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            "content": "\ud83e\udd16\u2728 Hallo, ich bin Simon, Ihr pers\u00f6nlicher Assistent! \ud83c\udf1f Wie kann ich Ihnen heute behilflich sein? Lassen Sie uns gemeinsam Ihre Fragen kl\u00e4ren, Herausforderungen meistern und Ihre Ziele erreichen! Einfach fragen, und ich bin f\u00fcr Sie da.\n\nWhich docs do you think are helpful for software devs?\n\n FOR ADDITIONAL CONTEXT:\n\nKnowledgebase name: 10.24 big files\nKnowledgebase files:\nA First Encounter with Machine Learning - Max Welling (PDF).pdf\nA MACHINE MADE THIS BOOK ten sketches of computer science - JOHN WHITINGTON (PDF).pdf\nAdvanced Algebra - Anthony W. Knapp (PDF).pdf\nCompetitive Programming, 2nd Edition - Steven Halim (PDF).pdf\nBIOS Disassembly Ninjutsu Uncovered 1st Edition - Darmawan Salihun (PDF) BIOS_Disassembly_Ninjutsu_Uncovered.pdf\nData Mining Concepts and Techniques - Jiawei Han, Micheline Kamber, Jian Pei (PDF).pdf\nAnalytic Geometry (1922) - Lewis Parker Siceloff, George Wentworth, David Eugene Smith (PDF).pdf\nKnowledgebases crawlers:\n"
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        },
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            "content": "Competitive programming is a sport where participants are given well-known computer science problems and must solve them as quickly as possible. It's not about research problems with unknown solutions, but rather about applying existing computer science knowledge to code solutions efficiently. \n\nThe goal of competitive programming is not just to win competitions, but to develop well-rounded computer scientists who are prepared to tackle complex problems in the future. \n\nThe book \"Competitive Programming, 2nd Edition\" by Steven Halim, which is part of the provided knowledge base, provides a comprehensive guide to competitive programming, covering various topics like data structures, algorithms, problem-solving paradigms, and specific problem categories. \n\nDo you have any specific questions about competitive programming that you'd like me to answer? For example, you could ask about:\n\n* **Popular competitive programming platforms** (like UVa Online Judge, ACM ICPC Live Archive, TopCoder, etc.)\n* **Specific problem-solving techniques** (like Complete Search, Divide and Conquer, Greedy algorithms, Dynamic Programming, etc.)\n* **Important data structures and algorithms** (like heaps, hash tables, graphs, trees, etc.)\n* **Tips for improving your competitive programming skills** (like practicing regularly, mastering programming languages, testing your code thoroughly, etc.)\n\nI'm here to help you learn more about competitive programming and achieve your goals!"
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FINAL QUERY 
Final Query: CONTEXT: ##########
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf

Page: 185

Context: FurtherReading171Chapter6FundamentalDataCompressionIdaMengyiPu.PublishedbyButter-worth-Heinemann(2006).ISBN0750663103.TheFaxModemSourcebookAndrewMargolis.PublishedbyWiley(1995).ISBN0471950726.IntroductiontoDataCompressionKhalidSayood.PublishedbyMor-ganKaufmaninTheMorganKaufmannSeriesinMultimediaIn-formationandSystems(fourthedition,2012).ISBN0124157963.Chapter7PythonProgrammingfortheAbsoluteBeginnerMikeDawson.Pub-lishedbyCourseTechnologyPTR(thirdedition,2010).ISBN1435455002.OCamlfromtheVeryBeginningJohnWhitington.PublishedbyCo-herentPress(2013).ISBN0957671105.SevenLanguagesinSevenWeeks:APragmaticGuidetoLearningPro-grammingLanguagesBruceA.Tate.PublishedbyPragmaticBook-shelf(2010).ISBN193435659X.Chapter8HowtoIdentifyPrintsBamberGascgoine.PublishedbyThames&Hudson(secondedition,2004).ISBN0500284806.AHistoryofEngravingandEtchingArthurM.Hind.PublishedbyDoverPublications(1963).ISBN0486209547.PrintsandPrintmaking:AnIntroductiontotheHistoryandTechniquesAntonyGriffiths.PublishedbyUniversityofCaliforniaPress(1996).ISBN0520207149.DigitalHalftoningRobertUlichney.PublishedbyTheMITPress(1987).ISBN0262210096.
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf

Page: 573

Context: # UEFI Execution Environment

- Uniprocessor
- Protected mode
- Paging mode not enabled
- Selectors are set to flat and are otherwise not used
- Interrupts are enabled—though no interrupt services are supported other than the UEFI boot services item functions (All loaded device drivers are serviced synchronously by "polling.")
- Direction flag in EFLAGS is clear
- Other general purpose flags registers are undefined
- 128 KB, or more, of available stack space

An application written to the specification may alter the processor execution mode, but the UEFI image must ensure firmware boot services and runtime services are executed with the prescribed execution environment.

## 2.3.4 x64 Platforms

All functions are called with C calling convention.

During boot services time, the processor is in the following execution mode:

- Uniprocessor
- Long mode, 64-bit mode
- Paging mode is enabled and any memory space defined by the UEFI memory map is identically mapped (virtual addresses equal physical addresses). The mappings to other regions are undefined and may vary from implementation to implementation.
- Selectors are set to flat and are otherwise not used.
- Interrupts are enabled—though no interrupt services are supported other than the UEFI boot services item functions (All loaded device drivers are serviced synchronously by "polling.")
- Direction flag in EFLAGS is clear
- Other general purpose flags registers are undefined
- 128 KB, or more, of available stack space

As you can see from the previous citation, the system is running in protected mode or long mode with flat memory addressing in order to run the UEFI routines. It’s also clear from the citation that the code that runs in one of these execution environments is compiled by using C compiler. C is chosen as the standard language because it’s well suited for system programming tasks like this. Note that the executable inside an UEFI image can be in the form of EFI byte code, i.e., not in the form of “native” executable binary of the platform in which it runs. EFI byte code is portable between different platforms because it’s executed inside an EFI interpreter that is required to be present in an UEFI-compliant firmware.

There is more to UEFI specification than what I’ve explained so far. Nonetheless, I can give you some pointers to understand the specification more easily. The specification is more than a thousand pages long; it’s hard to grasp without a “roadmap.” The key to the specification is in chapter 1 and chapter 2 of UEFI specification, especially section 1.5, UEFI design overview and all of the sections in chapter 2 of UEFI specification. Once you have grasped those sections, you are ready to delve into the next sections that you are interested in. This concludes this subsection. In the next subsection, I present some implementations of the UEFI/UEFI from two major firmware vendors, AMI and Phoenix Technologies.
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf

Page: 650

Context: ayraisesecurityandprivacyconcerns.Theseuniquekindsofdataprovidefertilelandfordatamining.Dataminingincomputersciencecanbeusedtohelpmonitorsystemstatus,improvesystemperformance,isolatesoftwarebugs,detectsoftwareplagiarism,analyzecomputersystemfaults,uncovernetworkintrusions,andrecognizesystemmalfunc-tions.Dataminingforsoftwareandsystemengineeringcanoperateonstaticordynamic(i.e.,stream-based)data,dependingonwhetherthesystemdumpstracesbeforehandforpostanalysisorifitmustreactinrealtimetohandleonlinedata.Variousmethodshavebeendevelopedinthisdomain,whichintegrateandextendmethodsfrommachinelearning,datamining,software/systemengineering,patternrecognition,andstatistics.Dataminingincomputerscienceisanactiveandrichdomainfordataminersbecauseofitsuniquechallenges.Itrequiresthefurtherdevelopmentofsophisticated,scalable,andreal-timedataminingandsoftware/systemengineeringmethods.
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf

Page: 47

Context: # Application Settings for the IDA Pro Plugin Project

1. In the **Application Settings** tab, from the **Application type** selection buttons, select **DLL**, and from the **Additional options** checkboxes choose **empty project**. Then, click **finish**. This step is shown in Figure 2.17.

   ![Figure 2.17 Application settings for the IDA Pro plugin project](path/to/figure2.17.png)

2. In the **Solution Explorer** on the right side of Visual Studio .NET 2003, right-click the **Source Files** folder and go to **Add** > **Add New Item...** or **Add** > **Add Existing Item...** to add the relevant source code files (*.cpp, *.c) into the plugin project as shown in Figure 2.18. 

   Start by creating new source code file, i.e., **main.cpp**. Then, copy the contents of the main source code file of the sample plugin from the IDA Pro SDK (`sdk\plugins\vc\sample\strings.cpp`) to **main.cpp**. 

   ![Figure 2.18 Source files in Solution Explorer](path/to/figure2.18.png)
####################
File: Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf

Page: 249

Context: ```markdown
# INDEX

- LA 2001 - Editor, 173
- LA 3001 - The Code, 132
- LA 3602 - Project Costing, 128
- LA 3899 - The Request constant genre, 139
- LA 3900 - Intermediary, 83
- LA 4001 - MODS, 128
- LA 4002 - Accessibility, 31
- LA 4003 - INTERLINK, 128
- LA 4100 - RACING, 60
- LA 4101 - Right Face Logo, 84
- LA 4102 - Budget Fundamentals, 61
- LA 4103 - Library, 128
- LA 4104 - Create X-Philanthropy, 115
- LA 4167 - JCPenney Format, 15
- LA 4200 - Help the Team, 155
- LA 4201 - Short & Bulky, 18
- LA 4202 - Evaluate a Marital Plan, 13
- LA 4204 - Causes of Phobias, 82
- LA 4210 - Channel Ports, 30
- LA 4299 - Shopping Don’s Day, 128
- LA 4300 - Marketplace, 154
- LA 4327 - Proximate End-Use, 202
- LA 4328 - C.A. Dwyer, 111
- LA 4329 - Est. No. 118, 211
- LA 4700 - Intro to Ethics, 214
- LA 4710 - Adult Education, 14
- LA 4711 - Syllabus, 292
- LA 4721 - Norma's Passport, 94
- LA 4722 - Event Re-Analysis, 210
- LA 4723 - Project Lead, 153
- LA 4724 - Introspective Approach, 202
- LA 4725 - Parent-Child, 100
- LA 4820 - Inventory, 211
- LA 8001 - Book List, 81
- LA 8002 - Amenable, 102
- LA 8003 - Required Substitution, 94
- LA 8004 - Bill Ten, 50
- LA 8005 - Thiessen Approach, 56
- LA 8006 - Activities Against, 128
- LA 8041 - Uproarious Joy, 103
- LA 8042 - Upside Down, 72
- LA 8110 - It's Friday, 66
- LA 8140 - Sharks Chocolate, 210
- LA 8143 - Sharks, 45

## Additional Items

- LA 4841 - Strung Popping, 45
- LA 854 - Password, 86
- LA 854 - Learn ACM-More, 132
- LA 884 - Your Bib, 89
- LA 6961 - Overlapping Zones, 46
- LA 5000 - Underwriter Systems, 202
- La 5001 - Java, 181
- Law of Causation, 141
- Last Common Multiple, 135
- Least-Turn Test, see CCW Text Libraries, 71
- Linear Diophantine Equation, 141
- Link List, 172
- Live Archive, 12
- Longitudinal Score Subsegment, 161
- Longest Causation Substring, 61
- Lowest Common Ancestor, 113

### Metrics

- Max Flow:
  - Max Flow with Vertex Capacities, 105
  - Maximum Independent Paths, 106
  - Min Cost (Max) Flow, 105
  - Min Flow, 61
  - Multi-source Minimal Sink Max Flow, 105
- Minimum Spanning Tree:
  - Minimum Spanning Tree, 86
  - Partial Minimum Spanning Tree, 86
- Shortest Path Simple Tree, 87
- McCabe Arithmetic, 107
- Myers, Case Study, 159

## Optimal Play

- Pascal, Blaze, 128
- Perfect Play, 145
```
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf

Page: 287

Context: • -R means traverse the directories recursively starting from the current directory and include in the tag file the source code information from all traversed directories. • * means create tags in the tag file for every file that ctags can parse.   Once you've invoked ctags like that, the tag file will be created in the current directory and named tags, as shown in shell snippet 9.8.  Shell snippet 9.8 The Tag File pinczakko@opunaga:~/Project/freebios_flash_n_burn> ls -l ... -rw-r--r--    1 pinczakko users       12794 Aug  8 09:06 tags ...   I condensed the shell output in shell snippet 9.8 to save space. Now, you can traverse the source code using vi. I'll start with flash_rom.c. This file is the main file of the flash_n_burn utility. Open it with vi and find the main function within the file. When you are trying to understand a source code, you have to start with the entry point function. In this case, it's main. Now, you can traverse the source code; to do so, place the cursor in the function call that you want to know and then press Ctrl+] to go to its definition. If you want to know the data structure definition for an object,5 place the cursor in the member variable of the object and press Ctrl+]; vi will take you to the data structure definition. To go back from the function or data structure definition to the calling function, press Ctrl+t. Note that these key presses apply only to vi; other text editors may use different keys. As an example, refer to listing 9.2. Note that I condensed the source code and added some comments to explain the steps to traverse the source code.  Listing 9.2 Moving flash_n_burn Source Code // -- file: flash_rom.c -- int main (int argc, char * argv[]) {   // Irrelevant code omitted    (void) enable_flash_write(); // You will find the definition of this                                // function. Place the cursor in the                                // enable_flash_write function call, then                                // press Ctrl+].    // Irrelevant code omitted }                                                   5 An object is a data structure instance. For example if a data structure is named my_type, then a variable of type my_type is an object, as in my_type a_variable; a_variable is an object.
####################
File: Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf

Page: 28

Context: # 1.2 TIPS TO BE COMPETITIVE  
**© Steven & Felix**

## 1.2.5 Tip 6: Practice and More Practice

Competitive programmers, like real athletes, must train themselves regularly and keep themselves programming-fit. This is not just a tip; it's a necessity. It’s one of the best ways to improve your problem-solving skills. Success comes as a result of continuous practice on ACM contests.

Universities or local programming clubs often organize regular ACM contests. These contests will expose you to the same problems and submit your solutions to the online judges. Errors, correctness, and the performance of your program will be reported as soon as possible. Try solving the problems mentioned in the book and see how you perform on the up-to-minute problems. The problems presented here can give you a ranking:

- **UVa (starting 12 August 2011)** is ranked (6) for solving 1260 UVa problems with 2950 submissions.
- **ACM ICPC Live Archive** is ranked (9) for solving 1118 problems since year 2003 and maintaining your performance over time.

![University of Valladolid (IVA) Online Judge](https://example.com)  
![ACM ICPC Live Archive](https://example.com)

USA Computing Olympiad has a very useful training website [20] and online contests for you to learn programming and problem-solving. This site is geared more towards IOI participants. Go straight to their website, register an account, and start training.  
The Sphere Online Judge [19] is another online judge where veterans can add problems too. This online judge is mostly for problems from Poland, Brazil, and Vietnam. We take SPOJ into account since it offers numerous problems for novice programmers.

![USA Computing Olympiad Training](https://example.com)  
![Sphere Online Judge](https://example.com)

## 1.2.6 Tip 7: Team Work (ICPC Only)

This tip is not just something that is teachable. But here are some ideas that may help with teamwork:

- Practice coding on a blank paper (useful when your teammate is solving the other problem).
- Submit and discuss your strategy. If you code a 1AC, ignore your partner. If it is still not AC, debug using their strategy too. This helps not only your performance but also increases your teamwork rating as expected by ICPC judges.

If your teammate is having difficulty coding his algorithm, prepare challenges for you to help prepare him. You want to learn and obtain the cases and exemplify the solutions as a team.

- The X-factor: Befriend your teammates outside the training sessions & actual competition.
####################
File: Competitive%20Programming%2C%202nd%20Edition%20-%20Steven%20Halim%20%28PDF%29.pdf

Page: 204

Context: # 7.3 Polygons with Libraries

Polygon is a plane figure that is bounded by a closed path (path that starts and ends at the same vertex) composed of a finite sequence of straight line segments. These segments are called edges or sides. The point where two edges meet is the polygon's vertex or corner. Polygon is a source of many geometry problems as it allows the problem setter to present more realistic objects.

## 7.3.1 Polygon Representation

The standard way to represent a polygon is to simply enumerate the vertices of the polygon in either clockwise or counterclockwise order, with the last vertex equal to the first vertex (some of the functions mentioned later require this arrangement).

```c
struct point { 
    double x, y; 
}; // reproduced here

// 6 points oriented in counter clockwise order, 0-based indexing
vector<point> P;
P.push_back(point(1, 1)); // P0
P.push_back(point(3, 1)); // P1
P.push_back(point(4, 2)); // P2
P.push_back(point(3, 4)); // P3
P.push_back(point(1, 4)); // P4
P.push_back(point(1, 1)); // important: loop back
```

## 7.3.2 Perimeter of a Polygon

The perimeter of a polygon with \( n \) vertices given in some order (clockwise or counter-clockwise) can be computed using the simple function below.

```c
double dist(point p1, point p2) { 
    return hypot(p1.x - p2.x, p1.y - p2.y); // as shown earlier
}

// returns the perimeter, which is the sum of Euclidean distances
// of consecutive line segments (polygon edges)
double perimeter(vector<point> P) { 
    double result = 0.0; 
    for (int i = 0; i < P.size() - 1; i++) // assume that the first vertex
        result += dist(P[i], P[i + 1]); // is equal to the last vertex
    return result; 
}
```

## 7.3.3 Area of a Polygon

The signed area A of (either convex or concave) polygon with \( n \) vertices given in some order (clockwise or counter-clockwise) is:

```
A = 1/2 * (x₁y₂ + x₂y₃ + ... + xₙy₁ - x₂y₁ - x₃y₂ - ... - xₙy₁)

A =               x₁    y₁
                 x₂    y₂
                 x₃    y₃
                  ...
                 xₙ    yₙ
                 x₀    y₀  ← cycle back to the first vertex
```

This structure provides a clear presentation of polygon-related algorithms and calculations while adhering to proper Markdown syntax.
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf

Page: 718

Context: ”5training,18transactional,13–14typesof,33web,597–598dataauditingtools,92
####################
File: Data%20Mining%20Concepts%20and%20Techniques%20-%20Jiawei%20Han%2C%20Micheline%20Kamber%2C%20Jian%20Pei%20%28PDF%29.pdf

Page: 75

Context: eACMInterna-tionalConferenceonWebSearchandDataMining(WSDM),theACMConferenceonInformationandKnowledgeManagement(CIKM),theEuropeanConferenceonInfor-mationRetrieval(ECIR),theTextRetrievalConference(TREC),andtheACM/IEEEJointConferenceonDigitalLibraries(JCDL).Othersourcesofpublicationincludemajorinformationretrieval,informationsystems,andWebjournals,suchasJournalofInformationRetrieval,ACMTransactionsonInformationSystems(TOIS),Informa-tionProcessingandManagement,KnowledgeandInformationSystems(KAIS),andIEEETransactionsonKnowledgeandDataEngineering(TKDE).
####################
File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf

Page: 153

Context: Chapter10.WordstoParagraphs139thosewordsareinthesamelanguage–werequireahyphenationdictionaryforeachlanguageappearinginthedocument).Forexample,inthetypesettingsystemusedforthisbook,thereare8527rules,andonly8exceptionalcaseswhichmustbelistedexplicitly:uni-ver-sityma-nu-scriptsuni-ver-sit-iesre-ci-pro-cityhow-everthrough-outma-nu-scriptsome-thingThusfar,wehaveassumedthatdecisionsonhyphenationaremadeoncewereachtheendofalineandfindweareabouttooverrunit.Ifweare,wealterthespacingbetweenwords,orhy-phenate,orsomecombinationofthetwo.Andso,atmostweneedtore-typesetthecurrentline.Advancedlinebreakingalgorithmsuseamorecomplicatedapproach,seekingtooptimisetheresultforawholeparagraph.(Wehavegoneline-by-line,makingthebestlinewecanforthefirstline,thenthesecondetc.)Itmayturnoutthatanawkwardsituationlaterintheparagraphispreventedbymakingaslightlyless-than-optimaldecisioninanearlierline,suchassqueezinginanextrawordorhyphenatinginagoodpositionwhennotstrictlyrequired.Wecanassign“demerits”tocertainsituations(ahyphenation,toomuchortoolittlespacingbetweenwords,andsoon)andoptimisetheoutcomefortheleastsumofsuchdemerits.Thesesortsofoptimisationalgorithmscanbequiteslowforlargeparagraphs,takinganamountoftimeequaltothesquareofthenumberoflinesintheparagraph.Fornormaltexts,thisisnotaproblem,sinceweareunlikelytohavemorethanafewtensoflinesinasingleparagraph.Wehavenowdealtwithsplittingatextintolinesandpara-graphs,butsimilarproblemsoccurwhenitcomestofittingthoseparagraphsontoapage.Therearetwoworryingsituations:whenthelastlineofaparagraphis“widowed”atthetopofthenextpage,andwhenthefirstlineofaparagraphis“orphaned”onthelastlineofapage.Examplesofawidowandanorphanareshownonthenextpage.Itisdifficulttodealwiththeseproblemswith-outupsettingthebalanceofthewholetwo-pagespread,butitcanbedonebyslightlyincreasingordecreasinglinespacingononeside.Anotheroption,ofcourse,istoeditthetext,andyoumaybesurprisedtolearnhowoftenthathappens.Furthersmalladjustmentsandimprovementstoreducetheamountofhyphenationcanbeintroducedusing
####################
File: BIOS%20Disassembly%20Ninjutsu%20Uncovered%201st%20Edition%20-%20Darmawan%20Salihun%20%28PDF%29%20BIOS_Disassembly_Ninjutsu_Uncovered.pdf

Page: 8

Context: Typographical Conventions  In this book, the courier font is used to indicate that text is one of the following: 1. Source code 2. Numeric values 3. Configuration file entries 4. Directory/paths in the file system 5. Datasheet snippets 6. CPU registers  Hexadecimal values are indicated by prefixing them with a 0x or by appending them with h. For example, the integer value 4691 will, in hexadecimal, look like 0x1253 or 1253h. Hexadecimal values larger than four digits will be accompanied by underscore every four consecutive hexadecimal digits to ease reading the value, as in 0xFFFF_0000 and 0xFD_FF00_0000.  Binary values are indicated by appending them with b. For example, the integer value 5 will, in binary, look like 101b.  Words will appear in the italic font, in this book, for following reasons: 1. When defining a new term 2. For emphasis  Words will appear in the bold font, in this book, for the following reasons: 3. When describing a menu within an application software in Windows 4. A key press, e.g. CAPSLOCK, G, Shift, C, etc. 5. For emphasis
####################
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Context: HAN04-fore-xix-xxii-97801238147912011/6/13:32Pagexxii#4xxiiForewordtoSecondEditionthefield.Thefieldisevolvingveryrapidly,butthisbookisaquickwaytolearnthebasicideas,andtounderstandwherethefieldistoday.Ifounditveryinformativeandstimulating,andbelieveyouwilltoo.JimGrayInhismemory
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Context: HAN11-ch04-125-186-97801238147912011/6/13:17Page184#60184Chapter4DataWarehousingandOnlineAnalyticalProcessing4.16Adatacube,C,hasndimensions,andeachdimensionhasexactlypdistinctvaluesinthebasecuboid.Assumethattherearenoconcepthierarchiesassociatedwiththedimensions.(a)Whatisthemaximumnumberofcellspossibleinthebasecuboid?(b)Whatistheminimumnumberofcellspossibleinthebasecuboid?(c)Whatisthemaximumnumberofcellspossible(includingbothbasecellsandaggregatecells)intheCdatacube?(d)WhatistheminimumnumberofcellspossibleinC?4.17Whatarethedifferencesbetweenthethreemaintypesofdatawarehouseusage:infor-mationprocessing,analyticalprocessing,anddatamining?DiscussthemotivationbehindOLAPmining(OLAM).4.8BibliographicNotesThereareagoodnumberofintroductory-leveltextbooksondatawarehousingandOLAPtechnology—forexample,Kimball,Ross,Thornthwaite,etal.[KRTM08];Imhoff,Galemmo,andGeiger[IGG03];andInmon[Inm96].ChaudhuriandDayal[CD97]provideanearlyoverviewofdatawarehousingandOLAPtechnology.Asetofresearchpapersonmaterializedviewsanddatawarehouseimplementationswerecol-lectedinMaterializedViews:Techniques,Implementations,andApplicationsbyGuptaandMumick[GM99].Thehistoryofdecisionsupportsystemscanbetracedbacktothe1960s.However,theproposaltoconstructlargedatawarehousesformultidimensionaldataanalysisiscreditedtoCodd[CCS93]whocoinedthetermOLAPforonlineanalyticalprocessing.TheOLAPCouncilwasestablishedin1995.Widom[Wid95]identifiedseveralresearchproblemsindatawarehousing.KimballandRoss[KR02]provideanoverviewofthedeficienciesofSQLregardingtheabilitytosupportcomparisonsthatarecommoninthebusinessworld,andpresentagoodsetofapplicationcasesthatrequiredatawarehousingandOLAPtechnology.ForanoverviewofOLAPsystemsversusstatisticaldatabases,seeShoshani[Sho97].Grayetal.[GCB+97]proposedthedatacubeasarelationalaggregationoperatorgeneralizinggroup-by,crosstabs,andsubtotals.Harinarayan,Rajaraman,andUllman[HRU96]proposedagreedyalgorithmforthepartialmaterializationofcuboidsinthecomputationofadatacube.Datacubecomputationmethodshave
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Context: # 6.4 STRING MATCHING

The solution for string matching in a 2D grid is usually a recursive backtracking (see Section 3.2.1). This is based on the ID counterpart where we always go to the right, at every recursive call (that is, for each row of the 2D grid, we never have close branches). 

To optimize the backtracking process, usually we employ this skipping strategy. Once the recursion depends on the length of the pattern, we can immediately prune that recursive branch. This is also called an *alpha-beta* search (see Section 3.2.3).

## Programming Exercises related to String Matching

- UVA 10405: **Podruk String** (finds s in s + s)
- UVA 10282: **Recursive String** (finds s in s + s, similar to UVA 455)
- UVA 11717: **Plank List** (substring search, matching)
- UVA 11756: **Sliding Slice** (modified string matching, complete suffix)
- UVA 11763: **Alternating Sum** (to check ‘alternated’, find reverse of s in s + s)

## In 2D Grid

1. UVA 10222: **Word Search, Wander!** (2D grid, backtracking)
2. UVA 10100: **Wormhole’s Winder** (discussed in the section)
3. UVA 11813: **Playing Boggle** (2D grid, backtracking, do not count twice)

## Profile of Algorithm Inventors

**Donald Erth Knuth** (born 1938) is a computer scientist and Professor Emeritus at Stanford University. He is the author of the popular Computer Science book: *The Art of Computer Programming*. Knuth has been called the “father” of the analysis of algorithms. Knuth is also the creator of TeX, the computer typesetting system used in his book.

**James Hiram Morris** (born 1941) is a Professor of Computer Science. He is a co-inventor of the Knuth-Morris-Pratt algorithm for string searching.

**Vaughan Ronald Pratt** (born 1944) is a Professor Emeritus at Stanford University. He was one of the earliest pioneers in the field of computer science. His work has contributed to foundational areas such as search algorithms, string algorithms, and primality testing. He is also a co-inventor of the Knuth-Morris-Pratt algorithm for string searching.

**Saul B. Needleman** and **Christian D. Wunsch** jointly published the string alignment dynamic programming algorithm in 1970. Their DP algorithm is described in this book.

**Temple F. P. H. Gibbons** is a prominent computer scientist and bioinformatician. He is known for his work with Michael Waterman in 1981. The Smith-Waterman algorithm was significant in computational biology for aligning sequences, particularly in comparing RNA, DNA, and proteins.

**Michael S. Waterman** is a professor at the University of Southern California. Waterman is one of the key contributors to computational biology and is known for his work in dynamic programming, especially for genomic analysis.

**Udi Manber** is a Russian-American computer scientist who is known for his work in string matching algorithms. He first introduced Suffix Array data structure (Naive approach). 

**Eugene “Gene” W. Myers** Jr. is an American computer scientist and bioinformatician, known for his development of the BLAST (Basic Local Alignment Search Tool) for sequence analysis. His 1990 paper that describes BLAST has received over 2000 citations making it among the most highly cited paper ever. He also invented Suffix Array with Udi Manber.
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Context: thesepapers:[SN88,Gal93,TS93,Avn95,LSL95,CS96,LGT97].ThemethodofruleextractiondescribedinSection9.2.4isbasedonLu,Setiono,andLiu[LSL95].CritiquesoftechniquesforruleextractionfromneuralnetworkscanbefoundinCravenandShavlik[CS97].Roy[Roy00]proposesthatthetheoreticalfoundationsofneuralnetworksareflawedwithrespecttoassumptionsmaderegardinghowconnectionistlearningmodelsthebrain.Anextensivesurveyofapplicationsofneuralnetworksinindustry,business,andscienceisprovidedinWidrow,Rumelhart,andLehr[WRL94].SupportVectorMachines(SVMs)grewoutofearlyworkbyVapnikandChervonenkisonstatisticallearningtheory[VC71].ThefirstpaperonSVMswaspresentedbyBoser,Guyon,andVapnik[BGV92].MoredetailedaccountscanbefoundinbooksbyVapnik[Vap95,Vap98].Goodstartingpointsincludethetuto-rialonSVMsbyBurges[Bur98],aswellastextbookcoveragebyHaykin[Hay08],Kecman[Kec01],andCristianiniandShawe-Taylor[CS-T00].Formethodsforsolvingoptimizationproblems,seeFletcher[Fle87]andNocedalandWright[NW99].Thesereferencesgiveadditionaldetailsalludedtoas“fancymathtricks”inourtext,suchastransformationoftheproblemtoaLagrangianformulationandsubsequentsolvingusingKarush-Kuhn-Tucker(KKT)conditions.
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Context: SECTIONS {         .text __boot_vect :     {        *( .text)        } = 0x00         .rodata ALIGN(4) :        {               *( .rodata)        } = 0x00         .data ALIGN(4) :     {               *( .data)     } = 0x00         .bss ALIGN(4) :     {               *( .bss)        } = 0x00  }   7.3.3.2. PCI PnP Expansion ROM Checksum Utility Source Code   The source code provided in this section is used to build the build_rom utility, which is used to patch the checksums of the PCI PnP expansion ROM binary produced by section 7.3.3.1. The role of each file as follows:  • makefile: Makefile used to build the utility • build_rom.c: C language source code for the build_rom utility  Listing 7.7 PCI Expansion ROM Checksum Utility Makefile # ----------------------------------------------------------------------- # Copyright (C) Darmawan Mappatutu Salihun # File name : Makefile # This file is released to the public for noncommercial use only # -----------------------------------------------------------------------  CC= gcc CFLAGS= -Wall -O2 -march=i686 -mcpu=i686 -c LD= gcc LDFLAGS=   31
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Context: AMACHINEMADETHISBOOKtensketchesofcomputerscience
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Context: rwithMorganKauf-mannPublishersatwww.booksite.mkp.com/datamining3e.Thesewebsitescontainmanysupplementalmaterialsforreadersofthisbookoranyoneelsewithaninterestindatamining.Theresourcesincludethefollowing:Slidepresentationsforeachchapter.LecturenotesinMicrosoftPowerPointslidesareavailableforeachchapter.
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Context: # Authors’ Profiles

## Steven Halim, PhD¹  
stevenhalim@gmail.com  

Steven Halim is currently a lecturer in the School of Computing, National University of Singapore (NUS). He teaches several programming courses in NUS, ranging from basic programming to advanced courses that introduce data structures and algorithms, and up to the "Computational Programming" module that serves as the book's foundation. He has participated in several ACM ICPC Regional contests, including Singapore 2004, 2008, 2010, and 2013, where he and other teams in NUS have successfully qualified for the ACM ICPC World Finals team (2010-2011) as well as ICPC world finalists.

As an aside, Steven is a happily married man. His wife, Grace Shyuan, is currently pregnant with their first baby during the time the second edition of this book is released.

## Felix Halim, PhD Candidate²  
felixhalim@gmail.com  

Felix Halim is currently a PhD student in the same University, SoC. He is fond of programming challenges. He has a minor in computer graphics and is also an active coder. He was on his first ICPC team during 2012 contest (representing NUS) and his ICPC friends at that time, Bin Shustar, obtained good ranks (6th and 10th ICPC respectively). Then, in his last team, they were in the ACM ICPC Singapore Regional 2016 and finished as 1st place in Taisey 2017 (GlobalCode Masters) and his team has ranked TopCoder Single Round Matches and his badge rating is a yellow color.

---

# Copyright

No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, scanning, uploading to any information storage and retrieval systems. 

---
*¹ PhD Thesis: "An Integrated Writer–Block Box Approach for Designing and Tuning Stochastic Local Search Algorithms"*

*² Research area: "Large Scale Data Processing."*
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Context: # Hex Workshop

![Figure 2.1: Hex Workshop 9557A-AE5R2 BIOS file opened with Hex Workshop](image_path)

A quick look in the American Standard Code for Information Interchange (ASCII) section (the rightmost section in the figure) reveals some string. The most interesting one is the `~1s` in the beginning of the binary file. An experienced programmer will be suspicious of this string, because it resembles a marker for a header of a compressed file. Further research will reveal that this is a string to mark the header of a file compressed with LHA.

You can try a similar approach to another kind of file. For example, every file compressed with WinZip will start with ASCII code `PK`, and every file compressed with WinRAR will start with ASCII code `Rar!`, as seen in a hex editor. This shows how powerful a preliminary assessment is.

## 2.2 Introducing IDA Pro

Reverse code engineering is carried out to comprehend the algorithm used in software by analyzing the executable file of the corresponding software. In most cases, the software only comes with the executable—without its source code. The same is true for the BIOS. Only the executable binary file is accessible. Reverse code engineering is carried out with the help of some tools: a debugger; a disassembler; a hexadecimal file editor, a.k.a. a hex editor, in-circuit emulator, etc. In this book, I only deal with a disassembler and a hex editor. The current chapter only deals with a disassembler, i.e., IDA Pro disassembler.

IDA Pro is a powerful disassembler. It comes with support for plugin and scripting facilities and support for more than 50 processor architectures. However, every powerful tool has its downside of being hard to use, and IDA Pro is not an exception. This chapter is designed to address this issue.

There are several editions of IDA Pro: freeware, standard, and advanced. The latest freeware edition as of the writing of this book is IDA Pro version 4.3. It's available for download at [http://www.dlfile.com/ida_pro_freeware_version.htm](http://www.dlfile.com/ida_pro_freeware_version.htm). It's the most limited of the IDA Pro versions. It supports only the x86 processor and doesn’t come with a plugin.
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Context: # 2.2 DATA STRUCTURES WITH BUILT-IN LIBRARIES

**Steven & Felix**

---

## Heap - C++ STL priority_queue (Java PriorityQueue)

Heap is another way to organize data as a tree structure. Heap is also a binary tree (BST) but is most notably different from the traditional BST (Binary Search Tree). Heap addresses the Heap property: In each subtree rooted at z, the entry in the left and the right subtrees are smaller than (or equal to) z (see Figure 2.1, right). This is also a part of the maximum (and minimum) heap used in section 3.3. This property influences how we can search for the maximum element and insert one element, which can easily be done in O(log n) (max heap) and O(1) (min heap).

### (Max) Heap used to model a Priority Queue, where items with the highest priority (the maximum element) are dequeued first from the priority queue in O(log n) per operation. The opposite priority queue is also available in C++ STL (using Java PriorityQueue). Priority Queue is an important component in algorithms like Prim's (and Kruskal's) algorithm for Minimum Spanning Tree (MST) problem (Section 4.3) and Dijkstra's algorithm for Single-Source Shortest Path (SSSP) problem (Section 4.4).

### Example code: 

