vi;
#define INF 1000000000
// 1 billion, safer than 2B for Floyd Warshall’s
// Common memset settings
//memset(memo, -1, sizeof memo);
// initialize DP memoization table with -1
//memset(arr, 0, sizeof arr);
// to clear array of integers
// Note that we abandon the usage of "REP" and "TRvii" in the second edition
// to reduce the confusion encountered by new programmers
The following shortcuts are frequently used in our C/C++/Java codes in this book:
// ans = a ? b : c;
// to simplify: if (a) ans = b; else ans = c;
// index = (index + 1) % n;
// from: index++; if (index >= n) index = 0;
// index = (index + n - 1) % n;
// from: index--; if (index < 0) index = n - 1;
// int ans = (int)((double)d + 0.5);
// for rounding to nearest integer
// ans = min(ans, new_computation)
// we frequently use this min/max shortcut
// some codes uses short circuit && (AND) and || (OR)
Problem Categorization
As of 1 August 2011, Steven and Felix – combined – have solved 1502 UVa problems (≈51% of
the entire UVa problems). About ≈1198 of them are discussed and categorized in this book.
These problems are categorized according to a ‘load balancing’ scheme: If a problem can be
classified into two or more categories, it will be placed in the category with a lower number of
problems. This way, you may find problems ‘wrongly’ categorized or problems whose category does
not match the technique you use to solve it. What we can guarantee is this: If you see problem X
in category Y, then you know that we have solved problem X with the technique mentioned in the
section that discusses category Y.
If you need hints for any of the problems, you may turn to the index at the back of this book and
save yourself the time needed to flip through the whole book to understand any of the problems.
The index contains a sorted list of UVa/LA problems number (do a binary search!) which will help
locate the pages that contains the discussion of those problems (and the required data structures
and/or algorithms to solve that problem).
Utilize this categorization feature for your training! To diversify your problem solving skill, it is
a good idea to solve at least few problems from each category, especially the ones that we highlight
as must try * (we limit ourself to choose maximum 3 highlights per category).
xii
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Context: 6.5.REMARKS316.5RemarksOneofthemainlimitationsoftheNBclassifieristhatitassumesindependencebe-tweenattributes(ThisispresumablythereasonwhywecallitthenaiveBayesianclassifier).Thisisreflectedinthefactthateachclassifierhasanindependentvoteinthefinalscore.However,imaginethatImeasurethewords,“home”and“mortgage”.Observing“mortgage”certainlyraisestheprobabilityofobserving“home”.Wesaythattheyarepositivelycorrelated.Itwouldthereforebemorefairifweattributedasmallerweightto“home”ifwealreadyobservedmortgagebecausetheyconveythesamething:thisemailisaboutmortgagesforyourhome.Onewaytoobtainamorefairvotingschemeistomodelthesedependenciesex-plicitly.However,thiscomesatacomputationalcost(alongertimebeforeyoureceiveyouremailinyourinbox)whichmaynotalwaysbeworththeadditionalaccuracy.Oneshouldalsonotethatmoreparametersdonotnecessarilyimproveaccuracybecausetoomanyparametersmayleadtooverfitting.6.6TheIdeaInaNutshellConsiderFigure??.Wecanclassifydatabybuildingamodelofhowthedatawasgenerated.ForNBwefirstdecidewhetherwewillgenerateadata-itemfromclassY=0orclassY=1.GiventhatdecisionwegeneratethevaluesforDattributesindependently.Eachclasshasadifferentmodelforgeneratingattributes.Clas-sificationisachievedbycomputingwhichmodelwasmorelikelytogeneratethenewdata-point,biasingtheoutcometowardstheclassthatisexpectedtogeneratemoredata.
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Context: 78Chapter6.SavingSpaceProblemsSolutionsonpage154.1.CountthefrequenciesofthecharactersinthispieceoftextandassignthemtotheHuffmancodes,fillinginthefollowingtable.Thenencodethetextupto“morelightly.”.’IhaveatheorywhichIsuspectisratherimmoral,’Smileywenton,morelightly.’Eachofushasonlyaquantumofcompassion.Thatifwelavishourconcernoneverystraycat,wenevergettothecentreofthings.’LetterFrequencyCodeLetterFrequencyCode11111010010011001110111100100111110001011001011101000101010011010100000010010100010000010100101101101010011101010101100010100010110010001101011010110101010110112.Considerthefollowingfrequencytableandtext.Decodeit.LetterFrequencyCodeLetterFrequencyCodespace20111s200011e12100d2110101t91011T1110100h70111n1110011o70110w1110010m60100p1110001r50011b1010111
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Context: HAN20-ch13-585-632-97801238147912011/6/13:26Page625#4113.6Summary625Furtherdevelopmentofprivacy-preservingdataminingmethodsisforeseen.Thecollaborationoftechnologists,socialscientists,lawexperts,governments,andcompaniesisneededtoproducearigorousprivacyandsecurityprotectionmech-anismfordatapublishinganddatamining.Withconfidence,welookforwardtothenextgenerationofdataminingtechnologyandthefurtherbenefitsthatitwillbring.13.6SummaryMiningcomplexdatatypesposeschallengingissues,forwhichtherearemanydedi-catedlinesofresearchanddevelopment.Thischapterpresentsahigh-leveloverviewofminingcomplexdatatypes,whichincludesminingsequencedatasuchastimeseries,symbolicsequences,andbiologicalsequences;mininggraphsandnetworks;andminingotherkindsofdata,includingspatiotemporalandcyber-physicalsystemdata,multimedia,textandWebdata,anddatastreams.Severalwell-establishedstatisticalmethodshavebeenproposedfordataanalysissuchasregression,generalizedlinearmodels,analysisofvariance,mixed-effectmod-els,factoranalysis,discriminantanalysis,survivalanalysis,andqualitycontrol.Fullcoverageofstatisticaldataanalysismethodsisbeyondthescopeofthisbook.Inter-estedreadersarereferredtothestatisticalliteraturecitedinthebibliographicnotes(Section13.8).Researchershavebeenstrivingtobuildtheoreticalfoundationsfordatamining.Sev-eralinterestingproposalshaveappeared,basedondatareduction,datacompression,probabilityandstatisticstheory,microeconomictheory,andpatterndiscovery–basedinductivedatabases.Visualdataminingintegratesdatamininganddatavisualizationtodiscoverimplicitandusefulknowledgefromlargedatasets.Visualdataminingincludesdatavisu-alization,dataminingresultvisualization,dataminingprocessvisualization,andinteractivevisualdatamining.Audiodataminingusesaudiosignalstoindicatedatapatternsorfeaturesofdataminingresults.Manycustomizeddataminingtoolshavebeendevelopedfordomain-specificapplications,includingfinance,theretailandtelecommunicationindustries,scienceandengineering,intrusiondetectionandprevention,andrecommendersystems
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Context: Figure 2.8 IDA Pro workspace
Up to this point, you have been able to open the binary file within IDA Pro. This is
not a trivial task for people new to IDA Pro. That's why it's presented in a step-by-step
fashion. However, the output in the workspace is not yet usable. The next step is learning
the scripting facility that IDA Pro provides to make sense of the disassembly database that
IDA Pro generates.
2.3. IDA Pro Scripting and Key Bindings
Try to decipher the IDA Pro disassembly database shown in the previous section
with the help of the scripting facility. Before you proceed to analyzing the binary, you have
to learn some basic concepts about the IDA Pro scripting facility. IDA Pro script syntax is
similar to the C programming language. The syntax is as follows:
9
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Context: 1.2. TIPS TO BE COMPETITIVE
c
⃝Steven & Felix
2. For multiple input test cases, you should include two identical sample test cases consecutively.
Both must output the same known correct results.
This is to check whether you have forgotten to initialize some variables, which will be easily
identified if the 1st instance produces the correct output but the 2nd one does not.
3. Your test cases must include large cases.
Increase the input size incrementally up to the maximum possible stated in problem descrip-
tion. Sometimes your program works for small input size, but behave wrongly (or slowly)
when input size increases. Check for overflow, out of bounds, etc if that happens.
4. Your test cases must include the tricky corner cases.
Think like the problem setter! Identify cases that are ‘hidden’ in the problem description.
Some typical corner cases: N = 0, N = 1, N = maximum values allowed in problem description,
N = negative values, etc. Think of the worst possible input for your algorithm.
5. Do not assume the input will always be nicely formatted if the problem description does not
say so (especially for a badly written problem). Try inserting white spaces (spaces, tabs) in
your input, and check whether your code is able to read in the values correctly (or crash).
6. Finally, generate large random test cases to see if your code terminates on time and still give
reasonably ok output (the correctness is hard to verify here – this test is only to verify that
your code runs within the time limit).
However, after all these careful steps, you may still get non-AC responses. In ICPC6, you and your
team can actually use the judge’s response to determine your next action. With more experience
in such contests, you will be able to make better judgment. See the next exercises:
Exercise 1.2.4: Situation judging (Mostly in ICPC setting. This is not so relevant in IOI).
1. You receive a WA response for a very easy problem. What should you do?
(a) Abandon this problem and do another.
(b) Improve the performance of your solution (optimize the code or use better algorithm).
(c) Create tricky test cases and find the bug.
(d) (In team contest): Ask another coder in your team to re-do this problem.
2. You receive a TLE response for an your O(N3) solution. However, maximum N is just 100.
What should you do?
(a) Abandon this problem and do another.
(b) Improve the performance of your solution (optimize the code or use better algorithm).
(c) Create tricky test cases and find the bug.
3. Follow up question (see question 2 above): What if maximum N is 100.000?
4. You receive an RTE response. Your code runs OK in your machine. What should you do?
5. One hour to go before the end of the contest. You have 1 WA code and 1 fresh idea for
another problem. What should you (your team) do?
(a) Abandon the problem with WA code, switch to that other problem in attempt to solve
one more problem.
(b) Insist that you have to debug the WA code. There is not enough time to start working
on a new code.
(c) (In ICPC): Print the WA code. Ask two other team members to scrutinize the code
while you switch to that other problem in attempt to solve two more problems.
6In IOI 2010-2011, contestants have limited tokens that they can use sparingly to check the correctness of their
submitted code. The exercise in this section is more towards ICPC style contest.
11
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Context: tion,youwilllearnaboutminingcontextualandcollectiveoutliers(Section12.7)andoutlierdetectioninhigh-dimensionaldata(Section12.8).c(cid:13)2012ElsevierInc.Allrightsreserved.DataMining:ConceptsandTechniques543
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Context: HAN14-ch07-279-326-97801238147912011/6/13:21Page314#36314Chapter7AdvancedPatternMiningPattern:“{frequent,pattern}”contextindicators:“mining,”“constraint,”“Apriori,”“FP-growth,”“rakeshagrawal,”“jiaweihan,”...representativetransactions:1)miningfrequentpatternswithoutcandidate...2)...miningclosedfrequentgraphpatternssemanticallysimilarpatterns:“{frequent,sequential,pattern},”“{graph,pattern}”“{maximal,pattern},”“{frequent,closed,pattern},”...Figure7.12Semanticannotationofthepattern“{frequent,pattern}.”Ingeneral,thehiddenmeaningofapatterncanbeinferredfrompatternswithsim-ilarmeanings,dataobjectsco-occurringwithit,andtransactionsinwhichthepatternappears.Annotationswithsuchinformationareanalogoustodictionaryentries,whichcanberegardedasannotatingeachtermwithstructuredsemanticinformation.Let’sexamineanexample.Example7.15Semanticannotationofafrequentpattern.Figure7.12showsanexampleofasemanticannotationforthepattern“{frequent,pattern}.”Thisdictionary-likeannotationpro-videssemanticinformationrelatedto“{frequent,pattern},”consistingofitsstrongestcontextindicators,themostrepresentativedatatransactions,andthemostsemanticallysimilarpatterns.Thiskindofsemanticannotationissimilartonaturallanguagepro-cessing.Thesemanticsofawordcanbeinferredfromitscontext,andwordssharingsimilarcontextstendtobesemanticallysimilar.Thecontextindicatorsandtherepre-sentativetransactionsprovideaviewofthecontextofthepatternfromdifferentanglestohelpusersunderstandthepattern.Thesemanticallysimilarpatternsprovideamoredirectconnectionbetweenthepatternandanyotherpatternsalreadyknowntotheusers.“Howcanweperformautomatedsemanticannotationforafrequentpattern?”Thekeytohigh-qualitysemanticannotationofafrequentpatternisthesuccessfulcontextmodelingofthepattern.Forcontextmodelingofapattern,p,considerthefollowing.Acontextunitisabasicobjectinadatabase,D,thatcarriessemanticinformationandco-occurswithatleastonefrequentpattern,p,inatleastonetransactioninD.Acontextunitcanbeanitem,apattern,orevenatransaction,dependingonthespeci
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Context: HAN08-ch01-001-038-97801238147912011/6/13:12Page33#331.8Summary33Invisibledatamining:Wecannotexpecteveryoneinsocietytolearnandmasterdataminingtechniques.Moreandmoresystemsshouldhavedataminingfunc-tionsbuiltwithinsothatpeoplecanperformdataminingorusedataminingresultssimplybymouseclicking,withoutanyknowledgeofdataminingalgorithms.Intelli-gentsearchenginesandInternet-basedstoresperformsuchinvisibledataminingbyincorporatingdataminingintotheircomponentstoimprovetheirfunctionalityandperformance.Thisisdoneoftenunbeknownsttotheuser.Forexample,whenpur-chasingitemsonline,usersmaybeunawarethatthestoreislikelycollectingdataonthebuyingpatternsofitscustomers,whichmaybeusedtorecommendotheritemsforpurchaseinthefuture.Theseissuesandmanyadditionalonesrelatingtotheresearch,development,andapplicationofdataminingarediscussedthroughoutthebook.1.8SummaryNecessityisthemotherofinvention.Withthemountinggrowthofdataineveryappli-cation,dataminingmeetstheimminentneedforeffective,scalable,andflexibledataanalysisinoursociety.Dataminingcanbeconsideredasanaturalevolutionofinfor-mationtechnologyandaconfluenceofseveralrelateddisciplinesandapplicationdomains.Dataminingistheprocessofdiscoveringinterestingpatternsfrommassiveamountsofdata.Asaknowledgediscoveryprocess,ittypicallyinvolvesdatacleaning,datainte-gration,dataselection,datatransformation,patterndiscovery,patternevaluation,andknowledgepresentation.Apatternisinterestingifitisvalidontestdatawithsomedegreeofcertainty,novel,potentiallyuseful(e.g.,canbeactedonorvalidatesahunchaboutwhichtheuserwascurious),andeasilyunderstoodbyhumans.Interestingpatternsrepresentknowl-edge.Measuresofpatterninterestingness,eitherobjectiveorsubjective,canbeusedtoguidethediscoveryprocess.Wepresentamultidimensionalviewofdatamining.Themajordimensionsaredata,knowledge,technologies,andapplications.Dataminingcanbeconductedonanykindofdataaslongasthedataaremeaningfulforatargetapplication,suchasdatabasedata,datawarehousedata,transactionaldata,andadvanceddatatypes.Advanceddatatyp
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Context: diamturpis,molestievitae,placerata,molestienec,leo.Maecenaslacinia.Namipsumligula,eleifendat,accumsannec,suscipita,ipsum.Morbiblanditligulafeugiatmagna.Nunceleifendconsequatlorem.Sedlacinianullavitaeenim.Pellentesquetinciduntpurusvelmagna.Integernonenim.Praesenteuismodnunceupurus.Donecbibendumquamintellus.Nullamcursuspulvinarlectus.Donecetmi.Namvulputatemetuseuenim.Vestibulumpellentesquefeliseumassa.102004006000200400600800xyYoucanseethatthechapterheading“Chapter1”beginsatabout(80,630).Noticethatthecoordinatesofthebottomleftofthepage(calledtheorigin)are,ofcourse,(0,0).Thechoiceofthebottomleftasouroriginissomewhatarbitrary–onecouldmakeanargumentthatthetopleftpoint,withverticalpositionsmeasureddownwards,isamoreappropriatechoice,atleastintheWestwherewereadtoptobottom.Ofcourse,onecouldalsohavetheoriginatthetoprightorbottomright,withhorizontalpositionsmeasuringleftward.Weshallbeusingsuchcoordinatestodescribethepositionandshapeofeachpartofeachletter,eachword,andeachparagraph,aswellasanydrawingsorphotographstobeplacedonthepage.Wewillseehowlinescanbedrawnbetweencoordinates,andhowtomaketheelegantcurveswhichformthelettersinatypeface.Oncewehavedeterminedwhatshapeswewishtoputoneachpage,wemustconsiderthefinalformofourdocument.Youmay
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Context: emostrecentlyaddedconjunctwhencon-sideringpruning.Conjunctsareprunedoneatatimeaslongasthisresultsinanimprovement.
