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As a third example, before deciding to read a book or document, it is useful to read its abstract For that purpose, we could decompose the document into sentences, then create a binary relation linking each sentence to nonempty words belonging to it An optimal concept linking the maximum number of sentences to the maximum number of words (or similar words) may be used as a base for a summary extraction from the document As a last application, assume that you have an instance of a table in a relational database model, how can we discover the entities of the database In Section 41, we will explain the main ideas of supervised learning using optimal concepts In Section 42, we give a method to explain how we discover entities from an instance of a database In Section 4.



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(3.3)

clearInterval()

Call clearInterval() with the ID returned from a setInterval() or setTimeout() call to cancel that interval. The delayed action is canceled immediately. Again, managing these global interval IDs is very un-OOP, so if you nd yourself needing clearInterval(), it s time to think about using a Timer.





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One problem with the sequence in (3.3) is that the linear complexity is, in a sense, concentrated in just a single bit. That is, the linear complexity is zero, until the last bit is processed, then the complexity jumps from the minimum to the maximum possible value. Recognizing this, Rueppel [125] proposes the linear complexity profile as a practical measure of the quality of a keystream. This profile is simply the graph of the linear complexity L of S O , s1,. . , s k for each k = 0 , 1 , 2 , .. .. The required L values are obtained . when the linear complexity of s is computed using the Berlekamp-Massey Algorithm, so it is efficient to determine such a profile. Rueppel has shown that most sequences have a linear complexity profile that follows the n/2 line closely but irregularly, and he proposes this as a criteria for judging the quality of a keystream. Figure 3.4 illustrates a linear complexity profile that satisfies Rueppel s criteria and would therefore be considered cryptographically strong by his definition.

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You won t be able to see what a typical class le looks like in its entirety until you ve learned about packages and classes in 4, but now you know that all ActionScript code is constructed of statements, expressions, and blocks of code. Let s learn how to make these useful.

Not to scare you away from programming if you happen to hate math, but programming is just like algebra: its power and generality are drawn from using variables to represent numbers. A formula like (x2 x1 )2 + (y2 y1 )2 that computes the Euclidean distance between two points (x1 , y1 ) and (x2 , y2 ) is powerful because it applies to any two points you can think of. To calculate it, you simply substitute your coordinates in the proper place and do the arithmetic. ActionScript 3.0 is built entirely around the use of variables just like the x and y variables in an algebraic equation. A variable is a representation of a number, string of characters, or some other data that can change values the way the x variable in algebra represents a number that may have any value. You might think of variables as containers used to hold pieces of information. In ActionScript, variables contain chunks of information known as objects for the time your program is running. They allow your program to hold on to that information and operate on it. Variables are used in every aspect of programming. You may also hear variables referred to as properties or elds when they are part of a class.

3, we explain how we can find the simplest software architecture with a minimal complexity Finally, in Section 44, we show that we can find the most important communicating groups in a network using optimal concepts..

Figure 3.4: Linear complexity profile. In [141] a different criteria for cryptographically strong sequences is considered. Although the keystream in (3.3) has the highest possible linear complexity, it differs by only one bit from the all-zero sequence, which has the minimum linear complexity. That is, the sequence in (3.3) is too close (in Hamming distance) to a sequence with small linear complexity. In general, if a sequence is close to a sequence with a relatively low linear complexity, then regardless of the linear complexity of the original sequence, it is an undesirable keystream. The k-error linear complexity is defined to be the smallest linear complexity that can be obtained when any k or fewer bits in one period of a sequence are changed from 0 to 1 or vice versa.

De ning your own variables is easy. Let s take a look at a typical variable declaration and step through its different parts in the list that follows:

var food:String; food = "pizza";

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