```cpp
#include <queue>
#include <vector>
#include <iostream>

int main() {
    std::priority_queue<int> pq;
    pq.push(10);
    pq.push(5);
    pq.push(20);

    while (!pq.empty()) {
        std::cout << pq.top() << " ";
        pq.pop();
    }
    return 0;
}
```

## Hash Table - C++ STL unordered_map (Java HashMap/Hashtable)

Hash tables are another C++ STL structure used for storing and accessing data in a geometric structure. The primary benefit of using a hash table is that it allows you to access the data in average O(1) time. 
When elements are stored, they are hashed into a unique number based on the input string; this unique number is used to access the element in the array. However, since hash functions can lead to collisions (where two inputs hash to the same output), methods must be implemented to handle them. This can be done using chaining (linked lists) or open addressing.

It is worth mentioning that the average case for search times is O(1), but the worst case can escalate to O(n) if many collisions occur. However, with a proper hash function, the worst-case scenario can be effectively minimized.

### Example code:

```cpp
#include <unordered_map>
#include <iostream>
#include <string>

int main() {
    std::unordered_map<std::string, int> umap;
    umap["apple"] = 1;
    umap["banana"] = 2;
    umap["grapes"] = 3;

    for (const auto& pair : umap) {
        std::cout << pair.first << ": " << pair.second << std::endl;
    }
    return 0;
}
```

--- 

This section provides an overview of data structures commonly used in the C++ STL and their relationships with Java counterparts, emphasizing efficiency and practical application in algorithms.
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Context: ructures(unlikeSQLqueriesindatabasesystems).Thetypicalapproachesininformationretrievaladoptprobabilisticmodels.Forexample,atextdocumentcanberegardedasabagofwords,thatis,amultisetofwordsappearinginthedocument.Thedocument’slanguagemodelistheprobabilitydensityfunctionthatgeneratesthebagofwordsinthedocument.Thesimilaritybetweentwodocumentscanbemeasuredbythesimilaritybetweentheircorrespondinglanguagemodels.Furthermore,atopicinasetoftextdocumentscanbemodeledasaprobabilitydis-tributionoverthevocabulary,whichiscalledatopicmodel.Atextdocument,whichmayinvolveoneormultipletopics,canberegardedasamixtureofmultipletopicmod-els.Byintegratinginformationretrievalmodelsanddataminingtechniques,wecanfind
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Context: HAN01-fm-i-vi-97801238147912011/6/13:29Pageiii#3DataModelingEssentials,3rdEditionGraemeC.Simsion,GrahamC.WittDevelopingHighQualityDataModelsMatthewWestLocation-BasedServicesJochenSchiller,AgnesVoisardManagingTimeinRelationalDatabases:HowtoDesign,Update,andQueryTemporalDataTomJohnston,RandallWeisDatabaseModelingwithMicrosoftR(cid:13)VisioforEnterpriseArchitectsTerryHalpin,KenEvans,PatrickHallock,BillMacleanDesigningData-IntensiveWebApplicationsStephanoCeri,PieroFraternali,AldoBongio,MarcoBrambilla,SaraComai,MaristellaMateraMiningtheWeb:DiscoveringKnowledgefromHypertextDataSoumenChakrabartiAdvancedSQL:1999—UnderstandingObject-RelationalandOtherAdvancedFeaturesJimMeltonDatabaseTuning:Principles,Experiments,andTroubleshootingTechniquesDennisShasha,PhilippeBonnetSQL:1999—UnderstandingRelationalLanguageComponentsJimMelton,AlanR.SimonInformationVisualizationinDataMiningandKnowledgeDiscoveryEditedbyUsamaFayyad,GeorgesG.Grinstein,AndreasWierseTransactionalInformationSystemsGerhardWeikum,GottfriedVossenSpatialDatabasesPhilippeRigaux,MichelScholl,andAgnesVoisardManagingReferenceDatainEnterpriseDatabasesMalcolmChisholmUnderstandingSQLandJavaTogetherJimMelton,AndrewEisenbergDatabase:Principles,Programming,andPerformance,2ndEditionPatrickandElizabethO’NeilTheObjectDataStandardEditedbyR.G.G.Cattell,DouglasBarryDataontheWeb:FromRelationstoSemistructuredDataandXMLSergeAbiteboul,PeterBuneman,DanSuciuDataMining:PracticalMachineLearningToolsandTechniqueswithJavaImplementations,3rdEditionIanWitten,EibeFrank,MarkA.HallJoeCelko’sDataandDatabases:ConceptsinPracticeJoeCelkoDevelopingTime-OrientedDatabaseApplicationsinSQLRichardT.SnodgrassWebFarmingfortheDataWarehouseRichardD.Hackathorn
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Context: # BIBLIOGRAPHY

[1] TopCoder Inc. PrimePairs. Copyright 2009 TopCoder, Inc. All rights reserved.  
   [Link](https://www.topcoder.com/tc?module=Static&d1=help&d2=14273)

[2] TopCoder Inc. Single Round Match (SRM).  
   [Link](https://www.topcoder.com/)

[3] Competitive Learning Institute. ACM ICPC Live Archive.  
   [Link](http://acm.uva.es/)

[4] IOI. International Olympiad in Informatics.  
   [Link](http://olympiads.org/)

[5] Jarek Kuczynski, Gianni Marazzini, and Simon J. Puglisi. Permuted Longest-Common-Prefix Arrays. In ECAI, LNCS 357, pages 181–192, 2006.

[6] Jon Kleinberg and Éva Tardos. Algorithm Design. Addison Wesley, 2006.

[7] Anany Levitin. Introduction to the Design and Analysis of Algorithms. Addison Wesley, 2002.

[8] Ruijin Liu. Algorithmic Contests for Beginners (in Chinese). Tsinghua University Press, 2000.

[9] Ruijin Liu and Liang Huang. The Art of Algorithms and Programming Contests (in Chinese). Tsinghua University Press, 2003.

[10] Institute of Mathematics and Informatics, Vilnius University. Informatics in Informatics.  
   [Link](http://www.mif.vu.lt/)

[11] University of Valladolid. Online Judge.  
   [Link](http://uva.onlinejudge.org/)

[12] USA Computing Olympiad. USACO Training Program Gateway.  
   [Link](http://train.usaco.org/)

[13] Joseph O’Rourke. Computational Geometry. C. Cambridge U Press, 2nd edition, 1998.

[14] Kenneth H. Rosen. Elementary Number Theory and Its Applications. Addison Wesley Longman, 4th edition, 2001.

[15] Robert Sedgewick. Algorithms in C++, Part 5: Advanced Data Structures, 2nd edition, 2002.

[16] Steven S. Skiena. The Algorithm Design Manual. Springer, 2008.

[17] Steven S. Skiena and Miguel A. Revilla. Programming Challenges. Springer, 2003.

[18] SPOJ. Sphere Online Judge.  
   [Link](http://www.spoj.pl/)

[19] Wing-Kin Sung. Algorithms in Bioinformatics: A Practical Introduction. CRC Press (Taylor & Francis Group), 1st edition, 2010.

[20] Edino U. Thokozani. On the reconstruction of suffix trees. Algorithmica, 14 (3-24):289–296, 1995.

[21] Bayley University. ACM International Collegiate Programming Contest.  
   [Link](http://icpc.fas.harvard.edu)

[22] Tom Veklenko. 24 Years of IOI Competition Tasks. Olympiads in Informatics, 3, 21496/2008.

[23] Adrian V. and Czesław Nougaret. Suffix arrays - a programming contest approach. 2008.

[24] Henry S. Warren. Hacker’s Delight. Pearson, 1st edition, 2002.

[25] Wikipedia. The Free Encyclopedia.  
   [Link](http://en.wikipedia.org/)
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Context: # Chapter 1. Putting Marks on Paper

& & &

On the left is an idealised high resolution shape. In the middle, just black and white at a lower resolution. On the right, prepared for display on a screen supporting grey as well as black and white, at the same lower resolution. This use of greys is known as antialiasing, since the jagged edges in low resolution lines are known as aliasing. This term originated in the field of signal processing and is used to describe problems stemming from low-resolution versions of high-resolution signals. Here is a photograph, taken under a microscope, of such an antialiased line on a modern computer screen:

![Microscope image of antialiased line](link-to-image)

The left image is magnified 20×; the right image 400×. The rectangular shapes you can see in the images are the separate Red, Green, and Blue sub-pixels, which a monitor uses to build up all the different colours and greys it may need (the monitor makes a picture by emitting light and Red, Green, and Blue are the primary colours of light).

What might a reasonable minimum resolution be? To simplify, let’s return to the scenario where we only have black and white dots – no antialiasing. The resolution required to make the page look smooth depends on the distance at which the typical viewer sees it. For a computer screen, this might be twenty inches. For a smartphone, eight inches. For a billboard, twenty feet (if you have never walked right up to a billboard and looked at the printing, do so – it is surprisingly coarse). The limit of the human optical system's ability to distinguish the colour of adjacent dots,
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Context: releasing source code version 3.5.0. With this utility, you can read, erase, and verify the supported flash ROMs in the PCI expansion card directly in Linux. Ctflasher supports kernel versions 2.4 and 2.6. Currently, ctflasher only supports some network interface cards (NICs), the proprietary ctflasher card, the SiS 630 motherboard, and a flasher card that connects through the IDE port.  The architecture of ctflasher is based on an LKM. Thus, to use it, you have to load the kernel module in advance. After the LKM has been loaded, you can access the flasher through the /proc interface by using the cat command. The HOWTO file from ctflasher version 3.5.0 explains the usage as follows:  First do a "make all." All modules will be placed in modules. Do a "cd modules." There should be 8 files.  For kernel 2.4, these files are  flash.o                  -- The main module, containing algorithms for programming flashprom ct.o                       -- Low-level driver for ctflasher  ide_flash.o           -- Low-level driver for ide-flasher e100_flash.o        -- Low-level driver for Intel nic e100 3c90xc_flash.o    -- Low-level driver for Intel nic 3c905c rtl8139_flash.o    -- Low-level driver for Realtek nic 8139 sis630_flash.o     -- Low-level driver for north–southbridge SiS 630 (BIOS) via-rhine_flash.o -- Low-level driver for via Rhine nic  While for kernel 2.6, these files are  flash.ko            -- The main module, containing algorithms for programming flashprom ct.ko               -- Low-level driver for ctflasher ide_flash.ko        -- Low-level driver for ide-flasher e100_flash.ko       -- Low-level driver for Intel nic e100 3c90xc_flash.ko     -- Low-level driver for Intel nic 3c905c rtl8139_flash.ko    -- Low-level driver for Realtek nic 8139 sis630_flash.ko     -- Low-level driver for north–southbridge SiS 630 (BIOS) via-rhine_flash.ko  -- Low-level driver for via Rhine nic   You must load the main module "flash.o" and the low-level driver (for example, ct.o). It doesn't matter what order the modules are loaded in.  For kernel 2.2 and 2.4 "insmod flash.o" "insmod ct.o"  For kernel 2.6 "insmod flash.ko" "insmod ct.ko"  Depending on the loaded modules you have 3 files. /proc/.../info /proc/.../data
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Context: software in this book because it varies so much. There is open-source activity around the UNIX implementation of WBEM at http://openwbem.org/. As for WMI, I offer a little explanation. However, this chapter covers the BIOS level implementation of the WBEM paradigm. Therefore, the operating system–specific layer of WBEM will not be the major theme here.  Even if figure 10.1 shows a kind of client–server relationship between the WBEM manager software and the system that hosts the manageable components, in the real world, the system doesn't have to be set up as client and server for the WBEM to work. For example, in Windows machines, as long as remote access to the WMI of the remote machine is granted, the local machine can "ask" the remote machine to perform management tasks.  The requirements and specifics about WBEM for hardware devices are available in the "Windows Hardware Instrumentation Implementation Guidelines" at http://download.microsoft.com/download/5/7/7/577a5684-8a83-43ae-9272-ff260a9c20e2/whiig-1.doc. The SMBIOS implementation guideline is provided in chapter 2.7 in the document: Static SMBIOS table data is provided to WMI using the WMI infrastructure  Required  Vendors who want to provide OEM-specific and system-specific instrumentation data may choose to use SMBIOS as the mechanism. In order to leverage the capabilities of the WMI infrastructure to surface this SMBIOS data, they must conform to any SMBIOS version from 2.0 to 2.3. Doing so will allow the Win32 provider to populate almost all of the SMBIOS-provided information into the [Common Information Model] CIMv2.0 namespace. In particular, almost all of the information will be put into Win32 classes. Some of these Win32 classes are derived from the CIMv2.0 physical [Managed Object Format] MOF.  This requirement does not imply a requirement to implement SMBIOS in a system.   It's clear in the preceding citation that the WMI subsystem in Windows will "parse" the SMBIOS data provided by the BIOS and then "export" it to the WBEM manager software as needed through the WMI interface.  In figure 10.1, an arrow runs from the power-on BIOS code to the SMBIOS structure tables. This arrow means the SMBIOS structures table is populated by the BIOS code that is executed during system initialization.  SMBIOS is a BIOS feature specific to the x86 platform. It's implemented as part of the WBEM initiative. The role of SMBIOS is to provide system-specific information to the upper layer in the WBEM implementation, i.e., the operating system layer. To easily understand the SMBIOS, you can download version 2.4 of its specification at http://www.dmtf.org/standards/smbios/. I often refer to the contents of this specification.  In the earlier implementation of SMBIOS, the information was presented as a "callable interface," i.e., platform-specific function calls. The current implementation of
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Context: Indexaccentedletter,125acidwash,104acid-resistant,104advancement,126AlbrechtDürer,102algorithm,41ofdeCasteljau,20aliasing,8ampersand,16ANSI,58antialiasing,8ASCII,31AugustDvorak,58backslash,33backwardcompatibility,35baseline,126BézierPierre,17Béziercurve,17billboard,8bit,28,69bitpattern,69bold,123Braille,38burin,102burnishing,104byte,29,69carriagereturn,31Cartesiancoordinate,1CasteljauPaulde,17chain,ofcurves,18characteraccented,33printable,31checkerboardpattern,100Chinesetone,63ChristopherLathamScholes,53circlefromBeziercurves,23circulararc,16Citroën,17coatedpaper,4composingstick,142compression,65lossy,74computerprogram,43condition,44controlpoint,17coordinate,1copper,102counter,44curvecontinuous,18discontinuous,18flatness,22join,18smoothcontinuous,18subdivision,20Cyrillic,33,124183
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Context: c.2003Int.Conf.DataMining(ICDM’03),pp.581–584,Melbourne,FL,Nov.2003.[LKF05]J.Leskovec,J.Kleinberg,andC.Faloutsos.Graphsovertime:Densificationlaws,shrinkingdiametersandpossibleexplanations.InProc.2005ACMSIGKDDInt.Conf.KnowledgeDiscoveryandDataMining(KDD’05),pp.177–187,Chicago,IL,Aug.2005.[LLLY03]G.Liu,H.Lu,W.Lou,andJ.X.Yu.Oncomputing,storingandqueryingfrequentpatterns.InProc.2003ACMSIGKDDInt.Conf.KnowledgeDiscoveryandDataMining(KDD’03),pp.607–612,Washington,DC,Aug.2003.[LLMZ04]Z.Li,S.Lu,S.Myagmar,andY.Zhou.CP-Miner:Atoolforfindingcopy-pasteandrelatedbugsinoperatingsystemcode.InProc.2004Symp.OperatingSystemsDesignandImplementation(OSDI’04),pp.20–22,SanFrancisco,CA,Dec.2004.[Llo57]S.P.Lloyd.LeastsquaresquantizationinPCM.IEEETrans.InformationTheory,28:128–137,1982(originalversion:TechnicalReport,BellLabs,1957).
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Context: ColophonThisbookwasdesignedandtypesetbytheauthorusingDonaldKnuth’sTEXsystemwithLeslieLamport’sLATEXmacrosin10ptPalatinoLinotype,ComputerModernSans,andBeraSansMono.Diagramswereconstructedusingthetikzpackage,andtheout-putwaspreparedbytheauthorasaPDFfileforprintandeBookformats.Inaddition,thefollowingpackageswereused:ragged2e,blindtext,amsmath,etex,gensymb,lettrine,booktabs,placeins,titlesec,makeidx,lineno,nameref,cleveref,xspace,comment,setspace,etoolbox,forest,multicol,tocloft,float,caption,longtable,tabu,enumerate,calc,textcomp,microtype,mathtools,hyperref,fancyhdr,emptypage,under-score,upquote,fancyvrb,idxlayout,xcolor,textgreek.ThecoverusestypefacesfromtheAdobeGaramondfamily.TheeBookwaspreparedfromLATEXsourceswithlatex4htandCalibre.ThephysicalbookwasprintedbyOn-DemandPublishingLLC,asubsidiaryofAmazonInc.ofSeattle,Washington.181
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Context: 1. IDA Pro scripts recognize only one type of variable, i.e., auto. There are no other variable types, such as int or char. The declaration of variable in an IDA Pro script as follows: auto variable_name; 2. Every statement in an IDA Pro script ends with a semicolon (;), just like in the C programming language. 3. A function can return a value or not, but there's no return-type declaration. The syntax is as follows: static function_name(parameter_1, parameter_n, ...) 4. A comment in an IDA Pro script starts with a double slash (//). The IDA Pro scripting engine ignores anything after the comment in the corresponding line. // comment statement; // comment 5. IDA Pro "exports" its internal functionality to the script that you build by using header files. These header files must be "included" in the script so that you can access that functionality. At least one header file must be included in any IDA Pro script, i.e., idc.idc. The header files are located inside a folder named idc in the IDA Pro installation directory. You must read the *.idc files inside this directory to learn about the functions exported by IDA Pro. The most important header file to learn is idc.idc. The syntax used to include a header file in an IDA Pro script is as follows: #include < header_file_name> 6. The entry point of an IDA Pro script is the main function, just as in the C programming language.   Now is the time to put the theory into a simple working example, an IDA Pro sample script (listing 2.2).  Listing 2.2 IDA Pro Code Relocation Script #include <idc.idc> // Relocate one segment static relocate_seg(src, dest) {        auto ea_src, ea_dest, hi_limit;         hi_limit = src + 0x10000;        ea_dest = dest;         for(ea_src = src; ea_src < hi_limit ; ea_src = ea_src + 4 )        {        PatchDword( ea_dest, Dword(ea_src));        ea_dest = ea_dest + 4;        }    Message("segment relocation finished"           "(inside relocate_seg function)...\n");   10
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Context: ts,539graphdata,14graphindexstructures,591graphpatternmining,591–592,612–613graphicdisplaysdatapresentationsoftware,44–45histogram,54,55
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Context: ```markdown
# 5.5  NUMBER THEORY
© Steven & Felix

## Programming Exercises related to Functions involving Prime Factors:

1. UVa 10924 - Divisors (numbD)
2. UVa 10841 - Perfect Factors (numbPF)
3. UVa 10718 - Irreducible Rational Functions (EulerPhi(01))
4. UVa 1029 - Relatives (EulerPhi(01))
5. UVa 1069 - Send a Tribe (numbD(0))
6. UVa 1063 - Number Theory 2 - EulerPhi(01) - numbD(0)
7. UVa 1168 - Composite Prime Factors with numbPF(0)
8. UVa 1136 - Restricting the exponent (super(1))
9. UVa 1137 - Enumerating Rational (generalized EulerPhi(0), V = 0 ...2003)
10. UVa 11128 - Alternate Task (numbD(0))
11. LA 540 - Final Torretics (numbD(0))

### 5.7 Modulo Arithmetic:

Some mathematical computations in programming problems can yield having very large positive (or very small negative) functional results that are beyond the range of largest built-in integer types, especially for the 64-bit Long Long C++ type. In Section 5.5, we have seen how to work with large numbers using prime factors. For these problems, we are only interested in the results modulo M (where M is often a prime number). 

For example, if you need to calculate \( (a + b) \mod M \), first calculate the result \( a + b \) and then take the modulo. 

1. \( (a \oplus b) \) is also valid. Note: \( \oplus \) is a symbol of modulo operations.
2. \( (a + b) \mod M \)
3. \( (a - b) \mod M \)
4. \( (a \times b) \mod M \)
5. \( (a \div b) \equiv (a \times (b^{-1} \mod M)) \mod M \)

## Programming Exercises related to Modulo Arithmetic:

1. UVa 10278 - Software Credit (n mod M, + (mod M) mod M)
2. UVa 10374 - Big Mod (n mod M, sample: write ourselves)
3. UVa 10610 - Sum of Digits (n mod M, digit counting)
4. UVa 10417 - LCM (Lowest Common Multiple)
5. UVa 10142 - Cubic Substrates (a - one Space)
6. UVa 10176 - Count Deep: Make Digit (assume that j is even)
7. UVa 10178 - The Least Non-Negative Digit (assume that our divisor exists)
8. UVa 10619 - Boxes of Chocolates (keep arithmetic computations small with modulo)

*Note: “Multiplying integers down to N mod M”, repeatedly use 10 (to discard the trailing zeros), then use % 1 billion to only maintain the last ten (10) zero digits.*
```
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Context: # Convention

There are a lot of C++ codes shown in this book. If they appear, they will be written using this font. Many of them use types, structs, or macros that are often used by competitive programmers to speed up the coding time. In the short section, it's for the second examples. Java support has been increased substantially in the second edition of this book. This book uses such, as usual, does not support macros and typedefs.

```cpp
// Suppress some compiler warning messages (only for VC++ users)
#define _CRT_SECURE_NO_DEPENDENCE
```

## Shortcuts for Common Data Types in Contexts

```cpp
typedef long long int ll;        // comments that are mixed with code
typedef pair<int, int> pii;      // are aligned to the right like this
typedef vector<int> vi;
typedef vector<vector<int>> vvi;
#define INF 1000000000            // 1 billion, safer than 2B for Floyd Warshall's
```

## Common Macro Settings

```cpp
// memset(arr, 0, sizeof(arr));   // initialize DP memoization table with -1
// memset(arr, 0, 1);              // to clear an array of integers
```

**Note:** Do not abandon the usage of "RHF" and "RHM" in the second edition to reduce the confusion encountered by new programmers.