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Context: in compressed state. The compressed component preceding awardext.rom is the compressed system BIOS, and the byte highlighted in pink is a custom checksum that follows the end-of-file marker for this compressed system BIOS. Other compressed components always end up with an end-of-file marker, and no checksum byte precedes the next compressed component in the BIOS binary. Proceed to the pure binary component of the Foxconn BIOS. The mapping of this pure binary component inside the hex editor as follows: 1. 6_A9C0h–6_BFFEh: The decompression block. This routine contains the LZH decompression engine 2. 7_E000h–7_FFFFh: This area contains the boot block code. Between of the pure binary components lay padding bytes. Some padding bytes re FFh bytes, and some are 00h bytes. Reverse Engineering e engineering. The boot BIOS. Understanding the reverse boot block is valuable, because these ifferent vendors. From this point on, I assemble the boot block routines. Now, I'll present some obscure and important areas of of the Foxconn 955X7AA-8EKRS2 you learned how to start ation here. All you have t the initial load address to 8_0000h–FFFh. Then, create new segments at FFF8_0000h–FFFD_FFFFh and relocate the h to that newly created segment to mimic the mapping of the dress map. You can use the IDA Pro script in listing 5.1 to e IDA Pro add the o make it a standalone script in an ASCII file, . a 5.1.2. Award Boot Block This section delves into the mechanics of boot block reversblock is the key into overall insight of the motherboard engineering tricks needed to reverse engineer thehniques tend to be applicable to BIOS from dtecisdthe BIOS code in the disassembled boot block motherboard BIOS dated November 11, 2005. In section 2.3 assembling a BIOS file with IDA Pro. I won't repeat that informdisto do is open the 512-KB file in IDA Pro and seF_Fcontents of 8_0000h–D_FFFFstem adBIOS binary in the syaccomplish this operation. The script in listing 5.1 must be executed directly in thrkspace scripting window that's called with Shift+F2 shortcut. You canwoappropriate include statements if you wish tas you learned in chapter 2 Listing 5.1 IDA Pro Relocation Script for Award BIOS with a 512-KB File auto ea, ea_src, ea_dest; /* Create segments for the currently loaded binary */ for(ea=0x80000; ea<0x100000; ea = ea+0x10000) { SegCreate(ea, ea+0x10000, ea>>4, 0,0,0); } /* Create new segments for relocation */ 6
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Context: HAN15-ch08-327-392-97801238147912011/6/13:21Page385#598.7Summary385usesoversamplingwheresynthetictuplesareadded,whichare“closeto”thegivenpositivetuplesintuplespace.Thethreshold-movingapproachtotheclassimbalanceproblemdoesnotinvolveanysampling.Itappliestoclassifiersthat,givenaninputtuple,returnacontinuousoutputvalue(justlikeinSection8.5.6,wherewediscussedhowtoconstructROCcurves).Thatis,foraninputtuple,X,suchaclassifierreturnsasoutputamapping,f(X)→[0,1].Ratherthanmanipulatingthetrainingtuples,thismethodreturnsaclas-sificationdecisionbasedontheoutputvalues.Inthesimplestapproach,tuplesforwhichf(X)≥t,forsomethreshold,t,areconsideredpositive,whileallothertuplesarecon-siderednegative.Otherapproachesmayinvolvemanipulatingtheoutputsbyweighting.Ingeneral,thresholdmovingmovesthethreshold,t,sothattherareclasstuplesareeas-iertoclassify(andhence,thereislesschanceofcostlyfalsenegativeerrors).Examplesofsuchclassifiersincludena¨ıveBayesianclassifiers(Section8.3)andneuralnetworkclas-sifierslikebackpropagation(Section9.2).Thethreshold-movingmethod,althoughnotaspopularasover-andundersampling,issimpleandhasshownsomesuccessforthetwo-class-imbalanceddata.Ensemblemethods(Sections8.6.2through8.6.4)havealsobeenappliedtotheclassimbalanceproblem.Theindividualclassifiersmakinguptheensemblemayincludeversionsoftheapproachesdescribedheresuchasoversamplingandthresholdmoving.Thesemethodsworkrelativelywellfortheclassimbalanceproblemontwo-classtasks.Threshold-movingandensemblemethodswereempiricallyobservedtooutper-formoversamplingandundersampling.Thresholdmovingworkswellevenondatasetsthatareextremelyimbalanced.Theclassimbalanceproblemonmulticlasstasksismuchmoredifficult,whereoversamplingandthresholdmovingarelesseffective.Althoughthreshold-movingandensemblemethodsshowpromise,findingasolutionforthemulticlassimbalanceproblemremainsanareaoffuturework.8.7SummaryClassificationisaformofdataanalysisthatextractsmodelsdescribingdataclasses.Aclassifier,orclassificationmodel,predictscategoricallabels(classes).Nu
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Context: viPREFACE
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Context: CONTENTS
c
⃝Steven & Felix
Topic
In This Book
Data Structures: Union-Find Disjoint Sets
Section 2.3.2
Graph: Finding SCCs, Max Flow, Bipartite Graph
Section 4.2.1, 4.6.3, 4.7.4
Math: BigInteger, Probability, Nim Games, Matrix Power
Section 5.3, 5.6, 5.8, 5.9
String Processing: Suffix Tree/Array
Section 6.6
More Advanced Topics: A*/IDA*
Section 8.3
Table 1: Not in IOI Syllabus [10] Yet
We know that one cannot win a medal in IOI just by mastering the current version of this book.
While we believe many parts of the IOI syllabus have been included in this book – which should
give you a respectable score in future IOIs – we are well aware that modern IOI tasks requires more
problem solving skills and creativity that we cannot teach via this book. So, keep practicing!
Specific to the Teachers/Coaches
This book is used in Steven’s CS3233 - ‘Competitive Programming’ course in the School of Com-
puting, National University of Singapore. It is conducted in 13 teaching weeks using the following
lesson plan (see Table 2). The PDF slides (only the public version) are given in the companion web
site of this book. Hints/brief solutions of the written exercises in this book are given in Appendix
A. Fellow teachers/coaches are free to modify the lesson plan to suit your students’ needs.
Wk
Topic
In This Book
01
Introduction
Chapter 1
02
Data Structures & Libraries
Chapter 2
03
Complete Search, Divide & Conquer, Greedy
Section 3.2-3.4
04
Dynamic Programming 1 (Basic Ideas)
Section 3.5
05
Graph 1 (DFS/BFS/MST)
Chapter 4 up to Section 4.3
06
Graph 2 (Shortest Paths; DAG-Tree)
Section 4.4-4.5; 4.7.1-4.7.2
-
Mid semester break
-
07
Mid semester team contest
-
08
Dynamic Programming 2 (More Techniques)
Section 6.5; 8.4
09
Graph 3 (Max Flow; Bipartite Graph)
Section 4.6.3; 4.7.4
10
Mathematics (Overview)
Chapter 5
11
String Processing (Basic skills, Suffix Array)
Chapter 6
12
(Computational) Geometry (Libraries)
Chapter 7
13
Final team contest
All, including Chapter 8
-
No final exam
-
Table 2: Lesson Plan
To All Readers
Due to the diversity of its content, this book is not meant to be read once, but several times. There
are many written exercises and programming problems (≈1198) scattered throughout the body
text of this book which can be skipped at first if the solution is not known at that point of time,
but can be revisited later after the reader has accumulated new knowledge to solve it. Solving
these exercises will strengthen the concepts taught in this book as they usually contain interesting
twists or variants of the topic being discussed. Make sure to attempt them once.
We believe this book is and will be relevant to many university and high school students as
ICPC and IOI will be around for many years ahead. New students will require the ‘basic’ knowledge
presented in this book before hunting for more challenges after mastering this book. But before
you assume anything, please check this book’s table of contents to see what we mean by ‘basic’.
