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Table below gives a comparison of the activities in the proposed model with those in the major existing models described in the previous chapter. Some of the relevant activities in other models are incorporated in the proposed model. However there are many activities like communication shielding and volatile evidence collection, which are unique for this model, as it is clear from the table.

in FORENSIC INVESTIGATION PROCESS MODEL FOR WINDOWS MOBILE DEVICES
by Anup Ramabhadran, Security Group, Tata Elxsi

Table 6: Proportions of Unique Words in Subjective Elements

in A Corpus Study of Evaluative and Speculative Language
by Janyce Wiebe , Rebecca Bruce , Matthew Bell, Melanie Martin , Theresa Wilson 2001
"... In PAGE 6: ..., 1998), we hypothesized that low-frequency words are associ- ated with subjectivity. Table6 provides evidence that the number of unique words (words that ap- pear just once) in subjective elements is higher than expected. The rst rowgives information for all words and the second gives information for words that appear just once.... ..."
Cited by 14

Table 4.2 shows that there are 8,159 unique words in the one-million-word corpus. 25% of the lexicon, or 1,885 words, are words related to city names. This empirical evidence supports the commonly-held view that the proper nouns are a major open class and that, in task-oriented spoken English, noun is also one of the most frequent word classes [18, 68].

in unknown title
by unknown authors 2005
Cited by 1

Table 9 has the results for one module. The Unix coverage experiments are discussed in detail in [5]. Several coverage experiments were done at Nortel by Burr and Young [2]. They also found that the AETG pair- wise test sets gave good coverage in a variety of situations. Of course, it is easy to construct examples where only one unique combination of test parameters will trigger a fault. However, there is growing evidence that for many real-world systems, a large number of faults are triggered by many parameter combinations. This is an area that mer- its further study.

in The AETG System: An Approach to Testing Based on Combinatorial Design
by David Cohen, Siddhartha R. Dalal, Michael L. Fredman, Gardner C. Patton 1997
Cited by 70

Table unique

in Adaptive-optics performance of Antarctic telescopes
by Jon S. Lawrence

Table 12: Evidence Gathering

in A method for reusable and adaptable reference models Table of Contents TABLE OF CONTENTS ERROR! NO TABLE OF CONTENTS ENTRIES FOUND.
by Student Peter Nes 2007
"... In PAGE 71: ....4.1. Evidence Gathering Table12 gives the elements of the Evidence Gathering Step. Table 12: Evidence Gathering ... ..."

Table 4, the following conclusions are evident:

in Term-weighting approaches in automatic text retrieval
by Gerard Salton, Christopher Buckley 1988
Cited by 925

Table 1. Goals and their evidences

in An Effective Conversational Agent with User Modeling based on Bayesian Network
by Seung-ik Lee, Chul Sung, Sung-Bae Cho 2001
"... In PAGE 4: ...ig. 3 shows the structure of the model. The evidence variables of a node or a goal are represented as parent nodes. For each of the goal variable, evidence variables are defined as in Table1 . Words in curly braces are synonyms with the one in front of the braces.... ..."
Cited by 6

Table 2: Evidence

in Empirical Evaluations Of Some Benchmark Bayesian Networks
by Igor Cadez, Hong Zhao, Stephen Bay, Scott Gafney, Dmitry Pavlov, Rina Dechter
"... In PAGE 22: ....2.2 Evidence For this experiment, I entered evidence that would be typical of an insurance client (myself). Table2 shows the variables observed and their values. JavaBayes and logic sampling return the following belief vector for the node PropCost:... ..."

Table 2: Evidence

in Empirical Evaluations Of Some Benchmark Bayesian Networks
by Igor Cadez, Hong Zhao, Stephen Bay, Scott Gafney, Dmitry Pavlov, Rina Dechter
"... In PAGE 22: ....2.2 Evidence For this experiment,Ientered evidence that would be typical of an insurance client#28myself#29. Table2 shows the variables observed and their values. JavaBayes and logic sampling return the following belief vector for the node PropCost:... ..."
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