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11.1.3.2 DRM and Content Distribution Business Models To establish an effective DRM system that links users, contents, and rights, one must take into account the way in which the content is distributed (i.e., the distribution model) and the allowable usage rights (which are related to the business model). Examples of several content distribution and business models are introduced in this section. The most fundamental type of content distribution is based on a client server paradigm in which a client requests and downloads information from a given server. The server is often centralized and assists all host computers. The content, depending on its format, may be streamed in real time or completely downloaded and stored for later use. Many rst-generation e-commerce and DRM systems make use of this basic download model. In contrast, in peer-to-peer le sharing systems, content is stored and distributed directly by end systems (termed peers ) without the need of a central server. Examples of such systems include the infamous Napster and Kazaa. Multicast distribution, which has been shown to scale more naturally than have client server models [12] for a large number of consumers, involves the distribution of content from a single source to multiple recipients. For example, cellular digital packet data (CDPD) technology [13], a speci cation to support wireless Internet access for mobile devices such as cell phones, supports multicast distribution. Effective business models are also an integral part in the establishment of a successful DRM system. Subscription-based models allow a given user to obtain access to a library of content with a prede ned scope based on the details of his/her membership rights. The membership is normally purchased ahead of time.

DDB% is calculated as 100 percent, or 1 divided by years of life: 100% / 5 20% 2 40% or 1/5 20% 2 40%

THE STRUCTURE OF AFFECTIVE EXPERIENCE Discrete Affect Models What are the basic constructs that need to be assessed in this domain Early mood research tended to emphasize the importance of discrete, speci c types of affect, such as fear-anxiety, sadness-depression, anger-hostility, and happiness-joy. This approach was supported by an extensive array of evidence; most notably, structural analyses of mood terms repeatedly identi ed well-de ned content factors corresponding to these speci c affective states. Moreover, a common core of discrete affects including fear, sadness, and anger emerged consistently across factor analyses re ecting different pools of items and diverse samples of respondents (e.g., McNair, Lorr, & Droppleman, 1971; Nowlis, 1965; Watson & Clark, 1994; Zuckerman & Lubin, 1985). Nevertheless, advocates of this approach eventually encountered a very serious problem, namely, that measures of different speci c affects are strongly interrelated and tend to show questionable discriminant validity. Correlations among affects of the same valence tend to be particularly strong. For instance, people who experience signi cant levels of one type of negative affect (e.g., anger) also tend to report elevated levels of other negative moods (e.g., fear, sadness, guilt); similarly, individuals who report one type of positive mood (e.g., joy) report many others (e.g., energy, enthusiasm, interest) as well. In fact, multitrait-multimethod analyses consistently demonstrate much stronger evidence for nonspeci city (i.e., signi cant positive correlations among measures of different, similarly valenced affects) than for speci city (i.e., unique relations between indicators of the same target affect) in mood data (see Bagozzi, 1993; Berenbaum, Fujita, & Pfennig, 1995; Diener, Smith, & Fujita, 1995; Watson & Clark, 1992). We should add that crossvalence correlations (e.g., between fear and enthusiasm) tend to be much weaker, but often are not negligible, an issue we shall return to later.

Illustrated TCP/IP by Matthew G. Naugle Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471196568 Pub Date: 11/01/98

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One of the most important differences between outdoor navigation and indoor navigation is certainly the third dimension. In fact, outdoor navigation is primarily a planar system. Knowledge of the altitude is interesting for general information, but is not really required for most applications. Indoors, the problem is totally different, as knowing the oor level is of primary concern (for emergency aspects for instance, or in order to cope with oor maps that are not identical from one oor to another). In that sense, barometers can be very helpful in allowing quite an accurate determination of the altitude. This can be achieved through the use of so-called micro-altimeters, whose accuracy is typically 1 m on a local and time-limited extension scale. Once this time has elapsed, meteorological uctuations are bound to occur and cause large bias to the measurement. The idea is then to reset the altimeter when entering the building at a known location (and known absolute altitude relative to any referential), and to use the micro-altimeter to determine the oor level. An accuracy of 1 m is enough to achieve such a goal. With an increased accuracy of a fraction of a

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A database is said to be distributed if any of its tables are stored at different sites; one or more of its tables are replicated and their copies are stored at different sites; one or more of its tables are fragmented and the fragments are stored at different sites; and so on. In general, a database is distributed if not all of its data is localized at a single site.