vii
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Context: edwith“null.”ScandatabaseDasecondtime.TheitemsineachtransactionareprocessedinLorder(i.e.,sortedaccordingtodescendingsupportcount),andabranchiscreatedforeachtransaction.Forexample,thescanofthefirsttransaction,“T100:I1,I2,I5,”whichcontainsthreeitems(I2,I1,I5inLorder),leadstotheconstructionofthefirstbranchofthetreewiththreenodes,(cid:104)I2:1(cid:105),(cid:104)I1:1(cid:105),and(cid:104)I5:1(cid:105),whereI2islinkedasachildtotheroot,I1islinkedtoI2,andI5islinkedtoI1.Thesecondtransaction,T200,containstheitemsI2andI4inLorder,whichwouldresultinabranchwhereI2islinkedtotherootandI4islinkedtoI2.However,thisbranchwouldshareacommonprefix,I2,withtheexistingpathforT100.Therefore,weinsteadincrementthecountoftheI2nodeby1,andcreateanewnode,(cid:104)I4:1(cid:105),whichislinkedasachildto(cid:104)I2:2(cid:105).Ingeneral,
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Context: 176TemplatesProblem2.1
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Context: FurtherReadingTherefollowsalistofinterestingbooksforeachchapter.Somearecloselyrelatedtothechaptercontents,sometangentially.Thelevelofexpertiserequiredtounderstandeachofthemvariesquiteabit,butdonotbeafraidtoreadbooksyoudonotunderstandallof,especiallyifyoucanobtainorborrowthematlittlecost.Chapter1ComputerGraphics:PrinciplesandPracticeJamesD.Foley,AndriesvanDam,StevenK.Fiener,andJohnF.Hughes.PublishedbyAddisonWesley(secondedition,1995).ISBN0201848406.ContemporaryNewspaperDesign:ShapingtheNewsintheDigitalAge–Typography&ImageonModernNewsprintJohnD.BerryandRogerBlack.PublishedbyMarkBatty(2007).ISBN0972424032.Chapter2ABookofCurvesE.H.Lockwood.PublishedbyCambridgeUniver-sityPress(1961).ISBN0521044448.FiftyTypefacesThatChangedtheWorld:DesignMuseumFiftyJohnL.Waters.PublishedbyConran(2013).ISBN184091629X.ThinkingwithType:ACriticalGuideforDesigners,Writers,Editors,andStudentsEllenLupton.PublishedbyPrincetonArchitecturalPress(secondedition,2010).ISBN1568989695.169
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Context: onglength)byaPattern-Fusionmethod.Toreducethenumberofpatternsreturnedinmining,wecaninsteadminecom-pressedpatternsorapproximatepatterns.Compressedpatternscanbeminedwithrepresentativepatternsdefinedbasedontheconceptofclustering,andapproximatepatternscanbeminedbyextractingredundancy-awaretop-kpatterns(i.e.,asmallsetofk-representativepatternsthathavenotonlyhighsignificancebutalsolowredundancywithrespecttooneanother).Semanticannotationscanbegeneratedtohelpusersunderstandthemeaningofthefrequentpatternsfound,suchasfortextualtermslike“{frequent,pattern}.”Thesearedictionary-likeannotations,providingsemanticinformationrelatingtotheterm.Thisinformationconsistsofcontextindicators(e.g.,termsindicatingthecontextofthatpattern),themostrepresentativedatatransactions(e.g.,fragmentsorsentences
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Context: HAN19-ch12-543-584-97801238147912011/6/13:25Page545#312.1OutliersandOutlierAnalysis545justifywhytheoutliersdetectedaregeneratedbysomeothermechanisms.Thisisoftenachievedbymakingvariousassumptionsontherestofthedataandshowingthattheoutliersdetectedviolatethoseassumptionssignificantly.Outlierdetectionisalsorelatedtonoveltydetectioninevolvingdatasets.Forexample,bymonitoringasocialmediawebsitewherenewcontentisincoming,noveltydetectionmayidentifynewtopicsandtrendsinatimelymanner.Noveltopicsmayinitiallyappearasoutliers.Tothisextent,outlierdetectionandnoveltydetectionsharesomesimilarityinmodelinganddetectionmethods.However,acriticaldifferencebetweenthetwoisthatinnoveltydetection,oncenewtopicsareconfirmed,theyareusuallyincorporatedintothemodelofnormalbehaviorsothatfollow-upinstancesarenottreatedasoutliersanymore.12.1.2TypesofOutliersIngeneral,outlierscanbeclassifiedintothreecategories,namelyglobaloutliers,con-textual(orconditional)outliers,andcollectiveoutliers.Let’sexamineeachofthesecategories.GlobalOutliersInagivendataset,adataobjectisaglobaloutlierifitdeviatessignificantlyfromtherestofthedataset.Globaloutliersaresometimescalledpointanomalies,andarethesimplesttypeofoutliers.Mostoutlierdetectionmethodsareaimedatfindingglobaloutliers.Example12.2Globaloutliers.ConsiderthepointsinFigure12.1again.ThepointsinregionRsignifi-cantlydeviatefromtherestofthedataset,andhenceareexamplesofglobaloutliers.Todetectglobaloutliers,acriticalissueistofindanappropriatemeasurementofdeviationwithrespecttotheapplicationinquestion.Variousmeasurementsarepro-posed,and,basedonthese,outlierdetectionmethodsarepartitionedintodifferentcategories.Wewillcometothisissueindetaillater.Globaloutlierdetectionisimportantinmanyapplications.Considerintrusiondetec-tionincomputernetworks,forexample.Ifthecommunicationbehaviorofacomputerisverydifferentfromthenormalpatterns(e.g.,alargenumberofpackagesisbroad-castinashorttime),thisbehaviormaybeconsideredasaglobaloutlierandthecorrespondingcomputerisasuspectedvictimofhacking
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Context: fedintothenetwork,andthenetinputandoutputofeachunit
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Context: ContentsPrefaceiiiLearningandIntuitionvii1DataandInformation11.1DataRepresentation.........................21.2PreprocessingtheData.......................42DataVisualization73Learning113.1InaNutshell.............................154TypesofMachineLearning174.1InaNutshell.............................205NearestNeighborsClassification215.1TheIdeaInaNutshell........................236TheNaiveBayesianClassifier256.1TheNaiveBayesModel......................256.2LearningaNaiveBayesClassifier.................276.3Class-PredictionforNewInstances.................286.4Regularization............................306.5Remarks...............................316.6TheIdeaInaNutshell........................317ThePerceptron337.1ThePerceptronModel.......................34i
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Context: HAN19-ch12-543-584-97801238147912011/6/13:25Page576#34576Chapter12OutlierDetectionAswithcontextualoutlierdetection,collectiveoutlierdetectionmethodscanalsobedividedintotwocategories.Thefirstcategoryconsistsofmethodsthatreducetheprob-lemtoconventionaloutlierdetection.Itsstrategyistoidentifystructureunits,treateachstructureunit(e.g.,asubsequence,atime-seriessegment,alocalarea,orasubgraph)asadataobject,andextractfeatures.Theproblemofcollectiveoutlierdetectionisthustransformedintooutlierdetectiononthesetof“structuredobjects”constructedassuchusingtheextractedfeatures.Astructureunit,whichrepresentsagroupofobjectsintheoriginaldataset,isacollectiveoutlierifthestructureunitdeviatessignificantlyfromtheexpectedtrendinthespaceoftheextractedfeatures.Example12.23Collectiveoutlierdetectionongraphdata.Let’sseehowwecandetectcollectiveout-liersinAllElectronics’onlinesocialnetworkofcustomers.Supposewetreatthesocialnetworkasanunlabeledgraph.Wethentreateachpossiblesubgraphofthenetworkasastructureunit.Foreachsubgraph,S,let|S|bethenumberofverticesinS,andfreq(S)bethefrequencyofSinthenetwork.Thatis,freq(S)isthenumberofdifferentsubgraphsinthenetworkthatareisomorphictoS.Wecanusethesetwofeaturestodetectoutliersubgraphs.Anoutliersubgraphisacollectiveoutlierthatcontainsmultiplevertices.Ingeneral,asmallsubgraph(e.g.,asinglevertexorapairofverticesconnectedbyanedge)isexpectedtobefrequent,andalargesubgraphisexpectedtobeinfrequent.Usingtheprecedingsimplemethod,wecandetectsmallsubgraphsthatareofverylowfrequencyorlargesubgraphsthataresurprisinglyfrequent.Theseareoutlierstructuresinthesocialnetwork.Predefiningthestructureunitsforcollectiveoutlierdetectioncanbedifficultorimpossible.Consequently,thesecondcategoryofmethodsmodelstheexpectedbehav-iorofstructureunitsdirectly.Forexample,todetectcollectiveoutliersintemporalsequences,onemethodistolearnaMarkovmodelfromthesequences.Asubsequencecanthenbedeclaredasacollectiveoutlierifitsignificantlydeviatesfromthemodel.Insummary,collectiveoutlierdetectionissubtledue
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Context: Chapter6.SavingSpace73problemofhavingtogatherfrequencydataforthewholepage,apre-preparedmastercodetableisused,uponwhicheveryoneagrees.Thetablehasbeenbuiltbygatheringfrequenciesfromthousandsoftextdocumentsinseverallanguagesandtypefaces,andthencollatingthefrequenciesofthevariousblackandwhiteruns.Hereisthetableofcodesforblackandwhiterunsoflengths0to63.(Weneedlength0becausealineisalwaysassumedtobeginwhite,andazero-lengthwhiterunisrequiredifthelineactuallybeginsblack.)RunWhiteBlackRunWhiteBlack000110101000011011132000110110000011010101000011101033000100100000011010112011111340001001100001101001031000103500010100000011010011410110113600010101000011010100511000011370001011000001101010161110001038000101110000110101107111100011390010100000001101011181011000101400010100100000110110091010000010041001010100000011011011000111000010042001010110000110110101101000000010143001011000000110110111200100000001114400101101000001010100130000110000010045000001000000010101011411010000000111460000010100000101011015110101000011000470000101000000101011116101010000001011148000010100001100100171010110000011000490101001000000110010118010011100000010005001010011000001010010190001100000011001115101010100000001010011200001000000011010005201010101000000100100210010111000011011005300100100000000110111220000001100000110111540010010100000011100023000010000000101000550101100000000010011124010100000000010111560101100100000010100025010101100000011000570101101000000101100026001001100001100101058010110110000010110012701001000000110010115901001010000000101011
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Context: | | (8 KB) | the temporary result of the decompression
process before being copied to the destination
address. |
| -------- | -------- | -------- |
| | | |
| 571Ch | 1 | LHA header length. |
| 571Dh | 1 | LHA header sum (8-bit sum). |
| ... | ... | ... |
Table 5.4 Memory map of scratch-pad used by the decompression engine
3. In t
segm
com
ts are not decompressed yet. However, their original header
information was stored at 0000:6000h–0000:6xxxh in RAM. Among this
information were the starting addresses10 of the compressed component.
d to 4000h by the
Decompression_Ngine procedure in the BIOS binary image at 30_0000h–
needed.
4. The 40xxh in the header behaves as an ID that works as follows:
•
(hi-byte) is an identifier that marks it as an "Extension BIOS" to be
•
xx is an identifier that will be used in system BIOS execution to refer to the
decompressed. This will be explained more thoroughly in the system BIOS
explanation later.
Engineering
previous section: I'll just highlight the places
here the "code execution path" is obscure. By now, you're looking at the disassembly of
erboard.
his stage, only the system BIOS that is decompressed. It is decompressed to
ent 5000h and later will be relocated to segment E000h–F000h. Other
pressed componen
Subsequently, their destination segments were patche
37_FFFFh. This can be done because not all of those components will be
decompressed at once. They will be decompressed one by one during system
BIOS execution and relocated from segment 4000h as
11
40
decompressed later during original.tmp execution.
component's starting address within the image of the BIOS binary12 to be
5.1.3. Award System BIOS Reverse
I'll proceed as in the boot block in the
w
the decompressed system BIOS of the Foxconn moth
5.1.3.1. Entry Point from the "Boot Block in RAM"
This is where the boot block jumps after relocating and write-protecting the system
BIOS.
10 The starting address is in the form of a physical address.
11 The 40xxh value is the destination segment of the LHA header of the compressed component.
12 This image of the BIOS binary is already copied to RAM at 30_0000h–37_FFFFh.
41
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Context: 34Chapter3.StoringWordsWemight,forexample,extendoursystemofspecialcharactersinthefollowingfashion:!SectionTitle!Thisisthe$first$paragraph,whichis*important*.Inthelanguageusedforwebpages,thestartingandendingsignifiers(theyarecalled“tags”)arenotsymmetrical.Atagsuchasbeginsbold,thetagendsit.Wealsouseandforitalic,and
fortheheading,andand
toexplicitlymarkparagraphs.(Inthepreviousmethod,wehadjustusedCarriageReturnsandLineFeedstomarkthem.)Wemaywrite:SectionTitle
Thisisthefirst,whichisimportant.
Inthetypesettinglanguageusedforwritingthisbook,mark-upisintroducedwiththebackslashescapecharacter,followedbyadescriptivenameofthechangebeingmade,withthecontentsenclosedincurlybrackets{and}:\section{SectionTitle}Thisisthe\textit{first}paragraph,whichis\textbf{important}.Here,wehaveused\section{}forthesectiontitle,\textit{}foritalic,and\textbf{}forbold.Thesedifferingmark-upsystemsarenotjusthistoricalartefacts:theyservedifferentpurposes.Therequirementsmaybewhollydifferentforadocumenttobeprinted,tobeputontheweb,ortobeviewedonaneBookreader.Wepromisedtotalkaboutrepresentingtheworld’smanylan-guagesandwritingsystems.Since1989,therehasbeenaninter-nationalindustrialeffort,undertheUnicodeinitiative,toencodemorethanonehundredthousandcharacters,givingeachanumber,anddefininghowtheymaybecombinedinvalidways.Therearemorethanamilliontotalslotsavailableforfutureuse.ItisimportanttosaythattheUnicodesystemisconcernedonlywithassigningcharacterstonumbers.Itdoesnotspecifytheshapesthosecharacterstake:thatisamatterfortypefacedesigners.Theprincipleisoneofseparationofconcerns:thateachpartofacom-putersystemshoulddoonejobwellandallowinteractionwiththeother,similarlywell-designedcomponents.ThisisparticularlydifficultfortheUnicodesystem,whichmustnavigateinnumerableculturaldifferencesandawidevarietyofpossibleuses.ThefollowingfivepagesgivesomeexamplesdrawnfromthehugeUnicodestandard.
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Context: 7.6 Pattern Exploration and Application
317
Table 7.4 Annotations Generated for Frequent Patterns in the DBLP Data Set
Pattern
Type
Annotations
| christos faloutsos | Context indicator Representative
transactions
Representative
transactions
Representative
transactions | spiros papadimitriou multi-attribute hash use gray code
recovery latent time-series observe sum
network tomography particle filter
index multimedia database tutorial |
| |Semantic similar
patterns | spiros papadimitriou&christos faloutsos;
spiros papadimitriou; flip korn;
timos k selli;
ramakrishnan srikant;
ramakrishnan srikant&rakesh agrawal |
| -------- | -------- | -------- | -------- | -------- | -------- | -------- |
| informationretrieval | Context indicator | w bruce croft; web information;monika rauch henzinger;james p callan; full-text |
| |Representative
transactions
Representative
transactions | web information retrieval
language model information retrieval |
| |Semantic similar
patterns | information use; web information;
probabilistic information; information
filter;
text information |
In both scenarios, the representative transactions extracted give us the titles of papers
that effectively capture the meaning of the given patterns. The experiment demonstrates
the effectiveness of semantic pattern annotation to generate a dictionary-like annota-
tion for frequent patterns, which can help a user understand the meaning of annotated
patterns.
The context modeling and semantic analysis method presented here is general and
can deal with any type of frequent patterns with context information. Such semantic
annotations can have many other applications such as ranking patterns, categorizing
and clustering patterns with semantics, and summarizing databases. Applications of
the pattern context model and semantical analysis method are also not limited to pat-
tern annotation; other example applications include pattern compression, transaction
clustering, pattern relations discovery, and pattern synonym discovery.