The following shortcuts are frequently used in our C/C++ classes like this block:

```cpp
// simplify: if (a) b = c; else c = c;
// index = (index + 1) % n;      // from: index if (index >= n) index = 0;
// index = (index - 1 + n) % n;  // from: index if (index < 0) index = n - 1;
// ans = (int)(doubleVal + 0.5);  // for rounding to nearest integer
// if (ans > max_ans, comparison)  // if frequently use this min/max shortcut
```

Some codes used short circuit as (AND) and (OR).

# Problem Categorization

As of 1 August 2011, Steven and Felix combined have solved 1502 VBA problems (≥ 1% of active UTF problems). About 11% of them are discussed and categorized in this book.

These problems are categorized according to a "hard discovery" criterion. If a problem can be classified into two or more categories, it will be placed in the category with the highest difficulty. The index reflects the whole range of problems categorized according to their problem structure, which helps understand problem-solving techniques.

Unlike discussions that merely find solutions, this book is aimed at solving the diversity (or problems solving skill). It is a must try (we limit ourselves to discuss maximum 3 highlights per category).
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Context: pliedtomanufacturingsettingstoestimatethelifespanofindustrialequipment.PopularmethodsincludeKaplan-Meierestimatesofsurvival,Coxproportionalhazardsregressionmodels,andtheirextensions.Qualitycontrol:Variousstatisticscanbeusedtopreparechartsforqualitycontrol,suchasShewhartchartsandCUSUMcharts(bothofwhichdisplaygroupsum-marystatistics).Thesestatisticsincludethemean,standarddeviation,range,count,movingaverage,movingstandarddeviation,andmovingrange.13.2.2ViewsonDataMiningFoundationsResearchonthetheoreticalfoundationsofdatamininghasyettomature.Asolidandsystematictheoreticalfoundationisimportantbecauseitcanhelpprovideacoherent
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Context: inline int msg(const char *format,...) {   va_list va;   va_start(va, format);   int nbytes = vmsg(format, va);   va_end(va);   return nbytes; }   The msg function is useful as a debugging aid while developing the IDA Pro plugin. To do so, you can log plugin-related messages in the IDA Pro message pane with this function. Experienced C/C++ programmers will recognize that the msg function is similar to variations of the printf function in C/C++.  Up to this point, the development of an IDA Pro plugin has been clear. However, you can develop another plugin that has a graphical user interface (GUI). It will be dialog based and use Windows message-loop processing during its execution. It will be more flexible than the script version. It is sometimes useful to have an easily accessible user interface for an IDA Pro plugin. That's why you will learn about that here.  The plugin will use a lot of Windows application programming interface (Win32 API). Hence, I recommend that you read a book by Charles Petzold, Programming Windows (5th edition, Microsoft Press, 1998) if you haven't been exposed to Win32 API. Use Win32 API to create a dialog box for the IDA Pro plugin. The relevant material in Petzold's book for this purpose is found in chapters 1, 2, 3, and 11. A thorough explanation about the source code will be presented. Nevertheless, it'll be hard to grasp this without significant background in Win32 API.  Start the plugin development. The first steps are just the same as explained in the previous plugin example. Proceed accordingly, until you can show a message in the IDA Pro message pane. Then, you have to modify three types of core functions in the IDA Pro plugin source code, i.e., init, term, and run. The term function is called when the plugin is in the process of being terminated, init is called when the plugin is being started (loaded to the IDA Pro workspace), and run is called when the plugin is activated by pressing its shortcut key or by invoking the plugin with RunPlugin in an IDA Pro script.  Initialize the user interface inside init, and clean up the corresponding user interface resources during the termination process inside term. Let's get down to the code.  Listing 2.7 BIOS Binary Analyzer Plugin Framework /*  * Filename: main.cpp  *  * This is the main file of the Award BIOS binary analyzer plugin.  * This file handles the user interface aspect of the plugin.  * It can be compiled by Microsoft Visual C++.  */  #include <windows.h>    25
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Context: • The link to the Phoenix TrustedCore SP3b datasheet is http://www.phoenix.com/NR/rdonlyres/C672D334-DD93-4926-AC40-EF708B75CD13/0/TrustedCore_SP3b_ds.pdf. • The link to the Phoenix TrustedCore white paper is https://forms.phoenix.com/whitepaperdownload/trustedcore_wp.aspx. Note that this link points to an electronic form that you have to fill in before you are allowed to download the white paper. The white paper is free. • The link to download the Phoenix TrustedCore Notebook white paper is http://www.phoenix.com/NR/rdonlyres/7E40E21F-15C2-4120-BB2B-01231EB2A2E6/0/trustedcore_NB_ds.pdf. This white paper is quite old. Nonetheless, it's worth reading.   With regard to TCG standards, there are two requirements for the BIOS boot block that are fulfilled by the Phoenix TrustedCore, as follows:  1. A host-platform manufacturer-approved agent or method modifies or replaces code or data in the boot block. 2. The manufacturer controls the update, modification, and maintenance of the BIOS boot block component, and either the manufacturer or a third-party supplier may update, modify, or maintain the POST BIOS component.   In this case, the boot block plays a role as the CRTM, which means it is used to measure the integrity of other modules in the PC firmware. Having read the preceding requirements, go back to the prevention method theme. What does Phoenix TrustedCore BIOS offer? To put it simply, this new approach to BIOS implementation provides two levels of protection against tampering for the BIOS boot block:  1. Any modification to BIOS code must meet strong authentication requirements. The system prevents a nonmanufacturer-approved BIOS flashing utility from writing into the CRTM. This is achieved by activating the hardware-based write-lock to the boot block except in a specific case, i.e., when a manufacturer-approved BIOS flashing utility is updating the boot block. 2. Any modification to BIOS code must meet strong verification requirements. The system uses a strong cryptographic method to verify the integrity of the firmware. This is achieved by using a strong cryptographic algorithm, such as RSA.   Phoenix provides details of implementation for both of the preceding protection levels in its TrustedCore white paper, as cited here:  The following details refer to a high-level implementation of a secure CRTM and BIOS design.  Hardware and Software:  • Use appropriate flash ROM parts that support lock down of the write-lock bit setting.
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Context: # GCC and BIOS-related Development

GCC, the GNU Compiler Collection, is a versatile compiler with interesting features for BIOS-related development:

1. **Mixed Language Development**: GCC supports mixed language development through inline assembly constructs inside C/C++ functions.
2. **Integration with GNU Assembler**: GCC comes with GNU Assembler (GAS). GAS output can be combined seamlessly with GCC C/C++ compiler output through the GNU LD linker. GAS supports AT&T assembly syntax and recently began to support Intel assembler syntax, too.
3. **Linker Script Support**: GCC features so-called linker script support. A linker script is a script that gives detailed control of the overall linking process.

Start with a review of the compilation steps in a C compiler to understand these features. These steps are implemented not only in GCC but also in other C compilers.

## Figure 3.2 C Compiler Compilation Steps

```
C Source Code
    ↓                        
Preprocessor
    ↓
Compiler
    ↓
Assembly Source Code
    ↓
Assembler
    ↓
Object File
    ↓
Library, Other Object Files
    ↓
Linker
    ↓
Executable File / Pure Machine Code
```

Figure 3.2 shows that the linker plays an important role; it links the object and the library files from various sources into an executable file or pure machine code.

[^1]: **PCI Expansion ROM Binary** is the software inside the ROM chip in a PCI expansion card. It's primarily used for the initialization of the card during boot. However, it may contain other features.
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Context: # 6.2 BASIC STRING PROCESSING SKILLS  
**Steven & Felix**

1. (a) Do you know how to store a string in your favorite programming language?  
   (b) How to read a given text input line by line?  
   (c) How to concatenate (combine) two strings into a larger one?  
   (d) How to check if a string starts with `.....` to stop reading input?  

I love CS2333 Competitive Programming. I also love Algorithm.

> **Note:** You must stop after reading this line as it starts with 7 dots; after the first input block, there will be `loooooooooooocccccc...`.

2. Suppose we have one big string. We want to check if another string can be found in it. Report all the indices where it appears.  
   For example, if `S`=`'I love CS2333 Competitive Programming.'` and `P`=`'l'`, the indices could be:  
   - If `P` is not found, return `-1`.  
   - If `P` is found at the character `i`, add `i` as part of the output. If `P` is `love`, then the output is `[2, 46]` (if `P` is `'book'`, then the output is `[]`).  

   - How to find the first occurrence of a substring in a string?  
   - Do we need to implement a string matching algorithm (like Knuth-Morris-Pratt (KMP) algorithm discussed in Section 6.4, etc.) or can we just use Brute Force?  

3. Suppose we want to do some simple analysis of the characters in a string and also to transform each character into 1-byte numbers. The required analysis is as follows:  
   How many digits, vowels, and consonants? (Let lower/uppercase alphabets be stored in the same count.)  
   What is the total frequency of each character in the string?  

   - How to store an array of strings?  

4. Next, we want to look at this string in terms of its string lexicographically and find the lexicographically smallest string. Thus, we want to base tokens called `['also', 'competitive', 'CS2333', 'i', 'love', 'programming']`.  
   - The given list has `one more item` like `'i'` or `'love'`, as follows:  
     - Which data structure best supports this word frequency counting problem?  

5. (a) How to read a string when we do not know its length in advance?  
   (b) The length of this list is less constrained. Count how many characters are there in the last item?  

> **Note:** *This text is not written like the one used in our common dictionary.*  

152
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Context: coveringregressionandothertopicsinstatis-ticalanalysis,suchasMathematicalStatistics:BasicIdeasandSelectedTopicsbyBickelandDoksum[BD01];TheStatisticalSleuth:ACourseinMethodsofDataAnalysisbyRamseyandSchafer[RS01];AppliedLinearStatisticalModelsbyNeter,Kutner,Nacht-sheim,andWasserman[NKNW96];AnIntroductiontoGeneralizedLinearModelsbyDobson[Dob90];AppliedStatisticalTimeSeriesAnalysisbyShumway[Shu88];andAppliedMultivariateStatisticalAnalysisbyJohnsonandWichern[JW92].Researchinstatisticsispublishedintheproceedingsofseveralmajorstatisticalcon-ferences,includingJointStatisticalMeetings,InternationalConferenceoftheRoyalStatisticalSocietyandSymposiumontheInterface:ComputingScienceandStatistics.OthersourcesofpublicationincludetheJournaloftheRoyalStatisticalSociety,TheAnnalsofStatistics,theJournalofAmericanStatisticalAssociation,Technometrics,andBiometrika.TextbooksandreferencebooksonmachinelearningandpatternrecognitionincludeMachineLearningbyMitchell[Mit97];PatternRecognitionandMachineLearningbyBishop[Bis06];PatternRecognitionbyTheodoridisandKoutroumbas[TK08];Introduc-tiontoMachineLearningbyAlpaydin[Alp11];ProbabilisticGraphicalModels:Principles
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Context: ## 3.2 COMPLETE SEARCH  
**© Steven & Felix**

You may want to tweak the “critical code” of your program first instead of painfully reducing the problem with a faster algorithm—which may not be trivial to design. Here are some tips that you may want to consider when designing your solution, especially a Complete Search solution, to bring it a better chance of passing the Time Limit.

### Tip 1: Filtering versus Generating  
Programs that generate lists of all candidate solutions and choose those that are correct (or remove the incorrect ones) are called "filters," e.g., the naive-search works with `O(n)` time complexity and the derivative algorithm with `O(2^n)`. Usually, "filter" programs are more efficient.

Programs that gradually build solutions and immediately prune invalid partial solutions are called "generators," e.g., the improved `8-queens` solves with `O(n!)` time complexity. Generally, filters are easier to code but slower. Do the math to see if a filter is good enough or if you need to create a generator.

### Tip 2: Prune Infeasible Search Space Early  
In generating solutions using recursive backtracking (see Tip 1 above), we may encounter a partial solution that will never lead to a full solution. We can prune the search at those parts of the search space. For example, see the diagonal check in `8-queens` solutions above. Suppose we have placed a queen at row 1, column 2, then placing another queen at row 2, column 2 + 3 will create a conflict, and placing another queen at row 2 will create a non-valid solution. Thus, we can prune the partial solutions right at this juncture, concentrating on only valid positions of `row[2] = {4, 5, 6, 7, 8}`; thus, we avoid wasting overall runtime.

### Tip 3: Utilize Symmetries  
Some problems have symmetries and we should try to exploit symmetries to reduce execution time! In the `8-queens` problem, there are `92` solutions that form every `12` unique (or functionally non-symmetric) solutions that are rotational and linearly identifiable in this problem. You can thus programmatically generate the `12` unique solutions and, if needed, generate the method by reflecting and rotating the `12` unique solutions. Example: row = `{7, 3, 1, 6, 2, 4}` is the mirrored reflection of Figure 1.

### Tip 4: Pre-Calculation a.k.a. Pre-Calculation  
Sometimes it is helpful to create tables or other data structures that allow the fastest possible response to the routine. This takes care of part of the program’s workload. This could be Pre-computable, which can save memory/space for time. However, this technique can be tricky and hard to zone for base programming consistently.

If you run into a problem like solving `8-queens` and know that there are `92` solutions, it seems pragmatic to create a generator program [2] that is `O(n!)` (which takes some runtime) for this `8-queen problem.` And thus, we can create a generator program (which slows some routines) if this is by any domination.  

### Tip 5: Solving the Problem Backwards  
Surprisingly, some current problems look far harder when they are viewed "backward" (from the idea defining approach). This can be solved using a funnel attack (from the more obvious angle). Be ready to process the data in the order that seems less than the obvious.

* It is said that every program is doing most of its task to ask about 10% of the code—the critical code.
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Context: BUILD: Saving C:\WINDDK\2600~1.110\build.dat... BUILD: Compiling f:\a-list_publishing\windows_bios_flasher\current\sys directory Compiling - bios_probe.c for i386 BUILD: Linking f:\a-list_publishing\windows_bios_flasher\current\sys directory Linking Executable - i386\bios_probe.sys for i386 BUILD: Done      2 files compiled     1 executable built    Now, I will show you the overall source code of the driver that implements components 2 and 3 in figure 9.1. I start with the interface file that connects the user-mode application and the device driver.  Listing 9.8 The interface.h File /*  *  This is the interface file that connects the user-mode application  *  and the kernel-mode driver.  *  *  NOTE:  *  -----  *  - You must use #include <winioctl.h> before including this  *    file in your user-mode application.  *  - You probably need to use #include <devioctl.h> before including  *    this file in your kernel-mode driver.  *  These include functions are needed for the CTL_CODE macro to work.  */  #ifndef __INTERFACES_H__ #define __INTERFACES_H__  #define IOCTL_READ_PORT_BYTE       CTL_CODE(FILE_DEVICE_UNKNOWN, 0x0801,                      METHOD_IN_DIRECT, FILE_READ_DATA | FILE_WRITE_DATA) #define IOCTL_READ_PORT_WORD       CTL_CODE(FILE_DEVICE_UNKNOWN, 0x0802,                      METHOD_IN_DIRECT, FILE_READ_DATA | FILE_WRITE_DATA) #define IOCTL_READ_PORT_LONG       CTL_CODE(FILE_DEVICE_UNKNOWN, 0x0803,                      METHOD_IN_DIRECT, FILE_READ_DATA | FILE_WRITE_DATA)  #define IOCTL_WRITE_PORT_BYTE       CTL_CODE(FILE_DEVICE_UNKNOWN, 0x0804,                      METHOD_OUT_DIRECT, FILE_READ_DATA | FILE_WRITE_DATA) #define IOCTL_WRITE_PORT_WORD       CTL_CODE(FILE_DEVICE_UNKNOWN, 0x0805,                      METHOD_OUT_DIRECT, FILE_READ_DATA | FILE_WRITE_DATA) #define IOCTL_WRITE_PORT_LONG       CTL_CODE(FILE_DEVICE_UNKNOWN, 0x0806,                      METHOD_OUT_DIRECT, FILE_READ_DATA | FILE_WRITE_DATA)  #define IOCTL_MAP_MMIO              CTL_CODE(FILE_DEVICE_UNKNOWN, 0x0809,
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Context: ContentsPrefacev1PuttingMarksonPaper12LetterForms153StoringWords274LookingandFinding415TypingitIn536SavingSpace657DoingSums818GreyAreas979OurTypeface12310WordstoParagraphs135Solutions147FurtherReading169Templates173Colophon181Index183v
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Context: # GETTING STARTED: THE AD HOC PROBLEMS

### Programming Exercises related to Ad Hoc problems:

**Easy Problems in UVA Online Judge (under 7 minutes)**

1. UVA 001: **TXQ Quotes** (simple replace all double quotes to TXQ style quotes)
2. UVA 004: **Wicky and Gumball** (count frequency of words in a line)  
3. UVA 109: **What's The Frequency?** (a bad box, 1D array manipulation)
4. UVA 10068: **Periodic Strings** (string periodicity, easy test)  
5. UVA 100: **Conundrum Box** (image, do == mean)
6. UVA 110: **Simon Says** (simple, easy test)
7. UVA 112: **Making Change** (general knowledge) 
8. UVA 1137: **Birthday of Neighbors** (multidimensional array, see list-subarray)
9. UVA 1137: **K Flavors** (more than one, make gist of the LED)
10. UVA 1177: **Card Counting** (use linear search to get max value)
11. UVA 1197: **Frog Jumping** (check if reachable in limited jumps)
12. UVA 1198: **Jumper's Dance** (jump through the list from last min)
13. UVA 1199: **Suspicious Stock** (check if it is trending/descending)
14. UVA 1201: **Coughs** (simple, string should not be null)
15. UVA 1209: **Card Exchange** (another easy manipulation problem)

### Case (Easy):

1. UVA 006: **Beggar My Neighbor** (card game simulation, straightforward)
2. UVA 012: **Bridge Hand Evaluator** (simulation, cards)
3. UVA 055: **Elderly Flash** (card game)
4. UVA 106: **Stack 'em Up** (card game)
5. UVA 109: **Check It Out!** (a shuffle with some rule, get certain card)
6. UVA 116: **Card Shuffle** (shuffle cards with numbers)
7. UVA 1168: **Queen (and box, chess)**

### Case (Hard):

1. UVA 0277: **People** (follow the game rules, 24 turtles)
2. UVA 0292: **Panic** (game, you range, any manipulation problem)
3. UVA 0293: **Game Maker** (another card game manipulation problem)
4. UVA 0294: **Special Assignment** (little turtles grab stacks)
5. UVA 0296: **Hanging Dance** (how to save)
6. UVA 0297: **Sailors** (difficult, can lead to your routing complexities)
7. UVA 0298: **One Last Drink** (simple)
8. UVA 0299: **Minmaxing** (simple Minmaxing game, similar to UVA 10279)
9. UVA 109: **Time Management** (see UVA 11804)

---

**Note: Items are non-alphabetical thereon. Can be solved ‘elegantly’ with Java Regular Expression.**
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Context: # 2.7 Bibliographic Notes

Methods for descriptive data summarization have been studied in the statistic literature long before the onset of computers. Good summaries of statistical descriptive data mining methods include Freedman, Pisani, and Purves [FP97] and Devore [Dev95].

## 2.6

Given two objects represented by the tuples (22, 1, 42, 10) and (20, 0, 36, 8):

1. Compute the Euclidean distance between the two objects.
2. Compute the Manhattan distance between the two objects.
3. Compute the Minkowski distance between the two objects, using \( q = 3 \).
4. Compute the supremum distance between the two objects.

## 2.7

The median is one of the most important holistic measures in data analysis. Propose 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:

|     | A1  | A2  |
|-----|-----|-----|
| x1  | 1.5 | 1.7 |
| x2  | 2.2 | 1.9 |
| x3  | 1.6 | 1.8 |
| x4  | 1.2 | 1.5 |
| x5  | 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.
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Context: # Data Mining
## Concepts and Techniques

**Third Edition**

**Authors:**
- Jiawei Han
- Micheline Kamber
- Jian Pei

## Table of Contents
1. **Introduction**
   - Overview of Data Mining
   - Importance of Data Mining
   - Data Mining Process
2. **Data**
   - Types of Data
   - Data Preprocessing
3. **Data Warehouse and OLAP**
   - Data Warehouse Basics
   - OLAP Operations
4. **Data Mining Techniques**
   - Classification
   - Clustering
   - Association Rule Learning

## Key Concepts
- **Data Mining:** The process of discovering patterns in large data sets.
- **OLAP (Online Analytical Processing):** Tools for managing and analyzing large data sets efficiently.

## Useful Links
- [Data Mining Resources](https://example.com)
- [Online Courses](https://example.com)

## Conclusion
Data mining techniques are essential in extracting valuable insights from large amounts of data.
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Context: 714SelectedReferencesFarkas,H.M.,andI.Kra,RiemannSurfaces,Springer-Verlag,NewYork,1980;secondedition,1992.Freyd,P.,AbelianCategories:AnIntroductiontotheTheoryofFunctors,HarperandRow,NewYork,1964.Fr¨ohlich,A.,andM.J.Taylor,AlgebraicNumberTheory,CambridgeUniversityPress,Cambridge,1991.Fulton,W.,AlgebraicCurves:AnIntroductiontoAlgebraicGeometry,Addison-WesleyPublishingCompany,RedwoodCity,CA,1989;originallypublishedbyW.A.Benjamin,Inc.,NewYork,1969,andreprintedin1974.Gauss,C.F.,DisquisitionesArithmeticae,EnglishtranslationoftheoriginalLatin,Springer-Verlag,NewYork,1986.Griffiths,P.A.,IntroductiontoAlgebraicCurves,AmericanMathematicalSociety,Providence,1989.Gunning,R.C.,IntroductiontoHolomorphicFunctionsofSeveralVariables,Vol-umeIII:HomologicalTheory,Wadsworth&Brooks/Cole,PacificGrove,CA,1990.Hall,M.,TheTheoryofGroups,TheMacmillanCompany,NewYork,1959.Hardy,G.H.,andE.M.Wright,AnIntroductiontotheTheoryofNumbers,ClarendonPress,Oxford,1938;secondedition,1945;thirdedition,1954;fourthedition,1960;fifthedition,1979.Harris,J.,AlgebraicGeometry:AFirstCourse,Springer-Verlag,NewYork,1992;reprintedwithcorrections,1995.Hartshorne,R.,AlgebraicGeometry,Springer-Verlag,NewYork,1977.Hasse,H.,NumberTheory,EnglishtranslationoftheoriginalGerman,Springer-Verlag,Berlin,1980;reprinted,2002.Hecke,E.,LecturesontheTheoryofAlgebraicNumbers,EnglishtranslationoftheoriginalGerman,Springer-Verlag,NewYork,1981.Hilton,P.J.,andU.Stammbach,ACourseinHomologicalAlgebra,Springer-Verlag,NewYork,1971;secondedition,1997.Hua,L.-K.,IntroductiontoNumberTheory,EnglishtranslationoftheoriginalChi-nese,Springer-Verlag,Berlin,1982.Hungerford,T.W.,Algebra,Holt,RinehartandWinston,NewYork,1974;reprinted,Springer-Verlag,NewYork,1980;reprintedwithcorrections,1996.Ireland,K.,andM.RosenAClassicalIntroductiontoModernNumberTheory,Springer-Verlag,NewYork,1982;secondedition,1990.Jacobson,N.,BasicAlgebra,VolumeI,W.H.FreemanandCo.,SanFrancisco,1974;secondedition,NewYork,1985.VolumeII,W.H.FreemanandCo.,SanFrancisco,1980;secondedition
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Context: # Appendix B

## uHunt

[uHunt](http://felix-halim.net/vua/hunting.php) is a web-based tool created by one of the authors of this book (Felix Halim) to complement the UVa online judge [28]. This tool is created to help UVa users in keeping track which problems they have (and have not) solved. Considering that UVa has 2500 problems, today’s programmers indeed need a tool like this (see Figure B.1).

![Figure B.1: Steven's statistics as of 1 August 2011](link_to_image_here)

The original version of uHunt (that stands for UVa hunting) is to help UVa users in finding the 2500 accepted problems for such users. The rationale for this is simple: whenever a UVa user needs to build up his/her confidence, he/she needs to solve problems with gradual difficulty. This is important because distinguishing hard problems and easy problems in competitive programming (AC) is tricky without knowing what’s wrong. The 1162 UVa users actually contributed statistical information, and their data can be analyzed for this purpose; thus, the statistics will help users to identify their skills at the different levels of AC problems. For example, a UVa user can determine which skills are their strong points and which areas they need to improve.

