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XML enables an author to create entirely new markup languages with tags that contain industry-specific language. The markup language creator can define the language with a Document Type Definition, or DTD. In short, DTDs define a set of rules that govern the relationships among the tags contained in a document. For example, a DTD describing a company's personnel may specify that every Employee element have at least one Hire_Date child element and one and only one Name child element. When the tags in an XML document conform to the specifications in an associated DTD, the XML document is said to be valid. An XML document can be well-formed without being valid. Also, DTD keywords are case sensitive. barcode generator open source
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The probability of failure function Fs t of a series system can be determined using the formula Fs t 1
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that is, let dom(C) = {c1 , . . . , cm }, and let U = {A1 , . . . , An } be a set of other attributes used to describe a case or an object. These other attributes may be symbolic, that is, dom(Ak ) = {ak,1 , . . . , ak,mk }, or numeric, that is, dom(Ak ) = IR.1 If the second index of an attribute value is irrelevant, it is dropped and we simply write ak for a value of an attribute Ak . With this notation a case or an object can be described, as usual, by an instantiation (a1 , . . . , an ) of the attributes A1 , . . . , An . For a given instantiation (a1 , . . . , an ) a naive Bayes classifier tries to compute the conditional probability P(C = ci | A1 = a1 , . . . , An = an ) for all ci and then predicts the class ci for which this probability is highest. Of course, it is usually impossible to store all of these probabilities explicitly, so that the most probable class can be found by a simple lookup. If there are numeric attributes, this is obvious (in this case some parameterized function is needed). But even if all attributes are symbolic, we have to store a class (or a class probability distribution) for each point of the Cartesian product of the attribute domains, the size of which grows exponentially with the number of attributes. To cope with this problem, naive Bayes classifiers exploit as their name indicates Bayes rule and a set of (naive) conditional independence assumptions. With Bayes rule the conditional probabilities are inverted. That is, naive Bayes classifiers consider2 P(C = ci | A1 = a1 , . . . , An = an ) f(A1 = a1 , . . . , An = an | C = ci ) P(C = ci ) . = f(A1 = a1 , . . . , An = an ) Of course, for this inversion to be always possible, the probability density function f(A1 = a1 , . . . , An = an ) must be strictly positive. There are two observations to be made about this inversion. In the first place, the denominator of the fraction on the right can be neglected, since for a given case or object to be classified it is fixed and therefore does not have any influence on the class ranking (which is all we are interested in). In addition, its influence can always be restored by normalizing the distribution on the classes (compare also page 126). That is, we can exploit f(A1 = a1 , . . . , An = an )
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For example, if an XML element was <P class="foo">here is a paragraph</P>, a rule such as .foo {font-size=20px} would match.
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generate, create code-128 special none for .net projects 128b Customised Applications for Mobile Enhanced Logic (CAMEL) The basic operator requirements to nd a means of services customisation in order to differentiate themselves in competition and the creation of the work item for CAMEL is described in 5, Section 1, paragraph 2.4. It turned out that the CAMEL concept of offering services creation and portability based on Intelligent Network (IN) concepts was such a formidable task that it had to be developed in phases. The service requirements of CAMEL phase 1 was approved by SMG#19 in June 1996. These service requirements contained basic mechanisms: trigger detection points, event detection points and operations of the Intelligent Network Application Part (INAP) protocol. Regarding the protocol speci cation a discussion emerged between the ETSI Technical Committees SMG and SPS (signalling protocols and switching). SPS was responsible for all protocols in ISDN. This included Signalling System No. 7 used in ISDN. The key mobility handling protocol in GSM is MAP (Mobile Application Part) a high level protocol using the transport capabilities of Signalling System No. 7. In the past SMG had produced stage 2 MAP speci cations (architecture aspects) and SPS stage 3 MAP speci cations. SPS had handed back the MAP stage 3 to SMG in 1995, since they saw INAP, the Intelligent Network Application Part, as the main avenue into the future also providing mobility management in broadband-ISDN and UMTS. CAMEL, however, needed to use some existing INAP functions and to create new INAP functions. SPS wanted to take over this work. SMG however felt that it was so deeply connected to MAP and the rest of the GSM work that it could not be separated. Another mismatch was the timing. The SMG demand was much more urgent than the demand for the INAP development, which was driven by the xed network demand and was aligned with the ITU INAP development. After a longer dialogue between SPS and SMG the following solution was found: SMG3 produces CAMEL stage 3 speci cations under the heading CAMEL Application Part (CAP). SPS takes this work and mirrors it into the ETSI INAP speci cation, which is aligned with the ITU INAP. SPS would introduce as much of the CAP material into INAP as possible. The
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then provide power to a water pump and return to their initial starting point via a Ferris wheel. The generator analogy is that the generator pumps electrons at a measurable ow rate through a circuit from a lower to a higher electrical energy level, increasing their potential energy. The electrons then ow through the circuit from the higher energy level toward the lower energy level as a current. As the electrons ow along the circuit they may be used to power devices such as electric motors. Electrons at the terminal point of the circuit (same as starting point) all have the same energy level. Table 2.1 summarizes this analogy. Standard units are necessary to measure current, energy, and power. We present the standard industry units. Current: Ampere The unit for current is the ampere. This provides the ow rate of electrons that are owing through a cross section of the circuit. Electric Potential Level: Volt The electric potential level of a point on a circuit (analogous to height in the mechanical example) is measured in volts. Electrons at the same voltage level have the same potential energy. Within a circuit, cross sections that are transverse to the direction of the current have nearly the same voltage. Power: Megawatt Utilities most often measure power in megawatts (MW). In the mechanical example the power input is determined by multiplying the change in height, 20 feet, by the ow rate of the bowling balls. Similarly, one attains the power input in an electrical circuit by multiplying the change in voltage around the circuit by the current ( ow rate). Accordingly the unit volt-ampere is a power unit. One volt-ampere is the amount of power associated with moving a current with a ow rate of 1 ampere
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