7.6.2 Applications of Pattern Mining
We have studied many aspects of frequent pattern mining, with topics ranging from effi-
cient mining algorithms and the diversity of patterns to pattern interestingness, pattern
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Context: 0000:001A0044 dd 40000h ; dest seg = 4000h; size = 5D56h (relocated) 0000:001A0048 dd 80005D56h 0000:001A004C dd 0A8530h ; dest seg = A853h; size = 82FCh (relocated) 0000:001A0050 dd 800082FCh 0000:001A0054 dd 49A90h ; dest seg = 49A9h; size = A29h (relocated) 0000:001A0058 dd 80000A29h 0000:001A005C dd 45D60h ; dest seg = 45D6h; size = 3D28h (relocated) 0000:001A0060 dd 80003D28h 0000:001A0064 dd 0A0000h ; dest seg = A000h; size = 55h (relocated) 0000:001A0068 dd 80000055h 0000:001A006C dd 0A0300h ; dest seg = A030h; size = 50h (relocated) 0000:001A0070 dd 80000050h 0000:001A0074 dd 400h ; dest seg = 40h; size = 110h (NOT relocated) 0000:001A0078 dd 110h 0000:001A007C dd 510h ; dest seg = 51h; size = 13h (NOT relocated) 0000:001A0080 dd 13h 0000:001A0084 dd 1A8E0h ; dest seg = 1A8Eh; size = 7AD0h (relocated) 0000:001A0088 dd 80007AD0h 0000:001A008C dd 0 ; dest seg = 0h; size = 400h (NOT relocated) 0000:001A0090 dd 400h 0000:001A0094 dd 266F0h ; dest seg = 266Fh; size = 101Fh (relocated) 0000:001A0098 dd 8000101Fh 0000:001A009C dd 2EF60h ; dest seg = 2EF6h; size = C18h (relocated) 0000:001A00A0 dd 80000C18h 0000:001A00A4 dd 30000h ; dest seg = 3000h; size = 10000h 0000:001A00A4 ; (NOT relocated) 0000:001A00A8 dd 10000h 0000:001A00AC dd 4530h ; dest seg = 453h; size = EFF0h 0000:001A00AC ; (NOT relocated) 0000:001A00B0 dd 0EFF0h 0000:001A00B4 dd 0A8300h ; dest seg = A830h; size = 230h (relocated) 0000:001A00B8 dd 80000230h 0000:001A00BC dd 0E8000h ; dest seg = E800h; size = 8000h 0000:001A00BC ; (NOT relocated) 0000:001A00C0 dd 8000h 0000:001A00C4 dd 0A7D00h ; dest seg = A7D0h; size = 200h 0000:001A00C4 ; (NOT relocated) 0000:001A00C8 dd 200h 0000:001A00CC dd 0B0830h ; dest seg = B083h; size = F0h (relocated) 0000:001A00D0 dd 800000F0h 0000:001A00D4 dd 0A8000h ; dest seg = A800h; size = 200h 0000:001A00D4 ; (NOT relocated) 0000:001A00D8 dd 200h 0000:001A00DC dd 530h ; dest seg = 53h; size = 4000h 0000:001A00DC ; (NOT relocated) 0000:001A00E0 dd 4000h 0000:001A00E4 dd 0A7500h ; dest seg = A750h; size = 800h 0000:001A00E4 ; (NOT relocated) 0000:001A00E8 dd 800h 0000:001A00EC dd 0C0000h ; dest seg = C000h; size = 20000h 0000:001A00EC ; (NOT relocated) 96
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Context: ning,dataintegration,datareduction,anddatatransformation.Datacleaningroutinesworkto“clean”thedatabyfillinginmissingvalues,smooth-ingnoisydata,identifyingorremovingoutliers,andresolvinginconsistencies.Ifusersbelievethedataaredirty,theyareunlikelytotrusttheresultsofanydataminingthathasbeenapplied.Furthermore,dirtydatacancauseconfusionfortheminingprocedure,resultinginunreliableoutput.Althoughmostminingroutineshavesomeproceduresfordealingwithincompleteornoisydata,theyarenotalwaysrobust.Instead,theymayconcentrateonavoidingoverfittingthedatatothefunctionbeingmodeled.Therefore,ausefulpreprocessingstepistorunyourdatathroughsomedatacleaningroutines.Section3.2discussesmethodsfordatacleaning.GettingbacktoyourtaskatAllElectronics,supposethatyouwouldliketoincludedatafrommultiplesourcesinyouranalysis.Thiswouldinvolveintegratingmultipledatabases,datacubes,orfiles(i.e.,dataintegration).Yetsomeattributesrepresentinga
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Context: HAN14-ch07-279-326-97801238147912011/6/13:21Page308#30308Chapter7AdvancedPatternMiningpattern/ruleinterestingnessandcorrelation(Section6.3)canalsobeusedtohelpconfinethesearchtopatterns/rulesofinterest.Inthissection,welookattwoformsof“compression”offrequentpatternsthatbuildontheconceptsofclosedpatternsandmax-patterns.RecallfromSection6.2.6thataclosedpatternisalosslesscompressionofthesetoffrequentpatterns,whereasamax-patternisalossycompression.Inparticular,Section7.5.1exploresclustering-basedcompressionoffrequentpatterns,whichgroupspatternstogetherbasedontheirsimilar-ityandfrequencysupport.Section7.5.2takesa“summarization”approach,wheretheaimistoderiveredundancy-awaretop-krepresentativepatternsthatcoverthewholesetof(closed)frequentitemsets.Theapproachconsidersnotonlytherepresentativenessofpatternsbutalsotheirmutualindependencetoavoidredundancyinthesetofgener-atedpatterns.Thekrepresentativesprovidecompactcompressionoverthecollectionoffrequentpatterns,makingthemeasiertointerpretanduse.7.5.1MiningCompressedPatternsbyPatternClusteringPatterncompressioncanbeachievedbypatternclustering.ClusteringtechniquesaredescribedindetailinChapters10and11.Inthissection,itisnotnecessarytoknowthefinedetailsofclustering.Rather,youwilllearnhowtheconceptofclusteringcanbeappliedtocompressfrequentpatterns.Clusteringistheautomaticprocessofgroupinglikeobjectstogether,sothatobjectswithinaclusteraresimilartooneanotheranddis-similartoobjectsinotherclusters.Inthiscase,theobjectsarefrequentpatterns.Thefrequentpatternsareclusteredusingatightnessmeasurecalledδ-cluster.Arepresenta-tivepatternisselectedforeachcluster,therebyofferingacompressedversionofthesetoffrequentpatterns.Beforewebegin,let’sreviewsomedefinitions.AnitemsetXisaclosedfrequentitemsetinadatasetDifXisfrequentandthereexistsnopropersuper-itemsetYofXsuchthatYhasthesamesupportcountasXinD.AnitemsetXisamaximalfrequentitemsetindatasetDifXisfrequentandthereexistsnosuper-itemsetYsuchthatX⊂YandYisfrequentinD.Usingtheseconceptsaloneisnotenoughtoobt
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Context: Chapter 6. Saving Space
79
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4
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4
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c
4
11011
y
1
01010001
u
4
10101
.
1
01010010
i
3
10100
1101000111100001110011100100011100111010001100100
1001100110110001111111001001111010011011011111100
1000111001110100001011010110011110101110001111011
0000001110110110011011101001010101110110111111000
1101110101000000001110000011000111110110111100010
0111011011011101011110001010110100010100001001101
0111100101011111101101111001111011101000100100111
1011011110001010001111011011011110111010100110101
0010
3. Encode the following fax image. There is no need to use zero-
length white runs at the beginning of lines starting with a
black pixel.
4. Decode the following fax image to the same 37x15 grid. There
are no zero-length white runs at the beginning of lines starting
with a black pixel.
0001011000001110001111110001111000001110000001001
0110000100100000010001111111001010001011001001111
1110010000011111111011011110111111011111111011000
0111111100100111111011110111111100100000111000100
1000111011110111000100011100010010001110111101110
0010001111111001001111110111101111111001000001111
1111011011111101111011111111011000011111111011011
1101110100111111110110000111111110110111011110011
1000111110110000111000010010000000100100000010001
110000111000111111001011100010101100010110
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Context: Chapter 10. Words to Paragraphs
143
The finished paragraphs of type are arranged in a galley. This
will be used to make prints of the page (or pages – two or four may
be printed from one galley, then folded and cut). You can imagine
how long it takes to make up the galleys for a book, and how much
time is required to justify each line by inserting exactly the right
spaces and hyphenating by hand. Mistakes found after test prints
can be very costly to fix, since they necessitate taking apart the
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Context: Chapter3ProblemSolvingParadigmsIfallyouhaveisahammer,everythinglookslikeanail—AbrahamMaslow,19623.1OverviewandMotivationInthischapter,wehighlightfourproblemsolvingparadigmscommonlyusedtoattackproblemsinprogrammingcontests,namelyCompleteSearch,Divide&Conquer,Greedy,andDynamicProgramming.BothIOIandICPCcontestantsneedtomasteralltheseproblemsolvingparadigmssothattheycanattackthegivenproblemwiththeappropriate‘tool’,ratherthan‘hammering’everyproblemwiththebrute-forcesolution(whichisclearlynotcompetitive).Ouradvicebeforeyoustartreading:Donotjustrememberthesolutionsfortheproblemsdiscussedinthischapter,butremembertheway,thespiritofsolvingthoseproblems!3.2CompleteSearchCompleteSearch,alsoknownasbruteforceorrecursivebacktracking,isamethodforsolvingaproblembysearching(upto)theentiresearchspacetoobtaintherequiredsolution.Inprogrammingcontests,acontestantshoulddevelopaCompleteSearchsolutionwhenthereisclearlynocleveralgorithmavailable(e.g.theproblemofenumeratingallpermutationsof{0,1,2,...,N−1},whichclearlyrequiresO(N!)operations)orwhensuchcleveralgorithmsexist,butoverkill,astheinputsizehappenstobesmall(e.g.theproblemofansweringRangeMinimumQueryasinSection2.3.3butonastaticarraywithN≤100–solvablewithanO(N)loop).InICPC,CompleteSearchshouldbethefirstsolutiontobeconsideredasitisusuallyeasytocomeupwiththesolutionandtocode/debugit.Rememberthe‘KISS’principle:KeepItShortandSimple.Abug-freeCompleteSearchsolutionshouldneverreceiveWrongAnswer(WA)responseinprogrammingcontestsasitexplorestheentiresearchspace.However,manyprogrammingproblemsdohavebetter-than-Complete-Searchsolutions.ThusaCompleteSearchsolutionmayreceiveaTimeLimitExceeded(TLE)verdict.Withproperanalysis,youcandeterminewhichisthelikelyoutcome(TLEversusAC)beforeattemptingtocodeanything(Table1.4inSection1.2.2isagoodgauge).IfCompleteSearchcanlikelypassthetimelimit,thengoahead.ThiswillthengiveyoumoretimetoworkontheharderproblemswhereCompleteSearchistooslow.InIOI,weusuallyneedbetterproblemsolvingtechniquesasCompleteSearchsolutionsareusu
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Context: HAN11-ch04-125-186-97801238147912011/6/13:17Page179#554.6Summary179Adatacubeconsistsofalatticeofcuboids,eachcorrespondingtoadifferentdegreeofsummarizationofthegivenmultidimensionaldata.Concepthierarchiesorganizethevaluesofattributesordimensionsintogradualabstractionlevels.Theyareusefulinminingatmultipleabstractionlevels.Onlineanalyticalprocessingcanbeperformedindatawarehouses/martsusingthemultidimensionaldatamodel.TypicalOLAPoperationsincluderoll-up,anddrill-(down,across,through),slice-and-dice,andpivot(rotate),aswellasstatisticaloperationssuchasrankingandcomputingmovingaveragesandgrowthrates.OLAPoperationscanbeimplementedefficientlyusingthedatacubestructure.Datawarehousesareusedforinformationprocessing(queryingandreporting),analyticalprocessing(whichallowsuserstonavigatethroughsummarizedanddetaileddatabyOLAPoperations),anddatamining(whichsupportsknowledgediscovery).OLAP-baseddataminingisreferredtoasmultidimensionaldatamin-ing(alsoknownasexploratorymultidimensionaldatamining,onlineanalyticalmining,orOLAM).Itemphasizestheinteractiveandexploratorynatureofdatamining.OLAPserversmayadoptarelationalOLAP(ROLAP),amultidimensionalOLAP(MOLAP),orahybridOLAP(HOLAP)implementation.AROLAPserverusesanextendedrelationalDBMSthatmapsOLAPoperationsonmultidimensionaldatatostandardrelationaloperations.AMOLAPservermapsmultidimensionaldataviewsdirectlytoarraystructures.AHOLAPservercombinesROLAPandMOLAP.Forexample,itmayuseROLAPforhistoricdatawhilemaintainingfrequentlyaccesseddatainaseparateMOLAPstore.Fullmaterializationreferstothecomputationofallofthecuboidsinthelatticedefiningadatacube.Ittypicallyrequiresanexcessiveamountofstoragespace,particularlyasthenumberofdimensionsandsizeofassociatedconcepthierarchiesgrow.Thisproblemisknownasthecurseofdimensionality.Alternatively,partialmaterializationistheselectivecomputationofasubsetofthecuboidsorsubcubesinthelattice.Forexample,anicebergcubeisadatacubethatstoresonlythosecubecellsthathaveanaggregatevalue(e.g.,count)abovesomeminimumsupportthreshold.O
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Context: 2.7 Bibliographic Notes
81
(c) Numeric attributes
(d) Term-frequency vectors
2.6 Given two objects represented by the tuples (22, 1, 42, 10) and (20, 0, 36, 8):
(a) Compute the Euclidean distance between the two objects.
(b) Compute the Manhattan distance between the two objects.
(c) Compute the Minkowski distance between the two objects, using q = 3.
(d) Compute the supremum distance between the two objects.
2.7 The median is one of the most important holistic measures in data analysis. Pro-
pose several methods for median approximation. Analyze their respective complexity
under different parameter settings and decide to what extent the real value can be
approximated. Moreover, suggest a heuristic strategy to balance between accuracy and
complexity and then apply it to all methods you have given.
2.8 It is important to define or select similarity measures in data analysis. However, there
is no commonly accepted subjective similarity measure. Results can vary depending on
the similarity measures used. Nonetheless, seemingly different similarity measures may
be equivalent after some transformation.
