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While the Internet, with its chat rooms, electronic banking and payments, phishing and pharming, malware, and spyware, has certainly added a dimension to the crime, the basics are also still employed by identity thieves: common theft, mail theft and change of address, Dumpster diving, database and network hacking, and insider theft. The purposes for which identity thieves can use the stolen personal identifier information has been exacerbated by the Internet, as online credit applications, purchases, bank transfers, and the like eliminate the need for face-to-face contact. The findings presented here must be used to improve and increase proactive measures that law enforcement and fraud investigators use to combat identity theft, including investigation, prevention, detection, and prosecution. The information concerning offender characteristics and modus operandi should be used in law enforcement training. The picture that this study paints of identity theft offenses and offenders should be used in prioritizing and managing cases and resources. Law enforcement executives will be able to use this information to develop policy, allocate resources, and advocate training. Secret Service Case Classification Exhibit 19.1 displays the most frequent primary case types represented by the 517 cases. Fifty percent of the cases were classified as Fraudulent Use of Account Numbers, Fraudulent Access Device Applications, Stolen Bank Issued Cards, Financial Institution Fraud (FIF) Involving Check Fraud, Counterfeit Bank Issued Credit Cards, Counterfeit Commercial Checks, and Counterfeit State Driver s licenses. A quarter



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public StockTradeResult(String status, String cName, int nt, String ss) { statusMsg = status ; custName = cName ; numberTraded = nt; stockSymbol = ss; } public String getStatusMsg() { return public String getCustName() { return public int getNumberTraded() { return public String getStockSymbol() { return }





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It turns out that the redundancy property alone does not provide edge removal based construction of a planar and connected topology Refer to Figure 155b for an example The depicted graph satis es the redundancy property Planarity can be obtained by removing either the edges (ui , vi ) or the edges (ui , w) However, in both cases either the nodes vi or the node w get isolated from the remaining network On the contrary, the fact that any UDG can be planarized (for instance, by applying the Gabriel graph method) reveals that these graphs have besides the redundancy an additional structural property that assures that an example like depicted in Figure 155b cannot be constructed An explicit formal graph theoretic de nition of this property is still missing.

based on the results of the evaluation, follow one of the four following paths:

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The data summary report is used to examine preprocessed data in order to nd and correct inconsistencies in the data that can reduce the validity of statistical inference. Unlike other procedures, there are no golden standards for this step. It is a good practice to evaluate the data summary report before and after data preprocessing. Approaches used to inspect the data include the evaluation of a histogram to provide information about data distribution in one microarray, a boxplot of the whole data set to check the similarities of all data distributions, and the evaluation of correlation coef cient maps (see Fig. 4.7) to check consistency among arrays. Correlation coef cient heat maps plot the values of correlation coef cients between pairs of arrays. For a given pair of arrays, #i and #j, their expression pro les are observed as vectors and the correlation coef cient between the two vectors is plotted as two pixels in symmetrical positions (ij) and (ji) in the heat map (the magnitude of correlation coef cient is indicated by the color of the pixel). Correlation coef cients are normally expected to be high, since we assume that the majority of gene expression levels are similar in different arrays. A horizontal (and the corresponding vertical) line in a heat map represents all of the correlation coef cients between a given array and all other arrays. If a line has a near-constant color representing a very low value, we should suspect a problem with the corresponding array.

A localized rule in order to decide which one of the two intersecting edges (a, b) or (c, d) has to be removed can be described as follows For edge (a, b) compute the maximum over the angles acb and adb For edge (c, d) compute the maximum over the angles cad and cbd The edge with the larger maximum value is removed For instance, in Figure 155a the angle = acb is the maximum one in the quadrilateral (a, b, c, d) Thus, the edge (a, b) will be removed Ties, that is, both edges have the same maximum value, are broken by using a given edge ordering and removing the smaller edge In the following, the described construction method will be referred as angle-based direct planarization (ABDP) Since for each intersection at least one edge is removed, it is obvious that the resulting topology is planar.

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