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The MPEG encoder is the first element that manages digital video. This component receives inputs from the NTSC/PAL receivers (off-the-air content), premium feed/content (VPNs, direct link, cable) and inputs from the satellite receivers. Up to that moment, the content has not been encoded, and any attempts to steal the content would require a very large container or a prolonged connection to the system. MPEG encoders would require a connection to the network to allow for administrative and support activities. This connection can be abused at the operating system or application. The operating system of the MPEG encoder must be properly patched to avoid common security exploits and viruses. Additionally, the network access should be controlled to avoid unauthorized access to this system. VLANs can be set up for the MPEG encoder and transcoder to be able to communicate with the IP encapsulator on a dedicated VLAN, leaving another VLAN for administrative functions. In some cases the NTSC/PAL receivers and satellite receivers would be on the same computer rack as the MPEG video encoders. A direct connection would be possible without using the network. Servers hosting the encoder and transcoder applications must be configured to remove any removable media or USB connector. This will reduce the chances of removal of digital assets. Table 6.3 includes references to MPEG and video transcoder countermeasures.

Making Predictions The third objective of this exercise was to develop a predictive model to classify patients into two categories: (1) patients that will develop diabetes in the next ve years and (2) patients that will not develop diabetes in the next ve years Since the response variable (Class) is categorical, we must develop a classi cation model There are many alternative classi cation modeling approaches that we could consider Since there is no need to explain how these results were calculated, selecting a method that generates explanations or con dence values is not necessary We decided to select k-Nearest Neighbors (described in Section 73) and neural networks (described in Section 75) approaches to build the models For both types of models, an experiment was designed to optimize the parameters used in generating the models.

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JSPs were designed to contain static HTML. The Use JSPs refactoring (found later in this chapter) will help you rework your servlets that are stuck in this AntiPattern, so they take advantage of JSPs. Look for ways to isolate to JSPs the HTML that is being built in your servlets. The static HTML text can be captured in a way that is much easier to maintain and understand by keeping it in a JSP instead of writing it to the response in a servlet.

Since we are interested in both speci city and sensitivity of the results, the experiments will measure both scores The models will be tested using a 10% cross validation (described in Section 715) The analysis performed so far is critical to the process of developing prediction models It helps us understand which variables are most in uential, as well as helping us to interpret the results Table 910 illustrates the optimization of the kNN (k-Nearest Neighbors) model using different descriptor variables with an optimal value for k, and using the Euclidean distance The resulting model accuracy is displayed using the format sensitivity/speci city along with the best value of k Table 911 shows a section of the optimization of the neural network models, using different input variables, different numbers of iterations, and different numbers of hidden layers.

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