Suppose we have the following 2-D data set:
| | A
1 | A
2 |
| -------- | -------- | -------- |
| x
1 | 1.5 | 1.7 |
| x
2 | 2 | 1.9 |
| x3 | 1.6 | 1.8 |
| x
4 | 1.2 | 1.5 |
| x
5 | 1.5 | 1.0 |
(a) Consider the data as 2-D data points. Given a new data point, x = (1.4,1.6) as a
query, rank the database points based on similarity with the query using Euclidean
distance, Manhattan distance, supremum distance, and cosine similarity.
(b) Normalize the data set to make the norm of each data point equal to 1. Use Euclidean
distance on the transformed data to rank the data points.
2.7 Bibliographic Notes
Methods for descriptive data summarization have been studied in the statistics literature
long before the onset of computers. Good summaries of statistical descriptive data min-
ing methods include Freedman, Pisani, and Purves [FPP07] and Devore [Dev95]. For
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Context: The last thing to note
the
normal boot block code
tion
i
that takes place if the system BIO
As promised, I now delv
e d
f the decompression routine for the
system BIOS, mentioned in point
ompressed c
po
LZH le
header for
Th
ill be
located after decompression are
t. The format is provided in
table 5.2. Remember that it applies t
is that the
path, wh
S is corrupt
e into th
boot block explanation here only covers
ch means it didn't explain the boot block POST
ed.
etails o
execu
5. Start by learn
nent in an
e address ra
contained with
o all com
ing the prerequisites.
Award BIOS uses a modified version of the
nges where these BIOS components w
in this forma
The c
vel-1
om
mat.
pressed components.
| | Starting | | |
| -------- | -------- | -------- | -------- |
| Starting Offset | | | |
| |Offset in | Size in | |
| from First Byte | | | Contents |
| |LZH Basic | Bytes | |
| (from Preheader) | | | |
| |Header | | |
| | | 1 for | The header length of the component. It
depends on the file/component name. The
formula is header_length = filename_length +
25. |
| | | preheader, | |
| 00h | N/A | N/A for | |
| | | LZH basic | |
| | | header | |
| | | 1 for | The header 8-bit checksum, not including the
first 2 bytes (header length and header
checksum byte). |
| | | preheader, | |
| 01h | N/A | N/A for | |
| | | LZH basic | |
| | | header | |
| | | | LZH method ID (ASCII string signature). In
Award BIOS, it's "-lh5-," which means: 8-KB
sliding dictionary (max 256 bytes) + static
Huffman + improved encoding of position and
trees. |
| 02h | 00h | 5 | |
| | | | Compressed file or component size in little
endian dword value, i.e., MSB8 at 0Ah, and so
forth. |
| 07h | 05h | 4 | |
| | | | Uncompressed file or component size in little
endian dword value, i.e., MSB at 0Eh, and so
forth. |
| 0Bh | 09h | 4 | |
| | | | Destination offset address in little endian word
value, i.e., MSB at 10h, and so forth. The
component will be decompressed into this
offset address (real-mode addressing is in
effect here). |
| 0Fh | 0Dh | 2 | |
| | | | Destination segment address in little endian
word value, i.e., MSB at 12h, and so forth. The |
| 11h | 0Fh | 2 | |
8 MSB stands for most significant bit.
26
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Context: Figure 12.3 shows that a file system API is installed into the kernel of the operating
system. Therefore, every time a call to the file system API is made, this hook is executed.
Note that after the hook is installed, the execution in CIH virus source code is no longer
"linear"; the file system API hook code is dormant and executes only if the operating
system requests it—much like a device driver. As you can see in the virus segment source
code, this hook checks the type of operation carried out and infects the file with a copy of
the virus code if the file is an executable file. Don't forget that at this point the file system
hook is a resident entity in the system—think of it as part of the kernel. It has been copied
to system memory allocated for hooking purposes by the virus code in the beginning of
listing 12.6. Figure 12.4 shows the state of the CIH virus in the system's virtual address
space right after file system API hook installation. This should clarify the CIH code
execution up to this point.
Figure 12.4 CIH state in memory after file system API hook installation
Don't forget that the file system API hook will be called if the operating system interacts
with a file, such as when opening, closing, writing, or reading it.
The file system API hook is long. Therefore, I only show its interesting parts in listing
12.7. In this listing, you can see how the virus destroys the BIOS contents. I focus on that
subject.
Listing 12.7 File System API Hook
; **************************************
; * IFSMgr_FileSystemHook entry point *
; **************************************
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Context: HAN03-toc-ix-xviii-97801238147912011/6/13:32Pagexii#4xiiContents4.1.4DataWarehousing:AMultitieredArchitecture1304.1.5DataWarehouseModels:EnterpriseWarehouse,DataMart,andVirtualWarehouse1324.1.6Extraction,Transformation,andLoading1344.1.7MetadataRepository1344.2DataWarehouseModeling:DataCubeandOLAP1354.2.1DataCube:AMultidimensionalDataModel1364.2.2Stars,Snowflakes,andFactConstellations:SchemasforMultidimensionalDataModels1394.2.3Dimensions:TheRoleofConceptHierarchies1424.2.4Measures:TheirCategorizationandComputation1444.2.5TypicalOLAPOperations1464.2.6AStarnetQueryModelforQueryingMultidimensionalDatabases1494.3DataWarehouseDesignandUsage1504.3.1ABusinessAnalysisFrameworkforDataWarehouseDesign1504.3.2DataWarehouseDesignProcess1514.3.3DataWarehouseUsageforInformationProcessing1534.3.4FromOnlineAnalyticalProcessingtoMultidimensionalDataMining1554.4DataWarehouseImplementation1564.4.1EfficientDataCubeComputation:AnOverview1564.4.2IndexingOLAPData:BitmapIndexandJoinIndex1604.4.3EfficientProcessingofOLAPQueries1634.4.4OLAPServerArchitectures:ROLAPversusMOLAPversusHOLAP1644.5DataGeneralizationbyAttribute-OrientedInduction1664.5.1Attribute-OrientedInductionforDataCharacterization1674.5.2EfficientImplementationofAttribute-OrientedInduction1724.5.3Attribute-OrientedInductionforClassComparisons1754.6Summary1784.7Exercises1804.8BibliographicNotes184Chapter5DataCubeTechnology1875.1DataCubeComputation:PreliminaryConcepts1885.1.1CubeMaterialization:FullCube,IcebergCube,ClosedCube,andCubeShell1885.1.2GeneralStrategiesforDataCubeComputation1925.2DataCubeComputationMethods1945.2.1MultiwayArrayAggregationforFullCubeComputation195
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Context: HAN13-ch06-243-278-97801238147912011/6/13:20Page271#296.4Summary271differentvaluesonsomesubtlydifferentdatasets.Let’sexaminedatasetsD5andD6,shownearlierinTable6.9,wherethetwoeventsmandchaveunbalancedconditionalprobabilities.Thatis,theratioofmctocisgreaterthan0.9.Thismeansthatknowingthatcoccursshouldstronglysuggestthatmoccursalso.Theratioofmctomislessthan0.1,indicatingthatmimpliesthatcisquiteunlikelytooccur.TheallconfidenceandcosinemeasuresviewbothcasesasnegativelyassociatedandtheKulcmeasureviewsbothasneutral.Themaxconfidencemeasureclaimsstrongpositiveassociationsforthesecases.Themeasuresgiveverydiverseresults!“Whichmeasureintuitivelyreflectsthetruerelationshipbetweenthepurchaseofmilkandcoffee?”Duetothe“balanced”skewnessofthedata,itisdifficulttoarguewhetherthetwodatasetshavepositiveornegativeassociation.Fromonepointofview,onlymc/(mc+mc)=1000/(1000+10,000)=9.09%ofmilk-relatedtransactionscontaincoffeeinD5andthispercentageis1000/(1000+100,000)=0.99%inD6,bothindi-catinganegativeassociation.Ontheotherhand,90.9%oftransactionsinD5(i.e.,mc/(mc+mc)=1000/(1000+100))and9%inD6(i.e.,1000/(1000+10))contain-ingcoffeecontainmilkaswell,whichindicatesapositiveassociationbetweenmilkandcoffee.Thesedrawverydifferentconclusions.Forsuch“balanced”skewness,itcouldbefairtotreatitasneutral,asKulcdoes,andinthemeantimeindicateitsskewnessusingtheimbalanceratio(IR).AccordingtoEq.(6.13),forD4wehaveIR(m,c)=0,aperfectlybalancedcase;forD5,IR(m,c)=0.89,aratherimbalancedcase;whereasforD6,IR(m,c)=0.99,averyskewedcase.Therefore,thetwomeasures,KulcandIR,worktogether,presentingaclearpictureforallthreedatasets,D4throughD6.Insummary,theuseofonlysupportandconfidencemeasurestomineassocia-tionsmaygeneratealargenumberofrules,manyofwhichcanbeuninterestingtousers.Instead,wecanaugmentthesupport–confidenceframeworkwithapatterninter-estingnessmeasure,whichhelpsfocustheminingtowardruleswithstrongpatternrelationships.Theaddedmeasuresubstantiallyreducesthenumberofrulesgener-atedandleadstothediscoveryofmoremeaningfulrule
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Context: HAN17-ch10-443-496-97801238147912011/6/13:44Page488#46488Chapter10ClusterAnalysis:BasicConceptsandMethodsconsiderclusteringC2,whichisidenticaltoC1exceptthatC2issplitintotwoclusterscontainingtheobjectsinLiandLj,respectively.Aclusteringqualitymeasure,Q,respectingclusterhomogeneityshouldgiveahigherscoretoC2thanC1,thatis,Q(C2,Cg)>Q(C1,Cg).Clustercompleteness.Thisisthecounterpartofclusterhomogeneity.Clustercom-pletenessrequiresthatforaclustering,ifanytwoobjectsbelongtothesamecategoryaccordingtogroundtruth,thentheyshouldbeassignedtothesamecluster.Clustercompletenessrequiresthataclusteringshouldassignobjectsbelongingtothesamecategory(accordingtogroundtruth)tothesamecluster.ConsiderclusteringC1,whichcontainsclustersC1andC2,ofwhichthemembersbelongtothesamecategoryaccordingtogroundtruth.LetclusteringC2beidenticaltoC1exceptthatC1andC2aremergedintooneclusterinC2.Then,aclusteringqualitymeasure,Q,respectingclustercompletenessshouldgiveahigherscoretoC2,thatis,Q(C2,Cg)>Q(C1,Cg).Ragbag.Inmanypracticalscenarios,thereisoftena“ragbag”categorycontain-ingobjectsthatcannotbemergedwithotherobjects.Suchacategoryisoftencalled“miscellaneous,”“other,”andsoon.Theragbagcriterionstatesthatputtingahet-erogeneousobjectintoapureclustershouldbepenalizedmorethanputtingitintoaragbag.ConsideraclusteringC1andaclusterC∈C1suchthatallobjectsinCexceptforone,denotedbyo,belongtothesamecategoryaccordingtogroundtruth.ConsideraclusteringC2identicaltoC1exceptthatoisassignedtoaclusterC(cid:48)(cid:54)=CinC2suchthatC(cid:48)containsobjectsfromvariouscategoriesaccordingtogroundtruth,andthusisnoisy.Inotherwords,C(cid:48)inC2isaragbag.Then,aclusteringqualitymeasureQrespectingtheragbagcriterionshouldgiveahigherscoretoC2,thatis,Q(C2,Cg)>Q(C1,Cg).Smallclusterpreservation.Ifasmallcategoryissplitintosmallpiecesinacluster-ing,thosesmallpiecesmaylikelybecomenoiseandthusthesmallcategorycannotbediscoveredfromtheclustering.Thesmallclusterpreservationcriterionstatesthatsplittingasmallcategoryintopiecesismoreharmfulthansplittinga
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Context: Figure 5.6 Stack values during _j27 routine execution
Now, as you arrive in the decomp_block_start function, right before the ret
struction
in
, the stack values shown in figure 5.6 have already been popped, except the value
in the bottom of the stack, i.e., 0xA091. Thus, when the ret instruction executes, the code
will jump to offset 0xA091. This offset contains the code shown in listing 5.31.
Listing 5.31 Decompression Block Handler Routine
8000:A091 decomp_block_entry proc near
8000:A091 call init_decomp_ngine ; On ret, ds = 0
8000:A094 call copy_decomp_result
8000:A097 call call_F000_0000
8000:A09A retn
8000:A09A decomp_block_entry endp
5.2.3.3. Decompression Engine Initialization
gine initialization is rather complex. Pay attention to its
ngine initialization is shown in listing 5.32.
utine
The decompression en
e
execution. The decompression
Listing 5.32 Decompression Block Initialization Ro
8000:A440 init_decomp_ngine proc near ; decomp_block_entry
8000:A440 xor ax, ax
8000:A442 mov es, ax
8000:A444 assume es:_12000
8000:A444 mov si, 0F349h
8000:A447 mov ax, cs
8000:A449 mov ds, ax ; ds = cs
8000:A44B assume ds:decomp_block
8000:A44B mov ax, [si+2] ; ax = header length
8000:A44E mov edi, [si+4] ; edi = destination addr
8000:A452 mov ecx, [si+8] ; ecx = decompression engine
8000:A452 ; byte count
8000:A456 add si, ax ; Point to decompression engine
64
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Context: 3.6. CHAPTER NOTES
c
⃝Steven & Felix
3.6
Chapter Notes
Many problems in ICPC or IOI require one or combination (see Section 8.2) of these problem
solving paradigms. If we have to nominate a chapter in this book that contestants have to really
master, we will choose this one.