As a side note: there is a different meaning of AC here. It means any program that is accepted as correct according to the problem statement in a given programming contest. The strong point of the tool is that it shows the variants of accepted AC codes. A quick example based on the user statistics might be that a novice user does not know a certain data structure or algorithm that other students perform well on. The tool can show the statistical construct; for example, if you have a friend (or a better UVa user than yourself), you can compare your list of solved problems with him/her and then try to solve the problems that your friend can solve.
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Context: Chapter 7 PCI Expansion ROM Software Development 195 7.1. Plug and Play BIOS and Expansion ROM Architecture 195 7.1.1. Power-On Self-Test Execution Flow 196 7.1.2. Expansion ROM Support 198 7.2. PCI Expansion ROM Peculiarities 200 7.3. Implementation Sample 200 7.3.1. Hardware Testbed 200 7.3.2. Software Development Tool 201 7.3.3. Expansion ROM Source Code 202 7.3.3.1. Core PCI PnP Expansion ROM Source Code 202 7.3.3.2. PCI PnP Expansion ROM Checksum Utility Source Code 211 7.2. Building the Sample 216 7.3. Testing the Sample            217 7.4. Potential Bug and Its Workaround            218  Chapter 8 PCI Expansion ROM Reverse Engineering                                          219 a. Binary Architecture                                                                     219 b. Disassembling the Main Code                                                     202  Part IV BIOS Ninjutsu  Chapter 9 Accessing BIOS within the Operating System                                    208 5.1. General Access Mechanism                                                       208 5.2. Accessing Motherboard BIOS Contents in Windows                217 5.3. Accessing Expansion ROM Contents in Windows                    226 5.4. Accessing Motherboard BIOS Contents in Linux                     235 5.5. Accessing Expansion ROM Contents in Linux                         244  Chapter 10 Low-Level Remote Server Management                                             263 - DMI and SMBIOS Protocols                                                     263 - Remote Server Management Code Implementation                  275  Chapter 11 BIOS Security Measures 285 15.1.1. Password Protection Code 287 15.1.2. BIOS Code Integrity Checks 308 15.1.3. Remote Server Management Security Measures 327  Chapter 12 BIOS Rootkits Engineering 346 a. Looking Back to BIOS Exploitation History 346 b. DMI and SMBIOS Protocol Flaws 355 c. DMI and SMBIOS Protocol Exploitation 364  Chapter 13 BIOS Defense Techniques 374 1. Prevention Methods 374 2. Recognizing Compromised Systems 382 3. Healing Compromised Systems 392  Part V Other Applications of BIOS Technology  Chapter 14 Embedded x86 BIOS Technology 402 Embedded x86 BIOS Architecture 402 Embedded x86 BIOS Implementation Samples 405 i.TV Set-Top Boxes 408 ii.Routers  412 iii.Kiosks  415
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Context: PrefaceItcanbetremendouslydifficultforanoutsidertounderstandwhycomputerscientistsareinterestedinComputerScience.Itiseasytoseethesenseofwonderoftheastrophysicist,oroftheevolutionarybiologistorzoologist.Wedon’tknowtoomuchaboutthemathe-matician,butweareinaweanyway.ButComputerScience?Well,wesupposeitmusthavetodowithcomputers,atleast.“Com-puterscienceisnomoreaboutcomputersthanastronomyisabouttelescopes”,thegreatDutchcomputerscientistEdsgerDijkstra(1930–2002),wrote.Thatistosay,thecomputerisourtoolforex-ploringthissubjectandforbuildingthingsinitsworld,butitisnottheworlditself.Thisbookmakesnoattemptatcompletenesswhatever.Itis,asthesubtitlesuggests,asetoflittlesketchesoftheuseofcomputersciencetoaddresstheproblemsofbookproduction.Bylookingfromdifferentanglesatinterestingchallengesandprettysolutions,wehopetogainsomeinsightintotheessenceofthething.Ihopethat,bytheend,youwillhavesomeunderstandingofwhythesethingsinterestcomputerscientistsand,perhaps,youwillfindthatsomeoftheminterestyou.vii
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Context: COHERENTPRESSCambridgePublishedintheUnitedKingdombyCoherentPress,Cambridge©CoherentPress2016Thispublicationisincopyright.SubjecttostatutoryexceptionnoreproductionofanypartmaytakeplacewithoutthewrittenpermissionofCoherentPress.FirstpublishedMarch2016AcataloguerecordforthisbookisavailablefromtheBritishLibraryISBN978-0-9576711-2-6PaperbackbythesameauthorPDFExplained(O’Reilly,2012)OCamlfromtheVeryBeginning(Coherent,2013)MoreOCaml:Algorithms,Methods&Diversions(Coherent,2014)
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Context: HAN05-pref-xxiii-xxx-97801238147912011/6/13:35Pagexxviii#6xxviiiPrefacebookorhandbook,shouldyoulaterdecidetoperformin-depthresearchintherelatedfieldsorpursueacareerindatamining.Whatdoyouneedtoknowtoreadthisbook?Youshouldhavesomeknowledgeoftheconceptsandterminologyassociatedwithstatistics,databasesystems,andmachinelearning.However,wedotrytoprovideenoughbackgroundofthebasics,sothatifyouarenotsofamiliarwiththesefieldsoryourmemoryisabitrusty,youwillnothavetroublefollowingthediscussionsinthebook.Youshouldhavesomeprogrammingexperience.Inparticular,youshouldbeabletoreadpseudocodeandunderstandsimpledatastructuressuchasmultidimensionalarrays.TotheProfessionalThisbookwasdesignedtocoverawiderangeoftopicsinthedataminingfield.Asaresult,itisanexcellenthandbookonthesubject.Becauseeachchapterisdesignedtobeasstandaloneaspossible,youcanfocusonthetopicsthatmostinterestyou.Thebookcanbeusedbyapplicationprogrammersandinformationservicemanagerswhowishtolearnaboutthekeyideasofdataminingontheirown.Thebookwouldalsobeusefulfortechnicaldataanalysisstaffinbanking,insurance,medicine,andretailingindustrieswhoareinterestedinapplyingdataminingsolutionstotheirbusinesses.Moreover,thebookmayserveasacomprehensivesurveyofthedataminingfield,whichmayalsobenefitresearcherswhowouldliketoadvancethestate-of-the-artindataminingandextendthescopeofdataminingapplications.Thetechniquesandalgorithmspresentedareofpracticalutility.Ratherthanselectingalgorithmsthatperformwellonsmall“toy”datasets,thealgorithmsdescribedinthebookaregearedforthediscoveryofpatternsandknowledgehiddeninlarge,realdatasets.Algorithmspresentedinthebookareillustratedinpseudocode.ThepseudocodeissimilartotheCprogramminglanguage,yetisdesignedsothatitshouldbeeasytofollowbyprogrammersunfamiliarwithCorC++.Ifyouwishtoimplementanyofthealgorithms,youshouldfindthetranslationofourpseudocodeintotheprogramminglanguageofyourchoicetobeafairlystraightforwardtask.BookWebSiteswithResourcesThebookhasawebsiteatwww.cs.uiuc.edu/∼hanj/bk3andanotherwithMorganKauf-mann
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Context: HAN10-ch03-083-124-97801238147912011/6/13:16Page124#42124Chapter3DataPreprocessingwasproposedinSiedleckiandSklansky[SS88].Awrapperapproachtoattributeselec-tionisdescribedinKohaviandJohn[KJ97].UnsupervisedattributesubsetselectionisdescribedinDash,Liu,andYao[DLY97].Foradescriptionofwaveletsfordimensionalityreduction,seePress,Teukolosky,Vet-terling,andFlannery[PTVF07].AgeneralaccountofwaveletscanbefoundinHubbard[Hub96].Foralistofwaveletsoftwarepackages,seeBruce,Donoho,andGao[BDG96].DaubechiestransformsaredescribedinDaubechies[Dau92].ThebookbyPressetal.[PTVF07]includesanintroductiontosingularvaluedecompositionforprincipalcom-ponentsanalysis.RoutinesforPCAareincludedinmoststatisticalsoftwarepackagessuchasSAS(www.sas.com/SASHome.html).Anintroductiontoregressionandlog-linearmodelscanbefoundinseveraltextbookssuchasJames[Jam85];Dobson[Dob90];JohnsonandWichern[JW92];Devore[Dev95];andNeter,Kutner,Nachtsheim,andWasserman[NKNW96].Forlog-linearmodels(knownasmultiplicativemodelsinthecomputerscienceliterature),seePearl[Pea88].Forageneralintroductiontohistograms,seeBarbar´aetal.[BDF+97]andDevoreandPeck[DP97].Forextensionsofsingle-attributehistogramstomultipleattributes,seeMuralikrishnaandDeWitt[MD88]andPoosalaandIoannidis[PI97].SeveralreferencestoclusteringalgorithmsaregiveninChapters10and11ofthisbook,whicharedevotedtothetopic.AsurveyofmultidimensionalindexingstructuresisgiveninGaedeandG¨unther[GG98].TheuseofmultidimensionalindextreesfordataaggregationisdiscussedinAoki[Aok98].IndextreesincludeR-trees(Guttman[Gut84]),quad-trees(FinkelandBentley[FB74]),andtheirvariations.Fordiscussiononsamplinganddatamining,seeKivinenandMannila[KM94]andJohnandLangley[JL96].Therearemanymethodsforassessingattributerelevance.Eachhasitsownbias.Theinformationgainmeasureisbiasedtowardattributeswithmanyvalues.Manyalterna-tiveshavebeenproposed,suchasgainratio(Quinlan[Qui93]),whichconsiderstheprobabilityofeachattributevalue.OtherrelevancemeasuresincludetheGiniindex(Breiman,Friedman,Olshen,andStone[BFOS84]),the
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Context: Additionally, we have a few other rules of thumb that are useful in programming contests:

- \( 2^{10} = 1,024 \approx 10^3 \)
- Max-32-bit signed integer: \( 2^{31} - 1 = 2,147,483,647 \) (for up to 9 decimal digits).
- Max-64-bit signed integer: \( \approx 2^{63} \) (for up to 19 decimal digits).
- Use `unsigned` if slightly higher positive number is needed (e.g., \( 2^{32} - 1 \)).
- If you need to store integers \( > 2^{31} \), you need to use the Big Integer technique (Section 5.3).

1. Program with nested loops of depth \( k \) returning back to its base (O\( (n^k) \) complexity).
   - If your program is recursive with a recursive call per level and has \( L \) items, the program has roughly O\( (L^k) \) complexity. But this is in the upper bound. The actual complexity depends on what actions you perform and whether some pruning are possible.

2. There are \( n! \) permutations and \( 2^n \) subsets (or combinations) of \( n \) elements.

3. Dynamic Programming algorithms which fill in a 2D matrix in O\( (n) \) per call is in O\( (n^2) \).
   - More details in Section 3 later.

4. The best time complexity of a comparison-based sorting algorithm is O\( (n \log n) \).

5. Most of the time, O\( (n^2) \) algorithms will be sufficient for most contest problems.

6. The largest input size for typical programming contest problems must be < 1M, because beyond that, the time needed to read the input (O\( (n) \) time) will be the bottleneck.

### Exercise 1.2.2
Please answer the following questions below using your current knowledge about algorithms and their complexities. After you have finished reading this task book now, it may be beneficial to attempt this exercise again.

1. There are \( n \) webpages (n ≤ 10M). Each webpage has a different page rank \( r \). You want to pick the top 10 pages with highest page ranks. Which method is more feasible?
   - (a) Load all webpages' page rank to memory, sort (Section 2.1), and pick the top 10.
   - (b) Use priority queue data structure (heap) (Section 2.2).

2. Given a list of L up to 100 integers. You need to frequently ask the value of sum \( S_j \), i.e. the sum of \( L[j] + L[j+1] + ... + L[i] \). Which data structure should you use?
   - (a) Simple Array (Section 2.2.1).
   - (b) Simple Array that has been pre-processed with Dynamic Programming (Section 2.2.1 & 3.5).
   - (c) Balanced Binary Search Tree (Section 2.2.2).
   - (d) Hash Table (Section 2.2.2).
   - (e) Segment Tree (Section 2.3.3).
   - (f) Ruin Tree (Section 2.4.3).
   - (g) Fenwick Tree (Section 6.6).
   - (h) Suffix Array (Section 6.6.4).

3. Given a set of \( S \) points randomly scattered on 2D plane, \( N \leq 1000 \). Find two points \( p \) that has the greatest Euclidean distance. Is O\( (N^2) \) complex search algorithm that try all pairs possible?
   - (a) Yes, such complete search is possible.
   - (b) No, we must find another way.
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Context: ixChapter7introducesmoreprogramming,ofaslightlydifferentkind.Webeginbyseeinghowcomputerprogramscalculatesimplesums,followingthefamiliarschoolboyrules.Wethenbuildmorecomplicatedthingsinvolvingtheprocessingoflistsofitems.Bythenendofthechapter,wehavewrittenasubstantive,real,program.Chapter8addressestheproblemofreproducingcolourorgreytoneimagesusingjustblackinkonwhitepaper.Howcanwedothisconvincinglyandautomatically?Welookathistori-calsolutionstothisproblemfrommedievaltimesonwards,andtryoutsomedifferentmodernmethodsforourselves,comparingtheresults.Chapter9looksagainattypefaces.Weinvestigatetheprincipaltypefaceusedinthisbook,Palatino,andsomeofitsintricacies.Webegintoseehowlettersarelaidoutnexttoeachothertoformalineofwordsonthepage.Chapter10showshowtolayoutapagebydescribinghowlinesoflettersarecombinedintoparagraphstobuildupablockoftext.Welearnhowtosplitwordswithhyphensattheendoflineswithoutugliness,andwelookathowthissortoflayoutwasdonebeforecomputers.
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Context: # CONTENTS
© Steven & Fitz

## 3 Algorithms  
### 3.1 Interesting Usages of Binary Search  
### 3.2 Geometry  
#### 3.2.1 Examples  
### 3.3 Dynamic Programming  
#### 3.3.1 DP Illustration  
#### 3.3.2 Classical Examples  
#### 3.3.3 Non-Classical Examples  
### 3.4 Chapter Notes  

## 4 Graph  
### 4.1 Overview and Motivation  
### 4.2 Graph Traversal  
#### 4.2.1 Depth First Search (DFS)  
#### 4.2.2 Breadth First Search (BFS)  
#### 4.2.3 Finding Connected Components (in an Unrestricted Graph)  
#### 4.2.4 Proof of Labeling/Clustering the Connected Components  
#### 4.2.5 Topological Sort of a Directed Acyclic Graph  
#### 4.2.6 Bipartite Graph Check  
### 4.3 Minimum Spanning Tree  
#### 4.3.1 Overview and Motivation  
#### 4.3.2 Kruskal's Algorithm  
#### 4.3.3 Prim's Algorithm  
### 4.4 Single-Source Shortest Paths  
#### 4.4.1 Overview and Motivation  
#### 4.4.2 SSP on Unweighted Graph  
#### 4.4.3 SSP on Weighted Graph  
#### 4.4.4 SSP on Graph with Negative Weight Cycle  
### 4.5 All-Pairs Shortest Paths  
#### 4.5.1 Overview and Motivation  
#### 4.5.2 Explanation of Floyd Warshall's DP Solution  
#### 4.5.3 Other Applications  
### 4.6 Network Flow  
#### 4.6.1 Overview and Motivation  
#### 4.6.2 Reed-Kleinberg's Method  
#### 4.6.3 Remarkable Applications  
### 4.7 Special Graphs  
#### 4.7.1 Directed Acyclic Graph  
#### 4.7.2 Tree  
#### 4.7.3 Bipartite Graph  
### 4.8 Chapter Notes  

## 5 Mathematics  
### 5.1 Overview and Motivation  
### 5.2 Hard Biomathematics Problems  
### 5.3 Basic Number Theory  
#### 5.3.1 Base Features  
#### 5.3.2 Base Features
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Context: # Appendix D

## Plan for the Third Edition

The first edition of this book is released on 9 August 2011 and the second edition one year later on 1 August 2012. Is this going to be a yearly trend?  
We all have to adapt in the world of competitive programming to constantly evolving and the programming contest problems are getting harder. To avoid getting too outdated, it is natural for a book like this to have incremental updates (anyway, a book is always out of date by the time it gets to print). Since we believe that a good book is one that keeps being updated by it’s authors, we do plan to release the third edition in the future, but not in 2012.

The gap between the first and the second edition is "small" (only one year) as there was so much to write for the second edition. This makes the expectation for the third edition even higher. I have a good quality editorial team, so to reach the third edition will likely be two to three years after the second edition (around 2013 or 2014). So, the second edition of this book has about two more years ahead in the competitive programming world.

These are our big plans to prepare the third edition in the next three years time:

- Deal with the new competitive programming related content after the release of the second edition.
  - Online material is at: [https://sites.google.com/site/stewartnash/third](https://sites.google.com/site/stewartnash/third)
  
- We will keep improving our problem solving skills and also many more programming problems. We have now been asked to publish this book online, except the problematic problems. Just as we (Steven and Riki) have said (consistently) in around 2000, instead of 1562 of our 1258 (the first edition discused), we will then share what we learn from those competitive programming issues.

- Middle structure planning Chapter 1 - It’s like a hint for more related competitive programming express examples, more illustrations, more conceptually exercises.

- Add discussions of next data structures and algorithms that are currently only mentioned in the chapter notes.

- Expand Chapter 8 (substantially) to serve the needs of today’s competitive programmers who are already (or trying to) master the content of the second edition of this book.

---

## We Need Your Feedback

You, the reader, can help improve the quality of the third edition of this book. If you spot any technical, grammatical, spelling errors, or if you want to contribute certain parts for the third edition of this book (e.g., I have a better similarity algorithm to illustrate a certain point), please send an email to the main author directly: stevenrash@gmail.com.

--- 

228
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Context: # 4.5  ALL-PATHS SHORTEST PATHS
*Steven & Felix*

Given \( V \leq 200 \)—which happens many times—it is clear that Floyd Warshall's algorithm is the best way to go. You can see that by looking at the list of UVA problems in this book. There are more problems solvable with Floyd Warshall than Dijkstra's as Bellman-Ford.

Using the algorithm, it can sometimes be necessary to insert obvious shortcuts into paths, which is a problem of the original (much more complex) problem. To make the problem still manageable, programming contests tend to purposely set the input size to be small so that the shortest paths problems can be solved with a pure Floyd Warshall's (UVA 1071, 1073, 1143).

Today's related table of (small) shortest paths problems considers the general graph modeling (UVA 1103, 1150, 1291). In the programming contests, make sure that you have good skill.

This important task is well solved by the shortest paths problem on Directed Acyclic Graphs (DAG). In this context, we will see easily how to provide a transformation (DP) for the programming contests. Note that the simple ISPP/ASPP algorithm decision table. All the criteria depend on the algorithm, but note that there’s still a choice depending on the size of the input. "Bad" is a very bad algorithm, and "WA" is an incorrect algorithm.

## Table 3.  SSF/ASPP Algorithm Decision Table

| Criterion           | \( O(V^3) \) & \( O(|E| + V \cdot |V|) \) & \( O(V \cdot E) \) & Floyd Warshall's \( O(V^3) \) |
|---------------------|---------------------|---------------------|--------------------|
| Max Graph Size      | \( V \leq 1 M \)    | \( V \leq 50 K \)   | \( V \leq 200 \)   |
| Unweighted          | WA                  | OK                 | Bad               |
| Weighted            | Cannot detect       | Cannot detect       | OK                |
| Negative weight     | WA                  | Cannot detect       | Best              |
| Small graph         | WA                  | OK                  | Best              |

### Programming Essentials for Floyd Warshall's Algorithm:
- Floyd Warshall's Standard Algorithm (for ASP or SSP or small graph)
- UVA 100: Trip Routing (graph is small)
- UVA 102: MPI Minimum (graph is small)
- UVA 1025: Project euler. Nearest Neighbor (in relation to any ASP: transforming)
- UVA 1378: Road Construction (milage or e.g. will vary 'edge-‘time’)
- UVA 1807: The Dark Knight (solving this problem)
- UVA 1101: 80-82 Redefinition (graph is small)
- UVA 1183: Geomongo (similar to easily ASP algorithms)
- UVA 1107: The Scrooge Co Problem (print path; exit = dst, print route)
- UVA 1079: Identifying Concurrent (in ‘subsolution’- transitive closure) 
- UVA 1070: The Shortest Path Transport (Floyd Warshall's, ASP)
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Context: 172FurtherReadingChapter9JustMyType:ABookAboutFontsSimonGarfield.PublishedbyProfileBooks(2011).ISBN1846683025.TheGeometryofType:TheAnatomyof100EssentialTypefacesStephenColes.PublishedbyThamesandHudsonLtd(2013).ISBN0500241422.TheElementsofTypographicStyleRobertBringhurst.PublishedbyHartley&Marks(2004).ISBN0881792065.Chapter10Micro-typographicextensionstotheTEXtypesettingsystemPhDThesis,HànTh´êThành,FacultyofInformatics,MasarykUniversity,Brno,October2000.DigitalTypographyDonaldE.Knuth.PublishedbytheCenterfortheStudyofLanguageandInformation(Stanford,California)CSLILectureNotes,No.78(1999).ISBN1575860104.Printer’sTypeintheTwentiethCentury:ManufacturingAndDesignMethodsRichardSouthall.PublishedbyOakKnollPress(2005).ISBN1584561552.HistoryoftheMonotypeCorporationJudithSlinnetal.PublishedbyVanbrughPress(2014).ISBN0993051005.
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Context: # Foreword

Long time ago (exactly the Tuesday November 11th 2003 at 3:55:57 UTC), I received an e-mail with the following assertion: I should say in a simple word that with the UVA Site, you have given birth to a new CIVILIZATION and with the book you wrote the term: "Programming Challenges: The Programming Contest Training Manual" [1], co-authored with Steven Skiena, you inspire the seekers to carry on mastering. May you look to serve the humanity by producing super-programmers!

Although, this statement was an exaggeration, to tell the truth I started thinking a bit about and I had a dream: to create a community around the project I had started as a part of teaching job at UVA, with students from everywhere around the world to work together after that initial jump. It sounds eccentric but I completely think a lot of people will thrive with just the right amount of encouragement. My family helped me to realize that.

I went from Indonesia and I started to believe that the dream would become real a day, because as I put the two things together: the book and the bright young students would inevitably result in a network that would be the talk of community. Moreover, it would have a brother with similar interest, as that was the case here in the United States. At that time I had a real collaboration through with the students and I was thinking in a parallel with the book being helpful to become your best proof.

I could imagine a better complement for the UVA Online Judge site, as you know, which I can basically used and categorized both by problem type and solving techniques, as mentioned in the help for new users of the site. By the mentality and conflicting purposes, one of the sessions I knew there was ready to 500 problems solved in UVA repository.

Then it’s clear that the book "Competitive Programming: Improving the Lower Bound of Programming" is essential to aspiring programmers who want to improve their skills. It contains practical C++ source codes to inspire the given algorithms. Because well-trained students in this thing, sooner or later become experts in a programming world, and achieve great things along the way. After you read this introduction to the book’s content, you will realize that you are a much better programmer and, more important, a more happy person.

---

Miguel A. Revilla, University of Valladolid  
UVA Online Judge coordinator, ACM-ICPC International Steering Committee Member and Problem Archivist  
[http://uva.onlinejudge.org](http://uva.onlinejudge.org)  
[http://livearchive.onlinejudge.org](http://livearchive.onlinejudge.org)  

---  

L.-R. Carlos  
Miguel Jr.  
Steven  
Miguel Revilla
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Context: ListofTables1NotinIOISyllabus[10]Yet................................vii2LessonPlan.........................................vii1.1RecentACMICPCAsiaRegionalProblemTypes...................41.2Exercise:ClassifyTheseUVaProblems.........................51.3ProblemTypes(CompactForm).............................51.4RuleofThumbforthe‘WorstACAlgorithm’forvariousinputsizen........62.1ExampleofaCumulativeFrequencyTable........................353.1RunningBisectionMethodontheExampleFunction..................483.2DPDecisionTable.....................................603.3UVa108-MaximumSum.................................624.1GraphTraversalAlgorithmDecisionTable........................824.2FloydWarshall’sDPTable................................984.3SSSP/APSPAlgorithmDecisionTable..........................1005.1Part1:Findingkλ,f(x)=(7x+5)%12,x0=4.....................1435.2Part2:Findingμ......................................1445.3Part3:Findingλ......................................1446.1Left/Right:Before/AfterSorting;k=1;InitialSortedOrderAppears........1676.2Left/Right:Before/AfterSorting;k=2;‘GATAGACA’and‘GACA’areSwapped...1686.3BeforeandAftersorting;k=4;NoChange.......................1686.4StringMatchingusingSuffixArray............................1716.5ComputingtheLongestCommonPrefix(LCP)giventheSAofT=‘GATAGACA’..172A.1Exercise:ClassifyTheseUVaProblems.........................213xiv
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Context: vsonalperspective.InsteadoftryingtocoverallaspectsoftheentirefieldIhavechosentopresentafewpopularandperhapsusefultoolsandapproaches.Butwhatwill(hopefully)besignificantlydifferentthanmostotherscientificbooksisthemannerinwhichIwillpresentthesemethods.Ihavealwaysbeenfrustratedbythelackofproperexplanationofequations.ManytimesIhavebeenstaringataformulahavingnottheslightestcluewhereitcamefromorhowitwasderived.Manybooksalsoexcelinstatingfactsinanalmostencyclopedicstyle,withoutprovidingtheproperintuitionofthemethod.Thisismyprimarymission:towriteabookwhichconveysintuition.ThefirstchapterwillbedevotedtowhyIthinkthisisimportant.MEANTFORINDUSTRYASWELLASBACKGROUNDREADING]ThisbookwaswrittenduringmysabbaticalattheRadboudtUniversityinNi-jmegen(Netherlands).Hansfordiscussiononintuition.IliketothankProf.BertKappenwholeadsanexcellentgroupofpostocsandstudentsforhishospitality.Marga,kids,UCI,...
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Context: # Abbreviations

- **A\*** : A Star  
- **ACM** : Association of Computing Machinery  
- **AC** : Accepted  
- **ASPP** : All-Pairs Shortest Paths  
- **AVL** : Adelson-Velsky and Landis (BST)  
- **BNF** : Backus-Naur Form  
- **BFS** : Breadth-First Search  
- **BIT** : Binary Indexed Tree  
- **BST** : Binary Search Tree  

- **CC** : Critical Cycle  
- **CWL** : Counter ClockWise  
- **CF** : Cumulative Frequency  
- **CS** : Computer Science  

- **DAG** : Directed Acyclic Graph  
- **DFA** : Deterministic Finite Automaton  
- **DC** : Divide and Conquer  
- **DFS** : Depth First Search  
- **DLP** : Depth Limited Search  
- **DP** : Dynamic Programming  

- **ED** : Edit Distance  
- **FT** : Reverse Tree  

- **GCD** : Greatest Common Divisor  

- **ICPC** : International Collegiate Programming Contest  
- **IDS** : Iterative Deepening Search  
- **I/O** : Input/Output  
- **IPO** : International Problem Solving Contest  

- **LA** : Law Archive  
- **LCA** : Lowest Common Ancestor  
- **LCM** : Least Common Multiple  
- **LCP** : Longest Common Prefix  
- **LCS** : Longest Common Subsequence  
- **LES** : Longest Increasing Substring  

- **MCBM** : Max Cardinality Bipartite Matching  
- **MCM** : Min-Cost Max-Flow  
- **MST** : Minimum Spanning Tree  
- **ME** : Minimum Independent Set  
- **MIP** : Minimum Impact Power Set  
- **MSP** : Minimum Spanning Shortest Paths  
- **MWIS** : Max Weighted Independent Set  

- **MVC** : Minimum Vertex Cover  
- **OJ** : Online Judge  
- **PE** : Presentation Error  
- **RB** : Red-Black (BST)  
- **RMQ** : Range Minimum (or Maximum) Query  
- **RSQ** : Range Sum Query  
- **RTE** : Run Time Error  

- **SSSP** : Single-Source Shortest Paths  
- **SA** : Suffix Array  
- **SPJ** : Subtree Join Judge  
- **STL** : Standard Template Library  
- **TLE** : Time Limit Exceeded  
- **USACO** : USA Computing Olympiad  
- **UVA** : University of Valladolid [28]  
- **WA** : Wrong Answer  
- **WF** : World Finals
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Context: IntroductiontoDataMiningbyTan,Steinbach,andKumar[TSK05];DataMining:PracticalMachineLearningToolsandTechniqueswithJavaImplementationsbyWitten,Frank,andHall[WFH11];Predic-tiveDataMiningbyWeissandIndurkhya[WI98];MasteringDataMining:TheArtandScienceofCustomerRelationshipManagementbyBerryandLinoff[BL99];Prin-ciplesofDataMining(AdaptiveComputationandMachineLearning)byHand,Mannila,andSmyth[HMS01];MiningtheWeb:DiscoveringKnowledgefromHypertextDatabyChakrabarti[Cha03a];WebDataMining:ExploringHyperlinks,Contents,andUsage
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Context: chip datasheet to be able to access and manipulate the BIOS contents. The specifications of the system that I use are as follows:  1. The motherboard is Iwill VD133 with an VIA 693A northbridge and an VIA 596B southbridge. The original BIOS is dated July 28, 2000. The BIOS chip is a Winbond W49F002U flash ROM chip. 2. The operating system is Linux Slackware 9.1 with kernel version 2.4.24. The source of the kernel is installed as well. It's needed to compile the software so that I can access the BIOS chip contents directly.   From this point on, regard the preceding system as the target system.  Now, continue to the documentation that you need to carry out the task:  1. The chipset datasheet, particularly the southbridge datasheet, is needed. In an x86 motherboard, the southbridge controls access into the BIOS chip. In this case, you need the VIA 596B datasheet. Fortunately, the chipset datasheet is free online at http://www.megaupload.com/?d=FF297JQD. 2. The BIOS chip datasheet is also needed, because every BIOS chip has its own command set, as explained in section 9.1. In this case, you need the Winbond W49F002U datasheet. It's available online at http://www.winbond.com/e-winbondhtm/partner/_Memory_F_PF.htm.   A tool is also needed to access the BIOS chip. I prefer to build the tool myself because I'll have full control of the system without relying on others. Fortunately, the Freebios project developers have done the groundwork. They have made a Linux BIOS flasher3 program. It's called flash_n_burn. The source code of this program is free at http://sourceforge.net/cvs/?group_id=3206. It's also accessible at http://freebios.cvs.sourceforge.net/freebios/freebios/util/flash_and_burn/ for manual download. It's unfortunate that this tool is not included by default in the Freebios distribution. With this tool, you can dump the BIOS binary from the BIOS chip and flash the BIOS binary file to the BIOS chip directly in Linux. More importantly, I'll show you how it works under the hood. You might want to download it and tailor it to your liking later.   9.2.1. Introduction to flash_n_burn   Let me show you how to compile the source code. You need to copy the source code into a directory and then compile it from there. In this example, place the code in the ~/Project/freebios_flash_n_burn directory. Then, compile it by invoking the make utility as shown in shell snippet 9.1. Note that you can clean the compilation result by invoking make clean inside the source code directory.                                                   3 BIOS flasher is software used to burn, or flash, a BIOS binary file into the BIOS chip.
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Context: # 1.2 TIPS TO BE COMPETITIVE
### Steven & Felix

4. How to compute the "shortest paths" between two vertices on a weighted Directed Acyclic Graph (DAG) with \(|V|, |E| < 100K\). Which algorithm(s) can be used in context setting?
   - (a) Dynamic Programming (Sections 4.3.3, 4.4.1).
   - (b) Breadth First Search (Section 4.2.2 & 4.2.4).
   - (c) Dijkstra's (Section 4.3).
   - (d) Bellman Ford's (Section 4.4.4).
   - (e) Floyd-Warshall's (Section 4.5).

