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1

String searching algorithms. Singapore: World Scientific, 1994.

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2

Mailund, Thomas. String Algorithms in C. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5920-7.

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3

1951-, Aoe Jun-ichi, ed. Computer algorithms: String pattern matching strategies. Los Alamitos, Calif: IEEE Computer Society Press, 1994.

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4

1948-, Apostolico Alberto y Research Institute for Advanced Computer Science (U.S.), eds. Efficient parallel algorithms for string editing and related problems. [Moffett Field, Calif.?]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1988.

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5

Smyth, Bill. Computing patterns in strings. Harlow, England: Pearson Addison-Wesley, 2003.

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6

Efficient recovery algorithms with restricted access to strings. [New York, N.Y.?]: [publisher not identified], 2022.

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7

United States. National Aeronautics and Space Administration., ed. An algorithm for unsteady flows with strong convection. [Washington, DC]: National Aeronautics and Space Administration, 1988.

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8

Schannath, Heinz. Polynomiale und streng polynomiale Algorithmen für Netzwerkfluss-Probleme. Wiesbaden: Deutscher Universitäts-Verlag, 1991.

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9

Gusfield, Dan. Algorithms on strings, trees, and sequences: Computer science and computational biology. Cambridge [England]: Cambridge University Press, 1999.

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10

Gusfield, Dan. Algorithms on strings, trees, and sequences: Computer science and computational biology. Cambridge [England]: Cambridge University Press, 1997.

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11

Augurzky, Boris. Assessing the performance of matching algorithms when selection into treatment is strong. Bonn, Germany: IZA, 2004.

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12

Castillo, Oscar y Luis Rodriguez. A New Meta-heuristic Optimization Algorithm Based on the String Theory Paradigm from Physics. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-82288-0.

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13

Landau, Gad M. An efficient string matching algorithm with k differences for nucleotide and amino acid sequences. New York: Courant Institute of Mathematical Sciences, New York University, 1985.

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14

Landau, Gad M. An efficient string matching algorithm with k differences for nucleotide and amino acid sequences. New York: Courant Institute of Mathematical Sciences, New York University, 1985.

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15

1973-, Raffinot Mathieu, ed. Flexible Pattern Matching in Strings: Practical on-line search algorithms for texts and biological sequences. Cambridge: Cambridge University Press, 2002.

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16

Reading machines: Toward an algorithmic criticism. Urbana, Ill: University of Illinois Press, 2011.

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17

Mrzygłód, Mirosław. Algorytm optymalizacji topologicznej konstrukcji ciągłych z ograniczeniami zmęczeniowymi: Algorithm of topology optimization of continuous structures with fatigue constraints = [Algoritm topologicheskoĭ optimizat︠s︡ii nepreryvnykh konstrukt︠s︡ii s ustalostnymi ogranichenii︠a︡mi]. Kraków: Politechnika Krakowska im. Tadeusza Kościuszki, 2013.

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18

United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Strain gage selection in loads equations using a genetic algorithm: Sigurd A. Nelson II. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1994.

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19

Nakov, Svetlin. Fundamentals of Computer Programming with C#: The Bulgarian C# Book. Sofia, Bulgaria: Svetlin Nakov, 2013.

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20

1974-, Zomorodian Afra J., ed. Advances in applied and computational topology: American Mathematical Society Short Course on Computational Topology, January 4-5, 2011, New Orleans, Louisiana. Providence, R.I: American Mathematical Society, 2012.

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21

Lecroq, Thierry y Christian Charras. Handbook of Exact String Matching Algorithms. King's College Publications, 2004.

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22

Aoe, Jun-ichi. Computer Algorithms: String Pattern Matching Strategies (Practitioners). Wiley-IEEE Computer Society Pr, 1994.

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23

Mailund, Thomas. String Algorithms in C: Efficient Text Representation and Search. Apress, 2020.

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24

Holloway, James Lee. Algorithms for string matching with applications in molecular biology. 1992.

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25

Hancart, Christophe, Thierry Lecroq y Maxime Crochemore. Algorithms on Strings. Cambridge University Press, 2014.

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26

Hancart, Christophe, Thierry Lecroq y Maxime Crochemore. Algorithms on Strings. Cambridge University Press, 2007.

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27

Algorithms on Strings. Cambridge University Press, 2007.

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28

Hancart, Christophe, Thierry Lecroq y Maxime Crochemore. Algorithms on Strings. Cambridge University Press, 2007.

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29

Hancart, Christophe, Thierry Lecroq y Maxime Crochemore. Algorithms on Strings. Cambridge University Press, 2009.

