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A CGI script is executed only after it passes all the security checks performed by suEXEC suEXEC also logs the script request in its log file The log entry for my request looks as follows:

catch (Exception e) { System err println(e) ; try { if (pizzaServices != null) pizzaServices remove (); } catch (Exception ex) { throw new P2GException (ex toString ()); } try { conn close ( ) ; } catch (SQLException se) { throw new P2GException (se toString ());

If you are really interested in knowing that the script is running under the user s UID, insert a sleep command (such as sleep(10);) inside the foreach loop, which will slow the execution and allow you to run commands such as top or ps on your Web server console to learn the UID of the process running testpl You also can

.

try { price = Utilformat(price); } catch(ParseException pel { throw new P2GException(UtilgetStackTraceAsString(pe); } return price; }

change the ownership of the script by using the chown command; try to access the script via your Web browser after changing ownership, and see the error message that suEXEC logs For example, when I change the ownership of the testpl script in the ~kabir/public_html directory as follows:

I get a server error, and the log file shows the following line:

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The deliverOrder () method is used to assemble the final order and update the inventory: /**

[2001-03-29 16:00:22]: uid/gid (500/500) mismatch with directory (500/500) or program (0/500)

are observed among all the data variables under all the conditions Figure 91 shows an example of each data pattern Figure 91a shows two kinds of the spike pattern, one with the upward spikes only, and another with mostly the downward spikes There is an association between the data patterns and their probability distributions as shown by the histograms in Figure 91 Figure 92 shows the data plots and histograms for another set of data variables with the spike, random uctuation, step change and steady change patterns A spike pattern leads to a skewed probability distribution of data (see Figure 91a and Figure 92a) As shown in Figure 91a, an upward spike pattern leads to a right skewed distribution with a right tail, meaning that most data observations have values falling into the lower part of the data range and a few data observations spread over a part of larger values than those in the lower part A downward spike pattern leads to a left skewed distribution with a left tail, meaning that most data observations have values falling into the upper part of the data range and a few data observations spread over a part of smaller values than those in the upper part A random uctuation pattern leads to a symmetric, normal distribution as shown in Figure 91b and Figure 92b A step change pattern leads to a multimodal distribution as shown in Figure 91c and Figure 92c A step change with two dominant levels of values, as shown in Figure 92c, leads to a bimodal distribution A step change involving several distinctive levels of values, such as the step change shown in Figure 91c with one dominant level and a few other levels, leads to a multimodal distribution with more than two modes A steady change pattern as shown in Figure 91d and Figure 92d leads to a uniform distribution A sine-cosine wave with noise pattern may lead to a normal distribution if there is much noise as shown in Figure 91e, or a uniform distribution if there is little noise and the sine-cosine wave is well formed Based on the observation of the ve data patterns and the association of the data patterns with their corresponding probability distributions, we expect to observe ve types of probability distributions for the data variables in the collected data of the Windows performance objects described in 7:.

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