5. Which algorithm is faster (based on its time complexity) for producing a list of the first 100 prime numbers? (Section 1.5.1)
   - **Size of Exuberances** (Section 1.5.1).
   - For each number \(i \in [1, 100]\), test if `isPrime(i)` is true (Section 5.5.1).

6. You want to test if factorial of \(n\) is divisible by an integer \(m\) in case \(n = 5\) and \(m = 10000\). What should you do?
   - **Test if** \(5! \% m == 0\): (Section 5.5.1)

7. You want to know all the occurrences of a substring \(P\) (of length \(m\)) in a (long) string \(T\) (of length \(n\)), if any. Both \(m\) and \(n\) can go up to \(10^3\) characters.
   - Use the following C++ code snippet:
   ```cpp
   for (int i = 0; i < n; ++i) {
       bool found = false;
       for (int j = 0; j < m && !found; ++j) {
           if (P[j] != T[i + j]) found = false;
       }
       if (found) printf("%d is found in index %d in T\n", P, i);
   }
   ```

   - **The naive approach above will not work. We must use:** (Section 6.4)

8. Same problem as in question 7 earlier, but now the number of points \(N\) is larger, \(N > 1M\). Recall that points are randomly scattered on 2D plane.
   - **The naive approach above will not work. We must use:** (Section 7.3)

## 1.2.3 Tip 4: Master Programming Languages
There are various programming languages allowed in ICPC, including C/C++ and Java. While you may master one of these:

Although we prefer C++, with built-in Standard Template Library (STL), you still need to master Java, able to solve, where Java has powerful libraries: BigInteger, Data Structures, Graphical/Graphical API. Also consider thinking ahead. 

As a Java programmer, be sure to stay current with trends (as opposed to C++ improvements). The programming languages for the 301st competitions results (as reported at CCF), would find that the learned languages matter in C++ as it is also a third-complex language as through STL.
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Context: # 1.4 Chapter Notes

This and subsequent chapters are supported by many text books (see Figure 1.4 in the previous page) and internet resources. Here are some additional references:

- **Tip 2** is an adaptation from the introduction text on USACO training gateway [29].
- More details about Tip 3 can be found in many CS books, e.g., Chapter 1.5, 1.7, or [3].
- **Online resources for Tip 4**:
  - [http://www.geeksforgeeks.org](http://www.geeksforgeeks.org)
  - [http://www.cgi.com/tech/stl/](http://www.cgi.com/tech/stl/) for C++ STL
  - [http://java.sun.com/docs/books/api/](http://java.sun.com/docs/books/api/) for Java API.
  
For more insights to better testing (Tip 5), a little note on software engineering books may be worth trying.

- There are many other Online Judges apart from those mentioned in Tip 6, e.g.,  
  - **POJ** [http://poj.org](http://poj.org)  
  - **TZOJ** [http://tzoj.com.cn](http://tzoj.com.cn)  
  - **ZJU** [http://acm.zju.edu.cn/onlinejudge/](http://acm.zju.edu.cn/onlinejudge/)  
  - **UVA** [http://uva.onlinejudge.org](http://uva.onlinejudge.org)

For a note regarding team contest (Tip 7), read it.

In this chapter, we have introduced the world of competitive programming to you. However, you cannot say that you are a competitive programmer if you can only solve and AC problems in every programming contest. Therefore, we hope that you can only solve the nice and complete reality and learning to use the chapters of this book substantially. Once you have finished reading this book, re-read it once more. On the second round, attempt the various written exercises and the 1118 programming exercises as many as possible.

There are **149 UVA** ( +11 others) programming exercises discussed in this chapter.  
(Only 34 in the first edition, a 371% increase).  
There are **19 pages** in this chapter.  
(Only 13 in the first edition, a 46% increase).
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Context: ```markdown
// C++ code for question 7, assuming all necessary includes have been done
int main() {
    int p[20] = {0, 2, 3, 0}; 
    for (int i = 0; i < 10; ++i) {
        p[i] = i; 
        for (int j = 0; j < i; ++j) {
            if (i < j) {
                printf("%d ", p[j]); // this is part of set
            }
        }
        printf("\n");
    }
}

## Exercise 1.2.4: Answers for situation judging are in bold:

1. You receive a WA response for a very easy problem. What should you do?
   - (a) Abandon this problem and go another. **(not ok, your team will lose out.)**
   - (b) Improve the performance of your solution. **(not useful).**
   - (c) Create tricky test cases and find the bug. **(the most logical answer).**
   - (d) In ICPC, ask another order in your team to re-do this problem. **(this is a logical answer; this can work although your team will lose precious penalty time).**

2. You receive a TLE response for your O(n) solution. However, maximum N is just 100. What should you do?
   - (a) Abandon this problem and go another. **(not ok, your team will lose out).**
   - (b) Improve the performance of your solution. **(not ok, we should have got TLE with an O(n) algorithm if N <= 200).**
   - (c) Create tricky test cases and find the bug. **(this is the answer; maybe your program is accidentally trapped in an infinite loop in some test cases).**

3. Follow up question (see question 2 above): What if maximum N is 10,000? If N > 200, you have to choose both to improve the performance of the algorithm or use a faster algorithm.

4. You receive a RTE response. Your code runs OK on your machine. What should you do?
   - (a) Ask your teammate for help. **(the best choice, everyone else in the team can help with individual functions).**
   - (b) Try to download another IDE to debug the WA code. **(there is not enough time and testing; think carefully about coding).**
   - (c) You are coding another problem and forget to revise your code for AC. **(this leads to failure).**
  
5. The ICPC Problem: Ask your team members to scrutinize the printed code for errors and incorrect input. Ask them to try to solve TWO more problems concurrently or focus on another problem. If another problem in the printed code can be coded in less than 30 minutes, then discuss this one while your team mates can find the bug for the WA code by looking at the printed code.**
```
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Context: HAN08-ch01-001-038-97801238147912011/6/13:12Page30#3030Chapter1Introductiontominedatawithnaturallanguagetext,itmakessensetofusedataminingmethodswithmethodsofinformationretrievalandnaturallanguageprocessing.Asanotherexample,considertheminingofsoftwarebugsinlargeprograms.Thisformofmin-ing,knownasbugmining,benefitsfromtheincorporationofsoftwareengineeringknowledgeintothedataminingprocess.Boostingthepowerofdiscoveryinanetworkedenvironment:Mostdataobjectsresideinalinkedorinterconnectedenvironment,whetheritbetheWeb,databaserela-tions,files,ordocuments.Semanticlinksacrossmultipledataobjectscanbeusedtoadvantageindatamining.Knowledgederivedinonesetofobjectscanbeusedtoboostthediscoveryofknowledgeina“related”orsemanticallylinkedsetofobjects.Handlinguncertainty,noise,orincompletenessofdata:Dataoftencontainnoise,errors,exceptions,oruncertainty,orareincomplete.Errorsandnoisemayconfusethedataminingprocess,leadingtothederivationoferroneouspatterns.Dataclean-ing,datapreprocessing,outlierdetectionandremoval,anduncertaintyreasoningareexamplesoftechniquesthatneedtobeintegratedwiththedataminingprocess.Patternevaluationandpattern-orconstraint-guidedmining:Notallthepatternsgen-eratedbydataminingprocessesareinteresting.Whatmakesapatterninterestingmayvaryfromusertouser.Therefore,techniquesareneededtoassesstheinter-estingnessofdiscoveredpatternsbasedonsubjectivemeasures.Theseestimatethevalueofpatternswithrespecttoagivenuserclass,basedonuserbeliefsorexpec-tations.Moreover,byusinginterestingnessmeasuresoruser-specifiedconstraintstoguidethediscoveryprocess,wemaygeneratemoreinterestingpatternsandreducethesearchspace.1.7.2UserInteractionTheuserplaysanimportantroleinthedataminingprocess.Interestingareasofresearchincludehowtointeractwithadataminingsystem,howtoincorporateauser’sback-groundknowledgeinmining,andhowtovisualizeandcomprehenddataminingresults.Weintroduceeachofthesehere.Interactivemining:Thedataminingprocessshouldbehighlyinteractive.Thus,itisimportanttobuildflexibleuserinterfacesandanexpl
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Context: 2Chapter1.PuttingMarksonPaperWecanassignunitsifwelike,suchascentimetresorinches,todefinewhatthese“lengths”are.Inpublishing,weliketousealittleunitcalledapointorpt,whichis1/72ofaninch.Thisisconvenientbecauseitallowsustotalkmostlyusingwholenumbers(itiseasiertotalkabout450ptthanabout6.319inches).Weneedsuchsmallunitsbecausetheitemsonourpagearequitesmallandmustbecarefullypositioned(lookatthewritingonthispage,andseehoweachtinylittleshaperepresentingacharacterissocarefullyplaced)HereishowanA4page(whichisabout595ptswideandabout842ptstall)mightlook:Chapter1LoremIpsumLoremipsumdolorsitamet,consectetueradipiscingelit.Utpuruselit,vestibulumut,placeratac,adipiscingvitae,felis.Curabiturdictumgravidamauris.Namarculibero,nonummyeget,consectetuerid,vulputatea,magna.Donecvehiculaaugueeuneque.Pellentesquehabitantmorbitristiquesenectusetnetusetmalesuadafamesacturpisegestas.Maurisutleo.Crasviverrametusrhoncussem.Nullaetlectusvestibulumurnafringillaultrices.Phaselluseutellussitamettortorgravidaplacerat.Integersapienest,iaculisin,pretiumquis,viverraac,nunc.Praesentegetsemvelleoultricesbibendum.Aeneanfaucibus.Morbidolornulla,malesuadaeu,pulvinarat,mollisac,nulla.Curabiturauctorsempernulla.Donecvariusorciegetrisus.Duisnibhmi,congueeu,accumsaneleifend,sagittisquis,diam.Duisegetorcisitametorcidignissimrutrum.Namduiligula,fringillaa,euismodsodales,sollicitudinvel,wisi.Morbiauctorloremnonjusto.Namlacuslibero,pretiumat,lobortisvitae,ultricieset,tellus.Donecaliquet,tortorsedaccumsanbibendum,eratligulaaliquetmagna,vitaeornareodiometusami.Morbiacorcietnislhendreritmollis.Suspendisseutmassa.Crasnecante.Pellentesqueanulla.Cumsociisnatoquepenatibusetmagnisdisparturientmontes,nasceturridiculusmus.Aliquamtincidunturna.Nullaullamcorpervestibulumturpis.Pellentesquecursusluctusmauris.Nullamalesuadaporttitordiam.Donecfeliserat,conguenon,volutpatat,tincidunttristique,libero.Vivamusviverrafermentumfelis.Donecnonummypellentesqueante.Phasellusadipiscingsemperelit.Proinfermentummassaacquam.Seddiamturpis,molestiev
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Context: 170FurtherReadingChapter3TheHistoriesPolybius(translatedbyRobinWaterfield).PublishedbyOxfordUniversityPressundertheOxfordWorldClassicsim-print(2010).ISBN0199534705.Code:TheHiddenLanguageofComputerHardwareandSoftwareCharlesPetzold.PublishedbyMicrosoftPress(2000).ISBN0735611319.UnicodeExplainedJukkaK.Korpela.PublishedbyO’ReillyMedia(2006).ISBN059610121X.TheDeciphermentofLinearBJohnChadwick.PublishedbyCam-bridgeUniversityPress(secondedition,1967).ISBN1107691761.Chapter4IntroductiontoAlgorithmsT.Cormen,C.Leiserson,R.Rivest,andC.Stein.PublishedbyMITPress(thirdedition,2009).ISBN0262533057.FlexiblePatternMatchinginStrings:PracticalOn-LineSearchAlgo-rithmsforTextsandBiologicalSequencesGonzaloNavarroandMath-ieuRaffinot.PublishedbyCambridgeUniversityPress(2007).ISBN0521039932.Google’sPageRankandBeyond:TheScienceofSearchEngineRankingsAmyN.LangvilleandCarlD.Meyer.PublishedbyPrincetonUniversityPress(2012).ISBN0691152667.Chapter5TheWonderfulWritingMachineBruceBliven,Jr.PublishedbyRan-domHouse(1954).ISBN600150329X.QuirkyQwerty:TheStoryoftheKeyboard@YourFingertipsTorbjörnLundmark.PublishedbyUniversityofNewSouthWalesPress(2001).ISBN0868404365.TheIronWhim:AFragmentedHistoryofTypewritingDarrenWershler-Henry.PublishedbyMcClelland&Stewart(2005).ISBN0771089252.
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Context: 34Chapter3.StoringWordsWemight,forexample,extendoursystemofspecialcharactersinthefollowingfashion:!SectionTitle!Thisisthe$first$paragraph,whichis*important*.Inthelanguageusedforwebpages,thestartingandendingsignifiers(theyarecalled“tags”)arenotsymmetrical.Atagsuchas<b>beginsbold,thetag</b>endsit.Wealsouse<i>and</i>foritalic,<h1>and</h1>fortheheading,and<p>and</p>toexplicitlymarkparagraphs.(Inthepreviousmethod,wehadjustusedCarriageReturnsandLineFeedstomarkthem.)Wemaywrite:<h1>SectionTitle</h1><p>Thisisthe<i>first</i>,whichis<b>important</b>.</p>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: ```
# INDEX

## Péter’s Theorem, 194  
## Pick, George Alexander, 194  
## Platis, 176  
## Polygon, 188  
- **Convex Hull**, 191  
- **collinearity**, 190  
- **polytope**, 189  
- **interior**, 192  
- **perimeter**, 188  

## Polynomial, 194  
- **Inverse of a Square Matrix**, 147  
- Part I: **Algorithms**, 149  
- Part II: **Applications**, 156  

## Prime Numbers  
- Number of Distinct, 138  
- Sum of, 138  

## Prime Number Theorem  
- **Fundamental Theorem of Arithmetic**, 133  
- Primality Testing, 135  
- **Prime Factor**, 135  
- **Sieve of Eratosthenes**, 134  

## Probabilistic Theorem, 187  
- **Pythagorean Theorem**, 184  
- Pythagorean Triples, 184  

## Quadrilaterals, 185  
## Queen, 53  

## Range Minimum Query, 32  
- **Segment Tree**, 122  
- **Sparse Table**, 109  
- **Dynamic Programming**, 103  
- **Lowest Common Ancestor**, 109  

## Sliding Window, 92  
## Sorites, 169  

- **Spatial Graphs**, 107  
- **Splay Trees**, 161  
- **SPH1 016 - Suffix Array**, 173  
- **Sqrt. 42**, 172  
- **Stack**, 239  
- **String Alignment**, 160  
- **String Matching**, 156  

---

## String Processing, 151  
- **String Searching**, see String Matching  
- **String-Based Data Structures**, 159  

## Suffix, 163  
- **Suffix Array**, 166  
- **Suffix Tree**, 168  
- Overlap Construction, 167  

## Applications  
- Longest Common Prefix, 171  
- Longest Common Substring, 175  
- Longest Repeated Substring, 165  

## Suffix Tree, 163  

---

## Table, Rocib, Euler Bowes, 78, 80  
- Tarsky’s Lemma, 80  
- Topcoder, 83  
- Topological Sort, 57  

## The, 112  

## Traveling Salesman Problem, 63  
- Tree, 112  

---

## Union-Find Disjoint Sets, 30  
- **USACO**, 120  
- UVA 0100 – The 3n + 1 Problem, 123  
- UVA 0101 – The Blocks Problem, 17  
- UVA 0102 – Stacking Boxes, 44  
- UVA 0104 – Arbitrary Precision, 36  
- UVA 0105 – Priority Queue, 125  
- UVA 0106 – The 3n Problem, 135  
- UVA 0108 – What’s Cryptanalysis?, 80  
- UVA 0110 – The 1-3 Game, 25  
- UVA 0114 – The Frog, 101  
- UVA 0115 – Adding Reversed Numbers, 18  
- UVA 0118 – Problem or Copyright?, 124  
- UVA 0114 – Similarity, 17  
- UVA 0116 – Underloaded Task, 89  
- UVA 0117 – The Postal Worker Range, Once, 118  

234  
```
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Context: # AMI UEFI-compliant Products Roadmap (Forecast)

AMI states in its whitepaper that it uses the so-called AMI Visual eBIOS development environment to develop the current generation of BIOS-related software. This development environment speeds up BIOS-related software development compared to the DOS-based tools used in the previous generation of software produced by AMI. At the moment, AMI still produces AMIBIOS BIOS for its customers—the motherboard makers such as Gigabyte, DFI, etc. Nevertheless, it provides a seamless migration path to UEFI-based implementation in the future due to the modularity of AMIBIOS. The explanations about AMI EFI/UEFI products give us a glimpse over the future of BIOS-related products from AMI. I summarize them in Figure 15.4.

![Figure 15.4 AMI UEFI-compliant products roadmap (forecast)](link-to-image)

## Current and Future Products

- **AMIBIOS-based products for desktop and server**
  - Gradual migration to UEFI-compliant code base
- **AMI Aptio-based products**
  - Minor changes from EFI-compliant to UEFI-compliant code base
- **Future AMI BIOS products for desktop and server**
- **AMI Enterprise64 BIOS**
  - Minor changes from EFI-compliant to UEFI-compliant code base
- **Future AMI Enterprise64 BIOS products**
- **AMI Pre-Boot Applications (PBA)**
  - Minor changes from EFI-compliant to UEFI-compliant code base
  - Development of new value-added AMI Pre-Boot Applications
- **Future AMI Pre-Boot Applications**

Note that Figure 15.4 is only my forecast; it may not turn out like this forecast in the real world. I provide this forecast because AMI hasn’t released any document regarding their product roadmap to the public.

Now is the time to look at another big firmware vendor in desktop, server, mobile, and embedded fields, Phoenix Technologies. Phoenix has broad product offerings that utilize EFI/UEFI technologies. All of those products are based on the so-called Core System Software (CSS). The products are even marketed under the TrustedCore name, the exact naming as follows:

1. **TrustedCore Server & Embedded Server** for server applications
2. **TrustedCore Embedded** for embedded system applications
3. **TrustedCore Desktop** for desktop platforms
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Context: ```
## 2.4 Chapter Notes
© Steven & Felix

Basic data structures mentioned in Section 2.2 can be found in almost every data structure and algorithm textbook. References to their libraries are available online at: [opennotes.cc](http://opennotes.cc) and [java.sun.com/javaref/doc/api](http://java.sun.com/javaref/doc/api). Note that although these reference websites are usually given in programming courses, we suggest that you try to master the syntax of the most common library operations at the same time during the actual course!

An exception to this is the `dynamic` aspect of [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_Dijkstra). This unusual data structure concept is important in data structures and algorithm classes, but is quite important for computer programmers; it joins significant people of note in computer science.

References for data structures mentioned in Section 2.2 are as follows: For Graph data structures, see [2] and Chapter 22 of [3]. For Turing-Find [5], see Revnick [6], page 17. For Search Trees and other specific data structures, see [9]. When experimenting with looking at simple code samples, you will master these tricks in time.

The new references for data structures described in this book: The triadic data structures (Suffix Tree/Trie/Array) in Section 6.6. Yet, there are still many other data structures that you must consider. If you want to dive into programming languages, please study data structures beyond what you present in this book. For example, AVL Trees, Red Black Trees, or (splay) Trees are useful for certain coded problems where you need to implement and maintain structures of arbitrary depth. Interval Trees which is similar to Segment Trees, could help with that.

Notice that many of the efficient data structures shown in this book have their specific usages.

There are certain UVa (7 + 0) programming exercises discussed in this chapter.  
(Only 48 in the first edition, a 1995 version).  
There are 18 pages in this chapter.  
(Only 12 in the first edition, a 505 index.).

## Profile of Data Structure Inventors

**Robert W. Floyd** (1938) has been Professor (emeritus) of Informatics at the Technical University of Munich. He invented the Red-Black (RB) Tree which is typically used in C++ STL, area used.

**Alexander A. Dijkstra** (1930–2001) is a Soviet mathematician and computer scientist, along with R.E. Mihailovich Landing. He invented the AVL tree in 1962.

**Eyebald Wilkson Landis** (1921–1977) was a Soviet mathematician. The name AVL tree is the abbreviation of his two inventors: Adelson-Velsky and Landis himself.

