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Framework for Fraud There are supporting factors within a nonprofit or any organization that facilitate opportunities for fraud: Motivation: People have to want to engage in fraudulent activities and believe that there will be few if any consequences if they are ever caught The occasion for the fraud to take place: In other words, there is an open door or opportunity to engage in fraudulent activities Sloppy or nonexistent internal controls: It s easier to cover one s tracks when there are no protocols or records kept Access to electronic databases and online checking: Often, electronic records will need to be altered to cover the fraud Individuals who have access to sensitive databases are in a position to set up sham accounts and issue checks to themselves Organizational culture: The environment either denies the possibility of anyone committing fraud, or even more insidious, a culture that transforms staff and volunteers into martyrs How many times have you heard people say, We work so hard here for so little money Even more serious is the Executive Team s enabling of this dysfunctional attitude We pay these people so little, we really can t expect them to agree to these requirements A board of directors that is asleep at the wheel: How often do we hear stories about fraud committed at nonprofit organizations only to learn that the board knew nothing about it and suspected nothing SOX requirements will provide individuals with the opportunity to report waste, fraud, or abuse without fear of retaliation Document preservation will facilitate more efficient record keeping and provide auditors (external and internal) with better data for their review The overall strengthening of the internal controls that comes with the implementation of best practices will further reduce the opportunities for fraud, and will introduce a change in the organizational culture The Executive Team s Role in Setting the Organization s Values and Shaping its Culture In 3, we discussed the elements of organizational culture and how this invisible element shapes the profile of a nonprofit We examined how an organization s culture can contribute to its dysfunction Organizational culture is not set in stone it can be changed and in order to implement Sarbanes-Oxley requirements and best practices, often the ssence of a nonprofit s culture needs to change The primary change agents are the board and the Executive Team These leaders begin the process and use strategies along the way that bring about a sea change in the way in which a nonprofit does business, looks at its programs, its clients, its donors, its staff, and its future.

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The number of wavelengths on a single fiber The serial bit rate per wavelength The physical type of fiber The amplification mechanism Erbium-doped fiber amplifiers (EDFAs), for example, do not require conversion of the optical signal to electrical format before it an be regenerated or amplified The number of nodes through which the signal passes before it reaches the egress node or before regeneration. Making DataMatrix In .NET Using Barcode creation for ASP.NET Control to generate, create Data Matrix .Related: UPC-E Generation .NET , .NET ISBN Generation , Intelligent Mail Generating .NET

As more business transactions move online, it has become imperative to provide the QoS assurance on the Internet which does not currently exist This chapter describes two new resource reservation protocols, I-RSVP and SI-RSVP, to guarantee the end-to-end delay of instantaneous jobs The end-to-end delay guarantee through I-RSVP and SI-RSVP is veri ed in the testing of I-RSVP and SI-RSVP in comparison with the best effort model through the small-scale network simulation and the large-scale network simulation I-RSVP and SI-RSVP demonstrate their additional advantage to the best effort model in reventing traf c congestion. Making EAN13 In Java Using Barcode creation for Java Control to generate, create EAN13 image in Java .Related: Creating Intelligent Mail .NET

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of a given attack, are useful for detecting events of the attack Such data variables are identi ed for each of the eleven attacks in s 8 11 A feature is a measure of a property which exists in a sample of data observations for one or multiple data variables Only univariate mathematical/statistical features features of a data sample from one data variable are investigated in Part III These univariate mathematical/ statistical features include the statistical mean in 8, the probability distribution in 9, the autocorrelation in 10, and the wavelet-based signal strength in 11 covering the Haar wavelet, the Daubechies wavelet, the Derivative of Gaussian wavelet, the Paul wavelet and the Morlet wavelet These wavelets are used to extract the time-frequency signal changes associated with the data patterns of step change, steady change random change, spike change and sine-cosine wave with noise s 8 11 provide mathematical/statistical methods of extracting the mean, probability distribution, autocorrelation, and wavelet features from attack and normal use data Among the four features, the distribution feature gives a more comprehensive picture of a data sample than the mean feature Both the wavelet feature and the autocorrelation feature reveal relations of data observations over time The autocorrelation feature focuses on the general autocorrelation aspect of time series data, whereas the wavelet feature focuses on special forms of time-frequency data patterns Both various wavelet forms and various probability distributions are linked to certain data patterns The distribution feature describes the general pattern of the data, whereas the wavelet feature reveals time locations and frequencies of special data patterns Hence, the wavelet feature reveals more special data features than the distribution feature and the autocorrelation feature Note that there are other types of univariate features (eg, features extracting other trends or patterns of data) as well as multivariate features (ie, features of data from multiple data variables) which are not investigated in Part III but may be useful in revealing data characteristics of various attacks If one or more events of a given attack cause a signi cant change in a speci c feature of a data variable, this change is considered a data characteristic of the attack s 8 11 describe statistical tests to identify a signi cant change in a given data feature, and reveal data characteristics of eleven attacks in the mean, probability distribution, autocorrelation, and wavelet features If a speci c data characteristic appears during a given attack but not during other attacks or normal use activities, this data characteristic is considered a unique data characteristic of that attack and can be used to uniquely detect and identify this attack Note that an event may manifest through more than one data characteristic (eg, more than one data variable or more than one feature of a data variable) The identi ed attack characteristics in the mean, distribution, autocorrelation and wavelet features are used to uncover the similarity and difference of the attacks The data characteristics of attack and normal use activities discovered in Part III are essential to building attack detection models for detection accuracy and earliness Attack detection models are covered in Parts IV VI.

Why It s Needed When an analog video signal such as a National Television Standards Committee (NTSC) or Phase Alternate Line (PAL) signal is digitized, this is usually done in accordance to the CCIR 601 standard This standard accounts for a true and high-quality representation of the video signal at the cost of high data rate requirements Figure 172 depicts the data rates of two digital video representations and contrasts them with the bandwidth available over some popular networks As can be seen in the upper row, the digital format CCIR 601 requires a bandwidth of approximately 140 Mbps Even if the video is subsampled to a lower resolution for example, the Common Intermediate Format (CIF) with 352 ixels and 288 lines and only every other field is transmitted, the resulting data rate is no lower than about 35 Mbps. Paint UPC-A In Java Using Barcode maker for Java Control to generate, create UCC - 12 .Related: 

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