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Figure 4.3 An example of a use case for a login process. A new user would rst need to register successfully before logging in. A registered user would need to login successfully, and then she would be redirected to her account information.
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How do the people responsible for the information technology in an organization relate to the needs of those seeking to improve knowledge exchange and transfer through computer technologies What issues consume IT s attention today, and how do those issues affect its ability and willingness to work with knowledge-networking advocates It s important that we understand some practical and cultural realities about the people charged with IT responsibilities before we assume that the implementation of the right technologies is simply a matter of asking that they be done. IT is a busy place, and the technologies required by an effective knowledge network can be complex.
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Microsoft provides a runtime infrastructure called .NET compact framework, which provides a subset of the .NET framework functionality customized to mobile terminals. .NET compact framework is based on an intermediate language that is compiled on demand before execution. Figure 7.7 depicts the .NET framework. .NET compact framework is similar to the standard framework but removes certain services and libraries that are not considered essential for mobile devices, thereby reducing its size and resource requirements, and making it appropriate for mobile resource constrained devices. The Common Language Runtime (CLR) provides a secure and robust execution environment. It provides functionality for memory management, multiple language support, exception management, threading, inheritance, and security. The CLR is composed of two components: the Common Type System (CTS), and the Execution Engine (EE). The CTS de nes the types supported by the execution environment (everything is an object) and speci es rules for compound types, such as class, struct, enum, and interface. The EE compiles MSIL into native code before execution (just-in-time compilation), handles garbage collection, handles exceptions, enforces code access security (sandbox), and handles veri cation (managed versus unmanaged). The Base Class Library (BCL) is similar to Java s system namespace, and it is used by all .NET applications. It provides support for IO, threading, database, text, graphics, console, sockets/web/mail, security, cryptography, COM, re ection, and assembly generation. The Data and XML component illustrated in Figure 7.7 provides additional support for database management and XML manipulation. The Web Services and User Interface components provide support to run and interact with web services and support for user interface management. The .NET framework de nes a common language speci cation (CLS) that is supported by all .NET languages. It is very similar to CORBA IDL (interface description language)
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It is a simple matter to write an iteration to obtain a GARCH updating forecast for several periods in the future. The long-run forecast is called unconditional volatility, and it is a constant, provided the regression coef cients satisfy the inequality: a1 + g1 < 1. Then conditionally heteroscedastic GARCH models are said to be a mean reverting with a constant unconditional variance. Given data on nancial returns it is customary to use the maximum likelihood (ML) method to estimate the GARCH(1, 1) model. For a discussion of the ML method, see Sections 4.4.4 and 6.5.1. Several computer programs are available for this purpose. McCullough and Renfro (1999) offer a benchmark computer program for testing if a computer program is correct. McCullough and Vinod (1999, 2003a, b) discuss the importance of numerical accuracy in these calculations, since the results are sensitive to starting values and various settings in the program. The authors note that different software packages give different results and emphasize a need for benchmarks. We will explore these numerical issues in greater depth in 9. The Ljung-Box test with 15 time lags (k = 15) is popularly used for testing the adequacy of the model. Ljung and Box (1978) show that under the null
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Are you constantly developing systems or do you have four or five concepts that you follow I ve got about four concepts. I just keep going down those same tunnels looking for some cheese. My mind is in one mode. I keep mining the same approaches. Do you think there is an advantage in diversification (You can diversify over time, markets and strategies.) I don t like to diversify. If you have a trend-following system that catches trends, you probably need some diversification. I just trade S&P s and bonds. That s it, nothing else. I have some stuff that s held up pretty well after a long time period so I m just going to trade that. My only diversification would be various entry techniques. Where do you think it s all going I talked to a floor trader that trades in the S & P pit for a bank and he was concerned, a lot of guys in Chicago are worried that the pits are not going to be around forever. When you look across the world you see the Sydney Futures Exchange has gone electronic, it s happened in Europe; my prediction is that the Chicago Board of Trade is going to become a very nice shopping center. There will be some warehouse full of computers and that will be the future of the commodity exchange. So you have four or five different basic systems I use approaches. I use volatility break out. I use pattern recognition. I use a little momentum and I rely a lot on the fundamental set ups of the market. Do you have a mechanism that you screen for potential set ups and then you go in with discretion Not much discretion, it s pretty clear-cut. Like today, I had a specific buy at yesterday s high and when the price went to that point the signal said to go long, so I went long. What kind of stop do you put on that Stops are interesting. What I ve found to be the best type of stop, it s not some magical price point. I don t believe that. That s a bunch of bunk. I think the purpose of the stop is to protect your equity. My stops are based on dollar amounts. I know that I cannot lose more than x number of dollars, I don t care if there s a chart point real close or if that supposedly valid chart point is a mile away. It s not about my money, my money can only be protected absolutely, and it s much
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These results lead to more than mild concern. As the FULTON and LAVOTE examples demonstrate, these discrepancies are not problems with EI or EzI per se, but rather, are more likely to be a result of differences between Gauss s implementation on HP-Unix and Windows operating systems than a direct result of the small differences in EI code between these two platforms. Ironically, although the published results are from the EI version of the program, the EzI replications are more faithful to the published results in these two examples, whereas the Gauss replications are not. In four examples; FULTON, KYCK88, LAVOTE, and SCSP, note that the CML search algorithm converged at a higher local optimum in the EI v1.63 replications than in the v1.0 replications, indicating, at least in some small part, improvement of the overall EI algorithm over time. However, these results show evidence of implementation dependence across the platform and version of King s ecological inference solution. Users of the program should use the most current version of EI and all programs in general, and if concerned about implementation dependence, should run the analysis on the EI and EzI versions of the program.
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