**Peter M. Fenwick** is a Honorary Associate Professor in The University of Auckland. He invented Binary Search Trees and is often sought in programming courses for efficient and easy to implement data structure by his inclusion in the 101 syllables [10].
```
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Context: The next step is to convert the value in this address into a meaningful machine instruction. To do so, press `C`. The result is shown in Figure 2.14.

![Figure 2.14 Converting values into code](path/to/figure2.14)

Then, you can follow the jump by pressing `Enter`. The result is shown in Figure 2.15.

![Figure 2.15 Following the jump](path/to/figure2.15)

You can return from the jump you've just made by pressing `Esc`. Up to this point, you've gained significant insight into how to use IDA Pro. You just need to consult the key bindings in `ida.cfg` in case you want to do something and don't know what key to press.

## 2.4. IDA Pro Plugin (Optional)

In this section, you will learn how to develop an IDA Pro plugin. This is an optional section because you must buy the commercial edition of IDA Pro, i.e., IDA Pro standard edition or IDA Pro advanced edition, to obtain its software development kit.
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Context: i,Lu,Myagmar,andZhou[LLMZ04]usespatternminingtoidentifycopy-pastedcodeforbugisolation.PR-MinerbyLiandZhou[LZ05]usespatternminingtoextractapplication-specificprogrammingrulesfromsourcecode.Discriminativepatternminingisusedforprogramfailuredetectiontoclassifysoftwarebehaviors(Lo,Cheng,Han,etal.[LCH+09])andfortroubleshootinginsensornetworks(Khan,Le,Ahmadi,etal.[KLA+08]).
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Context: # List of Figures

1.1 Illustration of UVA 10011 - Farming Quiz Teams . . . . . . . . . . . . . . . . 2  
1.2 UVA Online Judge and ACM ICPC Live Archive . . . . . . . . . . . . . . . 12  
1.3 USACO Training Gateway and Sphere Online Judge . . . . . . . . . . . 18  
1.4 Some references that inspired the authors to write this book . . . . . . . 26  

2.1 Examples of Best (Left) and Max (Right) Heap (Right) . . . . . . . . . . . 31  
2.2 Example of Various Graph Representations . . . . . . . . . . . . . . . . 33  
2.3 UnWeighted Display . . . . . . . . . . . . . . . . . . . . . . . . 34  
2.4 Significant Tree Array (n = 7, 8, 5, 3, 5, 10) . . . . . . . . . . . 36  
2.5 Updating Array A to T (3, 7, 8, 5, 10, 10) . . . . . . . . . . . . 40  
2.6 Example of a Fibonacci Tree . . . . . . . . . . . . . . . . . . . . 42  

3.1 k-Queens . . . . . . . . . . . . . . . . . . . . . . . . . . 45  
3.2 UVA 10193 . . . . . . . . . . . . . . . . . . . . . . . . . 46  
3.3 Visualization of UVA 473 - Robots on Ice . . . . . . . . . . . 47  
3.4 My Ancestor . . . . . . . . . . . . . . . . . . . . . . . . . 48  
3.5 Visualization of UVA 410 - State Balance . . . . . . . . . . . 52  
3.6 UVA 410 - Observation 1 . . . . . . . . . . . . . . . . . . 54  
3.7 UVA 410 - Observation 2 . . . . . . . . . . . . . . . . . . 56  
3.8 UVA 10382 - Weather Grass . . . . . . . . . . . . . . . . . 58  
3.9 Bottom-Up DP . . . . . . . . . . . . . . . . . . . . . . . . 60  
3.10 Longest Increasing Subsequence . . . . . . . . . . . . . . . 61  
3.11 Coin Change . . . . . . . . . . . . . . . . . . . . . . . . 62  

4.1 Sample Graph . . . . . . . . . . . . . . . . . . . . . . . . 83  
4.2 Examination of BFS (from UVA 368 [26]) . . . . . . . . . . . . . 84  
4.3 Example of Present on DAG . . . . . . . . . . . . . . . . . . 85  
4.4 Visualization of DFS . . . . . . . . . . . . . . . . . . . . 86  
4.5 Introduction to More DFS Attributes: from start and allow . . . 87  
4.6 Finding Bridges . . . . . . . . . . . . . . . . . . . . . . . 88  
4.7 An Example of a Directed Graph and its Strongly Connected Components (SCC) . . . . . . . . . . . . . . . . . . . . 89  
4.8 An Example of MST Problem . . . . . . . . . . . . . . . . . 90  
4.9 Animation of Kruskal’s Algorithm for an MST Problem . . . . . . 91  
4.10 Forming the MST . . . . . . . . . . . . . . . . . . . . . . . 92  
4.11 From Root to Leaf: MST - Minimum ST, Partial Minimum ST, MS Forest . . . . . . . . . . . . . . . . . . . . 93  
4.12 Probing the Second Best Spanning Tree from the MST . . . . . . 94  
4.13 K-Exploration . . . . . . . . . . . . . . . . . . . . . . . . 95  
4.14 Edge Weights . . . . . . . . . . . . . . . . . . . . . . . . 96  
4.15 Minimum (UVA 1063 [25]) . . . . . . . . . . . . . . . . . . 97  
4.16 Weighted Graph . . . . . . . . . . . . . . . . . . . . . . . . 98  
4.17 K-Edge Weight . . . . . . . . . . . . . . . . . . . . . . . 99  
4.18 Bellman Ford’s can detect the presence of negative cycle (from UVA 555 [28]) . . . . . . . 100
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Context: # CONTENTS  
**© Steven & Felix**  

## Topic  
- Data Structures: Union-Find Disjoint Sets  
- Graphs: Prims, Kruskal, Max Flow, bipartite Graph  
- Linear Algebra: Eigenstuff, Markov Chains, Matrix Power  
- Probability: Random Variables  
- String Processing: Suffix Tree/Array  
- More Advanced Topics: DP/AI/Graph  

| Table | In the NOT IOI Syllabus |  
|-------|-------------------------|  
| 1     | Not in IOI Syllabus [1] |  

We know that one cannot win a medal in IOI just by mastering the content of this book. While we believe many parts of the IOI syllabus have been included in this book, which should give you a respectable foundation for future IOIs - we are well aware that not IOI tasks require more problem solving skills and creativity that we cannot teach via this book. So, keep practicing!

---

## Specific to the Teachers/Coaches  

This book is based on Steven's CS3232 - *Competitive Programming* course in the School of Computing, National University of Singapore. It is conducted in its teaching version, using the following lesson plan (see Table 2). The PDF slides (only the public versions) are to be used in conjunction with the content of this book. Lastly, 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 | Greedy: Search, Divide & Conquer, Greedy | Section 3.2.4           |  
| 04 | Dynamic Programming I (Basic Ideas) | Section 3.3               |  
| 05 | DP (Graphs/BFS/DFS)                 | Section 4.1 up to Section 4.3 |  
| 06 | Graph 1 (Shortest Paths, DAG-Tree)  | Section 4.4 - 4.5, 4.7-4.72 |  
| 07 | **Mid semester bonus contest**       |                           |  
| 08 | Dynamic Programming II (More Techniques) | Section 6.5.4, 6.8.3  |  
| 09 | Graphs 3 (Max Flow; Bipartite Graph) | Section 6.4.3, 4.7.4    |  
| 10 | Mathematics (Power)                  | Chapter 5                 |  
| 11 | Computing (Processing Basics, Suffix Array) | Chapter 6           |  
| 12 | Computational Geometry (Libraries)   | Chapter 7                 |  
|    | **Final exam content**             | All, including Chapter 8   |  

| Table | Lesson Plan |  
|-------|-------------|  

---  

## To All Readers  

Due to the diversity of this content, this book is not meant to be read once, but several times. There are a number of topics that are intermingled throughout the text, and readers are encouraged to view the corresponding problems (see [**Chapter 1**](#)) after studying that portion of the text to solidify and enhance their understanding of the material. In this way, relative twists are captured within their context. Make sure to pay attention to the practical examples taken from competitive programming!  

IPM will not be thorough as the book mentions how to assist students as efficiently as possible, so consider the sections and exercises must be perceived in this book before focusing on 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 “bask”.
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Context: # Chapter 7

## (Computational) Geometry

> Let no man ignorant of geometry enter here.  
> — Plato's Academy in Athens

### 7.1 Overview and Motivation

(Computational) Geometry is yet another topic that frequently appears in programming contests. Almost all ICPC problems have at least one geometry problem. If you are lucky, it will ask for some geometry solution that you have learned before. Usually, you draw the geometrical object and then use the solution from some basic geometric formulas. However, many geometry problems are the complications that often require some complex algorithms.

In fact, the existence of geometry-specific problems depends on the tasks described by the Scientific Committee for the last two years (2000 and 2001). ICPC has to date featured geometry problems. However, in other years, there exists one or two geometry-related problems per [10] list.

We have observed that many contestants, especially ICPC contestants, are afraid to tackle geometry-related problems due to two significant reasons:

1. The solutions for geometry-related problems have lower probability of getting Accepted (AC) during contests compared to the solutions for other problem types in this domain, like Complete Search or Dynamic Programming. This makes many contestants tend to skip the problem as more worthwhile than spending precious minutes coding geometry solutions that have lower probability of acceptance.
   - There are actually tricky 'special case' issues in geometry problems, e.g., What if the lines are vertical (i.e., respectively vertical)? What if the points are collinear? What if the polyline is convex, etc.? It is usually a good idea to join your team’s geometry solution with its detailed cases before you submit it to judges.
   - There is a possibility of having floating-point precision errors that cause even a 'correct' algorithm to be a Wrong Answer (WA) response.

2. The contestants are not well prepared.

The contestants often forget some important basic formulas or unable to derive the necessary formulas from the basic ones.

- The conclusion is that despite some well-written library functions and their attempts to code such functions that assist students relate time and size with (b) or (g) analogs, the ICPC, the top teams really find suitable parts of their hard copy material (which can bring valuable information) in textbooks instead of geometry libraries and library functions.
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Context: HAN21-bib-633-672-97801238147912011/6/13:27Page641#9Bibliography641[CWL+08]G.Cong,L.Wang,C.-Y.Lin,Y.-I.Song,andY.Sun.Findingquestion-answerpairsfromonlineforums.InProc.2008Int.ACMSIGIRConf.ResearchandDevelopmentinInformationRetrieval(SIGIR’08),pp.467–474,Singapore,July2008.[CYHH07]H.Cheng,X.Yan,J.Han,andC.-W.Hsu.Discriminativefrequentpatternanalysisforeffectiveclassification.InProc.2007Int.Conf.DataEngineering(ICDE’07),pp.716–725,Istanbul,Turkey,Apr.2007.[CYHY08]H.Cheng,X.Yan,J.Han,andP.S.Yu.Directdiscriminativepatternminingforeffectiveclassification.InProc.2008Int.Conf.DataEngineering(ICDE’08),pp.169–178,Cancun,Mexico,Apr.2008.[CYZ+08]C.Chen,X.Yan,F.Zhu,J.Han,andP.S.Yu.GraphOLAP:Towardsonlineanalyticalprocessingongraphs.InProc.2008Int.Conf.DataMining(ICDM’08),pp.103–112,Pisa,Italy,Dec.2008.[Dar10]A.Darwiche.Bayesiannetworks.CommunicationsoftheACM,53:80–90,2010.[Das91]B.V.Dasarathy.NearestNeighbor(NN)Norms:NNPatternClassificationTechniques.IEEEComputerSocietyPress,1991.[Dau92]I.Daubechies.TenLecturesonWavelets.CapitalCityPress,1992.[DB95]T.G.DietterichandG.Bakiri.Solvingmulticlasslearningproblemsviaerror-correctingoutputcodes.J.ArtificialIntelligenceResearch,2:263–286,1995.[DBK+97]H.Drucker,C.J.C.Burges,L.Kaufman,A.Smola,andV.N.Vapnik.Supportvec-torregressionmachines.InM.Mozer,M.Jordan,andT.Petsche(eds.),AdvancesinNeuralInformationProcessingSystems9,pp.155–161.Cambridge,MA:MITPress,1997.[DE84]W.H.E.DayandH.Edelsbrunner.Efficientalgorithmsforagglomerativehierarchicalclusteringmethods.J.Classification,1:7–24,1984.[De01]S.DzeroskiandN.Lavrac(eds.).RelationalDataMining.NewYork:Springer,2001.[DEKM98]R.Durbin,S.Eddy,A.Krogh,andG.Mitchison.BiologicalSequenceAnalysis:ProbabilityModelsofProteinsandNucleicAcids.CambridgeUniversityPress,1998.[Dev95]J.L.Devore.ProbabilityandStatisticsforEngineeringandtheSciences(4thed.).DuxburyPress,1995.[Dev03]J.L.Devore.ProbabilityandStatisticsforEngineeringandtheSciences(6thed.).DuxburyPress,2003.[DH73]W.E.DonathandA.J.Hoffman.Lowerboundsfor
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Context: ence(IJCAI’95),pp.1146–1152,Montreal,Quebec,Canada,Aug.1995.[RC07]R.RamakrishnanandB.-C.Chen.Exploratoryminingincubespace.DataMiningandKnowledgeDiscovery,15:29–54,2007.[Red92]T.Redman.DataQuality:ManagementandTechnology.BantamBooks,1992.[Red01]T.Redman.DataQuality:TheFieldGuide.DigitalPress(Elsevier),2001.[RG03]R.RamakrishnanandJ.Gehrke.DatabaseManagementSystems(3rded.).McGraw-Hill,2003.[RGN10]L.DeRaedt,T.Guns,andS.Nijssen.Constraintprogrammingfordataminingandmachinelearning.InProc.2010AAAIConf.ArtificialIntelligence(AAAI’10),pp.1671–1675,Atlanta,GA,July2010.[RH01]V.RamanandJ.M.Hellerstein.Potter’swheel:Aninteractivedatacleaningsystem.InProc.2001Int.Conf.VeryLargeDataBases(VLDB’01),pp.381–390,Rome,Italy,Sept.2001.[RH07]A.RosenbergandJ.Hirschberg.V-measure:Aconditionalentropy-basedexternalclusterevaluationmeasure.InProc.2007JointConf.EmpiricalMethodsinNaturalLan-guageProcessingandComputationalNaturalLanguageLearning(EMNLP-CoNLL’07),pp.410–420,Prague,CzechRepublic,June2007.
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Context: xiAcknowledgmentsThewordliston67isfromTheReadingTeacher’sBookofLists,FourthEdition(Fry,Kress&Fountoukidis),PrenticeHall,2000.TheliteraryquotationsusedasexampletextsinChapters3andtheproblemsinChapter6arefromJohnLeCarré’sTinker,Tailor,Soldier,Spy.ThetextinChapter6isfromFranzKafka’sTheTrial.LikewiseinChapter10fromhisMetamorphosis.ThehyphenationexamplesinChapter10arefromMicro-typographicextensionstotheTEXtypesettingsystem,thePhDThesisofHànTh´êThành,FacultyofInformatics,MasarykUniversity,Brno,October2000.ThecoverimageshowsaPaigeCompositor,courtesyoftheUnitedStatesPatentOffice.ThedrawingofFrenchCurvesonpage17wasmodifiedfromonecreatedbyJoshuaCertain.Thetablesonpages35–39appearbykindpermissionoftheUnicodeConsortium.UnicodeisaregisteredtrademarkofUnicode,Inc.intheUnitedStatesandothercountries.Thefacsimilepatentsonpages54,55,56,57,and59wereprovidedbytheUnitedStatesPatentandTrademarkOffice.ThepictureofaUnivackeyboardonpage60appearscourtesyoftheRetrocomputingSocietyofRhodeIsland.ThepictureofanIBMModelMkeyboardonpage60wastakenbySalCangeloso.Thewoodblockprintonpage101isDerFormschneider(TheBlockcutter)fromthePanopliaomniumilliberaliummechanicarum(BookofTrades);itwasprintedin1568andisintheBritishMuseum.Thepictureonpage102isadetailoftheengravingDerKreuzbrunnenzuMarienbadpublishedbyFranzSartoriin1819.TheengravingMelencoliaIbyAlbrechtDüreronpage103isheldattheMinneapolisInstituteofArt.Theimageofamezzotintplateonpage104wastakenbyDavidLadmore.ThepictureofthemezzotintprintbyFranzKrugeronpage105iscourtesyoftheImagePermanenceInstitute.TheRembrandtetchingTheHundredGuilderPrintonpage106isheldattheRijksmuseuminAmsterdam.Thephotographoffilmgrainonpage108wasprovidedbyKeithCooper.TheelectronmicroscopeimageoffilmgrainonthesamepageiscourtesyoftheUniversityofRochester.ThehalftonedpictureoftheSteinwayHallonEast14thStreetinManhattanonpage109isfromTheDailyGraphic,December2nd,1873.TheexamplesofZapfinoalternateglyphsonpage127arebasedontheinstructionsofDarioTaraborelli.ThetablesofPalatinoLinotypeonpages13
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Context: O04]S.C.MadeiraandA.L.Oliveira.Biclusteringalgorithmsforbiologicaldataanalysis:Asurvey.IEEE/ACMTrans.ComputationalBiologyandBioinformatics,1(1):24–25,2004.[MP69]M.L.MinskyandS.Papert.Perceptrons:AnIntroductiontoComputationalGeometry.Cambridge,MA:MITPress,1969.[MRA95]M.Metha,J.Rissanen,andR.Agrawal.MDL-baseddecisiontreepruning.InProc.1995Int.Conf.KnowledgeDiscoveryandDataMining(KDD’95),pp.216–221,Montreal,Quebec,Canada,Aug.1995.[MRS08]C.D.Manning,P.Raghavan,andH.Schutze.IntroductiontoInformationRetrieval.CambridgeUniversityPress,2008.[MS03a]M.MarkouandS.Singh.Noveltydetection:Areview—part1:Statisticalapproaches.SignalProcessing,83:2481–2497,2003.
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Context: 7.3.2. Software Development Tool   I needed three kinds of software for the development of this sample:  1. A development environment that provides a compiler, assembler, and linker for x86. I used GNU software, i.e., GNU AS assembler, GNU LD linker, GNU GCC compiler, and GNU Make. These development tools were running on Slackware Linux 9.0 in the development PC. I used Vi as the editor and Bourne Again Shell (bash) to run these tools. Note that the GNU LD linker must support the ELF object file format to be able to compile the sample source code (provided in a later section). Generally, all Linux distribution supports this object file format by default. As an addition, I used a hex dump utility in Linux to inspect the result of the development. 2. A PCI PnP expansion ROM checksum patcher. As shown in section 7.1, a valid PCI expansion ROM has many checksum values that need to be fulfilled. Because the development environment cannot provide that, I developed a custom tool for it. The source code of this tool is provided in a later section. 3. An Adaptec PCI expansion ROM flash utility for AHA-2940UW. The utility is named flash4.exe; it comes with the Adaptec AHA-2940UW BIOS version 2.57.2 distribution. It's used to flash the custom-made expansion ROM code into the flash ROM of the card. I used a bootable CD-ROM to access real-mode DOS and invoke the flash utility; it also needs DOS4GW. DOS4GW is provided with the Adaptec PCI BIOS distribution.   7.3.3. Expansion ROM Source Code   The basic rundown of what happens when the compiled source code executed is as follows:  1. During POST, the system BIOS look for implemented PCI expansion ROMs from every PCI expansion card by testing the XROMBAR of each card. If it is implemented,9 then system BIOS will copy the PCI expansion ROM from the address pointed to by the XROMBAR, i.e., the expansion ROM chip to RAM in the expansion ROM area.10 Then the system BIOS will jump to the INIT function of the PCI expansion ROM. After the PCI expansion ROM has done its initialization, execution is back to the system BIOS. The system BIOS will check the runtime size of the PCI expansion ROM that was initialized previously. It will                                                   9 XROMBAR consumed address space. 10 Expansion ROM area in RAM is at the C0000h–DFFFFh physical address.   21
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Context: ContentsForewordvPrefaceviAuthors’ProfilesandCopyrightxiConventionandProblemCategorizationxiiListofAbbreviationsxiiiListofTablesxivListofFiguresxv1Introduction11.1CompetitiveProgramming.................................11.2TipstobeCompetitive..................................21.2.1Tip2:QuicklyIdentifyProblemTypes.....................41.2.2Tip3:DoAlgorithmAnalysis...........................51.2.3Tip4:MasterProgrammingLanguages.....................81.2.4Tip5:MastertheArtofTestingCode......................101.2.5Tip6:PracticeandMorePractice........................121.2.6Tip7:TeamWork(ICPCOnly).........................121.3GettingStarted:TheAdHocProblems.........................131.4ChapterNotes.......................................192DataStructuresandLibraries212.1OverviewandMotivation.................................212.2DataStructureswithBuilt-inLibraries..........................222.2.1LinearDataStructures..............................222.2.2Non-LinearDataStructures............................262.3DataStructureswithOur-OwnLibraries.........................292.3.1Graph........................................292.3.2Union-FindDisjointSets.............................302.3.3SegmentTree....................................322.3.4FenwickTree....................................352.4ChapterNotes.......................................383ProblemSolvingParadigms393.1OverviewandMotivation.................................393.2CompleteSearch......................................393.2.1Examples......................................403.2.2Tips.........................................413.3DivideandConquer....................................47i
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Context: # 1. TCG Architecture Overview

![Figure 1.3 Steps in comprehending TCG standards implementation in PC architecture](figure-1.3.png)

Figure 1.3 shows that the first document you have to read is the **TCG Specification Architecture Overview**. Then, proceed to the platform-specific design guide document, which in the current context is the PC platform specification document. You have to consult the concepts explained in the TPM main specification, parts 1–4, and the TSS document while reading the PC platform specification document—the dashed blue arrows in figure 1.3 mean "consult." You can download the **TCG Specification Architecture Overview** at [https://www.trustedcomputinggroup.org/specs/TSS](https://www.trustedcomputinggroup.org/specs/TSS), and the TSS document is available for download at [https://www.trustedcomputinggroup.org/specs/PCCLIENT](https://www.trustedcomputinggroup.org/specs/PCCLIENT).

The PC platform specification document consists of several files; the relevant ones are:

- **TCG PC Client–Specific Implementation Specification for Conventional BIOS** (as of the writing of this book, the latest version of this document is 1.20 final)
- **PC Client TPM Interface Specification FAQ**

Reading these documents will give you a glimpse of the concepts of trusted computing and some details about its implementation in PC architecture.

Before moving forward, I’ll explain a bit more about the fundamental concept of **trusted computing** that is covered by the TCG standards. The **TCG Specification Architecture Overview** defines trust as the "expectation that a device will behave in a particular manner for a specific purpose." The advanced features that exist in a trusted platform are protected capabilities, integrity measurement, and integrity reporting. The focus is on the integrity measurement feature because this feature relates directly to the BIOS. As per the **TCG Specification Architecture Overview**, integrity measurement is "the process of obtaining metrics of platform characteristics that affect the integrity (trustworthiness) of a platform; storing those metrics; and putting digests of those metrics in PCRs [platform configuration registers]." 

I'm not going to delve into this definition or the specifics about PCRs. Nonetheless, it's important to note that in the TCG standards for PC architecture, the core **Root of Trust Measurement (CRTM)** is synonymous with **BIOS boot block**. At this point, you have
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Context: implementation of the flash ROM chip handler exists in the support file for each type of flash ROM. • flash.h. This file contains the definition of a data structure named flashchip. This data structure contains the function pointers and variables needed to access the flash ROM chip. The file also contains the vendor identification number and device identification number for the flash ROM chip that bios_probe supports. • error_msg.h. This file contains the display routine that declares error messages. • error_msg.c. This file contains the display routine that implements error messages. The error-message display routine is regarded as a helper routine because it doesn't posses anything specific to bios_probe. • direct_io.h. This file contains the declaration of functions related to bios_probe device driver. Among them are functions to directly write and read from the hardware port. • direct_io.c. This file contains the implementation of functions declared in direct_io.h and some internal functions to load, unload, activate, and deactivate the device driver. • jedec.h. This file contains the declaration of functions that is "compatible" for flash ROM from different manufacturers and has been accepted as the JEDEC standard. Note that some functions in jedec.h are not just declared but also implemented as inline functions. • jedec.c. This file contains the implementation of functions declared in jedec.h. • Flash_chip_part_number.c. This is not a file name but a placeholder for the files that implement flash ROM support. Files of this type are w49f002u.c, w39v040fa.c, etc. • Flash_chip_part_number.h. This is not a file name but a placeholder for the files that declare flash ROM support. Files of this type are w49f002u.h, w39v040fa.h, etc.   Consider the execution flow of the main application. First, remember that with ctags and vi you can decipher program flow much faster than going through the files individually. Listing 9.12 shows the condensed contents of flash_rom.c.  Listing 9.12 Condensed flash_rom.c /*  * flash_rom.c: Flash programming utility for SiS 630/950 M/Bs  *  *  * Copyright 2000 Silicon Integrated System Corporation  *  *     This program is free software; you can redistribute it and/or  *     modify it under the terms of the GNU General Public License as  *     published by the Free Software Foundation; either version 2 of the  *     License, or (at your option) any later version.  *  *     ...
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Context: HAN09-ch02-039-082-97801238147912011/6/13:15Page82#4482Chapter2GettingtoKnowYourDatastatistics-basedvisualizationofdatausingboxplots,quantileplots,quantile–quantileplots,scatterplots,andloesscurves,seeCleveland[Cle93].PioneeringworkondatavisualizationtechniquesisdescribedinTheVisualDis-playofQuantitativeInformation[Tuf83],EnvisioningInformation[Tuf90],andVisualExplanations:ImagesandQuantities,EvidenceandNarrative[Tuf97],allbyTufte,inadditiontoGraphicsandGraphicInformationProcessingbyBertin[Ber81],VisualizingDatabyCleveland[Cle93],andInformationVisualizationinDataMiningandKnowledgeDiscoveryeditedbyFayyad,Grinstein,andWierse[FGW01].MajorconferencesandsymposiumsonvisualizationincludeACMHumanFactorsinComputingSystems(CHI),Visualization,andtheInternationalSymposiumonInfor-mationVisualization.ResearchonvisualizationisalsopublishedinTransactionsonVisualizationandComputerGraphics,JournalofComputationalandGraphicalStatistics,andIEEEComputerGraphicsandApplications.Manygraphicaluserinterfacesandvisualizationtoolshavebeendevelopedandcanbefoundinvariousdataminingproducts.Severalbooksondatamining(e.g.,DataMiningSolutionsbyWestphalandBlaxton[WB98])presentmanygoodexamplesandvisualsnapshots.Forasurveyofvisualizationtechniques,see“Visualtechniquesforexploringdatabases”byKeim[Kei97].Similarityanddistancemeasuresamongvariousvariableshavebeenintroducedinmanytextbooksthatstudyclusteranalysis,includingHartigan[Har75];JainandDubes[JD88];KaufmanandRousseeuw[KR90];andArabie,Hubert,anddeSoete[AHS96].MethodsforcombiningattributesofdifferenttypesintoasingledissimilaritymatrixwereintroducedbyKaufmanandRousseeuw[KR90].
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Context: HAN22-ind-673-708-97801238147912011/6/13:27Page683#11Index683rowsandcolumns,68astwo-modematrix,68datamigrationtools,93datamining,5–8,33,598,623adhoc,31applications,607–618biologicaldata,624complexdatatypes,585–598,625cyber-physicalsystemdata,596datastreams,598datatypesfor,8datawarehousesfor,154databasetypesand,32descriptive,15distributed,615,624efficiency,31foundations,viewson,600–601functionalities,15–23,34graphsandnetworks,591–594incremental,31asinformationtechnologyevolution,2–5integration,623interactive,30asinterdisciplinaryeffort,29–30invisible,33,618–620,625issuesin,29–33,34inknowledgediscovery,7asknowledgesearchthroughdata,6machinelearningsimilarities,26methodologies,29–30,585–607motivationfor,1–5multidimensional,11–13,26,33–34,155–156,179,227–230multimediadata,596OLAPand,154aspattern/knowledgediscoveryprocess,8predictive,15presentation/visualizationofresults,31privacy-preserving,32,621–622,624–625,626querylanguages,31relationaldatabases,10scalability,31sequencedata,586socialimpacts,32societyand,618–622spatialdata,595spatiotemporaldataandmovingobjects,595–596,623–624statistical,598textdata,596–597,624trends,622–625,626ubiquitous,618–620,625userinteractionand,30–31visualandaudio,602–607,624,625Webdata,597–598,624dataminingsystems,10datamodelsentity-relationship(ER),9,139multidimensional,135–146dataobjects,40,79similarity,40terminologyfor,40datapreprocessing,83–124cleaning,88–93formsillustration,87integration,93–99overview,84–87quality,84–85reduction,99–111inscienceapplications,612summary,87tasksin,85–87transformation,111–119dataquality,84,120accuracy,84believability,85completeness,84–85consistency,85interpretability,85timeliness,85datareduction,86,99–111,120attributesubsetselection,103–105clustering,108compression,100,120datacubeaggregation,110–111dimensionality,86,99–100,120histograms,106–108numerosity,86,100,120parametric,105–106principlecomponentsanalysis,102–103sampling,108strategies,99–100theory,601wavelettransforms,100–102Seealsodatapreprocessingdatarich
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Context: ```
INDEX
© Steven & Felis

- UVa 10721 - Bar Codes, 69
- UVa 10724 - Road Construction, 100
- UVa 1073 - Test Case, 83
- UVa 1074 - Digits, 132
- UVa 1075 - String Matching, 135
- UVa 1076 - Snakes on a Plane, 62
- UVa 1078 - Cake Walk - The Sequel, 107
- UVa 1079 - Maximum Subsequence, 154
- UVa 1073 - Foreign Exchange, 95
- UVa 10748 - Calculating Machine, 122
- UVa 1074 - Selective Problem, 107
- UVa 1078 - Captain Price's MIA, 138
- UVa 1078 - Acid Rain, 122
- UVa 1075 - Debugging, 128
- UVa 1078 - The Mad Muni, 54
- UVa 1077 - The Shopping Party, 157
- UVa 1079 - Minimum Sum, 106
- UVa 1078 - The One Attack, 140
- UVa 1081 - Not That Kind of Graph, 155
- UVa 1080 - The Roping, 61
- UVa 1081 - Lumber Mountain, 100
- UVa 1082 - Adventurous, 207
- UVa 1083 - Digit Distribution, 207
- UVa 1084 - Ultra Efficient, 16
- UVa 1085 - Job Scheduling, 16
- UVa 1086 - Numbering Phrases, 126
- UVa 1087 - Extra Exercises, 154
- UVa 1088 - Adjectives, 198
- UVa 1089 - The Judge, 206
- UVa 1090 - Number of Paths, 154
- UVa 1091 - The Last Survivor, 292
- UVa 1092 - Operations Research, 206
- UVa 1093 - Consuming the Cable Wires, 130
- UVa 1094 - Remainders, 154
- UVa 1095 - The Crazy List, 292
- UVa 1096 - Simon's Trip, 196
- UVa 1097 - Recursion, 148
- UVa 1098 - Close the Tape, 194
- UVa 10879 - Code Redaction, 123
- UVa 10880 - Call and Return, 235
- UVa 10881 - The Check, 232
- UVa 10882 - Local Minimum, 104
- UVa 10883 - Same Family, 159
- UVa 10884 - Hatty's Prank, 154
- UVa 10886 - Known Password Attack, 154
- UVa 10887 - Terminating Disturbance, 167
- UVa 10888 - Pick Pockets-First, 154
- UVa 10889 - Children's Games, 28
- UVa 1090 - Mark's Distributing, 69
- UVa 1091 - Simple Minded Thinking, 620
- UVa 1092 - Walking on a Grid, 112
- UVa 1093 - Factor Problem, 145
- UVa 1094 - A Walk Through the Reset, 202
- UVa 1095 - The Investing Puzzles, 17
- UVa 1096 - Data Debugging, 87
- UVa 1097 - Phone Tag, 138
- UVa 1098 - A Really Bad Game, 154
- UVa 1099 - Spirals, 124
- UVa 1100 - The Last Number, 112
- UVa 1101 - Finding the Tailgating, 154
- UVa 1102 - It's the End, 142
- UVa 1103 - End of the World, 202
- UVa 1104 - Family Dynamics, 111
- UVa 1105 - Butserth Walk, 125
- UVa 1106 - The Last Footprints, 89
- UVa 1107 - Special Experience, 295
- UVa 1108 - Thought-Provoking Journey, 17
- UVa 1109 - A Symphony, 21
- UVa 1110 - Cloud in Space, 215
- UVa 1111 - Backtrack, 101
- UVa 1112 - Thang Chang, 150
- UVa 1113 - Lacing Up, 134
- UVa 1114 - Flowering Cacti, 173
- UVa 1115 - Cornered Away, 119
- UVa 1116 - Move to Move, 76
- UVa 1117 - Center, 37
- UVa 1118 - On the Line, 101
- UVa 1119 - The Hurdles, 37
- UVa 1120 - Giga - University, 122
- UVa 1121 - Pizza and Patience, 23
- UVa 1122 - Rock and Roll, 73
- UVa 1123 - The Surprise Riddle, 94
- UVa 1124 - The Snatching Wizard, 17
- UVa 1125 - The Chocolate Wizard, 18
- UVa 1126 - Your Favorite Salad, 91
- UVa 1127 - Back to Magic, 95
- UVa 1128 - Sentencing email, 95
```
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Context: ```markdown
printf("%d\n", fs.req(1, 1));  // => req[1] = 0
printf("%d\n", fs.req(1, 20)); // => req[1] = 1
printf("%d\n", fs.req(10, 30)); // => req[1] + 5 + 2 = 7
printf("%d\n", fs.req(1, 8));   // => req[1] = 0
printf("%d\n", fs.req(10, 8));  // => req[10] = 2 - 1

// Return 0
}

Example code: `ch2_10/functions/trees.cpp`, `ch2_10/functions/trees.java`

### Exercise 2.3.4.1: Revise Trees

Revise Trees has an additional operation: Find the index with a given cumulative frequency. For example, we want to know what is the minimum index in Table 2.1 so that there are at least k 'stars' covered (Answer = index/stars covered). Implement this feature!

