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CGI scripts are typically used to take user input and perform one or more operations based on the input and return results in a HTML page In this section I show you how to create a simple script to get started and to understand CGI scripting concepts The script that we will develop here will perform a single task: it will take a user s full name, format it properly, and return a personalized greeting message Listing 12-2 shows greetingspl script that does exactly that

#!/usr/bin/perl # # # Name: greetingspl # # Purpose: # # Show a greeting message # ########################################################## use strict; use CGI; # # Get user data # my $query = new CGI; my $name = $query->param( name ); # # Process data # $name = lc($name); my @strArray = (); foreach my $str (split(/ /,$name)) { push(@strArray, ucfirst($str)); } my $formattedName = join( , @strArray); # # Display results # print $query->header; print $query->start_html( Greetings ); print $query->p( Hello $formattedName , ); print $query->p( Thanks for coming to our Web site ); print $query->end_html; # # Terminate # exit 0;

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As a result of the rule construction method it is quite possible for rules to overlap 3114 LERS LERS (learning from examples based on rough sets) [119] induces a set of rules from examples and also handles inconsistent data through the use of rough sets Lower and upper approximations of concepts are used for this, generating certain and possible rules The process operates by examining attribute-value pairs and how well they approximate the decision concepts Through the de nition of minimal complexes and local coverings [120,351], minimal sets of attribute-value pairs can be discovered from data that adequately describe decision values These sets are then directly translated into decision rules, forming the antecedent part of the rule with the associated decision value forming the consequent For unseen data, the decision to which concept the data belongs to is based on three factors: strength, speci city, and support.

The Security Migration Editor (Sidwalk.msc) is an MMC snap-in that enables you to map old SIDs created by the Showaccs.exe file to the new SIDs. The Security Migration Editor is the third tool that makes up SIDWalker, and you use this graphical snap-in to complete the migration to the new SIDs so that SIDWalk.exe can read the updates to the mapping file for the actual SID conversion. Sidwalk.msc requires the Sidwkr.dll file, and before you can use it, you have to initialize the file at the command prompt. To complete this action, follow these steps: 1. Click Start Run and type cmd in the dialog box. Click OK. 2. At the command prompt, type regsvr32 sidwkr.dll and press Enter. 3. Click OK to the message that appears. Then type exit and press Enter. To use the Security Migration Editor snap-in, manually load the snap-in into an MMC, or from the command line, type sidwalk.msc and press enter.

Now, lets take a close look at this script The very first line is a special line:

This line tells the system to run Perl whenever executing this script If you have installed Perl in a non-standard directory, then you must modify this line accordingly For example, if you installed Perl in /usr/local/bin/perl, then you must change this line to reflect that All the lines (other than the very first one) that start with a # sign are a commented out and ignored by Perl The next code segment is:

Strength is the total number of objects correctly classi ed by the rule during training Speci city is the number of attribute-value pairs in the antecedent part of the rule Rules containing a larger number of such pairs that match a test object are considered to be more speci c..

use strict; use CGI;

The Security Migration Editor appears, shown in Figure C-4. The snap-in works like all other MMCs where you can use the Console menu to map accounts. Use the Help menu for more information about the Security Migration Editor.

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