourceusageofhisapplicationontheVMs

aswellasthephysicalresourceusageoftheVMsonthephysicalmachines.Thisenablestheresearcherto

closelymonitortheapplicationandmakemodificationsattheapplicationlevelorattheVMlevel.Thiswould

helptheresearchertooptimizeperformanceormanageinfrastructureeffectively.VOtusalsoscalestolarge

clustersandcanbeusedforreal-timemonitoringandtoarchiveperformancemetricsfordetailedanalysis

inthefuture.VOtusshouldprovetobeanimportanttoolforresearcherswhoplantodesign,developand

deploydistributedapplicationsonvirtualizedclusters.

51

Formorethanacentury,CarnegieMellonUniversityhasbeeninspiring

innovationsthatchangetheworld.Consistentlytopranked,Carnegie

Mellonhasmorethan11,000students,90,000alumniand5,000faculty

andstaffglobally.

In 2004, Qatar Foundation invited Carnegie Mellon to join Education

City,agroundbreakingcenter forscholarshipandresearch.Students

from 39 different countries enroll at our world-class facilities in

EducationCity.

Carnegie Mellon Qatar offers undergraduate programs in biological

sciences, business administration, computational biology, computer

scienceandinformationsystems.

Learnmoreatwww.qatar.cmu.edu

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