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For the collected data of the Windows performance objects described in 7, the same data screening procedure as described in Section 82 is performed to eliminate the data variables, each of which has the observations of the same value under all three conditions: the inactive, attack and norm conditions Each of the remaining data variables is analyzed to extract the autocorrelation feature and discover the utocorrelation change characteristics of the attacks in the following steps 1 For the 10-minute data under the inactive condition and the attack data for the entire attack period from Run 1 of the data collection: (a) Compute the lag-i autocorrelation coef cient of the inactive data and determine the statistical signi cance of the autocorrelation coef cient, for i = 1, , 10 If the autocorrelation coef cient for every one of the ten lags is statistically signi cant, the autocorrelation of the inactive data is considered high (AH) If the autocorrelation coef cient is not statistically signi cant for any of the ten lags, the autocorrelation of the inactive data is considered low (AL) If there is at least one but not all autocorrelation coef cients for the ten lags are statistically signi cant, the autocorrelation of the inactive data is considered medium (AM) (b) Repeat Step 1a for the attack data to determine if the attack data is considered AL, AM, or AH (c) Compare the autocorrelation levels of the inactive data and the attack data, if the autocorrelation level increases from the inactive condition to the attack condition, mark the autocorrelation increase, A+, as the autocorrelation change characteristic that occurs in this variable under this attack; if the autocorrelation level decreases from the inactive condition to the attack condition, mark the autocorrelation decrease, A , as the autocorrelation change characteristic that occurs in this variable under this attack; 2 Repeat Step 1 but replace the 10-minute inactive data and the attack data from Run 1 with the 10-minute inactive data and 10-minute norm data of text editing from Run 2 of the data collection to identify the autocorrelation change characteristics of the text editing norm 3 Repeat Step 1 but use the 10-minute inactive data and 10-minute norm data of web browsing from Run 3 of the data collection to identify the autocorrelation change characteristics of the web browsing norm For each attack, each autocorrelation change characteristic of the attack is examined to see if the same characteristic (the same variable with the same autocorrelation change) also manifests.

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The focus of this book is on the Sun Certified Programmer for the Java Platform Standard Edition 6 exam (SCJP 16), which is a prerequisite for taking many of the certification exams for the Java technology The goal of the programmer exam is to test practical knowledge of the Java language The exam tests for thorough understanding of both the syntax and semantics of the Java programming language The exam covers a wide variety of topics, as defined in the objectives for the programmer exam (see Appendix B, p 857) It covers everything from the basic syntax of the language to detailed knowledge of threading and the core APIs, such as the javalang package and the collections framework The need for real-world experience for this exam cannot be stressed enough It is next to impossible to pass the test without having some actual experience programming in Java Simply reading straight through this book is not recommended Readers should take time to try out what they have learned every step of the way Readers are encouraged to test their newly acquired knowledge using the review questions provided after every major topic Experimenting with the examples and working through the programming exercises in the book will serve to give the reader a much better chance of passing the test The exam is considered to be difficult, and requires a fair amount of studying on the part of the candidate When the reader feels ready for the exam, she should test her skills n the sample exam that is provided in the back of the book (Appendix F) This will give an indication of how well the reader is prepared for the exam, and which topics need fur-.

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eyeball merely visits vertices in topological order. Encode Bar Code .The implementation of the shortest-path algorithm for acyclic graphs is shown in Figure 1533 We use a queue to perform the topological sort and maintain the indegree information in the scratch data member Lines 1318 compute the indegrees, and at lines 20-25 we place any indegree 0 vertices on the queue We then repeatedly remove a vertex from the queue at line 30 Note that, if the queue is empty, the f o r loop is terminated by the test at line 28 If the loop terminates because of a cycle, this fact is reported at line 50 Otherwise, the loop at line 31 steps through the adjacency list and a value of w is obtained at line 34 Immediately we lower w's indegree at line 36 and, if it has fallen to 0, we place it on the queue at line 37 Recall that if the current vertex v appears prior to S in topological order, v must be unreachable from S Consequently, it still has D,, = co and thus cannot hope to provide a path to any adjacent vertex w We perform a test at line 8, and if a path cannot be provided, we do not attempt any distance calculations Otherwise, at lines 41 to 46, we use the same calculations as in Dijkstra's algorithm to update D, if necessary. AIM Code 128 In Java Using Barcode creation for .Related: Print Barcode Excel , VB.NET Winforms Barcode Generator , ASP.NET C# Barcode Generating

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