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As with the operating system, the application server requirements for the dashboard software must fall within the domain of supportable application servers within the organization. Some of the popular application servers are Windows 2000 and 2003, IBM Websphere, BEA Weblogic, Apache Tomcat, Apple WebObjects, JBoss, SAP AG Application Server, Sun Java Application Server, Oracle Application Server, Macromedia MX, and JRun Servers. There must be a match between the version of the application server on both sides the dashboard software requirements must also match what is supported within the organization.

Illustrated TCP/IP by Matthew G. Naugle Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471196568 Pub Date: 11/01/98

Corrections made in this manner are thorough and can run for great lengths of time especially on particularly outdated distributions and packages, or machines working with conservative system specifications. On a 2.2 GHz AMD Athlon 64 processor with 1 GB of DDR400 RAM, the aforementioned process was timed with the following results:

Steganography involves hiding data in an overt message and doing it in such a way that it is difficult for an adversary to detect and difficult for an adversary to remove. Based on this goal, three core principles can be used to measure the effectiveness of a given steganography technique: amount of data, difficulty of detection, and difficulty of removal.

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An optimal (shortest expected length) pre x code for a given distribution can be constructed by a simple algorithm discovered by Huffman [283]. We will prove that any other code for the same alphabet cannot have a lower expected length than the code constructed by the algorithm. Before we give any formal proofs, let us introduce Huffman codes with some examples. Example 5.6.1 Consider a random variable X taking values in the set X = {1, 2, 3, 4, 5} with probabilities 0.25, 0.25, 0.2, 0.15, 0.15, respectively. We expect the optimal binary code for X to have the longest codewords assigned to the symbols 4 and 5. These two lengths must be equal, since otherwise we can delete a bit from the longer codeword and still have a pre x code, but with a shorter expected length. In general, we can construct a code in which the two longest codewords differ only in the last bit. For this code, we can combine the symbols 4 and 5 into a single source symbol, with a probability assignment 0.30. Proceeding this way, combining the two least likely symbols into one symbol until we are nally left with only one symbol, and then assigning codewords to the symbols, we obtain the following table:

LATENT CLASS ANALYSIS 664 The Concept of a Latent Class 664 The LCA Mathematical Model and Related Issues 665 The Use of Parameter Restrictions 667 Model Selection and Goodness-of-Fit 668 Assessing the Quality of Categorical Measures 669 A LATENT CLASS EXAMPLE: ADOLESCENT DEPRESSION 670 The Mathematical Model and Parameters Estimated 670 The Five-Class Solution 671 Examining the Effect of Gender 672 EXTENSIONS OF LCA TO REPEATED MEASURES 673 Latent Markov Models 674 Latent Transition Analysis 674

needed in sport. The point of the model is to clarify the objectives for mental training programs by emphasizing that mental skill foundations, personal development abilities, and team skills, along with performance skills, are key mental training targets for sport psychology consultants. As discussed in the next section, the mental training process includes many different approaches that consultants may adopt as they target specific mental skills or sets of skills for enhancement. A FRAMEWORK FOR UNDERSTANDING MENTAL SKILLS TRAINING IN SPORT A framework for understanding mental skills training in sport is shown in Figure 13.2. The targets for mental training are foundation, performance, personal development, and team skills. The process of mental training includes the philosophy, model, strategies, and techniques that define the consultant s approach to enhancing mental skills. This process is mediated by the interpersonal and technical effectiveness of the consultant. The two arrows on each side of the framework represent the influence of physical training and the socialcultural influences of sport and society on the mental

evolving fuzzy modeling approach eTS [5] (by Angelov & Filev). The PL concept is based on unsupervised clustering and hence is a natural candidate to nd rule-base structures in adaptive fuzzy modeling procedures. Another variant is the FLEXFIS approach [41] (FLEXible Fuzzy Inference Systems), which exploits an evolving version of vector quantization [40] including an alternative winner selection strategy and a splitand-merge procedure for rule evolution and antecedent update, which is connected with recursive weighted least squares for local learning of rule consequents (achieving a high exibility and convergence of the consequent parameters).

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