The main source of the ‘Complete Search’ material in this chapter is the USACO training
gateway [29]. We adopt the name ‘Complete Search’ rather than ‘Brute-Force’ as we believe that
some Complete Search solution can be clever and fast enough, although it is complete. We believe
the term ‘clever Brute-Force’ is a bit self-contradicting. We will discuss some more advanced search
techniques later in Section 8.3, e.g. A* Search, Depth Limited Search (DLS), Iterative Deepening
Search (IDS), Iterative Deepening A* (IDA*).
Divide and Conquer paradigm is usually used in the form of its popular algorithms: binary
search and its variants, merge/quick/heap sort, and data structures: binary search tree, heap,
segment tree, etc. We will see more D&C later in Computational Geometry (Section 7.4).
Basic Greedy and Dynamic Programming (DP) techniques techniques are always included in
popular algorithm textbooks, e.g. Introduction to Algorithms [3], Algorithm Design [23], Algorithm
[4]. However, to keep pace with the growing difficulties and creativity of these techniques, especially
the DP techniques, we include more references from Internet: TopCoder algorithm tutorial [17]
and recent programming contests. In this book, we will revisit DP again on four occasions: Floyd
Warshall’s DP algorithm (Section 4.5), DP on (implicit) DAG (Section 4.7.1), DP on String (Section
6.5), and More Advanced DP (Section 8.4).
However, for some real-life problems, especially those that are classified as NP-Complete [3],
many of the approaches discussed so far will not work. For example, 0-1 Knapsack Problem which
has O(NS) DP complexity is too slow if S is big; TSP which has O(N2 ×2N) DP complexity is too
slow if N is much larger than 16. For such problems, people use heuristics or local search: Tabu
Search [15, 14], Genetic Algorithm, Ants Colony Optimization, Beam Search, etc.
There are ≈179 UVa (+ 15 others) programming exercises discussed in this chapter.
(Only 109 in the first edition, a 78% increase).
There are 32 pages in this chapter.
(Also 32 in the first edition, but some content have been reorganized to Chapter 4 and 8).
70
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Context: 4CHAPTER1.DATAANDINFORMATION1.2PreprocessingtheDataAsmentionedintheprevioussection,algorithmsarebasedonassumptionsandcanbecomemoreeffectiveifwetransformthedatafirst.Considerthefollowingexample,depictedinfigure??a.Thealgorithmweconsistsofestimatingtheareathatthedataoccupy.Itgrowsacirclestartingattheoriginandatthepointitcontainsallthedatawerecordtheareaofcircle.Inthefigurewhythiswillbeabadestimate:thedata-cloudisnotcentered.Ifwewouldhavefirstcentereditwewouldhaveobtainedreasonableestimate.Althoughthisexampleissomewhatsimple-minded,therearemany,muchmoreinterestingalgorithmsthatassumecentereddata.Tocenterdatawewillintroducethesamplemeanofthedata,givenby,E[X]i=1NNXn=1Xin(1.1)Hence,foreveryattributeiseparately,wesimpleaddalltheattributevalueacrossdata-casesanddividebythetotalnumberofdata-cases.Totransformthedatasothattheirsamplemeaniszero,weset,X′in=Xin−E[X]i∀n(1.2)ItisnoweasytocheckthatthesamplemeanofX′indeedvanishes.Anillustra-tionoftheglobalshiftisgiveninfigure??b.Wealsoseeinthisfigurethatthealgorithmdescribedabovenowworksmuchbetter!Inasimilarspiritascentering,wemayalsowishtoscalethedataalongthecoordinateaxisinordermakeitmore“spherical”.Considerfigure??a,b.Inthiscasethedatawasfirstcentered,buttheelongatedshapestillpreventedusfromusingthesimplisticalgorithmtoestimatetheareacoveredbythedata.Thesolutionistoscaletheaxessothatthespreadisthesameineverydimension.Todefinethisoperationwefirstintroducethenotionofsamplevariance,V[X]i=1NNXn=1X2in(1.3)wherewehaveassumedthatthedatawasfirstcentered.Notethatthisissimilartothesamplemean,butnowwehaveusedthesquare.Itisimportantthatwehaveremovedthesignofthedata-cases(bytakingthesquare)becauseotherwisepositiveandnegativesignsmightcanceleachotherout.Byfirsttakingthesquare,alldata-casesfirstgetmappedtopositivehalfoftheaxes(foreachdimensionor
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Context: 8.1.THENON-SEPARABLECASE43thataresituatedinthesupporthyperplaneandtheydeterminethesolution.Typi-cally,thereareonlyfewofthem,whichpeoplecalla“sparse”solution(mostα’svanish).Whatwearereallyinterestedinisthefunctionf(·)whichcanbeusedtoclassifyfuturetestcases,f(x)=w∗Tx−b∗=XiαiyixTix−b∗(8.17)AsanapplicationoftheKKTconditionswederiveasolutionforb∗byusingthecomplementaryslacknesscondition,b∗= XjαjyjxTjxi−yi!iasupportvector(8.18)whereweusedy2i=1.So,usinganysupportvectoronecandetermineb,butfornumericalstabilityitisbettertoaverageoverallofthem(althoughtheyshouldobviouslybeconsistent).Themostimportantconclusionisagainthatthisfunctionf(·)canthusbeexpressedsolelyintermsofinnerproductsxTixiwhichwecanreplacewithker-nelmatricesk(xi,xj)tomovetohighdimensionalnon-linearspaces.Moreover,sinceαistypicallyverysparse,wedon’tneedtoevaluatemanykernelentriesinordertopredicttheclassofthenewinputx.8.1TheNon-SeparablecaseObviously,notalldatasetsarelinearlyseparable,andsoweneedtochangetheformalismtoaccountforthat.Clearly,theproblemliesintheconstraints,whichcannotalwaysbesatisfied.So,let’srelaxthoseconstraintsbyintroducing“slackvariables”,ξi,wTxi−b≤−1+ξi∀yi=−1(8.19)wTxi−b≥+1−ξi∀yi=+1(8.20)ξi≥0∀i(8.21)Thevariables,ξiallowforviolationsoftheconstraint.Weshouldpenalizetheobjectivefunctionfortheseviolations,otherwisetheaboveconstraintsbecomevoid(simplyalwayspickξiverylarge).PenaltyfunctionsoftheformC(Piξi)k
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Context: 178TemplatesProblem8.2
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Context: Chapter9.OurTypeface125ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789(cid:362)(cid:363)(cid:364)(cid:365)(cid:366)(cid:367)(cid:368)(cid:369)(cid:370)(cid:371)IJ(cid:276)(cid:277)æœfiflffffiffl(cid:292)(cid:293)(cid:294)(cid:306)st(cid:308)(cid:309)(cid:278)(cid:279)(cid:280)(cid:107)NextaretheSmallCaps,whicharecapitalletterssettothesameheightaslowercaseletters.YoucanseeexamplesofSmallCapsinthefrontmatterofthisbook(thepartsbeforethefirstchapter).Noticethatthesmallcapsarenotjustscaled-downversionsoftheordinarycapitals–havingthesamegeneralweight,theymaybeusedalongsidethem.S(cid:1114)(cid:1102)(cid:1113)(cid:1113)C(cid:1102)(cid:1117)(cid:1120)S(cid:1114)(cid:1102)(cid:1113)(cid:1113)₁₂₃₄₅₆₇₈₉₀N(cid:1122)(cid:1114)(cid:1103)(cid:1106)(cid:1119)(cid:1120)ÄÀÅÁÃĄÂÇäàåáãąâç@£$%¶†‡©¥€`'``''!?(){}:;,./(cid:106)Next,wehaveaccentedletters,ofwhichonlyatinyportionareshownhere.Accentsattachindifferentplacesoneachletter,somanytypefacescontainanaccentedversionofeachcommonletter-accentpair,togetherwithseparateaccentmarkswhichcanbecombinedwithotherlettersasrequiredformoreesotericuses.S(cid:1114)(cid:1102)(cid:1113)(cid:1113)C(cid:1102)(cid:1117)(cid:1120)S(cid:1114)(cid:1102)(cid:1113)(cid:1113)₁₂₃₄₅₆₇₈₉₀N(cid:1122)(cid:1114)(cid:1103)(cid:1106)(cid:1119)(cid:1120)ÄÀÅÁÃĄÂÇäàåáãąâç@£$%¶†‡©¥€`'``''!?(){}:;,./(cid:106)Finally,herearesomeofthemanyotherglyphsinPalatino,forcurrencysymbolsandsoforth,andsomeofthepunctuation:S(cid:1114)(cid:1102)(cid:1113)(cid:1113)C(cid:1102)(cid:1117)(cid:1120)S(cid:1114)(cid:1102)(cid:1113)(cid:1113)₁₂₃₄₅₆₇₈₉₀N(cid:1122)(cid:1114)(cid:1103)(cid:1106)(cid:1119)(cid:1120)ÄÀÅÁÃĄÂÇäàåáãąâç@£$%¶†‡©¥€`'``''!?(){}:;,./(cid:106)
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File: Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf
Page: 3
Context: 6.7
Chapter Notes
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
7
(Computational) Geometry
175
7.1
Overview and Motivation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
7.2
Basic Geometry Objects with Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . 176
7.2.1
0D Objects: Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
7.2.2
1D Objects: Lines
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
iii
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File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 167
Context: Solutions153b)Theloveof\$\$\$istherootofallevil.c)Theloveof$\$\$\$$istherootofallevil.d)Theloveof*\$$\$$\$*istherootofallevil.Chapter41a)Thepatterndoesnotmatch.b)Thepatternmatchesatposition17.c)Thepatternmatchesatpositions28and35.d)Thepatternmatchesatposition24.2a)Thetextsaa,aaa,andaaaetc.match.b)Thetextsacandabconlymatch.c)Thetextsac,abc,andabbcetc.match.d)Thetextsad,abd,acd,abbd,accd,abcd,acbd,andabbbdetc.match.3a)Thepatternmatchesatpositions16and17.b)Thepatternmatchesatpositions0and24.c)Thepatternmatchesatpositions0,1,24,and25.d)Thepatternmatchesatpostiions0,1,24,and25.
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File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 86
Context: HAN09-ch02-039-082-97801238147912011/6/13:15Page49#112.2BasicStatisticalDescriptionsofData49Thequartilesgiveanindicationofadistribution’scenter,spread,andshape.Thefirstquartile,denotedbyQ1,isthe25thpercentile.Itcutsoffthelowest25%ofthedata.Thethirdquartile,denotedbyQ3,isthe75thpercentile—itcutsoffthelowest75%(orhighest25%)ofthedata.Thesecondquartileisthe50thpercentile.Asthemedian,itgivesthecenterofthedatadistribution.Thedistancebetweenthefirstandthirdquartilesisasimplemeasureofspreadthatgivestherangecoveredbythemiddlehalfofthedata.Thisdistanceiscalledtheinterquartilerange(IQR)andisdefinedasIQR=Q3−Q1.(2.5)Example2.10Interquartilerange.Thequartilesarethethreevaluesthatsplitthesorteddatasetintofourequalparts.ThedataofExample2.6contain12observations,alreadysortedinincreasingorder.Thus,thequartilesforthisdataarethethird,sixth,andninthval-ues,respectively,inthesortedlist.Therefore,Q1=$47,000andQ3is$63,000.Thus,theinterquartilerangeisIQR=63−47=$16,000.(Notethatthesixthvalueisamedian,$52,000,althoughthisdatasethastwomedianssincethenumberofdatavaluesiseven.)Five-NumberSummary,Boxplots,andOutliersNosinglenumericmeasureofspread(e.g.,IQR)isveryusefulfordescribingskeweddistributions.HavealookatthesymmetricandskeweddatadistributionsofFigure2.1.Inthesymmetricdistribution,themedian(andothermeasuresofcentraltendency)splitsthedataintoequal-sizehalves.Thisdoesnotoccurforskeweddistributions.Therefore,itismoreinformativetoalsoprovidethetwoquartilesQ1andQ3,alongwiththemedian.Acommonruleofthumbforidentifyingsuspectedoutliersistosingleoutvaluesfallingatleast1.5×IQRabovethethirdquartileorbelowthefirstquartile.BecauseQ1,themedian,andQ3togethercontainnoinformationabouttheend-points(e.g.,tails)ofthedata,afullersummaryoftheshapeofadistributioncanbeobtainedbyprovidingthelowestandhighestdatavaluesaswell.Thisisknownasthefive-numbersummary.Thefive-numbersummaryofadistributionconsistsofthemedian(Q2),thequartilesQ1andQ3,andthesmallestandlargestindividualobser-vations,writtenintheorderofMinimum,Q1,Med
####################
File: Analytic%20Geometry%20%281922%29%20-%20Lewis%20Parker%20Siceloff%2C%20George%20Wentworth%2C%20David%20Eugene%20Smith%20%28PDF%29.pdf
Page: 1
Context: PREFACE
This book is written for the purpose of furnishing college.
classes with a thoroughly usable textbook in analytic geometry.
It is not so elaborate in its details as to be unfitted for practical
classroom use; neither has it been prepared for the purpose
of exploiting any special theory of presentation; it aims solely
to set forth the leading facts of the subject clearly, succinctly,
and in the same practical manner that characterizes the other
textbooks of the series.
It is recognized that the colleges of this country generally
follow one of two plans with respect to analytic geometry.
Either they offer a course extending through one semester or
they expect students who take the subject to continue its.
study through a whole year. For this reason the authors have
so arranged the work as to allow either of these plans to be
adopted. In particular it will be noted that in each of the
chapters on the conic sections questions relating to tangents
to the conic are treated in the latter part of the chapter.