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30

Hancart, Christophe, Thierry Lecroq y Maxime Crochemore. Algorithms on Strings. Cambridge University Press, 2007.

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31

Algorithms on Strings. Cambridge University Press, 2007.

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32

String Algorithmics (Texts in Algorithmics). King's College Publications, 2004.

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33

Smyth, William. Computing Patterns in Strings (ACM Press Books). Addison Wesley, 2003.

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34

Carroll, John. Parsing. Editado por Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0012.

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This article introduces the concepts and techniques for natural language (NL) parsing, which signifies, using a grammar to assign a syntactic analysis to a string of words, a lattice of word hypotheses output by a speech recognizer or similar. The level of detail required depends on the language processing task being performed and the particular approach to the task that is being pursued. This article further describes approaches that produce ‘shallow’ analyses. It also outlines approaches to parsing that analyse the input in terms of labelled dependencies between words. Producing hierarchical phrase structure requires grammars that have at least context-free (CF) power. CF algorithms that are widely used in parsing of NL are described in this article. To support detailed semantic interpretation more powerful grammar formalisms are required, but these are usually parsed using extensions of CF parsing algorithms. Furthermore, this article describes unification-based parsing. Finally, it discusses three important issues that have to be tackled in real-world applications of parsing: evaluation of parser accuracy, parser efficiency, and measurement of grammar/parser coverage.
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35

Navarro, Gonzalo y Mathieu Raffinot. Flexible Pattern Matching in Strings. Cambridge University Press, 2002.

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36

Sedgewick, Robert. Algorithms in C, Parts 6-8, Strings, Geometry, and Advanced Topics. Pearson Education, Limited, 2020.

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37

Sedgewick, Robert. Algorithms in Java, Parts 6-8, Strings, Geometry, and Advanced Topics. Pearson Education, Limited, 2011.

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38

Gusfield, Dan. Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge University Press, 2010.

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39

Gusfield, Dan. Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge University Press, 2011.

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40

Sedgewick, Robert. Algorithms in C++, Parts 6-8, Strings, Geometry, and Advanced Topics. Pearson Education, Limited, 2020.

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41

Spencer, Joel H. The Strange Logic of Random Graphs (Algorithms and Combinatorics). Springer, 2010.

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42

Flexible Pattern Matching in Strings: Practical On-Line Search Algorithms for Texts and Biological Sequences. Cambridge University Press, 2007.

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43

Navarro, Gonzalo y Mathieu Raffinot. Flexible Pattern Matching in Strings: Practical on-Line Search Algorithms for Texts and Biological Sequences. Cambridge University Press, 2014.

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44

Navarro, Gonzalo y Mathieu Raffinot. Flexible Pattern Matching in Strings: Practical on-Line Search Algorithms for Texts and Biological Sequences. Cambridge University Press, 2014.

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45

Rodriguez, Luis y Oscar Castillo. New Meta-Heuristic Optimization Algorithm Based on the String Theory Paradigm from Physics. Springer International Publishing AG, 2021.

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46

Paparrizou, Anastasia. Efficient Algorithms for Strong Local Consistencies and Adaptive Techniques in Constraint Satisfaction Problems. Lulu Press, Inc., 2015.

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47

Marketing for the Mad Men of Tomorrow: Strong Brands in a World of Algorithms. Lannoo Publishers, 2018.

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48

Bos, Joppe y Martijn Stam, eds. Computational Cryptography. Cambridge University Press, 2021. http://dx.doi.org/10.1017/9781108854207.

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The area of computational cryptography is dedicated to the development of effective methods in algorithmic number theory that improve implementation of cryptosystems or further their cryptanalysis. This book is a tribute to Arjen K. Lenstra, one of the key contributors to the field, on the occasion of his 65th birthday, covering his best-known scientific achievements in the field. Students and security engineers will appreciate this no-nonsense introduction to the hard mathematical problems used in cryptography and on which cybersecurity is built, as well as the overview of recent advances on how to solve these problems from both theoretical and practical applied perspectives. Beginning with polynomials, the book moves on to the celebrated Lenstra–Lenstra–Lovász lattice reduction algorithm, and then progresses to integer factorization and the impact of these methods to the selection of strong cryptographic keys for usage in widely used standards.
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49

Mailund, Thomas. Introduction to Computational Thinking: Algorithms, Functions, Lexers, Parsers, Queues, Recursion, Sets, Strings, Stacks, and More. Apress L. P., 2021.

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50

Ramsay, Stephen. Reading Machines: Toward and Algorithmic Criticism. University of Illinois Press, 2011.

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