### Exercise 2.3.4.2: Extend the ID Revise Tree to 2D

Programming exercises that use data structures with our own libraries:

1. **Graph Data Structures Problems** (most graph problems are discussed in Chapter 4)
   1. UVA 10970 - The Forest of the Trees (apply the property of each forest)
   2. UVA 10279 - Graph Construction (multiple Eulerton's lists)
   3. UVA 10163 - Matrix Transpose
   4. UVA 1095 - Day for Night (sorting and adjacency list)
   5. UVA 11410 - Bus Stop Problem (using the adjacency list)
2. **Union-Find Data Structures**
   1. UVA 10094 - 2-Disk Murmuration (also solved with Union-Find in Section 4.2)
   2. UVA 11418 - Square Root Query (based on Bailey's data structure)
   3. UVA 11855 - Near-Monotonous Sequences (apply union-find)
3. **Advanced Topics**
   1. UVA 10018 - The blowfish (finds if a snack is a blowfish)
   2. UVA 10267 - Lizard (hunting & multi-snap queries)
   3. UVA 11398 - Marbles (require two weight queries)
   4. UVA 11397 - Spies (Killing the Lizard model)
4. **Tree Structures** (with sub-requirements on particle problems)
   1. UVA 11422 - Space Mice (query time)
   2. UVA 11107 - Non-connection (shortest path)
   3. UVA 12900 - Ternary Distributing (Standard form)
   4. UVA 12480 - Polygon (higher than top search query)

Also see Part 4 of the solution of harder problems in Chapter 8.

> "Since the graph given is a fixed (dated) problem, you can use the relations between the number of edges and the number of vertices to find the minimum for each case. See also Appendix B for weighted graphs on initial runs."
```
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Context: AMACHINEMADETHISBOOKtensketchesofcomputerscienceHowdowedecidewheretoputinkonapagetodrawlettersandpictures?Howcancomputersrepresentalltheworld’slanguagesandwritingsystems?Whatexactlyisacomputerprogram,whatandhowdoesitcalculate,andhowcanwebuildone?Canwecompressinformationtomakeiteasiertostoreandquickertotransmit?Howdonewspapersprintphotographswithgreytonesusingjustblackinkandwhitepaper?Howareparagraphslaidoutautomaticallyonapageandsplitacrossmultiplepages?InAMachineMadethisBook,usingexamplesfromthepublish-ingindustry,JohnWhitingtonintroducesthefascinatingdisciplineofComputerSciencetotheuninitiated.JOHNWHITINGTONfoundedacompanywhichbuildssoftwareforelectronicdocumentprocessing.Hestudied,andtaught,ComputerScienceatQueens’College,Cambridge.Hehaswrittentextbooksbefore,butthisishisfirstattemptatsomethingforthepopularaudience.
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Context: # Linker Script Basics

In this book, I am only concerned with pure machine code output because you are dealing with the hardware directly without going through any software layer.

Linker script can control every aspect of the linking process, such as the relocation of the compilation result, the executable file format, and the executable entry point. A linker script is a powerful tool when combined with various GNU binutils. Figure 2.2 shows that it’s possible to do **separate compilation**, i.e., compile some assembly language source code and then combine the object file result with the C language compilation object file result by using LD linker.

There are two routes to building a pure machine code or executable binary if you are using GCC:

1. Source code compilation → Object file → LD linker → Executable binary
2. Source code compilation → Object file → LD linker → Object file → Objcopy → Executable binary

This section deals with the second route. I explain the linker script that’s used to build the experimental PCI expansion ROM in part 3 of this book. It’s a simple linker script. Thus, it’s good for learning purposes.

Start with the basic structure of a linker script. The most common linker script layout is shown in Figure 3.3.

### Figure 3.3: Linker Script File Layout

- **Output File Format**
- **Target Machine Architecture**
- **Executable Code Entry Point**
- **Other Definitions …**
- **Sections Definition**

A linker script is just an ordinary plain text file. However, it conforms to certain syntax dictated by LD linker and mostly uses the layout shown in Figure 3.3. Consider the Makefile and the linker script used in Chapter 7 as an example. You have to review the Makefile with the linker script because they are tightly coupled.

> **Note**: 
> 1. The format of an executable file is operating system dependent.
> 2. GNU binutils is an abbreviation for GNU binary utilities, the applications that come with GCC for binary manipulation purposes.
> 3. Execution environment is the processor operating mode. For example, in a 32-bit x86-compatible processor, there are two major operating modes, i.e., 16-bit real mode and 32-bit protected mode.
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Context: # 1.2 Tips to Be Competitive

![Illustration of UVa 10911 - Forming Quiz Teams](path/to/image.png)

Now, ask yourself, which one of the following best describes you? Note that if you are unfamiliar with the material or terminology shown in this chapter, you can re-read it after going through this book.

1. Be a competitive programmer (A.k.a the shiny one):
   - **Step 1:** Treat the problem as difficult; hence, see it as a kind of problem before.
   - **Step 2:** Note that this is a common starting point for many possible results and outputs.
   - **Step 3:** Breathe and think about a direct solution via the problem.
   - **Step 4:** Establish erroneous practices by following Swing Away, Answer.
   - **Step 5:** Record debugging by tracking the **Time Limit Exceeded (TLE)**.
   - After 5 hours of typical coding time, an **Accepted (AC)** solution is produced.

2. Be a casual programmer:
   - **Step 1:** Realize there often exist kinds of problem solving, i.e., common where to start.
   - **Step 2:** Examine the history behind this problem: Dynamic Programming (DP).
   - **Step 3:** Read about validation problems to familiarize yourself.
   - **Step 4:** Establish some expectations for the "Technical Report".

3. Be an intermediate programmer (like me):
   - **Step 1:** Discover the problem and instead label it as "matching on general graph" problem.
   - **Step 2:** In the graph, identify that those results are not unique.
   - **Step 3:** Identify alternatives that could lead to solving objectives.
   - **Step 4:** Calculate all probable combinations and come up with a solution likely in under x.x...

4. Be a beginner programmer:
   - A very novice programmer (e.g., the red 'target' coders in TopCoder) should solve this kind of known problem in less than 15 minutes. 

References:
- [1]: ACM ICPC (International Collegiate Programming Contest)
- [2]: TopCoder
- [3]: Various programming resources (e.g., [3, 5, 10]) for better understanding of algorithms and problems. 

---

> Note: Ensure source attribution as it plays a crucial role in research integrity.
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Context: # 6.3 AD HOC STRING PROCESSING PROBLEMS
© Steven & Felix

## Problems

1. UVA 11136 - Minimum Finances (retail rate, simple/indirect function from a fan, op)
2. UVA 11142 - Persistent Character (simple mathematical expressions parsing)
3. UVA 10086 - Marmalade Mania (minimum cut, input formatting)
4. UVA 10044 - Matching Waves (data array manipulation, output formatting)
5. UVA 10007 - Longest Increasing Subsequence (dynamic programming problem)
6. UVA 10008 - Relaxing Routes (similarity and graph input formatting problem)
7. UVA 10009 - No Two Same Hidden (in a large list can we solve this ‘easy’ problem?)
8. UVA 10010 - Even Spaced (requires constructive proofs with only one query)
9. UVA 11937 - Number of Functions (set, function chart)

## String Comparisons

1. UVA 6000 - Errors, Errors (find terrible and compare; see if we can sense word)
2. UVA 6004 - Immediate Decodability (is a readable sequence; better know)
3. UVA 6001 - Full Char-Checker (conceptually comparable)
4. UVA 6002 - Automatic Correction (is flexible; using input dictionary)
5. UVA 6003 - Missing Arguments (requires non-transmitting occurrences)
6. UVA 12003 - Bell Daring (requires special string comparisons)
7. UVA 11794 - Harmonic Means (simplified string comparison)

## Just for fun

1. UVA 263 - Permutation Cipher (similar to UVA 314)
2. UVA 613 - Numbers (find primes; find some)
3. UVA 853 - Name Chains (find numbers; convert them into strings; simulating)
4. UVA 1248 - 15 Digit (checking if a 15 can match; confirming numbers)
5. UVA 1090 - Permuting Words (more like dictionary problems)
6. UVA 5129 - One-More-Indented-Type (is it similar to the problem description?)
7. UVA 1139 - I Love String (but asks: checking if a # is a member of set A)
8. UVA 513 - Classic Bingo (similar as proposed)
9. UVA 11649 - Matching Table (matching input, final output)
10. UVA 11230 - Apparently (challenges of the allocations)
11. UVA 11929 - Digital Rounder (might be A ≤ 2 in a terminologies)
12. UVA 401 - EPV (is similar to UVA 341; but a bit)
13. UVA 397 - The 2-Zero Triplet (random number equations)
14. UVA 610 - Circular Sorting (finished)
15. UVA 278 - Knapsack Problem (Alghabrak)

Although the problems listed in this section constitute 77% of the problems listed in this chapter, we have to make a reminder that most access problems in ACM ICPC (and also IOI) usually do not find hard tasks; however, the next section focuses on the “vanity problem” that matches numbers (constants) should be able to solve. In the next few sections, we will be examining problems (Section 6.4) related to intrinsic sorting achieving problems while deriving Programming (DP) (Section 6.5). This chapter will focus on string processing and dynamic programming problems where we have to think about the necessary components that will create data structures for strings like Suffix Tree, Suffix Array must be used. We discuss these data structures and several specialized algorithms using these data structures in Section 6.6.
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Context: GrossmanandO.Frieder.InformationRetrieval:AlgorithmsandHeuristics.NewYork:Springer,2004.[GR07]P.D.GrunwaldandJ.Rissanen.TheMinimumDescriptionLengthPrinciple.Cambridge,MA:MITPress,2007.[GRG98]J.Gehrke,R.Ramakrishnan,andV.Ganti.RainForest:Aframeworkforfastdecisiontreeconstructionoflargedatasets.InProc.1998Int.Conf.VeryLargeDataBases(VLDB’98),pp.416–427,NewYork,NY,Aug.1998.[GRS98]S.Guha,R.Rastogi,andK.Shim.CURE:Anefficientclusteringalgorithmforlargedatabases.InProc.1998ACM-SIGMODInt.Conf.ManagementofData(SIGMOD’98),pp.73–84,Seattle,WA,June1998.
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Context: ```markdown
# 5.2 AD HOC MATHEMATICS PROBLEMS
© Steven & Felix

## Mathematical Simulations (Brent Frensz)

1. UVA 00100 - The 1-2-3 Problem (just follow the description, note that j can be ≤ c)
2. UVA 00107 - Adder's Paradox (similar to UVa 100)
3. UVA 00112 - Parking (general tree division)
4. UVA 00126 - Consecutive Factors (brute force up to \(10^6\), find the pattern)
5. UVA 00134 - Super U (see remarks on specific problems)
6. UVA 00141 - Range Minimum Query (find it in \(O \log n\))
7. UVA 00151 - Power Assembly (find the number of proper combinations)
8. UVA 00171 - Average Scores (example answers, second round are your observations)
9. UVA 00175 - Height Survey (similar to UVa 100)
10. UVA 00185 - Cold Redundant (must be better)
11. UVA 00193 - Shortest Path (similar to UVa 106)
12. UVA 00197 - Pixland (use pixland references)
13. UVA 00219 - Multi-Source Shortest Path (similar to the answer to be safe)
14. UVA 00221 - Simple Queries (similar to UVa 1046)
15. UVA 00273 - The Airport (answers: input, let's choose the smaller one!)
16. UVA 00279 - Lucky Numbers (square numbers, divisibility check, etc.)

## Plotting Patterns in Pascal

1. UVA 00143 - Sum and the Odd Numbers (derive the short formula)
2. UVA 00144 - Simple Calculations (derive the required formula)
3. UVA 00146 - Antidotes (general call on Fibonacci numbers)
4. UVA 00149 - The Lots (some necessary representations)
5. UVA 00150 - The Body (identify Pascal's triangle, derive it step by step)
6. UVA 00164 - Enough Expressions (simplifying)
7. UVA 00170 - Binomial Sequences (degeneracy factors)
8. UVA 00171 - The Magic Square (generating required symmetric and flip)
9. UVA 00196 - The Little Lance (simple formulas)
10. UVA 00197 - Reverse Funding (correct start points)
11. UVA 00198 - Consistent Examples (application of the theorem)
12. UVA 00201 - The Number of Lattice Points (variety of first two terms)
13. UVA 00218 - Counting Solutions (a statement of two)
14. UVA 00225 - Just Another System (crucial checks)
15. UVA 00257 - A Caution (care for bounds)
16. UVA 00282 - Can You Solve It? (the answer is UVa 264)

---

* The area from 0 to \( \frac{c}{2} \) in the graph, \( d \leq 0 \) in general case where the data does not pass through the building (build table to the right (and top) so that we will only deal with one straight line instead of bouncing lines.
```
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Context: HAN08-ch01-001-038-97801238147912011/6/13:12Page38#3838Chapter1IntroductionandTechniquesbyKollerandFriedman[KF09];andMachineLearning:AnAlgorithmicPerspectivebyMarsland[Mar09].Foraneditedcollectionofseminalarticlesonmachinelearning,seeMachineLearning,AnArtificialIntelligenceApproach,Volumes1through4,editedbyMichalskietal.[MCM83,MCM86,KM90,MT94],andReadingsinMachineLearningbyShavlikandDietterich[SD90].Machinelearningandpatternrecognitionresearchispublishedintheproceed-ingsofseveralmajormachinelearning,artificialintelligence,andpatternrecognitionconferences,includingtheInternationalConferenceonMachineLearning(ML),theACMConferenceonComputationalLearningTheory(COLT),theIEEEConferenceonComputerVisionandPatternRecognition(CVPR),theInternationalConferenceonPatternRecognition(ICPR),theInternationalJointConferenceonArtificialIntel-ligence(IJCAI),andtheAmericanAssociationofArtificialIntelligenceConference(AAAI).Othersourcesofpublicationincludemajormachinelearning,artificialintel-ligence,patternrecognition,andknowledgesystemjournals,someofwhichhavebeenmentionedbefore.OthersincludeMachineLearning(ML),PatternRecognition(PR),ArtificialIntelligenceJournal(AI),IEEETransactionsonPatternAnalysisandMachineIntelligence(PAMI),andCognitiveScience.TextbooksandreferencebooksoninformationretrievalincludeIntroductiontoInformationRetrievalbyManning,Raghavan,andSchutz[MRS08];InformationRetrieval:ImplementingandEvaluatingSearchEnginesbyB¨uttcher,Clarke,andCormack[BCC10];SearchEngines:InformationRetrievalinPracticebyCroft,Metzler,andStrohman[CMS09];ModernInformationRetrieval:TheConceptsandTechnologyBehindSearchbyBaeza-YatesandRibeiro-Neto[BYRN11];andInformationRetrieval:Algo-rithmsandHeuristicsbyGrossmanandFrieder[GR04].Informationretrievalresearchispublishedintheproceedingsofseveralinforma-tionretrievalandWebsearchandminingconferences,includingtheInternationalACMSIGIRConferenceonResearchandDevelopmentinInformationRetrieval(SIGIR),theInternationalWorldWideWebConference(WWW),theACMInterna-tionalCo
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Context: ```markdown
![Figure 3.1 FASMW code editor](path_to_image)

FASM will place the assembly result in the same directory as the assembly source code. FASM will give the result a name similar to the source file name but with a `.com` extension, not `.asm` as the source code file did. The dump of the binary result is not shown here because it’s just the same as the one assembled with NASM previously. Note that Fasm version 1.67 will emit a binary file with a `.bin` extension for the source code in listing 3.2.

Even though using FASM or NASM is a matter of taste, I recommend FASM because it’s a little easier to use than NASM. Furthermore, FASM was built with operating system development usage in mind. BIOS-related development would benefit greatly because both types of software development are dealing directly with "bare metal." However, note that this recommendation is valid only if you intend to use assembly language throughout the software development process, i.e., without mixing it with another programming language. The next section addresses this issue in more detail.

## 3.2. BIOS-Related Software Development with GCC

In the previous section, you developed a BIOS patch using only assembly language. For a simple BIOS patch, that’s enough. However, for complicated system-level software development, you need to use a higher level of abstraction, i.e., a higher-level programming language. That means the involvement of a compiler is inevitable. This scenario sometimes occurs in the development of a BIOS plugin¹ or in the development of 

