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Note that a critical component of the work ow system is an enterprise directory structure that can be scaled across the enterprise. We have seen that there is a need to hold details on people s access rights and passwords; the provision of call-centre, eMail and collaborative working applications require consistent addressing and numbering plans; elsewhere we describe how scheduling and other people-centric processes are critical elements of supply-chain management. In database terms, individual people act as unique principal keys to other information

We used to talk about two steps forward and three steps back but today it s more like 50 steps sideways and 2000 steps forward. Networked, social-based opportunities are so explosive today that when we pursue them we re flung forward at pace. James Governor

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I think we are all interested But, what if it is the occasional pen or mechanical pencil Still occupational fraud, but then so is looking out the window and daydreaming about fishing, if we want to get technical about it The issue of reporting is extremely important and is one, I believe, that warrants much further study What the study seems to imply, nagging questions aside, is that there may be a significant amount of occupational fraud, of at least some sort, in Canada Given these characteristics of fraud, we in the forensic profession may, as the saying goes, Be in a heap of trouble We know we have a huge problem on our hands: it probably measures in at least the hundreds of billions of dollars annually Every indication we have is that it is not shrinking much and may be growing.

Trends are relatively slow-moving changes in the marketplace that can occur for a variety of reasons, and businesses ignore them at their peril. Sometimes trends are business led and sometimes consumers lead the way. General fashions can change very quickly and go from one extreme to another whereas trends in clothing fashion tend to be slower and build upon themselves rather than ignore what went before.

37 We know from experience that most of it remains hidden and that it tends to continue unless stopped That is one of the more interesting aspects of occupational fraud it almost always keeps going The person robbing a bank does not stand there, unless they are truly stupid, for six hours and keep robbing it They would surely get caught They might go down the street and rob another bank or come back a week later and rob this one again, but they do not continue to rob the same one in a temporal sense Fraudsters, on the other hand, and especially occupational fraudsters, usually do precisely the opposite Once they have committed a fraud and not gotten caught, they gain a greater sense of confidence They may repeat it endlessly at a like amount or increase the amount of their next take to test the system.

Uncaught and absent some truly significant personal event to change their behavior, they may continue for years or decades Thus the bank robber is like a shark that swims up, takes a quick, violent bite, and swims away The occupational fraudster is more like the parasite that seeks to remain attached to the lucrative host for as long as possible, feeding constantly It may also be that some of these parasites travel in packs A survey conducted of members by the ACFE in March 2002 asked the following question: In your latest internal fraud case, how many people were involved The results were surprising, since many in the general population and many in the forensic field consider fraud to typically be the work of the lone perpetrator:38.

Both parts together obviously prove the theorem. The first part is carried out by showing that for the (precise) tuple in support(D), which determines the s (support(D)) value of X (t), there must be a corresponding (imprecise) tuple in closure(D) with a weight no less than that of . The second part is analogous. s The full proof can be found in Section A.8 in the appendix.

so that each j is updated conditional on the latest value of for the other components. This latest value will be at the current iteration for -values that have been updated previously, and at the previous iteration for -values that have not yet been updated. This process is repeated until the required number of samples from the posterior are obtained. An initial burn-in period of iterations is also required to achieve approximate convergence to the posterior. We illustrate the basics of using Gibbs sampling with owgraphs for a simple series model.

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