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Once you have the unread message count, you need to use the XML::RSS module to produce the feed. Listing 8-5 gives the entire script an airing and shows how this works. Listing 8-5: Producing a New Mail Count in an RSS Feed

cable) long enough to reach between your computer and the Ethernet jack located on a nearby wired/wireless router or on a nearby wall (such as in your of ce, hotel room, and so forth).

Third, while for Furby this promulgation of a self-determining subject indicates a repressive role of psychology s social control interventions, Baistow takes this one step further to show a more productive potential of psychology s self-control ideologies Drawing on Nikolas Rose s (1992) extension of Foucauldian analysis to psychology, Baistow (2000) shows how, for example, increased senses of internality could eventuate in challenges to the status quo, such as black civil rights protests and the rise of black militancy In these cases, increasingly widespread notions of locus of control introduced as solutions to problems of disadvantaged groups may have helped to make possible empowerment talk, now commonplace in political rhetoric in the USA and the UK in recent years and a seemingly paradoxical objective of government policy and professional activities (p 112) Contrary, then, to being overly individualized and depoliticized psychological notions of control, locus of.

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mandatory for food safety. In small food workshops, they can per se be the foundation of so-called light HACCP. It may be assumed in extremis that a well-trained person with access to guidance is able to implement inhouse HACCP methods.

Figure 12-1

The unsupervised classi cation of images [1, 2] can be viewed as a simple computational procedure that uses the results of the preliminary similarity analysis, namely the computed similarity degrees between the new (unknown) image and all existing images from the image base. For classi cation purposes, these results have to be sorted in descending order of the similarity degrees for each pair of images. Then, according to a preliminary, assumed reasonable threshold for the similarity degree, the new (unknown) image is classi ed as belonging to the class of the most similar image or rejected as non-belonging to any known class of images. Now it becomes clear that in order to achieve a satisfactory solution to this problem we rst need to extract the most representative characteristics (features) from the original images in appropriate numerical form that corresponds to the speci c of this image. We can view each separate image as a kind of data cloud in the three-dimensional RGB space, which has its speci c density distribution, shape, and location in this space. All three correspond in some indirect way to the diversity and the overall ratio of the colors in the original two-dimensional image. Therefore, the problem of computing the similarity degree between a given pair of images becomes a problem of evaluating the difference between the respective features of the three-dimensional data clouds in the RGB space. It seems natural to assume that the rst (and probably the most important) feature that can be extracted from a given data cloud is its center of gravity (COG), de ned as the vector CG CG1 ; CG2 ; CG3 , which can be directly computed from the original raw data as follows: CGj

(2.39)

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where the approximation is based on the error probability for the BSC being a small number p 1. In order to determine the error-correction capability of a linear block code Cb (n, k), an integer number t that ts the condition 2t + 1 dmin 2t + 2 (42)

8.1 (a) There are at least seven cycles of length 4; the 1 s involved in these cycles are seen in matrix H below: 0 1 0 1 0 1 1 1 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 0 H= 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1

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