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R = O1 O3 O5 P1 P4 O2 O4 P2 P3 P5 P6 Because all objects contained in the first concept are in the class C1, we extract the first association rule: P1 AND P2 THEN CLASS = C1 WITH CERTAINTY DEGREE = 1 By the same means, from the second concept, we can extract the second rule: P2 AND P3 AND P5 AND P6 THEN CLASS = C2 WITH CERTAINTY DEGREE = 1 From the minimal coverage, we can, in this way, extract a minimal number of rules associated with each concept. When we have to decide about the class of an object with respect to relation R, we can use these association rules to give the best approximation to the predicted class of this object. However, a concept may contain objects belonging to different classes. In that case, assume that a concept O2 O4 P1 P2 P3 contains only 57 % of objects belonging to class C1, then we create the rule: IF P1 AND P2 AND P3 THEN CLASS = C1 WITH CERTAINTY DEGREE = 0 57 So, we select the class corresponding to the majority of objects in the concept. When we have to decide about the class of an object, we can deduce different alternatives, but we only take the best one, corresponding to the class that we can deduce with the highest certainty degree. Experiences realized on several public databases (such as heart disease and flower tables) have proved that the proposed approach is defendable with respect to other known methods. The learning time corresponding to rule generation is polynomial and lower than the other methods. By using an incremental approach, we have been able to improve time efficiency by only updating a few concepts in each database update.



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Summary

LFSRs in Figure 3.7, are identical to the single LFSR in Figure 3.6, except for the initial fills. Let the initial fill of LFSRo be identical to the initial fill of the LFSR in Figure 3.6, while LFSRl has as its initial fill the initial fill of LFSRo stepped once and, in general, the initial fill of LFSRi is the initial fill of LFSRo stepped i times. Then the nonlinear combining function f in Figure 3.7 has access to precisely the same bits at each step as t,he function f in Figure 3.6. That is, the stream cipher in Figure 3.6 is a special case of that in Figure 3.7. Consequently, in the discussion below, we restrict our attention to the more general case, as represented by Figure 3.7.





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Timers are useful for a number of things including delaying a function, repeating a function, and synchronizing several functions. Timers have a delay time and a repeat count. Setting the repeat count to zero causes them to continue ring inde nitely. Timers re the events TimerEvent.TIMER and TimerEvent.TIMER_COMPLETE. Event.ENTER_FRAME is dispatched by display objects on the display list before each frame is rendered. This event can be used for animation.

The getTimer() function retrieves the time in milliseconds since the application started up and is useful for accurate time measurements.

In this section we discuss a correlation attack on a shift register-based stream cipher. Consider the keystream generator in Figure 3.8, which consists of three small shift registers and a nonlinear combining function f. The feedback functions of the shift registers X , Y ,and 2 are

ue to the efforts of the Open Screen Project (http://openscreen project.org), Flash Player is appearing on a growing number of

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smartphones. Simultaneously, consumers, desktop and laptop manufacturers, and makers of desktop operating systems are embracing touchscreens. The net effect is that Flash Player has access to a wider array of input than ever before. The user is no longer limited to a keyboard and mouse. In fact, she may not even have a keyboard or mouse installed, although chances are in that case that a software keyboard is provided and touch input can be interpreted as mouse input. In this chapter you ll learn how to deal with not just touch-based input, but information from various sensors Flash Player may have access to, including accelerometers, and geolocation sensors such as GPS. I ll mention it, but I won t go into depth on geolocation because it s currently available only to AIR or native apps. Between the keyboard and mouse ( 21, Interactivity with the Mouse and Keyboard ), the camera and microphone ( 33, Capturing Sound and Video ), and touch and accelerometer (this chapter), modern hardware has a surprisingly extensive repertoire of interaction modes.

Figure 3.8: Keystream generator. Lct Ix he the length of the cycle grrierated by the shift register X , and define l y and t z similarly. Then the keystream generated by stream cipher in Figure 3.8 has a cycle length of lcm(i x, t y , i z ) . For the registers in Figure 3.8, the cycle length are i ,y = 7, l y = 15, and l , = 31, provided that none of the

flash.ui.Multitouch flash.events.TouchEvent flash.events .GestureEvent flash.sensors .Accelerometer flash.events .AccelerometerEvent

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