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Using MythGallery is a simple and straightforward affair. However, accessing MythGallery through anything other than the frontend client is not. The remainder of this chapter describes how to access your MythGallery repository through the MythWeb interface so you can view it with any Web-enabled browser.
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Illustrated TCP/IP by Matthew G. Naugle Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471196568 Pub Date: 11/01/98
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As seen in previous sections, a linear cyclic code Ccyc (n, k) generated by the generator polynomial g(X ) = 1 + g1 X + + gn k 1 X n k 1 + X n k is spanned by the k code polynomials, g(X ), Xg(X ), . . . , X n k g(X ), which can be represented as row vectors of a generator matrix of dimension k n: g0 g1 g2 gn k 0 0 0 0 g g g gn k 0 0 0 1 n k 1 . G= (19) . . . . . . . . . . . . . . 0 0 g0 g1 g2 gn k where g0 = gn k = 1. This generator matrix is not of systematic form. In general, and by operating over the rows of this matrix, a systematic form generator matrix can be obtained. Example 3.3: For the linear cyclic code Ccyc (7, 4) generated by the polynomial g(X ) = 1 + X + X 3 , determine the corresponding generator matrix and then convert it into a systematic generator matrix. In this case the matrix is of the form 1 1 0 1 0 0 0 0 1 1 0 1 0 0 G= 0 0 1 1 0 1 0 0 0 0 1 1 0 1 Linear row operations over this matrix can be carried out to obtain a systematic form of the generator matrix. These row operations are additions and multiplications in the binary eld GF(2). Thus, and by replacing the third row by the addition of the rst and third rows, the matrix becomes 1 1 0 1 0 0 0 0 1 1 0 1 0 0 G = 1 1 1 0 0 1 0 0 0 0 1 1 0 1 And replacing the fourth row by the addition of the rst, second and fourth rows, the matrix becomes 1 1 0 1 0 0 0 0 1 1 0 1 0 0 G = 1 1 1 0 0 1 0 1 0 1 0 0 0 1 This last modi ed matrix G generates the same code as that of the generator matrix G, but the assignment between the message and the code vector spaces is different in each case. Observe that the modi ed and systematic generator matrix G is the same as that of the linear block code Cb (7, 4) introduced in 2.
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Effective alert handling must also include the capability to forward alerts to other dashboard users. This facilitates effective communication to address a situation that may require the attention of several people within the organization.
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Binary Weighted Method The binary weighted method of item scaling involves the use of items that are initially scored in a binary fashion, and then weighted according to some scaling scheme by their supposed importance for the construct. For example, a personality characteristic might be noted as either present or absent (binary), and then a score for that item is added to the scale total that re ects the weighting of that characteristic. One well-known example of such a scaling method is the Holmes and Rahe (1967) Social Readjustment Rating Scale, which asked respondents to indicate whether certain stressful life events had occurred in the recent past and then weighted these binary responses according to Life Change Units derived by the test authors. Thus, the death of a spouse counts for 100 points in the total Life Change Unit score, whereas a traf c violation counts for 10 points. In the Holmes and Rahe scale, these weights were derived by survey; in other applications, weights are sometimes derived empirically, using regression coef cients, factor loadings, or discriminant function weights. Unlike the binary summative method, the assumption of the binary weighted method is that not all items are comparable indicators; some are assumed to be more important than others and thus are assigned greater weight in determining the nal scale score. In principle, this may seem congruent with theoretical assumptions; in practice, however, experimenterassigned weights often appear to make little difference in the nal result. For example, Skinner and Lei (1980), in studying the Social Readjustment Rating Scale (Holmes & Rahe, 1967) previously described, found that the total Life Change Unit score correlated .97 with an unweighted unit scoring ( present-absent) of the questionnaire. Other studies have suggested that item weightings based upon regression coef cients or factor scores tend to correlate highly with unitweighted versions of the scale, with these correlations nearly always higher than the