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makes Apache look for /home/joe/public_html (assuming /home/joe is joe s home directory) If the directory exists, the index page for that directory will be sent to the requesting client One way to add CGI support for each user is to add the following configuration in the httpdconf file:

<Directory ~ /home/[a-z]+/public_html/cgi-bin > Options ExecCGI AddHandler cgi-script cgi pl </Directory>

SIDWalker (security identifier) refers to a collection of administrative tools that help you access control policies for both Windows 2000 and NT Servers. With SIDWalker, you can modify access control lists (ACLs). This is no small task. The process of changing ACLs requires careful planning and analysis. So, why would SIDWalker be useful Primarily, you can use SIDWalker to clean up ACLs of merged groups and migrated groups in an interforest migration. The process of integrating two forests is not a simple task because new SIDs must be assigned to the merged or migrated groups. Fortunately, SIDWalker can be used to aid in this complicated task. SIDWalker is made up of three tools, two of which are command-line utilities and the third is an MMC snap-in. Let s look at the three tools that collectively make up SIDWalker.

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The task of learning rules from data has a long history in machine learning, with the rst rule learning algorithms traceable back to its early days. One of the main attractions of rule induction is that the rules are much more transparent and easier to interpret than, say, a regression model or a trained neural network. Rules are arguably the most comprehensive concept representation formalism. The task itself is to nd a set of classi cation rules from data that can be used for the prediction or classi cation of new unlabeled examples. Restrictions imposed by the language used to describe the data and the language used to describe the induced ruleset must be taken into account. The data description language imposes a bias on the form of data, and the hypothesis restriction language imposes a bias on the form of the induced rules. Induction takes place using a set of classi ed objects described in the data description language, with the aim of nding a set of rules in the hypothesis language that is both consistent (does not cover any negative examples) and complete (covers all positive examples).

Or you can use this configuration:

<DirectoryMatch /home/[a-z]+/public_html/cgi-bin > Options ExecCGI AddHandler cgi-script cgi pl </DirectoryMatch>

http://wwwyourcompanycom/~username/cgi-bin/ requests to /home/ username/public_html/cgi-bin/ and permits any CGI program with the proper extension (cgi or pl) to execute

Showaccs.exe is a command-line tool, shown in Figure C-2, that enables you to examine ACLs and memberships in local groups. Using showaccs.exe, you can examine the following: Specific file system directories The registry An entire system

3.1.1.1 AQ15 AQ15 [229] associates conjunctions in a generated rule with weights; those with the least weights are removed to avoid over tting the data. First, a complete matching is considered where all attribute-value pairs of a rule must match all values of the corresponding attributes for the example. If this is not possible, a partial matching is done, where some attribute-value pairs of a rule match the values of corresponding attributes. The best rule is chosen based on probability estimates. Each rule is weighted by the percentage of positive examples in the set of examples covered by it. The weights of rules of the same class are combined to a weight for the entire class and the class with the highest weight will be returned. 3.1.1.2 CN2 CN2 induces propositional classi cation rules from data [58]. The algorithm consists of two main procedures: the search procedure and the control procedure. The search procedure performs beam search to locate a single rule, employing the classi cation accuracy of the rule as a guiding heuristic. For this, conditional probabilities need to be estimated (the probability of a class

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