¹ A BIOS plugin is system-level software that's integrated into the BIOS as a component to add functionality to the BIOS. For example, you can add CD-playing capability to the BIOS for diskless machines.
```
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Context: HAN15-ch08-327-392-97801238147912011/6/13:21Page391#658.9BibliographicNotes391theoreticalandempiricalstudybyKohavi[Koh95].SeeFreedman,Pisani,andPurves[FPP07]fortheconfidencelimitsandstatisticaltestsofsignificance.ForROCanalysis,seeEgan[Ega75],Swets[Swe88],andVukandCurk[VC06].Bag-gingisproposedinBreiman[Bre96].FreundandSchapire[FS97]proposedAdaBoost.Thisboostingtechniquehasbeenappliedtoseveraldifferentclassifiers,includingdeci-siontreeinduction(Quinlan[Qui96])andna¨ıveBayesianclassification(Elkan[Elk97]).Friedman[Fri01]proposedthegradientboostingmachineforregression.Theensem-bletechniqueofrandomforestsisdescribedbyBreiman[Bre01].SeniandElder[SE10]proposedtheImportanceSamplingLearningEnsembles(ISLE)framework,whichviewsbagging,AdaBoost,randomforests,andgradientboostingasspecialcasesofagenericensemblegenerationprocedure.FriedmanandPopescu[FB08,FP05]presentRuleEnsembles,anISLE-basedmodelwheretheclassifierscombinedarecomposedofsimplereadablerules.Suchensembleswereobservedtohavecomparableorgreateraccuracyandgreaterinterpretability.Therearemanyonlinesoftwarepackagesforensembleroutines,includingbagging,AdaBoost,gradientboosting,andrandomforests.Studiesontheclassimbalanceproblemand/orcost-sensitivelearningincludeWeiss[Wei04],ZhouandLiu[ZL06],ZapkowiczandStephen[ZS02],Elkan[Elk01],andDomingos[Dom99].TheUniversityofCaliforniaatIrvine(UCI)maintainsaMachineLearningRepos-itoryofdatasetsforthedevelopmentandtestingofclassificationalgorithms.ItalsomaintainsaKnowledgeDiscoveryinDatabases(KDD)Archive,anonlinerepositoryoflargedatasetsthatencompassesawidevarietyofdatatypes,analysistasks,andappli-cationareas.Forinformationonthesetworepositories,seewww.ics.uci.edu/∼mlearn/MLRepository.htmlandhttp://kdd.ics.uci.edu.Noclassificationmethodissuperiortoallothersforalldatatypesanddomains.EmpiricalcomparisonsofclassificationmethodsincludeQuinlan[Qui88];Shavlik,Mooney,andTowell[SMT91];Brown,Corruble,andPittard[BCP93];CurramandMingers[CM94];Michie,Spiegelhalter,andTaylor[MST94];BrodleyandUtgoff[BU
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Context: # Chapter 2
## Data Structures and Libraries

> If I have seen further it is only by standing on the shoulders of giants.  
> —Isaac Newton

### 2.1 Overview and Motivation

Data structure is a way to store and organize data in order to support efficient insertions, searches, deletions, queries, and/or updates. Although a data structure by itself does not solve the given programming problem (the algorithm operating on it does), using the most efficient data structure for the given problem may go a long way toward solving the problem's time limit. There are many ways to configure the same data and sometimes one way is better than the other in a difficult context, as you will see in the discussion below. Particularly with data structures and libraries discussed in this chapter, it is a must in order to understand the semantics that these structures encapsulate.

As stated in the preface of this book, we assume that you are familiar with the basic data structures listed in Section 2.2 and thus do not review them again in this book. We highlight that in later chapters, we will build on libraries in C++ and API. Therefore, if you find that you are not quite with any of the terms of data structures introduced in Section 2.2, please consult the prior book, quickly explore and learn that particular material in reference books, etc., and come back here to get the basic concepts.

Note that for solving programming problems, you just have to be able to choose the right structure to solve the given problem. You just have to know the strengths, weaknesses, and performance implications of each data structure. Remember that normally you can’t construct any new data structure but adapt the existing ones you already know.

The chapter is divided into two parts, Section 2.2 describes the basic data structures in this domain and Section 2.3 more about their libraries. The discussion of each data structure in this chapter reflects the style in Section 1.3 where it is based on behavior.

In the second section of the book, we write out concrete code from C++ perspective. However, the Java version of the C++ code can be found in the supporting website of this book titled "Java resources". Help about methods when you are trying to get that listed structures and algorithms comes in CS curriculum.

For example, there is a hypothesis you want to prove in response to an action like #include <iostream> or a library from a list you want to use. The reference for each example code is indicated below the body text as a base link below.

[Download links from](https://sites.google.com/site/stevenashlin/home/material)
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Context: # 7.2 BASIC GEOMETRY OBJECTS WITH LIBRARIES

© Steven & Felix

1. A circle with diameter \( d \) has circumference (or perimeter) \( c = \pi \times d \).
2. A circle with radius \( r \) has area \( A = \pi \times r^2 \).

### Figure 7.2. Circles

3. **Arc** of a circle is defined as a connected section of the circumference of the circle. Given the central angle \( \theta \) (angle with vertex at the circle's center, see Figure 7.2, right) in degrees, we can compute the length of the corresponding arc as:
   \[
   \text{Arc length} = \frac{\theta}{360} \times 2\pi r
   \]

4. **Chord** of a circle is defined as the segment whose endpoints lie on the circle (diameter is the largest chord in a circle). A circle with radius \( r \) and a central angle in degrees \( \theta \) is represented in the figure below. The chord will correspond with length:
   \[
   \text{Chord length} = 2r \sin\left(\frac{\theta}{2}\right)
   \]

### Figure 7.3. Circle Through 2 Points and Radius

5. **Sector** of a circle is defined as the region of the circle bounded by two radii and an arc lying between those two radii. A sector with area \( A \) and a central angle \( \theta \) is represented in the figure. The area of a sector is:
   \[
   A = \frac{\theta}{360} \times \pi r^2
   \]

6. **Segment** of a circle is defined as a region of the circle bounded by a chord and an arc lying between the chord's endpoints. See Figure 7.3 for related information and note the relationship between the segment area and the triangle formed by the endpoints and the circle's center using the corresponding notation from the area of an isosceles triangle with bases \( r \) and chords.

7. Given 2 points on the circle (with polar coordinates \( (r, \theta_1) \) and \( (r, \theta_2) \)) of the corresponding circle, we can determine the distance between the two points as:
   \[
   d = r \cdot (\theta_2 - \theta_1)
   \]

**Example codes:** `ch7_p2_circles.cpp`, `ch7_p2_circles.java`

---

"Human reads with depth, but many mathematical functions must in programming languages work with notation. Check your programming language manual to verify that (as with C/C++/Java) do. To help with conversion, just remember that one radian equals 180 degrees."
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Context: # Appendix A

## Hints/Brief Solutions

In this appendix, you will see the hints or brief solutions for many – but not all – mini exercises in this book. Please refrain from looking at the solutions unless necessary.

## Chapter 1

### Exercise 1.2.1: The complete Table A.1 is shown below.

| UVa  | Title                          | Problem Type                          | Hint                          |
|------|--------------------------------|---------------------------------------|-------------------------------|
| 13004| Rail Attack                    | Complete Search or Dynamic Programming | Section 32 or 35             |
| 10334| Gap                          | Divide & Conquer (Brute Method)      | Section 3                     |
| 11292| Dragon of Loneliness           | Greedy (Non Classical)                | Section 4                     |
| 11054| Wedding Shopping               | DP (Non Classical)                    | Section 3                     |
| 10911| Forming Teams                  | Graph (Decomposition: Prayer's & BPS) | Section 4                     |
| 11055| Angry Program                  | Graph (Min Cut/Max Flow)              | Section 4.1                   |
| 10147| *GATTACA*                      | Dynamic Programming, Complete Search  | Section 6.6                   |
| 10625| Insects Title Problems         | Geometry (Convex Hull - Area of Polygon) | Section 6.7               |

Table A.1: Exercise: Classify These UVa Problems

### Exercise 1.2.2: The answers are:

1. (a) Use priority queue data structure (heap) (Section 2.2).
2. (b) If it is static: (i) Simple Array that is pre-processed with Dynamic Programming (Section 2.2.1 & 1.5). (ii) Dynamic (the (i) Round Robin Tree is a better answer (select to implement than simple heap)).

   - (a) Yes, such complete search is possible (Section 2.3).
   - (b) Dynamic Programming (Section 5.5, 4.2, 4.7).
   - (c) See Exercises (Section 5.5).

3. (a) The tricky approach above will not work. Use KMP or Suffix Array (Section 6.4 & 6.6)!
4. (b) You can also answer about: Find the Center Hull of the points in O(log n) (Section 7.3). Let the number of points in CH(S) = N, S ∈ C* N. Then, find the two furthest points in the convex hull for that set.

*"This book is based on official lecture material in Steven's course in MSCS C5230 - 'Competitive Programming'; it is necessary to look into the exercise for more accuracy as they will be used in class assessments."* 

213
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Context: # 1.3 Getting Started: The Ad Hoc Problems

We will kick this chapter by stating that the first problem type in ICPCs and IOIs: the Ad Hoc problems. According to [USACO, 29], Ad Hoc problems are problems that cannot be classified anywhere else, where each problem description and its corresponding solution are "unique".

Ad Hoc problems are always appear in a programming contest. Using a benchmark of total problems, there must be 1-2 Ad Hoc problems in an ICPC. If the problem is easy, it will probably be the first problem assigned to the teams in a programming contest. But there exists a reason for this problem list: the problem cannot seem to be solved by any conventional way, and you will encounter many problems that require you to think outside the box.

If you have the bare concepts, you can start with the simplest problems of this category and work your way up from there. There are 10 problems that you should try to solve. The cases I will take for each competition category (this is only a guide) are as follows:

1. If you start at 10, usually, you will find it hard to predict where you will end up with these cases: 2 may have little to no exceptions. However, the further you can draw these cases, the more time that you will have to work on the 2 + 3 = 5 challenging tasks.

## The main categories

We believe that you can make use of basic problems whilst using advanced data structures or algorithms that will be discussed in the later chapters. Many of these Ad Hoc problems are "simple" but have the simple method "tricky". Now, try to solve problems from each category before reading the next article.

### The categories:

1. **(Super) Easy**  
   You should try these problems `AC` in under `7 minutes` each!  
   If you are just starting competitive programming, we strongly recommend that you start your journey by solving some problems from this category.

2. **(Game Card)**  
   There exist Ad Hoc problems involving game problems. The first game type is related to cards. Usually you will need to parse the string input as external cards (suits such as `D(iamond)`, `C(lub)`, `H(eart)`, and `S(pade)` and so forth) with the basic quantity: `2, 3, 4, ..., 10, J(ack), Q(ueen), K(ing), A(ce)`. It may be a good idea to map the complicated strings to integer indices. For example, one possible mapping is `1` for `2`, `2` for `3`, ..., `DA` → `0`, `D2` → `1`, `D3` → `2`, ..., `SA` → `14`. Keep in mind that you will deal with the integer indices instead. 

3. **(Game (Others))**  
   Other than card-related games, there are many other popular programming contest problems one faces such that they must filter their way into programming contest problems. The To-Do, Read-Paper-Science, Sudoku, and more. These can be included in this section. Once you have the basics, look for many ways to put queries in `S × S` and those listed in Chapter 3.

In some other arrangements, let `A < Z`.
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Context: tomertransactions.InProc.1998Int.Conf.DataEngineering(ICDE’98),pp.494–502,Orlando,FL,Feb.1998.[SR81]R.SokalandF.Rohlf.Biometry.Freeman,1981.[SR92]A.SkowronandC.Rauszer.Thediscernibilitymatricesandfunctionsininformationsystems.InR.Slowinski(ed.),IntelligentDecisionSupport,HandbookofApplicationsandAdvancesoftheRoughSetTheory,pp.331–362.KluwerAcademic,1992.[SS88]W.SiedleckiandJ.Sklansky.Onautomaticfeatureselection.Int.J.PatternRecognitionandArtificialIntelligence,2:197–220,1988.[SS94]S.SarawagiandM.Stonebraker.Efficientorganizationoflargemultidimensionalarrays.InProc.1994Int.Conf.DataEngineering(ICDE’94),pp.328–336,Houston,TX,Feb.1994.[SS01]G.SatheandS.Sarawagi.IntelligentrollupsinmultidimensionalOLAPdata.InProc.2001Int.Conf.VeryLargeDataBases(VLDB’01),pp.531–540,Rome,Italy,Sept.2001.
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Context: # CONTENTS
### Steven & Felix

## Changes for the Second Edition

There are substantial changes between the first and second editions of this book. As the authors, we have learned a number of new things and solved hundreds of pressing problems during the one year gap between these two editions. We have received several feedbacks from readers, especially those from CSE233 Class Sem 2 AY2011 students, that we have incorporated in the second edition.

Here is a summary of the important changes for the second edition:

1. **The first noticeable change is the layout.** We now have more information entering each page. The 2nd edition seems like spacing instead of line spacing in the last edition. The positioning of small figures is also adjusted so that we have more clear structure. This is to avoid increasing the number of pages to which we also will not detract.

2. **Some minor bugs in our example codes** (both the codes shown in the book and the test copy files) have been improved. All source codes now have much needed simplifying comments to help readers understand the code.

3. **Several known language issues** (typos, grammatical, stylistic) have been corrected.

4. On top of enhancing the writing of existing data structures, algorithms, and programming problems, we also add these new materials in each chapter:
   -  **1.1** More real life applications to kick start the book (Section 1.3).
   -  **2.** Lightweight and Debloat (data manipulation techniques) (Section 2.1), Implicit Graph (Section 2.3.1), and Remarks (the data structures section) (Section 2.4).

5. **More DP.** Deeper explanation of the bottom-up DP (or, k by k) solutions for LFS problems, and incorporating of Radix/Subset Sum DP (without being Series) (Section 3.2), and revising 2 great paradigms in the book, Capricious (Section 5.3), string matching (Section 6.4), and all changed Suffic Tree for any explanation (Section 6.6).

6. Recognizing other methods/tricks (Chapter 5) and how Java Biographer, Converging (A*), Dynamics, Priorities, Probability Theory, Cycle-Filtering, Game Theory (new).

7. Basic string processing skills (Section 6.2), extra matching problems (Sections 6.3, 6.7), and filtering (Section 6.8) add on as well as other suffix filters.

8. **New Chapters.** Discovering libraries (Chapter 7), especially on points, lines, and polygons.
   - "A* Depth Limiting Search: Iterative Deepening (ID)," more advanced DP: note issues related to range DP states, and discuss on other harder DP problems.
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Context: architecture) and compression algorithm, computer science students might also find it useful. However, nothing prevents any people who is curious about BIOS technology to read this book and get benefit from it. Some prerequisite knowledge is needed to fully understand this book. It is not mandatory, but it will be very difficult to grasp some of the concepts without it. The most important knowledge is the understanding of x86 assembly language. Explanation of the disassembled code resulting from the BIOS binary and also the sample BIOS patches are  presented in x86 assembly language. They are scattered throughout the book. Thus, it’s vital to know x86 assembly language, even with very modest familiarity. It’s also assumed that the reader have some familiarity with C programming language. The chapter that dwell on expansion ROM development along with the introductory chapter in BIOS related software development uses C language heavily for the example code. C is also used heavily in the section that covers IDA Pro scripts and plugin development. IDA Pro scripts have many similarities with C programming language. Familiarity with Windows Application Programming Interface (Win32API) is not a requirement, but is very useful to grasp the concept in the Optional section of chapter 3 that covers IDA Pro plugin development.  THE ORGANIZATION  The first part of the book lays the foundation knowledge to do BIOS reverse engineering and Expansion ROM development. In this part, the reader is introduced with:  a. Various bus protocols in use nowadays within the x86 platform, i.e. PCI, HyperTransport and PCI-Express. The focus is toward the relationship between BIOS code execution and the implementation of protocols. b. Reverse engineering tools and techniques needed to carry out the tasks in later chapter, mostly introduction to IDA Pro disassembler along with its advanced techniques. c. Crash course on advanced compiler tricks needed to develop firmware. The emphasis is in using GNU C compiler to develop a firmware framework.  The second part of this book reveals the details of motherboard BIOS reverse engineering and modification. This includes indepth coverage of BIOS file structure, algorithms used within the BIOS, explanation of various BIOS specific tools from its corresponding vendor and explanation of tricks to perform BIOS modification.  The third part of the book deals with the development of PCI expansion ROM. In this part, PCI Expansion ROM structure is explained thoroughly. Then, a systematic PCI expansion ROM development with GNU tools is presented.  The fourth part of the book deals heavily with the security concerns within the BIOS. This part is biased toward possible implementation of rootkits within the BIOS and possible exploitation scenario that might be used by an attacker by exploiting the BIOS flaw.  Computer security experts will find a lot of important information in this part. This part is the central theme in this book. It’s presented to improve the awareness against malicious code that can be injected into BIOS.  The fifth part of the book deals with the application of BIOS technology outside of its traditional space, i.e. the PC. In this chapter, the reader is presented with various application of the BIOS technology in the emerging embedded x86 platform. In the end of this part, further application of the technology presented in this book is explained briefly. Some explanation regarding the OpenBIOS and Extensible Firmware Interface (EFI) is also presented.  SOFTWARE TOOLS COMPATIBILITY  This book mainly deals with reverse engineering tools running in windows operating system. However, in chapters that deal with PCI Expansion ROM development, an x86 Linux installation
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Context: HAN08-ch01-001-038-97801238147912011/6/13:12Page35#351.10BibliographicNotes35outlieranalysis.Giveexamplesofeachdataminingfunctionality,usingareal-lifedatabasethatyouarefamiliarwith.1.4Presentanexamplewheredataminingiscrucialtothesuccessofabusiness.Whatdataminingfunctionalitiesdoesthisbusinessneed(e.g.,thinkofthekindsofpatternsthatcouldbemined)?Cansuchpatternsbegeneratedalternativelybydataqueryprocessingorsimplestatisticalanalysis?1.5Explainthedifferenceandsimilaritybetweendiscriminationandclassification,betweencharacterizationandclustering,andbetweenclassificationandregression.1.6Basedonyourobservations,describeanotherpossiblekindofknowledgethatneedstobediscoveredbydataminingmethodsbuthasnotbeenlistedinthischapter.Doesitrequireaminingmethodologythatisquitedifferentfromthoseoutlinedinthischapter?1.7Outliersareoftendiscardedasnoise.However,oneperson’sgarbagecouldbeanother’streasure.Forexample,exceptionsincreditcardtransactionscanhelpusdetectthefraudulentuseofcreditcards.Usingfraudulencedetectionasanexample,proposetwomethodsthatcanbeusedtodetectoutliersanddiscusswhichoneismorereliable.1.8Describethreechallengestodataminingregardingdataminingmethodologyanduserinteractionissues.1.9Whatarethemajorchallengesofminingahugeamountofdata(e.g.,billionsoftuples)incomparisonwithminingasmallamountofdata(e.g.,datasetofafewhundredtuple)?1.10Outlinethemajorresearchchallengesofdatamininginonespecificapplicationdomain,suchasstream/sensordataanalysis,spatiotemporaldataanalysis,orbioinformatics.1.10BibliographicNotesThebookKnowledgeDiscoveryinDatabases,editedbyPiatetsky-ShapiroandFrawley[P-SF91],isanearlycollectionofresearchpapersonknowledgediscoveryfromdata.ThebookAdvancesinKnowledgeDiscoveryandDataMining,editedbyFayyad,Piatetsky-Shapiro,Smyth,andUthurusamy[FPSS+96],isacollectionoflaterresearchresultsonknowledgediscoveryanddatamining.Therehavebeenmanydatamin-ingbookspublishedinrecentyears,includingTheElementsofStatisticalLearningbyHastie,Tibshirani,andFriedman[HTF09];IntroductiontoDataMi
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Context: # 1.2 TIPS TO BE COMPETITIVE

## Exercise 1.2.8

Write the shortest codes that you can think of for the following tasks:

1. Given a string that represents a base X number, convert it to equivalent string in base Y. (e.g. `2, X = 5` → `FF` in base 16 (Hexadecimal) to `25` in base 10 (Decimal) and `11111111` in base Y = 2 (Binary). (More details in Section 5.2.3))
  
2. Given a list of integers `L` of size up to 1, determine whether a value `x` exists in `L` by not using more than `2` comparisons? (More details in Section 2.1)

3. Given a date, determine what day of the week (Monday, Tuesday, ..., Sunday) of that date? (e.g. 9 August 2010 - the famous date of the first edition of this book → Monday)

4. Given a string, replace all ‘special words’ of length `n` with `s` that starts with a numeric-alphabet character and followed by two consecutive digits, e.g. `s = "time"` and `**` will be replaced, but `a234` and `a572` will not.

5. Write the shortest possible Java code to read in a double (e.g. `1.472`, `1.23456789`, etc.) and print it again, just with minimum width for 3 digits after decimal point (e.g. `as` → `473 (where `s` denotes a space), `525.325`, `625`).

6. Generate all possible permutations of `(0, 1, 2, ... , N-1)` for `N = 20`.

7. Generate all possible subsets of `(0, 1, 2, ... , N-1)` for `N = 20`.

## 1.24 Tip 5: Master the Art of Testing Code

You thought that you have nailed a particular problem. You have identified its type, designed the algorithm for it, and calculated the algorithm's time-space complexity - it will involve testing the program, debugging, and optimizing the algorithm. But you might still not get points by solving the problem partially. In CPIC, you may only get credit if your team's code solves all the judging cases. That is, your program must provide correct results with high accuracy.

### Important Points

- **Time Limits:** Usually, time test (time spent running) in CPIC to check in I/O bounds and amplitude of problems must take less than a certain threshold (e.g. `1.00` seconds).
  
- Performant Algorithms: You need to consider all the cases extensively, and test cases may vary in size. If you are not sure whether your code is doing good, reproduce these test cases, especially for boundary conditions.
  
- **Improving Your Codes:** Prototype your solution and try to improve your code along the way.
  
### Tips for Testing:

- Ensure your tests include the simple case as you already have the answer given.
  
- Use `printf` functions and `if` in UNIX to check your outputs against the sample output.
  
- Avoid manual comparison as we are not good in performing such tests, especially for problems with delicate output format.
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Context: lsopublishesabi-annualnewsletter,SIGKDDExplorations.Thereareafewotherinternationalorregionalconferencesondatamining,suchastheEuropeanConferenceonMachineLearningandPrinciplesandPracticeofKnowledgeDiscoveryinDatabases(ECMLPKDD),thePacific-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD),andtheInternationalConferenceonDataWarehousingandKnowledgeDiscovery(DaWaK).Researchindatamininghasalsobeenpublishedinbooks,conferences,andjour-nalsondatabases,statistics,machinelearning,anddatavisualization.Referencestosuchsourcesarelistedattheendofthebook.PopulartextbooksondatabasesystemsincludeDatabaseSystems:TheCompleteBookbyGarcia-Molina,Ullman,andWidom[GMUW08];DatabaseManagementSystemsbyRamakrishnanandGehrke[RG03];DatabaseSystemConceptsbySilberschatz,Korth,andSudarshan[SKS10];andFundamentalsofDatabaseSystemsbyElmasriandNavathe[EN10].Foraneditedcollectionofseminalarticlesondatabasesystems,seeReadingsinDatabaseSystemsbyHellersteinandStonebraker[HS05].
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Context: # 3.2 COMPLETE SEARCH

*Stevens & Efic*

This tip is best shown using an example: UVA 10360 - Rat Attack. Imagine a 2D array (up to 1024 x 1024) containing rats. There are `n = 5000` rats on the grid, denoted by their coordinates `(x, y)`, and it should be established that the number of rats killed is minimized. This is the problem of data points in `M` space (see Figure 12).

The first step is to define this problem formally. By bombarding each of the `1024` cells and determining the number of rats killed within the square-bombing radius, we see that for each bomb cell `(i, j)`, we need to do `O(n)` to count how many rats are killed (in `2R^2` for each population area `x` is less than `1024`(1024). For each rat population `(x_i, y_i)` for all `i`, in the square-bombing radius `(x_i - d ≤ j ≤ x_i + d)` and `(y_i - d ≤ j ≤ y_i + d)`. This is the maximal density problem that involves `O(n^2)` operations. Then, to determine the best grid bomb placement, we need to consider the highest cost to ally rats killed, which can be done in `O(n)` operations. This backwards approach only requires `2000` cells `= 1024^2` divided by the worst case (i.e., `n = 2000`, so `2 * 50` times the maximum evaluated. This is `AC`.

## Tip 6: Optimizing Your Source Code

There are many tricks that can serve to optimize your code. Understanding computer hardware, especially I/O, memory, and cache behavior, can help you design a better program. Some examples:

1. Use the faster `C-style` `printf` rather than `cout`. For Java, prefer `BufferedReader` over `Scanner` for input.
2. Use the `opened (O) long` best-fitted dynamically (using C++ STL algorithms; avoid the `new` operator) instead of `malloc` or `free` for built-in array structures when possible.
3. Access a 2D array in a row-major fashion (row by row) rather than column by column.

4. Bit manipulation can often be more efficient than indexing an array of books (see the highlight section of Book in Section 22.1). If we need more than `15` bytes, use `C++` STL `string`. 

5. Use lower-level data types where relevant. For example, storing `int` equals to the maximum size of `int` instead of using resizable variables: use `32-bit integers` when possible, as `32-bit` is faster in most cases.

6. In C/C++, prefer the faster `ArrayList` rather than `Java Vector`. `Java Vector` is thread-safe but this is not used in competitive programming. 

7. The C++ `const` will provide compile-time optimization where possible.

8. Don't repeat code. Use functions to reduce duplication and improve maintainability.

9. Be careful with `String` manipulation in Java; using `C++` STL strings reduces overhead and improves performance. 

10. When accessing memory, ensure your data structure aligns with cache usage to avoid cache misses.
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Context: IndexA*,203ACM,1Adelson-Velskii,Georgii,38All-PairsShortestPaths,96FindingNegativeCycle,99MinimaxandMaximin,99PrintingShortestPaths,98TransitiveClosure,99AlternatingPathAlgorithm,116Array,22ArticulationPoints,77Backtracking,40BackusNaurForm,153Bayer,Rudolf,38BellmanFord’s,93Bellman,Richard,93Bellman,RichardErnest,95BigInteger,seeJavaBigIntegerClassBinaryIndexedTree,35BinarySearch,47BinarySearchtheAnswer,49,197BinarySearchTree,26BinomialCoefficients,130Bioinformatics,seeStringProcessingBipartiteGraph,114Check,76MaxCardinalityBipartiteMatching,114MaxIndependentSet,115MinPathCover,116MinVertexCover,115BisectionMethod,48,195Bitmask,23,65,205bitset,134BreadthFirstSearch,72,76,90,102Bridges,77BruteForce,39CatalanNumbers,131Catalan,Eug`eneCharles,128CCWTest,180ChinesePostman/RouteInspectionProblem,205Cipher,153Circles,181CoinChange,51,64Combinatorics,129CompetitiveProgramming,1CompleteGraph,206CompleteSearch,39ComputationalGeometry,seeGeometryConnectedComponents,73ConvexHull,191CrossProduct,180CutEdge,seeBridgesCutVertex,seeArticulationPointsCycle-Finding,143DataStructures,21DecisionTree,145Decomposition,197DepthFirstSearch,71DepthLimitedSearch,159,204Deque,26Dijkstra’s,91Dijkstra,EdsgerWybe,91,95DiophantusofAlexandria,132,141DirectAddressingTable,27DirectedAcyclicGraph,107CountingPathsin,108GeneralGraphtoDAG,109LongestPaths,108MinPathCover,116ShortestPaths,108DivideandConquer,47,148,195DivisorsNumberof,138Sumof,139DPonTree,110DynamicProgramming,55,108,160,205EditDistance,160EdmondsKarp’s,102Edmonds,JackR.,95,102EratosthenesofCyrene,132,133EuclidAlgorithm,135ExtendedEuclid,141EuclidofAlexandria,135,187Euler’sPhi,139Euler,Leonhard,132,139EulerianGraph,113,205EulerianGraphCheck,113PrintingEulerTour,114231
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Context: # 4.2 GRAPH TRAVERSAL  
© Steven & Felix  

- **Flood Fill / Finding Connected Components**  
    1. UVA 01220 - 2- Go along dx[3, 6 neighbors per cell]  
    2. UVA 01962 - Second War (count number of connected components (CC))  
    3. UVA 02977 - Password of Polish (count number of CCs, biconnected in this section)  
    4. UVA 059 - 20 Desktops (count number of CCs, similar to UVA 523)  
    5. UVA 10688 - The Big C (just the "one color" part)  
    6. UVA 10769 - Mice and Maze (Regular Graph, both cells have internet, formal output)  
    7. UVA 10776 - Schools and Ruling (response + what “we” remember we are inside the grid)  
    8. UVA 11325 - Peeking Cattle (Grid, farms)  
    9. UVA 11327 - Hiding Sheep (Grid, farms)  
    10. UVA 11328 - Cattle Corral (similar to UVA 11325)  
    11. UVA 11329 - Cattle Rush (as before, but it's also tricky)  
    12. UVA 11330 - Falling Giants (Grid and Hill runs minimum weight)  
    13. UVA 11401 - Counting Stars (count at most “H” by layers)  
    14. UVA 11417 - Spanning Trees (can be as hard “H” as we can, like simple CCs)  
    15. UVA 11458 - Getting Gold (not filled with extra blocking restriction)  
    16. UVA 11514 - Backstabbers (interpret as, test of blood and politeness)  
    17. UVA 12137 - The Superstars (Fundamentals, Connected Components)  

- **Topological Sort**  
    1. UVA 00211 - Railing Orders (use backtracking to generate valid toporders)  
    2. UVA 00312 - Race Order (similar to UVA 194, use backtracking)  
    3. UVA 01075 - Overlapping Attack (use topological algorithms discussed in this section)  
    4. UVA 11060 - Power Poses (treat as the "holistic" BFS topological sort)  
    5. UVA 11608 - Pk up (using possible) DAGS (combine see Section 4.7.1)  

- **Bipartite Graphs**  
    1. UVA 10004 - Bicoloring (bipartite graph)  
    2. UVA 11503 - Make a Match (bipartite graph, take max[lt, rt])  
    3. UVA 11180 - Place the Queens (4-figure graphs)  
    4. UVA 11360 - Clay Discombobulate (continually pick a logarithmic graph check)  

- **Finding Articulation Points / Bridges**  
    1. UVA 10001 - Network (finding articulation points)  
    2. UVA 10629 - Just Direct (finding bridges)  
    3. UVA 11538 - Classy Links (finding articulation points)  

- **Finding Strongly Connected Components**  
    1. UVA 11103 - Easy Char (SCC = printing solution)  
    2. UVA 11104 - Tunnel (SCCs)  
    3. UVA 11117 - Function (count SCCs) inherit in-degree from outside SCC)  
    4. UVA 11180 - Losing Away (similar to UVA 1130)  
    5. UVA 11309 - C (count the vertex graph is strongly connected)  
    6. UVA 11380 - Shift-In-Branch (turn, SCC)  

> "There is either way to solve this problem, i.e., by finding the patterns."
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Context: /proc/.../erase  The "..." stand for the hardware-dependent part of the path: ct.o                                  ctflasher ide_flash.o                        ide-flasher/PLCC32 and ide-flasher/DIL32 e100_flash.o                     e100-flash/device? 3c90xc_flash.o                  3c90xc-flash/device? rtl8139_flash.o                  rtl8139-flash/device? sis630_flash.o                   sis630-flash via-rhine_flash.o               via-rhine-flash/device?  So, the info file for the ide-flasher's PLCC socket is /proc/ide-flasher/PLCC32/info.  For information about the hardware and the inserted flash do  "cat /proc/.../info"  For erasing the flash do  "cat /proc/.../erase"  For reading the content of flash do  "cat /proc/.../data >my_file"  For programming (and erasing) the flash do  "cat my_image >/proc/.../data"  Verify is done automatically.  If you forget the main module "flash.o," you may get "cat: /proc/.../data: Device or resource busy."   Because ctflasher is released under general public license and BSD license, you can use the code without charge in your software. As explained in the previous subsections, to understand ctflasher source code without wasting your precious time, you can use ctags and vi to help traversing the source code. The directory structure of the source code is shown in figure 9.6.
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Context: # IDA Pro Workspace

![Figure 2.8 IDA Pro workspace](path_to_image)

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 analyze 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: 

1. **Basic Structure**
   - Start with a function definition.
   - Use semicolons to end each statement.
   - Use curly braces `{}` to define the scope of functions.

2. **Variables**
   - Declare variables with their types (e.g., `int`, `char`, `float`).
   - Example: `int x;`

3. **Control Structures**
   - Use `if`, `for`, and `while` as in C.

4. **Functions**
   - Functions are defined similarly to C.

5. **Comments**
   - Use `//` for single-line comments.
   - Use `/* comment */` for multi-line comments.

By mastering these basics, you will be better prepared to write scripts that allow you to leverage the full power of IDA Pro's analysis capabilities.
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Context: # 8.5 Chapter Notes

## New Advanced DP

1. **UVA 100 - **Beasts** (the input constraint is best used)¹²**
2. **UVA 673 - **The Ballot International**⁷**
3. **UVA 10677 - **The Malesk Mansion** (or, i.e., in an arbitrary order), try all**
4. **UVA 1089 - **Rat Divers Subsequence** (or Jon Baltringer)**
5. **UVA 1003 - **String Reapers** (or) possibly the last and main DP¹¹**
6. **UVA 1014 - **Number Game** (a bit number theory) (modular, backtracking)**
7. **UVA 1261 - **Dijkstra's Algorithm** (as in flowing)**
8. **UVA 1398 - **The Candyman Can** (drop two parameters between a, b, c)**
9. **UVA 10276 - **Playing Golf** (details to be inferred)**
10. **UVA 10635 - **Dividing Code** (similar to DP state to be stored in queue)**
11. **UVA 1133 - **Bridge Problem** (Ternary, DP on Trees)**
12. **UVA 1440 - **Masthead Wang** (backtracking)**
13. **UVA 1464 - **Number Transitions** (Singapore)**
14. **UVA 1441 - **Fewer Partitions** (Alashaft's problem author Rex Halim)**
15. **UVA 1189 - **Directed Paths** (Algebraic)**
16. **UVA 487 - **Partitioning Paths** (Augmenting)**
17. **UVA 831 - **Max Flow (Algebraic, Game Theory, DP)**
18. **UVA 687 - **Pile of Items (MaxChain, Game Theory, DP)**
19. **UVA 673 - **Reversing (Ratharb)**
20. **UVA 1791 - **The Islands (World Finals Harbof1, Bionic TSP)**

---

This chapter and the second edition of this book are here. After writing so much, we become more aware of the presence of more interesting techniques that we are not yet able to cover in the second edition of this book. Expect this chapter to be improved substantially in the editor’s notes.

To be continued... :)

---

There are 52 UVa (with 31 others) programming exercises discussed in this chapter.

There are 15 pages in this chapter.

¹² For a list, add the top parameter ‘id’ for looking at K ranges.  
⁷ Repeat each state to store DP data.  
¹¹ Place each trigger, or the arrangement, will roughly yield adjacent algorithms, DP states (pos, k=1).  
¹³ Compare the goal, use a relational, and drop as decisions symbolically, cov. It then moves to “DP states: (length, num, head)” and drop as the exact decision layout. Drop it to the most beneficial one.
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Context: sur.Dynamicitemsetcountingandimplicationrulesformarketbasketanalysis.InProc.1997ACM-SIGMODInt.Conf.ManagementofData(SIGMOD’97),pp.255–264,Tucson,AZ,May1997.[BN92]W.L.BuntineandT.Niblett.Afurthercomparisonofsplittingrulesfordecision-treeinduction.MachineLearning,8:75–85,1992.[BO04]A.BaxevanisandB.F.F.Ouellette.Bioinformatics:APracticalGuidetotheAnalysisofGenesandProteins(3rded.).JohnWiley&Sons,2004.[BP92]J.C.BezdekandS.K.Pal.FuzzyModelsforPatternRecognition:MethodsThatSearchforStructuresinData.IEEEPress,1992.[BP98]S.BrinandL.Page.Theanatomyofalarge-scalehypertextualwebsearchengine.InProc.7thInt.WorldWideWebConf.(WWW’98),pp.107–117,Brisbane,Australia,Apr.1998.
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Context: # Figure 9.6 Cflash directory structure

In figure 9.6, Cflash source code is placed in the directory named `flasher_3.5.0`. There are dedicated directories for the flash model that it supports, namely, `nics`, `bios`, `ct`, and `ide`. `Nics` contains source code related to PCI network interface cards that Cflash supports. `Bios` contains source code for a motherboard based on the SiS 630 chipset. `Ct` contains source code for the proprietary Cflash hardware. `Ide` contains files for the IDE flasher interface.

The directory named `modules` is empty at first. It will be filled by Cflash's LKM when you have finished compiling the code. The directory named `build2.6` contains the makefile for kernel 2.6. Finally, the directory named `flash` contains the source code for the flash ROM chip supported by Cflash.

Cflash source code is well structured, and it's easy to understand. For PCI NIC, you start to learn the Cflash source code by studying the NIC support files in the `nics` directory and then proceed to the `flash` directory to learn about the flash ROM-related routines. The PCI NIC support file provides routines needed to access the flash ROM onboard, and the flash ROM support file provides the specific write, erase, and read routines for the corresponding flash ROM chip.
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