reliability of the scale. Thus, although the binary weighted scaling method may appear to offer the opportunity to capture true score variance more precisely than the binary summative method, it is not clear that it typically does so to a signi cant extent. Any slight gain that does occur may be offset by greater complexity in scoring and potential scorer reliability problems. Guttman Scale Method The Guttman scale is a unidimensional scaling procedure in which items have a monotonic, deterministic pattern. The basic concept is that any individual who endorses an item on one scale will also endorse items lower on the scale. Thus, the scale is deterministic in that, if the evaluator knows how a person answered one item on a scale, the evaluator knows
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losses. In particular, these procedures allow one to make full use of any partial data one may have. As we shall see in the following pages, making use of partial data often proves to be a tremendous advantage. A similar concern in the early days, especially with respect to data imputation, was something along these lines: How can you say that this imputed value is what the person would have given if he or she had given us data It sounds like magic. Well, it would be magic if it were true. That is why we always tell people not to focus on the imputed values themselves. We do not impute a value because we are trying to fathom what an individual would have said if he or she had given us data. That would typically be impossible. Rather, we impute in order to preserve important characteristics of the whole data set. That is, we impute to get better estimates of population parameters (e.g., variances, covariances, means, regression coef cients, etc.) and distributions. As it turns out, this is a very possible goal. Any good procedure will yield unbiased and ef cient parameter estimates. By unbiased, we mean that the expected value of the parameter estimate (e.g., a b weight) is the same as the true population value. By ef cient, we mean that the variability around the estimated value is small. A second characteristic of a good missing data procedure is that it provides a reasonable estimate of the variability around the parameter estimate (i.e., standard errors or con dence intervals).
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You often end up creating scripts where you need more than one outcome depending on the properties of an object. As an example, when you display the results of a quiz, you display a different message depending on the player s score. When you need to evaluate more than one outcome, you use the elseif action and the else action.
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Sub UpdateChart(Item) Updates the chart using the selected dropdown item Dim TheChart As Chart Dim DataSheet As Worksheet Dim CatTitles As Range, SrcRange As Range Dim SourceData As Range Set TheChart = Sheets( Chart1 ) Set DataSheet = Sheets( Sheet1 ) With DataSheet Set CatTitles = .Range( A1:F1 ) Set SrcRange = .Range(.Cells(Item + 2, 1), _ .Cells(Item + 2, 6)) End With Set SourceData = Union(CatTitles, SrcRange) With TheChart .SetSourceData Source:=SourceData, PlotBy:=xlRows .ChartTitle.Left = TheChart.ChartArea.Left .Deselect End With End Sub
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In 11, I present some other sorting routines and compare them in terms of speed.
Ego Orientation (+) Approval of unsportsmanlike play /cheating (+) Approval of aggression (+) Likelihood to aggress (+) Intention to engage in unsportsmanlike play (+) Antisocial behavior in sport ( ) Sportspersonship (some dimensions) ( ) Moral functioning (multiple dimensions) ( ) High-stage moral reasoning Task Orientation (+) Sportspersonship (some dimensions) (+) Moral functioning (+) Prosocial behavior (+) Civic responsibility (+) Moral identity ( ) Unsportspersonlike attitudes Dunn and Dunn (1999), Lee et al. (2001), Lemyre et al. (2002) Gano-Overway et al. (2005), Stornes and Ommundsen (2004) Kavussanu and Ntoumanis (2003) Kavussanu (in press) Duda et al. ( 2004), LaVoi et al. (2004) LaVoi et al. (2004) Duda et al. (1991), Stuntz and Weiss (2003) Duda et al. (1991), Lee et al. (2001), Kavussanu and Ntoumanis (2003) Duda et al. (1991), Kavussanu and Roberts (2001), Stornes and Ommundsen (2004) Stephens and Kavanagh (2003) Stuntz and Weiss (2003) Kavussanu (in press), Sage et al. (in press) Dunn and Dunn (1999), Lemyre et al. (2002), Stornes and Ommundsen (2004) Kavussanu and Ntoumanis (2003), Kavussanu and Roberts (2001) Proios et al. (2004)
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