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Table 1: Overview of three experiments
"... In PAGE 5: ... One decides the range of a Discussion by hearing and looking the action records and by looking over the patterns of buttons, which reflects the turn-taking of participants. 4 Results and Discussion In Table1 , we present an overview of three experiments. The objective of all experiments was to develop a requirements speci cation, which was described on papers.... In PAGE 7: ... But they are enough fruitful to discuss the tendencies of human behaviors. The actual numbers of data in each experiment in Table1 , 2, Table3 and 4 are largely di erent. Especially in experiment#1, spending time in each meeting was long in spite of shorter products than the other experiment shown in Table1.... In PAGE 7: ...f another experiment. But they are enough fruitful to discuss the tendencies of human behaviors. The actual numbers of data in each experiment in Table1, 2, Table3 and 4 are largely di erent. Especially in experiment#1, spending time in each meeting was long in spite of shorter products than the other experiment shown in Table1 . This was why no computer system used in experiment#1 hence the scribe would not have enough data for writing rich documents.... ..."
Table 1: Example of a Fruit Carts Dialogue
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TABLE I A FRUIT RETRIEVAL EXAMPLE IN CCBR
Table 7. Distribution of households growing either vegetables or fruits or both in India (%) Farm category Vegetable growers Fruit growers
in and
2007
"... In PAGE 24: ...arge 22.2 0.0 15.4 23.3 55.1 28.8 Source: GOI (1999): Cultivation practices in India. Indeed, Table7 shows little convergence in crop choices between fruits and vegetables. Growers of fruits and vegetables are distinctly different in the sense that they rarely combine cultivation of fruits and vegetables.... ..."
Table 2: Fruits Selected in This Study Fruit Brand Weight/Size Observation
"... In PAGE 10: ... viii List of Tables Page Table 1: Retailers Selected in This Study 52 Table2 : Fruits Selected in This Study 53 Table 3: F Test 56 Table 4: Demographics Profile of Respondents 59 Table 5: Reasons for Not Buying Fruits Online 61 Table 6: Fruit Consumption and Buying Habits 62 Table 7: Likelihood of Buying Fruits Online in the Next Three to Twelve Months 63 Table 8: Likelihood of Buying Fruits Online Given Certain Criteria 63 Table 9: t tests on Prices 64 Table 10: Proportion of Times the Minimum Internet Price is less than 65 the Minimum Conventional Price Table 11: Important Factors to Consider when Choosing an Online Fruit Seller 68 Table 12: Convenience, Time Saving, Extra Information and Extra Services 69 Table 13: Price Factor 69 Table 14: Descriptive Data on Price Changes 70 Table 15: Proportion of Times Price Dispersion is Lower on the Internet 72 Table 16: Useful Services 74 Table 17: Summary of Findings for all Hypotheses 76 ... In PAGE 64: ... This will give us a good mix for the conventional fruit outlets sample. To control the differences between the fruits sold in the two markets, specific fruit of specific brand, size and weight were chosen to form our basket ( Table2 ). And for meaningful comparison, only fruit items whose prices were available across all the traditional fruit stores and online sellers chosen for this study were considered.... ..."
Table 13--Distribution of farmers by the value of fruit and vegetable sales Sales of fruits and vegetables
2004
"... In PAGE 44: ... Table 12--Distribution of farmers by marketed share of fruit and vegetable production Sales as a percentage of fruit amp; vegetable production Number of farmers Percent of all farmers Percent of the value of F amp;V production Percent of the value of F amp;V sales No sales 347 24 18 0 1 - 10 288 20 22 2 10 - 20 162 11 7 3 20 - 30 112 8 5 3 30 - 40 92 6 8 8 40 - 50 121 8 6 8 50 - 60 93 7 5 9 60 - 70 70 5 5 10 70 - 80 66 5 6 12 80 - 90 51 4 10 24 90 - 100 23 2 7 20 Total 1425 100 100 100 Source: Egerton/Tegemeo/MSU Rural Household Survey 2000 As mentioned above, most growers have relatively small sales of fruits and vegetables, but a few farms have quite sizeable sales. Table13 shows the distribution of farmers according to their fruit and vegetable sales. The average value of sales is relatively high, Ksh 17 thousand (US$ 226).... ..."
Table 6: Fruit Consumption and Buying Habits Variable Frequency Percent
"... In PAGE 10: ... viii List of Tables Page Table 1: Retailers Selected in This Study 52 Table 2: Fruits Selected in This Study 53 Table 3: F Test 56 Table 4: Demographics Profile of Respondents 59 Table 5: Reasons for Not Buying Fruits Online 61 Table6 : Fruit Consumption and Buying Habits 62 Table 7: Likelihood of Buying Fruits Online in the Next Three to Twelve Months 63 Table 8: Likelihood of Buying Fruits Online Given Certain Criteria 63 Table 9: t tests on Prices 64 Table 10: Proportion of Times the Minimum Internet Price is less than 65 the Minimum Conventional Price Table 11: Important Factors to Consider when Choosing an Online Fruit Seller 68 Table 12: Convenience, Time Saving, Extra Information and Extra Services 69 Table 13: Price Factor 69 Table 14: Descriptive Data on Price Changes 70 Table 15: Proportion of Times Price Dispersion is Lower on the Internet 72 Table 16: Useful Services 74 Table 17: Summary of Findings for all Hypotheses 76 ... ..."
Table 2: (b) Performance of Various Filters on the Fruits Image
"... In PAGE 4: ...edian filter and filter 7 is given in Fig. 2(c-d). The resulting MSE for various filters is given in Table 1(a). for the lenna image and in Table2 (a) for the fruits image. Table 1: (a) Performance of Various Filters on the lenna Image MSE Filter Type (i) Salt amp; pepper noise (10%) (ii) Gaussian noise 2 0.... In PAGE 5: ...59 702.17 Table2 : (a) Performance of Various Filters on the Fruits Image ... ..."
Table 4: Edge detection on fruit.jpg with different sizes
1996
"... In PAGE 10: ... Since we compute these edge parameters on a block by block basis by using DCT coefficients directly for each block, the process is much faster but the result is not so precise as that of the spatial domain pixel-based convolution. Table4 shows edge detection time (CPU time) for fruit image with different sizes. In the conventional approach, the processing time includes decompression and spatial domain Sobel edge detection.... ..."
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