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Researchers at IBM have considered this issue and have completed a characteristic study in this area.8 They found that a major issue arises in trying to identify and measure software development productivity, cost, and size. In their effort, they characterized productivity in terms of the number of lines of code produced per person per hour. However, it is clear that many different parameters can in uence the results. These include differences in the choice of programming language used (some being more favorable with respect to the quantitative measure than others), alternative methods for program development, and the effort placed on collecting data to demonstrate measured improvement. One nding was that small project teams produced source code with more positive credit for functionality than did large teams in a comparable amount of time. In addition, many studies don t effectively describe how they address variability in the performance of individual software writers, although it is clear that different individuals can possess markedly different capabilities. This is an indication that there are real artists in software development who are head and shoulders above the ordinary. Consequently, to date it has not been possible to accurately quantify the level of productivity improvement that could be expected as software methods advance, and neither has it been possible to come up with truly effective measures of individual or collective contributions.



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patterns, practices, and specific forms of activity that would be indicative of identity theft. Financial institutions and creditors must then use these red flags to adopt procedures to mitigate the risk of identity theft and other financial crimes. Specifically noted in the act is the risk of account takeovers by identity thieves using fraudulent changes of address. The act states that





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messages are transmitted in the network Queries can be ooding-based where a query is ooded to every node in the network Alternatively, they can be ERS based where a node does not relay a query that it can resolve Currently available query processing systems are summarized below: TinyDB [69]: a query processing system for extracting information from a network of TinyOS sensors TinyDB provides a simple SQL-like interface to specify the data, along with additional parameters such as the rate at which data should be refreshed much as in traditional databases Given a query specifying data interests, TinyDB collects data from nodes in the environment, lters and aggregates them TinyDB does this via power-ef cient in-network processing algorithms.

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Like IRQs, I/O address assignments can be viewed on a Windows PC through the Device Manager or the System Information applet. Figure 5-6 shows the Computer Properties window with the I/O addresses resources displayed. This list displays the I/O addresses assigned to the PC s devices. You might see entries such as In use by unknown device or Alias to for devices requiring additional space.

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Some key features of TinyDB are as follows: TinyDB provides metadata management, provides a declarative query language, supports multiple query resolution on the same set of nodes and supports different levels of in-network aggregation It also includes a facility for simple triggers or queries that execute some command when a result is produced Cougar [132]: a query layer for sensor networks which accepts queries in a declarative language that are then optimized to generate ef cient query execution plans with in-network processing which can signi cantly reduce resource requirements Active query forwarding scheme (ACQUIRE) [104]: this aims at reducing the number of nodes involved in queries In this scheme, each node that forwards a query tries to resolve it If the node resolves the query, it does not forward it further but sends the result back.

Necessity represents the degree to which the evidence considered to date supports the truth of a proposition or datum; possibility represents the extent to which the evidence considered to date fails to refute a proposition or datum Initially, in the lack of any data supporting or refuting a datum, its necessity is 0 and its possibility is 1 As more and more supporting data are considered the necessity of a datum tends to increase monotonically, always subject to the restriction that the necessity of a datum must be equal to or less than its possibility; as more and more refuting data are considered, the possibility of a datum tends to decrease monotonically In systems that maintain two truth values, possibility and necessity, a datum of which we have no knowledge at all has necessity 0 and possibility 1.

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