This arrangement allows of those subjects being omitted for
the shorter course if desired. Sections which may be omitted
without breaking the sequence of the work, and the omission
of which will allow the student to acquire a good working
knowledge of the subject in a single half year are as follows:
46-53, 56-62, 121-134, 145-163, 178-197, 225-245, and part
or all of the chapters on solid geometry. On the other hand,
students who wish that thorough foundation in analytic geom-
etry which should precede the study of the higher branches of
mathematics are urged to complete the entire book, whether
required to do so by the course of study or not.
iii
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File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf
Page: 87
Context: A.1.LAGRANGIANSANDALLTHAT75Hence,the“sup”and“inf”canbeinterchangedifstrongdualityholds,hencetheoptimalsolutionisasaddle-point.Itisimportanttorealizethattheorderofmaximizationandminimizationmattersforarbitraryfunctions(butnotforconvexfunctions).Trytoimaginea“V”shapesvalleywhichrunsdiagonallyacrossthecoordinatesystem.Ifwefirstmaximizeoveronedirection,keepingtheotherdirectionfixed,andthenminimizetheresultweendupwiththelowestpointontherim.Ifwereversetheorderweendupwiththehighestpointinthevalley.Thereareanumberofimportantnecessaryconditionsthatholdforproblemswithzerodualitygap.TheseKarush-Kuhn-Tuckerconditionsturnouttobesuffi-cientforconvexoptimizationproblems.Theyaregivenby,∇f0(x∗)+Xiλ∗i∇fi(x∗)+Xjν∗j∇hj(x∗)=0(A.8)fi(x∗)≤0(A.9)hj(x∗)=0(A.10)λ∗i≥0(A.11)λ∗ifi(x∗)=0(A.12)Thefirstequationiseasilyderivedbecausewealreadysawthatp∗=infxLP(x,λ∗,ν∗)andhenceallthederivativesmustvanish.Thisconditionhasaniceinterpretationasa“balancingofforces”.Imagineaballrollingdownasurfacedefinedbyf0(x)(i.e.youaredoinggradientdescenttofindtheminimum).Theballgetsblockedbyawall,whichistheconstraint.Ifthesurfaceandconstraintisconvextheniftheballdoesn’tmovewehavereachedtheoptimalsolution.Atthatpoint,theforcesontheballmustbalance.Thefirsttermrepresenttheforceoftheballagainstthewallduetogravity(theballisstillonaslope).Thesecondtermrepresentsthere-actionforceofthewallintheoppositedirection.Theλrepresentsthemagnitudeofthereactionforce,whichneedstobehigherifthesurfaceslopesmore.Wesaythatthisconstraintis“active”.Otherconstraintswhichdonotexertaforceare“inactive”andhaveλ=0.ThelatterstatementcanbereadoffromthelastKKTconditionwhichwecall“complementaryslackness”.Itsaysthateitherfi(x)=0(theconstraintissaturatedandhenceactive)inwhichcaseλisfreetotakeonanon-zerovalue.However,iftheconstraintisinactive:fi(x)≤0,thenλmustvanish.Aswewillseesoon,theactiveconstraintswillcorrespondtothesupportvectorsinSVMs!
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File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf
Page: 258
Context: | | |
| -------- | -------- |
| all: | build_rom.o |
| |$(LD) $(LDFLAGS) -o build_rom build_rom.o |
| | |
| cp build_rom ../ | |
| %.o: %.c | |
| $(CC) $(CFLAGS) -o $@ $< | |
| clean: | |
| rm -rf *~ build_rom *.o | |
Listing 7.8 build_rom.c
/* ----------------------------------------------------------------------
Copyright (c) Darmawan Mappatutu Salihun
File name : build_rom.c
This file is released to the public for noncommercial use only
Description :
This program zero-extends its input binary file and then patches it
into a valid PCI PnP ROM binary.
--------------------------------------------------------------------- */
#include
#include
#include
typedef unsigned char u8;
typedef unsigned short u16;
typedef unsigned int u32;
enum {
MAX_FILE_NAME = 100,
ITEM_COUNT = 1,
ROM_SIZE_INDEX = 0x2,
PnP_HDR_PTR = 0x1A,
PnP_CHKSUM_INDEX = 0x9,
PnP_HDR_SIZE_INDEX = 0x5,
ROM_CHKSUM = 0x10, /* Reserved position in PCI PnP ROM, that
can be used */
};
static int
ZeroExtend(char * f_name, u32 target_size)
{
FILE* f_in;
long file_size, target_file_size, padding_size;
32
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 585
Context: HAN19-ch12-543-584-97801238147912011/6/13:25Page548#6548Chapter12OutlierDetectionCollectiveoutlierdetectionhasmanyimportantapplications.Forexample,inintrusiondetection,adenial-of-servicepackagefromonecomputertoanotheriscon-siderednormal,andnotanoutlieratall.However,ifseveralcomputerskeepsendingdenial-of-servicepackagestoeachother,theyasawholeshouldbeconsideredasacol-lectiveoutlier.Thecomputersinvolvedmaybesuspectedofbeingcompromisedbyanattack.Asanotherexample,astocktransactionbetweentwopartiesisconsiderednor-mal.However,alargesetoftransactionsofthesamestockamongasmallpartyinashortperiodarecollectiveoutliersbecausetheymaybeevidenceofsomepeoplemanipulatingthemarket.Unlikeglobalorcontextualoutlierdetection,incollectiveoutlierdetectionwehavetoconsidernotonlythebehaviorofindividualobjects,butalsothatofgroupsofobjects.Therefore,todetectcollectiveoutliers,weneedbackgroundknowledgeoftherelationshipamongdataobjectssuchasdistanceorsimilaritymeasurementsbetweenobjects.Insummary,adatasetcanhavemultipletypesofoutliers.Moreover,anobjectmaybelongtomorethanonetypeofoutlier.Inbusiness,differentoutliersmaybeusedinvariousapplicationsorfordifferentpurposes.Globaloutlierdetectionisthesimplest.Contextoutlierdetectionrequiresbackgroundinformationtodeterminecontextualattributesandcontexts.Collectiveoutlierdetectionrequiresbackgroundinformationtomodeltherelationshipamongobjectstofindgroupsofoutliers.12.1.3ChallengesofOutlierDetectionOutlierdetectionisusefulinmanyapplicationsyetfacesmanychallengessuchasthefollowing:Modelingnormalobjectsandoutlierseffectively.Outlierdetectionqualityhighlydependsonthemodelingofnormal(nonoutlier)objectsandoutliers.Often,build-ingacomprehensivemodelfordatanormalityisverychallenging,ifnotimpossible.Thisispartlybecauseitishardtoenumerateallpossiblenormalbehaviorsinanapplication.Theborderbetweendatanormalityandabnormality(outliers)isoftennotclearcut.Instead,therecanbeawiderangeofgrayarea.Consequently,whilesomeout-lierdetectionmethodsassigntoeachobjectintheinputdata
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File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 105
Context: Chapter7.DoingSums91checkthatitworks(again,inourshortenedformofdiagram):reverse[1,2,3]=⇒reverse[2,3]•[1]=⇒(reverse[3]•[2])•[1]=⇒(([3]•reverse[])•[2])•[1]=⇒(([3]•[])•[2])•[1]=⇒[3,2,1]Letusapproachamorecomplicatedproblem.Howmightwesortalistintonumericalorder,whateverorderitisintostartwith?Forexample,wewanttosort[53,9,2,6,19]toproduce[2,6,9,19,53].Theproblemisalittleunapproachable–itseemsrathercomplex.Onewaytobeginistoseeifwecansolvethesimplestpartoftheproblem.Welljustlikeforreverse,sortingalistoflengthzeroiseasy–thereisnothingtodo:sortl=ifl=[]then[]else...Ifthelisthaslengthgreaterthanzero,ithasaheadandatail.Assumeforamomentthatthetailisalreadysorted–thenwejustneedtoinserttheheadintothetailatthecorrectpositionandthewholelistwillbesorted.Hereisadefinitionforsort,assumingwehaveaninsertfunction(weshallconcoctinsertinamoment):sortl=ifl=[]then[]elseinsert(headl)(sort(taill))Ifthelistisempty,wedonothing;otherwise,weinserttheheadofthelistintoitssortedtail.Assuminginsertexists,hereisthewholeevaluationofoursortingprocedureonthelist[53,9,2,6,19],showingonlyusesofsortandinsertforbrevity:sort[53,9,2,6,19]=⇒insert53(sort[9,2,6,19])=⇒insert53(insert9(sort[2,6,19]))=⇒insert53(insert9(insert2(sort[6,19])))=⇒insert53(insert9(insert2(insert6(sort[19]))))
####################
File: Analytic%20Geometry%20%281922%29%20-%20Lewis%20Parker%20Siceloff%2C%20George%20Wentworth%2C%20David%20Eugene%20Smith%20%28PDF%29.pdf
Page: 1
Context: iv
PREFACE
This book is intended as a textbook for a course of a full
year, and it is believed that many of the students who study
the subject for only a half year will desire to read the full text.
An abridged edition has been prepared, however, for students
who study the subject for only one semester and who do not
care to purchase the larger text.
It will be observed that the work includes two chapters on
solid analytic geometry. These will be found quite sufficient
for the ordinary reading of higher mathematics, although they
do not pretend to cover the ground necessary for a thorough
understanding of the geometry of three dimensions.
It will also be noticed that the chapter on higher plane
curves includes the more important curves of this nature,
considered from the point of view of interest and applications.
A complete list is not only unnecessary but undesirable, and
the selection given in Chapter XII will be found ample for
our purposes.
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf
Page: 111
Context: 13. 4_C86Ch–4_D396h: ppminit.rom. This is an expansion ROM for an onboard
device.
14. 4_D397h–4_E381h: \F1\foxconn.bmp. This is the Foxconn logo.
15. 4_E382h–4_F1D0h: \F1\64n8iip.bmp. This is another logo displayed during boot.
After the last compressed component there are padding FFh bytes. An example of
these padding bytes is shown in hex dump 5.2.
Hex dump 5.2 Padding Bytes after Compressed Award BIOS Components
Address Hex ASCII
0004F1A0 66DF 6FB7 DB2D 9B55 B368 B64B 4B4B 0054 f.o..-.U.h.KKK.T
0004F1B0 A4A4 A026 328A 2925 2525 AE5B 1830 6021 ...&2.)%%%.[.0`!
0004F1C0 0A3A 3A3B 59AC D66A F57A BD56 AB54 04A0 .::;Y..j.z.V.T..
0004F1D0 00FF FFFF FFFF FFFF FFFF FFFF FFFF FFFF ................
0004F1E0 FFFF FFFF FFFF FFFF FFFF FFFF FFFF FFFF ................
The compressed components can be extracted easily by copying and pasting it into
a new binary file in Hex Workshop. Then, decompress this new file by using LHA 2.55 or
WinZip. If you are into using WinZip, give the new file an .lzh extension so that it will be
automatically associated with WinZip. Recognizing where you should cut to obtain the new
file is easy. Just look for the -lh5- string. Two bytes before the -lh5- string is the
beginning of the file, and the end of the file is always 00h, right before the next compressed
file,3 the padding bytes, or some kind of checksum. As an example, look at the beginning
nd the e
a
nd of the compressed awardext.rom in the current Foxconn BIOS as seen within a
hex editor. The bytes highlighted in yellow are the beginning of the compressed file, and
he bytes highlighted in green are the end of compressed
t
awardext.rom.
Hex dum
ward BIOS Component Header Sample
p 5.3 Compressed A
Address
ASCII
Hex
00
0 6CE0 C1F9 041B C000 E725 1E2D 6C68 352D l........%.-lh5-
014DE
00014DF0 EC94 0000 40DC 0000 0000 7F40 2001 0C61 ....@......@ ..a
00014E00 7761 7264 6578 742E 726F 6D2C 0B20 0000 wardext.rom,. ..
00014E10 2CD0 8EF7 7EEB 1253 5EFF 7DE7 39CC CCCC ,...~..S^.}.9...
........
0001E2F0 ADAB 0F89 A8B5 D0FA 84EB 46B2 0024 232D ..........F..$#-
0001E300 6C68 352D 0D1B 0000 FC47 0000 0000 0340 lh5-.....G.....@
0
0 2001 0B41 4350 4954 424C 2E42 494E F3CD ..ACPITBL.BIN..
