### Table 21. Pervasiveness of the Internet

"... In PAGE 47: ... PERVASIVENESS One of the most common metrics to measure the state of the Internet is Pervasiveness, the fraction of the total population that uses the Internet regularly. Our usage of the term differs from commonly used Internet growth metrics in that the final measure of pervasiveness is not an absolute number, but a ranking of that number in one of five levels, shown in Table21 . An accurate determination of the number of users is, however, always problematic.... In PAGE 47: ... Nevertheless, tracking the numbers available over time provides an indication of trends. Table21 , which is based on the data in Table 22, indicates that India was at a very high Level 2 (Established) by March 2002, and likely crossed to a Level 3 (Common) by the end of the year. ... ..."

### Table 1: The eighteen Web and pervasive computing projects in 2001-2.

"... In PAGE 2: ... However, we believe that our program is unique because it focuses on value skills, serves the community, and is organized on a large scale with supporting project infrastructure. Eighteen projects were completed during the 2001-2 academic year ( Table1 ). Ten were Web interfaces to backend databases, four were medical applications, three concerned VoiceXML or InkXML, and one was a cluster and grid computing system.... In PAGE 8: ... This server was independent and separate from the other CSIS servers so that students could not corrupt data or interfere with operations on CSIS production servers. A variety of database-related software was used by the ten client/server systems and the VoiceXML absentee system that also used a backend database and a Web interface, the latter as an alternative to the telephone input and for displaying the results ( Table1 ). The student teams used three scripting languages (Cold Fusion, PHP, and ASP) to communicate with the databases and two database systems (Microsoft Access and MySQL).... ..."

### Table 1: Nested term candidates

"... In PAGE 2: ..., 1995) in order to facili- tate processing of grammatical agreements (if any) within term candidates. For each term candidate extracted by a filter, a set of nested term candidates is generated (see Table1 for an example in English). The proce- dure for the generation of nested term candidates is implemented via transformation rules for each morpho-syntactic filter that is used to extract 2 For example, nuclear receptor is a nested term in hormone nuclear receptor.... ..."

### Table 1: Signal objects de ned in SignalProcessing`Support`. For the syntax of CPulse, Dirichlet, FIR, IIR, LineImpulse and Pulse, consult the on-line documentation (e.g., ?CPulse).

"... In PAGE 5: ... We now examine them in more detail. Table1 lists the twelve new functions. There are discrete and continuous versions of the impulse (Impulse and Delta), step (Step and CStep), and pulse functions (Pulse and CPulse).... In PAGE 8: ... (However, certain obvious simpli cations are carried out: for example, InvZ[z,n][Z[n,z][f]] reduces to f.) Similarly, the functions of in Table1 are not reduced to Mathematica built-in objects until they appear as arguments to TheFunction. One may wish to reduce them, for example, in order to use Mathematica apos;s built-in plotting routines to plot them.... In PAGE 8: ... One may wish to reduce them, for example, in order to use Mathematica apos;s built-in plotting routines to plot them. Naturally, some functions in Table1 , like Delta and Unit, cannot be expressed in terms of Mathematica built-in objects, so TheFunction leaves them alone. Other Features Another facility provided by SignalProcessing`Support` is the plotting of signals and transforms.... In PAGE 18: ... Transforms of exponentials in the time domain are inverse-transformed by the exponential property rule, not by table lookup. Some strategies for inverting z-transforms ( Table1 0) are similar to those applied in taking forward z-transforms, but some new ones are also needed. Two such strategies are partial fractions and power series expansion.... In PAGE 19: ... complex cepstrum: Z?1flog X(z)g ! ? 1 nZ?1 ( z X(z) d dz X(z)) *9. apply the inverse z-transform to the rst N terms of a series expansion about z = 0 Table1 0: Strategies for inverse z-transforms. An asterisk means that once the rule is applied to an expression, it will no longer be applied to any part of that expression.... In PAGE 33: ...designing/analyzing 1-D analog lters DTFT discrete Fourier analysis EducationalTool interactive version of a conference paper describ- ing educational impact of Mathematica LaPlaceTest testing procedure for Laplace transforms PiecewiseConvolution tutorial on discrete/continuous convolution README brief introduction SignalProcessingExamples interactive version of paper in the The Mathemat- ica Journal SignalProcessingIntroduction introduction to Mathematica, signal processing, and the signal processing packages SignalProcessingUsage usage information about every new object de ned by the signal processing packages zTransformI z-transform tutorial, part I zTransformII z-transform tutorial, part II zTransformIII z-transform tutorial, part III Table1 1: List of the signal processing Notebooks transforms as long as the options are set properly. The default options are biased toward DTFT apos;s: Domain - gt; Continuous, DomainScale - gt; Linear, MagRangeScale - gt; Linear, PhaseRangeScale - gt; Degree, and PlotRange - gt; All.... In PAGE 40: ...Possible Values Meaning Apart Rational, All Partial fraction decomposition only applies to polynomials with real or rational coe cients Definition True, False Use the transform de nition if all else fails to nd the transform (does not apply to the inverse z or Laplace transforms) Dialogue False, True, All Ascending levels of justi cation Simplify True, False Apply SPSimplify to result Terms False or integer Number of terms in series expansion (False means none) TransformLookup list of rules Users can specify their own transform pairs, like {x[n] : gt; X[z]} or {y[t1,t2] : gt; Y[s1,s2]} Table1 2: Meaning of the Options for the Transform Rule Bases... In PAGE 41: ...Option Default Value CTFTransform Dialogue False Simplify True DFTransform Dialogue False InvDFTransform Dialogue False Terms False DTFTransform Dialogue False LaPlace Dialogue True Simplify True InvCTFTransform Apart Rational Dialogue False Simplify True Terms False InvDTFTransform Dialogue False Terms False InvLaPlace Apart Rational Dialogue True Simplify True Terms 10 InvZTransform Dialogue True Terms 10 ZTransform Dialogue True Table1 3: Options for the Transform Rule Bases. Definition always defaults to False and TransformLookup always defaults to an empty list.... ..."

