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In the last few years I have spoken at a number of seminars and conferences about how to get the best out of simulations One of the features of these events is the way in which simulation seems to have become synonymous with simulators and simulators have become seen as being largely computerbased equipment Of course, after a few minutes discussion, everyone in these audiences will tell you that they realise this is not true, but it is still interesting how the notion pervades most of the thinking about simulation now Not all that many years ago, I was asked to be a member of a steering group for a project commissioned by the Department for Education and Employment (Df EE) in the United Kingdom The aim of the project was to review developments in computer based assessment and training and to make recommendations on how the Df EE could support desirable developments The project team concluded that although computer based assessment had promised much, the economic and commercial realities of the world meant little of that promise had been delivered and was unlikely to be in the foreseeable future At the time, the steering group concluded that the project team were orrect Personal computers were slow, unreliable, with poor graphics capability and thinly spread around amongst users, whether individuals or organisations Many companies at the time who would have liked to use computers for assessment concluded that the capital and development costs were far too high to be contemplated About the same time, I was also part of a team that submitted a bid to the Df EE for funding to develop a crisis management simulator The funding committee agreed that the aim was worthwhile but did.

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Hamada and Scott (2001) discussed a CL model for distance learning courses They claim that learning is extricable intertwined with multidirectional activities such as work and play, and that learning is essentially a social activity They argue that position of learning is a process of applying it, because knowledge is temporary, developmental and socially and culturally mediated On these bases, they developed a collaborative learning and teaching (COLT) model to engage students The model required the students to conduct their own research for knowledge creation at local sites; connects students with different cultural backgrounds for direct cross-cultural interface; the collaboration for co-knowing is international; the students employ a much wider range of communication tools; the nal project is not only edited collaboratively, but also presented collaboratively and it intentionally creates a learning environment where participants need to manage uncertainty and uncertain knowledge In all, the COLT model allows collaborative groups to execute tasks that are too complex for one individual to undertake It provides opportunity for students to participate in cross-cultural group dynamics, to articulate, explicate and defend their ideas and hidden motives, and to manage their work ow amid high degree of uncertainty about how the project should be done At the end, they must create an intellectual product collaboratively Organisational learning as described by Patel et al (2000) is the ability of the organisation to collect and use information so that members exploit it to learn and to improve performance They went on to say that learning is something that pervades every individual s life in one form or another Organisations may be capable of learning and such organisational learning may in turn impact upon various aspects of an organisation s performance (Patel et al, 2000) The research discussed the role of IT in capturing and managing knowledge for organisational learning on construction projects known as KLICON The KLICON project s aim was to improve the understanding of the role of KM and how it adds value in the built environment This was achieved by studying the participating industrial organisations to analyse how experience and best practice were being captured The project also used IDEF0 and information models in EXPRESS to enhance understanding of generic construction knowledge and speci c project knowledge They also evaluated the issue of live project and identi ed key KM tools They concluded that KLICON provided an understanding amongst construction practitioners of how knowledge is gained and learning is formalised across the organisational interfaces within a project The role nd appropriateness of IT tools for knowledge capture and management was also clari ed Sadler-smith et al (2000) in suggesting that learning is one of the keys to sustained competitive advantage developed a model for CL in small rms They argued that resource constraints within smaller rms may mean that they sometimes fail to maximise the potential of learning.

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While more detail can be extracted by many of the tools resented in this chapter, these six elements are the core of any session data collection strategy In the context of this chapter, we care about session data for its ability to track intruder activities in a content-neutral way Unlike alert data tools (eg, intrusion detection systems), which rely on triggering a threshold or matching a pattern, session data applications collect everything they see The ability to track everything, or everything up to the limits of your session application, is invaluable for tracking stealthy intruders From a network investigation standpoint, full content data is more valuable than session data Full content data can be sliced and diced in any number of ways by multiple tools But because collecting full content data can be nearly impossible on high-traffic links, we turn to session data as the next best approximation of conversations between networked parties Support for session data collection is built in to many common pieces of networking infrastructure Session data has seen more use for accounting and billing purposes, but many analysts are realizing the security aspects of this important form of network audit data, especially as traffic loads increase Some thoughtful pioneers have been using session data for years to detect intruders Since the mid-1990s the Air Force Computer Emergency Response Team's (AFCERT) Automated Security Incident Measurement (ASIM) sensors have collected session data Analysts have queried or manually perused records of exchanges between Air Force systems and the world, finding novel intrusions not detected by ASIM's signature-matching logic In the late 1990s, Paul Myrick, then an officer in the Air Force Communications Agency, wrote a tool called Profiler to independently collect session data at Air Force bases In 1993, Carter Bullard, then with Carnegie Mellon, began work on the Audit Record Generation and Utilization System (Argus) program There are many ways to use session data on operational networks, but this book focuses on security We take an even more narrow approach when we use session data to track the activities of intruders We care about the intruder's IP address and the protocols he or she uses; we don't care so much about the bandwidth the intruder consumed I present enough information to get the tools running under normal circumstances, and then help decipher their output.

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In the next chapter, we'll look at statistical data about network traffic These statistics won't be used to predict activity, as is the case with mathematical probability cases Rather, we'll use statistical tools to get an overview of the sorts of traffic seen by a network probe Just as session data is a step above the detail of full ontent data, statistical data is a way of moving about session data to understand network activity. Day Up >. Creating Bar Code In VS .NET Using Barcode maker for .NET Control to generate, create bar .Related: Creating QR Code .NET , QR Code Creating .NET Image, .NET QR Code Generator Size

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