Examine what the different branches and layers of government do to address a policy using a current issue to illustrate it.
Complete a research essay on the government and national security. Remember this is being written for the president to read.
Here are the aspects and questions to address for this essay:
Identify a current issue being debated about American national security, and state a preferred solution to it and why it’s preferred (This is your thesis statement).
Explain two competing solutions to this problem while addressing the responsibilities of each level of government (federal, state, and local) and the three branches of the federal government (executive, legislative, and judicial).
Build the argument for why your chosen solution is preferable, what critics say about the solution, and finally why the critics are wrong.
Sample Solution
igure 2: Eigenfaces of the training set [26] 2) Calculate the eigenfaces from the training set, keeping only the M images that correspond to the highest eigenvalues. These M images define the face space. As new faces are experienced, the eigenfaces can be updated or recalculated. 3) Calculate the corresponding distribution in M-dimensional weight space for each known individual, by projecting their face images onto the âface spaceâ. After the initialization operations, there are carried out more operations in order to recognize new face images. 4) Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the eigenfaces. 5) Determine if the image is a face at all by checking to see if the image is sufficiently close to âface spaceâ. 6) If it is a face, classify the weight pattern as either a known person or as unknown. 7) (Optional) Update the eigenfaces and/or weight patterns. 8) (Optional) If the same unknown face is seen several times, calculate its characteristic weight pattern and incorporate into the known faces[24]. As mentioned earlier, there is a long list of methods that can be used for facial recognition. Four of them, i.e Eigenface Method, Correlation Method, Fisherface Method and the Linear Subspaces Method, are the most favorite. Below here, you can find the error rates of those four methods, considered pictures with close crop or the whole face. Figure 3: Graph and table of the result of an experiment with the four most used facial recognition techniques [1] As you can see, the Eigenface Method has the most errors, and the Fisherface Method the least. You can also see that the error rate is higher with images of close crops faces, compared to a full face image. This shows that it is harder for a facial recognition algorithm to recognize someone if their face is not fully shown in the picture and the features are thus not recognized. It also reminds us of the fact that facial recognition techniques are not completely accurate. Hopefully they will become more accurate in the future, so e.g crime can be prevented faster and better.>
igure 2: Eigenfaces of the training set [26] 2) Calculate the eigenfaces from the training set, keeping only the M images that correspond to the highest eigenvalues. These M images define the face space. As new faces are experienced, the eigenfaces can be updated or recalculated. 3) Calculate the corresponding distribution in M-dimensional weight space for each known individual, by projecting their face images onto the âface spaceâ. After the initialization operations, there are carried out more operations in order to recognize new face images. 4) Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the eigenfaces. 5) Determine if the image is a face at all by checking to see if the image is sufficiently close to âface spaceâ. 6) If it is a face, classify the weight pattern as either a known person or as unknown. 7) (Optional) Update the eigenfaces and/or weight patterns. 8) (Optional) If the same unknown face is seen several times, calculate its characteristic weight pattern and incorporate into the known faces[24]. As mentioned earlier, there is a long list of methods that can be used for facial recognition. Four of them, i.e Eigenface Method, Correlation Method, Fisherface Method and the Linear Subspaces Method, are the most favorite. Below here, you can find the error rates of those four methods, considered pictures with close crop or the whole face. Figure 3: Graph and table of the result of an experiment with the four most used facial recognition techniques [1] As you can see, the Eigenface Method has the most errors, and the Fisherface Method the least. You can also see that the error rate is higher with images of close crops faces, compared to a full face image. This shows that it is harder for a facial recognition algorithm to recognize someone if their face is not fully shown in the picture and the features are thus not recognized. It also reminds us of the fact that facial recognition techniques are not completely accurate. Hopefully they will become more accurate in the future, so e.g crime can be prevented faster and better.>
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