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The selection of a lymphocyte by a detected antigen for clonal proliferation, inspired the modeling of CLONALG De Castro and Von Zuben [186, 190] presented CLONALG as an algorithm that performs machine-learning and pattern recognition tasks All patterns are presented as binary strings The a nity between an ALC and a non-self pattern is measured as the Hamming distance between the ALC and the non-self pattern The Hamming distance gives an indication of the similarity between two patterns, ie a lower Hamming distance between an ALC and a non-self pattern implies a stronger a nity All patterns in the training set are seen as non-self patterns Algorithm 193 summarizes CLONALG for pattern recognition tasks The di erent parts of the algorithm are explained next The set of ALCs, C, is initialized with na randomly generated ALCs The ALC set is split into a memory set of ALCs, M, and the remaining set of ALCs, R, which are not in M Thus, C = M R and |C| = |M| + |R| (ie na = nm + nr ) The assumption in CLONALG is that there is one memory ALC for each of the patterns that needs to be recognized in DT Each training pattern, zp , at random position, p, in DT , is presented to C The a nity between zp and each ALC in C is calculated A subset of the nh highest a nity ALCs

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(b) = /4

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Algorithm 193 CLONALG Algorithm for Pattern Recognition t = tmax ; Determine the antigen patterns as training set DT ; Initialize a set of na randomly generated ALCs as population C; Select a subset of nm = |DT | memory ALCs, as population M C; Select a subset of na nm ALCs, as population R C; while t > 0 do for each antigen pattern zp DT do Calculate the a nity between zp and each of the ALCs in C; Select nh of the highest a nity ALCs with zp from C as subset H; Sort the ALCs of H in ascending order, according to the ALCs a nity; Generate W as the set of clones for each ALC in H; Generate W as the set of mutated clones for each ALC in W; Calculate the a nity between zp and each of the ALCs in W ; Select the ALC with the highest a nity in W as x; Insert x in M at position p; Replace nl of the lowest a nity ALCs in R with randomly generated ALCs; end t = t 1; end

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In this chapter, we are going to cover the Symbian OS Messaging subsystem in general and SMS in particular. Messaging is one of the most important features of Symbian OS phones. It provides a good example of creating a Connectivity application that involves programming on the phone as well as on the PC. A bonus is that having an SMS management application is downright useful I use the example I have built here to save me keying in messages using the numeric keypad. The features that we are going to cover include: retrieving messages from the phone for display sending messages via the phone deleting messages on the phone picking up incoming messages to keep the list of displayed messages up to date. This chapter will not be an exhaustive description of the Messaging APIs. Instead, it is intended to provide a general context and enough detail to write the SMS Management application. The Message Server is a prime example of a system server in Symbian OS. It controls access to a system resource the message store and it supports plug-ins (called MTMs Message Type Modules) for the speci c types of messages. It also accepts requests from multiple clients; these requests can be synchronous or asynchronous. Messages are stored as les in a dedicated directory and are accessed by means of Messaging APIs.

is selected from C as subset H The nh selected ALCs are then sorted in ascending order of a nity with zp Each ALC in the sorted H are cloned proportional to the calculated a nity with zp and added to set W The number of clones, nci , generated for an ALC, xi , at position i in the sorted set H, is de ned in [190] as nci = round nh i (191)

(c) = /2

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