### Table 9. Sources of pressure on forests in participating communities.

2005

"... In PAGE 6: ...able 8.Comparison of forest types enrolled in PES, 2003 and 2004............36 Table9 .... In PAGE 42: ... This definition does not include the other criteria detailed in the rules of operation, but it was used as a guide in the selection process for this year. The sources of deforestation pressures on forests held by PES recipients, detailed in Table9 , are not different from the deforestation pressures at a national level: agriculture and pastures, domestic use, and in some cases over extraction are the main causes. 25% of ejidos declared using forest clearings for subsistence agriculture while almost 65% of the ejidos use the forest for grazing of livestock.... ..."

### Table 7. KES Components evaluation (extract), KES QFD Matrix 2

"... In PAGE 3: ... ........................................................................35 T U Table7 . KES Components evaluation (extract), KES QFD Matrix 2U T.... In PAGE 36: ... Then all contributions are merged and consolidated. The results are collected in KES QFD Matrix 2, an extract is shown Table7 . In this iteration this was done using a 1 to mark that the functionality was provided by a KES Component Class, and 0 if it was not provided.... In PAGE 40: ... A snapshot of the resulting list of classes can be seen in Table 9, with the full list in Appendix 3. A snapshot of the first iteration of the ranking activity can be seen in Table7 and the results in D3.... ..."

### Table 1. The Roles of Pervasive Systems Development Layers

"... In PAGE 9: ...conceptual model is suggested to highlight the aspects of pervasive systems development in which four layers have been identified (physical, resource, abstract and intentional layers) (Ciarletta and Dima, 2000). Table1 elaborates on the role each one of these layers play in specialized pervasive system development. Table 1.... In PAGE 9: ... Table 1 elaborates on the role each one of these layers play in specialized pervasive system development. Table1 . The Roles of Pervasive Systems Development Layers A framework of four levels can provide a sound process for developing effective m- commerce applications (Varshney and Vetter, 2001).... ..."

### Table 1. The effectiveness (the percentage of test documents attributed with correct authorship) for 2, 3, 4, and 5-class attribution. The data is extracted from the AP collection [2], with function words as features, using Dirichlet smoothing.

"... In PAGE 1: ... Our present work arose out of experimenting with different forms of style markers in our quest to determine which works the best. Some sample results for two class or binary authorship attribution are shown in Table1 . (Technical details of the authorship attribution process is deferred to the following sections of the paper).... In PAGE 11: ... While using all types of features but FW/POS gives the best results of any three types of feature models. Table 5 shows the results of significance tests between the per- formance of the best baseline system from Table1 , and the best additive system using all feature models. The additive system performs significantly better, especially with the harder multi-class attribution tasks, with which the p-values are extremely small.... ..."

### Table 3: Axioms for signal extraction

"... In PAGE 7: ... Axiom TIns1 is the timed version of axiom Ins6, while axiom TIns2 expresses that signals are not in uenced by the passing of time, that is, if s ?! s0, then s0 inherits the signal of s, and vice versa. Table3 presents axioms Ext1-6 from [7] which de ne a function (F; s), which extracts the signal that frame F induces on state s. Axiom Ext4 expresses that... ..."

### Table 5 Evaluation results of the process extraction subtasks.

in Abstract

"... In PAGE 23: ...see Table 4). The overall scores of this task are 84 % recall and 86.8 % precision. Further analyses of these results (see Table5 for details) showed that they are mainly based on erroneous extractions of duration and iteration information. The results of each subtask has to be seen within the context of the benefit of the automatically generated data compared to the manual generation using DELT/A.... ..."