In the preceding hex dump, the last byte before the beginning of the compressed
awardext.rom is not an end-of-file marker,
001E31
00h
4 i.e., not
, even though the component is also
3 The -lh5- marker in its beginning also marks the next compressed file.
4 The end-of-file marker is a byte with 00h value.
5
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File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 474
Context: HAN16-ch09-393-442-97801238147912011/6/13:22Page437#459.8Summary437Backpropagationisaneuralnetworkalgorithmforclassificationthatemploysamethodofgradientdescent.Itsearchesforasetofweightsthatcanmodelthedatasoastominimizethemean-squareddistancebetweenthenetwork’sclasspredictionandtheactualclasslabelofdatatuples.Rulesmaybeextractedfromtrainedneuralnetworkstohelpimprovetheinterpretabilityofthelearnednetwork.Asupportvectormachineisanalgorithmfortheclassificationofbothlinearandnonlineardata.Ittransformstheoriginaldataintoahigherdimension,fromwhereitcanfindahyperplanefordataseparationusingessentialtrainingtuplescalledsupportvectors.Frequentpatternsreflectstrongassociationsbetweenattribute–valuepairs(oritems)indataandareusedinclassificationbasedonfrequentpatterns.Approachestothismethodologyincludeassociativeclassificationanddiscriminantfrequentpattern–basedclassification.Inassociativeclassification,aclassifierisbuiltfromassociationrulesgeneratedfromfrequentpatterns.Indiscriminativefrequentpattern–basedclassification,frequentpatternsserveascombinedfeatures,whichareconsideredinadditiontosinglefeatureswhenbuildingaclassificationmodel.Decisiontreeclassifiers,Bayesianclassifiers,classificationbybackpropagation,sup-portvectormachines,andclassificationbasedonfrequentpatternsareallexamplesofeagerlearnersinthattheyusetrainingtuplestoconstructageneralizationmodelandinthiswayarereadyforclassifyingnewtuples.Thiscontrastswithlazylearnersorinstance-basedmethodsofclassification,suchasnearest-neighborclassifiersandcase-basedreasoningclassifiers,whichstoreallofthetrainingtuplesinpatternspaceandwaituntilpresentedwithatesttuplebeforeperforminggeneralization.Hence,lazylearnersrequireefficientindexingtechniques.Ingeneticalgorithms,populationsofrules“evolve”viaoperationsofcrossoverandmutationuntilallruleswithinapopulationsatisfyaspecifiedthreshold.Roughsettheorycanbeusedtoapproximatelydefineclassesthatarenotdistinguishablebasedontheavailableattributes.Fuzzysetapproachesreplace“brittle”threshold
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File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf
Page: 81
Context: elocationofthemiddleorcenterofadatadistribution.Intuitivelyspeaking,givenanattribute,wheredomostofitsvaluesfall?Inparticular,wediscussthemean,median,mode,andmidrange.Inadditiontoassessingthecentraltendencyofourdataset,wealsowouldliketohaveanideaofthedispersionofthedata.Thatis,howarethedataspreadout?Themostcommondatadispersionmeasuresaretherange,quartiles,andinterquartilerange;thefive-numbersummaryandboxplots;andthevarianceandstandarddeviationofthedataThesemeasuresareusefulforidentifyingoutliersandaredescribedinSection2.2.2.Finally,wecanusemanygraphicdisplaysofbasicstatisticaldescriptionstovisuallyinspectourdata(Section2.2.3).Moststatisticalorgraphicaldatapresentationsoftware
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf
Page: 26
Context: 12Chapter1.PuttingMarksonPaperProblemsSolutionsonpage147.Gridsforyoutophotocopyorprintouthavebeenprovidedonpage173.Alternatively,usegraphpaperordrawyourowngrids.1.Givesequencesofcoordinateswhichmaybeusedtodrawthesesetsoflines.0246810121416182002468101214161820xy0246810121416182002468101214161820xy2.Drawthesetwosequencesofcoordinatesonseparate20x20grids,withlinesbetweenthepoints.Whatdotheyeachshow?(5,19)—(15,19)—(15,16)—(8,16)—(8,12)—(15,12)—(15,9)—(8,9)—(8,5)—(15,5)—(15,2)—(5,2)—(5,19)(0,5)—(10,10)—(5,0)—(10,3)—(15,0)—(10,10)—(20,5)—(17,10)—(20,15)—(10,10)—(15,20)—(10,17)—(5,20)—(10,10)—(0,15)—(3,10)—(0,5)3.Giventhefollowinglineson20x20grids,selectpixelstoap-proximatethem.
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf
Page: 451
Context: mov cl, (NumberOfSections-@8)[esi] mul cl ; *************************** ; * Set section table * ; *************************** ; Move ESI to the start of SectionTable lea esi, (StartOfSectionTable-@8)[esi] push eax ; Size push edx ; Pointer of file push esi ; Address of buffer ; *************************** ; * Code size of merged * ; * virus code section and * ; * total size of virus * ; * code section table must * ; * be smaller than or equal* ; * to unused space size of * ; * following section table * ; *************************** inc ecx push ecx ; Save NumberOfSections+1 shl ecx, 03h push ecx ; Save TotalSizeOfVirusCodeSectionTable add ecx, eax add ecx, edx sub ecx, (SizeOfHeaders-@9)[esi] not ecx inc ecx ; Save my virus first section code ; size of following section table... ; (do not include size of virus code section table) push ecx xchg ecx, eax ; ECX = size of section table ; Save original address of entry point mov eax, (AddressOfEntryPoint-@9)[esi] add eax, (ImageBase-@9)[esi] mov (OriginalAddressOfEntryPoint-@9)[esi], eax cmp word ptr [esp], small CodeSizeOfMergeVirusCodeSection jl OnlySetInfectedMark ; *************************** ; * Read all section tables * ; *************************** mov eax, ebp call edi ; VXDCall IFSMgr_Ring0_FileIO ; *************************** ; * Fully modify the bug: *
##########
"""QUERY: Please summarize the whole context. It is important that you include a summary for each file. All files should be included, so please make sure to go through the entire context"""
Consider the chat history for relevant information. If query is already asked in the history double check the correctness of your answer and maybe correct your previous mistake. If you find information separated by a | in the context, it is a table formatted in Markdown - the whole context is formatted as md structure.
Final Files Sources: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 82, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 353, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 584, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 351, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 352, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 612, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 717, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 10, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 167, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 618, BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf - Page 287, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 583, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 52, Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf - Page 611, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - Page 228, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 80, Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf - 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FINAL ANSWER
Answer: The provided context is a collection of excerpts from various books and articles related to computer science and data mining. Here's a summary of each file:
**File: A MACHINE MADE THIS BOOK ten sketches of computer science - JOHN WHITINGTON (PDF).pdf**
This book explores various aspects of computer science, starting from the basics of putting marks on paper to more complex topics like data compression and page layout. It uses a conversational and engaging style, often employing examples and metaphors to explain complex concepts.
* **Chapter 1: Putting Marks on Paper:** This chapter introduces the fundamental concepts of drawing lines and shapes on paper, considering the effects of different printing techniques.
* **Chapter 2: Letter Forms:** This chapter delves into the design of letters from a realistic typeface, exploring how typefaces are created and how letters are laid out on a page.
* **Chapter 3: Storing Words:** This chapter discusses how computers represent human language, including encoding different languages in a standard form and displaying text in various ways.
* **Chapter 4: Looking and Finding:** This chapter introduces basic computer programming concepts through the example of searching for words in a text.
* **Chapter 5: Typing It In:** This chapter explores the history of typewriters and modern methods of typing, including how Asian languages with complex writing systems can be typed.
* **Chapter 6: Saving Space:** This chapter focuses on data compression techniques, explaining how to reduce the size of words and images without losing essential detail.
* **Chapter 7: Doing Sums:** This chapter introduces more programming concepts, focusing on calculating sums and manipulating lists of items.
* **Chapter 8: Grey Areas:** This chapter explores the problem of reproducing color or grayscale images using only black ink on white paper.
* **Chapter 9: Our Typeface:** This chapter investigates the Palatino typeface used in the book, examining its intricacies and how letters are laid out next to each other to form lines of words.
* **Chapter 10: Words to Paragraphs:** This chapter explains how lines of letters are combined into paragraphs to build a block of text, covering topics like hyphenation and page layout.
**File: Data Mining Concepts and Techniques - Jiawei Han, Micheline Kamber, Jian Pei (PDF).pdf**
This book is a comprehensive guide to data mining, covering various concepts, techniques, and applications. It provides a detailed exploration of data preprocessing, frequent pattern mining, classification, clustering, outlier detection, and data warehousing.
* **Chapter 1: Introduction:** This chapter introduces the concept of data mining, its importance, and its relationship to other disciplines.
* **Chapter 2: Mining Frequent Patterns:** This chapter explores the concept of frequent patterns, their importance in data mining, and various algorithms for mining them.
* **Chapter 3: Data Preprocessing:** This chapter discusses the importance of data quality and various techniques for cleaning, integrating, reducing, and transforming data.
* **Chapter 4: Data Warehousing and Online Analytical Processing:** This chapter delves into data warehousing, its architecture, models, and OLAP operations.
* **Chapter 5: Data Cube Technology:** This chapter explores data cube technology, its computation methods, and various materialization strategies.
* **Chapter 6: Association Analysis:** This chapter discusses association rule mining, its concepts, algorithms, and measures of interestingness.
* **Chapter 7: Advanced Pattern Mining:** This chapter explores advanced pattern mining techniques, including mining compressed patterns, approximate patterns, and semantic annotations.
* **Chapter 8: Classification: Basic Concepts and Methods:** This chapter introduces the concept of classification, its various methods, and different types of classifiers.
* **Chapter 9: Classification: Advanced Methods:** This chapter delves into advanced classification techniques, including backpropagation, support vector machines, and classification based on frequent patterns.
* **Chapter 10: Cluster Analysis: Basic Concepts and Methods:** This chapter introduces the concept of cluster analysis, its various methods, and different types of clustering algorithms.
* **Chapter 11: Cluster Analysis: Advanced Methods:** This chapter explores advanced clustering techniques, including density-based clustering, model-based clustering, and constraint-based clustering.
* **Chapter 12: Outlier Detection:** This chapter focuses on outlier detection, its types, challenges, and various detection methods.
* **Chapter 13: Data Mining Trends and Research Frontiers:** This chapter discusses current trends and research frontiers in data mining, including mining complex data types, other methodologies, and applications.
**File: BIOS Disassembly Ninjutsu Uncovered 1st Edition - Darmawan Salihun (PDF) BIOS_Disassembly_Ninjutsu_Uncovered.pdf**
This book is a guide to reverse engineering BIOS code, focusing on the Award BIOS used in many motherboards. It provides a step-by-step approach to understanding the BIOS structure, its components, and how they interact.
* **Chapter 2: BIOS Disassembly with IDA Pro:** This chapter introduces IDA Pro, a powerful disassembler, and explains how to use it to disassemble BIOS code.
* **Chapter 3: BIOS Linker Script and Flat Binary File:** This chapter explains the role of linker scripts in BIOS development and how to generate a flat binary file from C and assembly code.
* **Chapter 4: BIOS Decompression Engine:** This chapter explores the LZH decompression engine used in Award BIOS and explains how it decompresses compressed BIOS components.
* **Chapter 5: Award BIOS Reverse Engineering:** This chapter delves into the reverse engineering of the Award BIOS, focusing on the boot block and system BIOS.
* **Chapter 6: PCI PnP Expansion ROM:** This chapter explains the concept of PCI PnP expansion ROM and how to create and patch it.
* **Chapter 7: PCI Expansion ROM Checksum Utility:** This chapter provides the source code for a utility that patches the checksums of PCI PnP expansion ROM binaries.
* **Chapter 8: BIOS Flash Programming Utility:** This chapter explains the concept of BIOS flashing and provides the source code for a utility that can read, write, and erase flash ROM chips.
* **Chapter 9: BIOS Flash Programming Utility for PCI Expansion ROM:** This chapter extends the BIOS flash programming utility to support PCI expansion ROM.
* **Chapter 10: BIOS Virus Analysis:** This chapter analyzes the CIH virus, explaining how it infects the system and destroys BIOS contents.
* **Chapter 11: Trusted Computing:** This chapter introduces the concept of trusted computing and explains how it is implemented in PC architecture.
* **Chapter 12: BIOS Security:** This chapter discusses BIOS security, including vulnerabilities and countermeasures.
**File: Competitive Programming, 2nd Edition - Steven Halim (PDF).pdf**
This book is a guide to competitive programming, focusing on the skills and techniques needed to succeed in programming contests like ICPC and IOI. It covers various data structures, algorithms, and problem-solving paradigms.
* **Chapter 1: Introduction:** This chapter introduces the world of competitive programming, its importance, and different types of contests.
* **Chapter 2: Data Structures & Libraries:** This chapter introduces various data structures, including arrays, lists, stacks, queues, trees, and graphs.
* **Chapter 3: Problem Solving Paradigms:** This chapter explores four common problem-solving paradigms: Complete Search, Divide & Conquer, Greedy, and Dynamic Programming.
* **Chapter 4: Graph:** This chapter delves into graph algorithms, including Depth-First Search, Breadth-First Search, Minimum Spanning Tree, Shortest Paths, and Maximum Flow.
* **Chapter 5: Mathematics:** This chapter covers various mathematical concepts and techniques relevant to competitive programming, including number theory, probability, and game theory.
* **Chapter 6: String Processing:** This chapter explores string processing techniques, including basic string manipulation, string matching, and suffix arrays.
* **Chapter 7: (Computational) Geometry:** This chapter introduces computational geometry, covering basic geometric objects, algorithms, and libraries.
* **Chapter 8: More Advanced Topics:** This chapter explores more advanced topics, including A* search, IDA* search, and advanced dynamic programming techniques.
**File: A First Encounter with Machine Learning - Max Welling (PDF).pdf**
This book provides an introduction to machine learning, focusing on the intuitive understanding and mathematical foundations of various algorithms. It covers topics like data representation, preprocessing, visualization, classification, and regression.
* **Chapter 1: Data and Information:** This chapter introduces the concept of data, its representation, and preprocessing techniques.
* **Chapter 2: Data Visualization:** This chapter explores various methods for visualizing data to gain insights.
* **Chapter 3: Learning:** This chapter introduces the concept of learning and its different types.
* **Chapter 4: Types of Machine Learning:** This chapter discusses different types of machine learning, including supervised, unsupervised, and reinforcement learning.
* **Chapter 5: Nearest Neighbors Classification:** This chapter explores the nearest neighbors classification algorithm and its applications.
* **Chapter 6: The Naive Bayesian Classifier:** This chapter introduces the Naive Bayesian classifier, its model, and how to learn and use it.
* **Chapter 7: The Perceptron:** This chapter explores the perceptron model, its limitations, and its applications.
* **Chapter 8: Support Vector Machines:** This chapter introduces support vector machines, their mathematical foundations, and how to use them for classification.
* **Chapter 9: Support Vector Regression:** This chapter explores support vector regression and its applications.
* **Chapter 10: Kernel Ridge Regression:** This chapter introduces kernel ridge regression and its applications.
* **Chapter 11: Kernel K-means and Spectral Clustering:** This chapter explores kernel K-means and spectral clustering algorithms.
* **Chapter 12: Kernel Principal Components Analysis:** This chapter introduces kernel principal components analysis and its applications.
* **Chapter 13: Fisher Linear Discriminant Analysis:** This chapter explores Fisher linear discriminant analysis and its applications.
* **Chapter 14: Kernel Canonical Correlation Analysis:** This chapter introduces kernel canonical correlation analysis and its applications.
This summary provides a high-level overview of the content covered in each file. For a more detailed understanding, you should refer to the original documents.
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