Massive Data MapReduce Fingerprint Discriminant Algorithm Based on Hadoop

2012 ◽  
Vol 263-266 ◽  
pp. 2655-2660 ◽  
Author(s):  
Wei Lu ◽  
Jun Huang ◽  
Lin Hong
Keyword(s):  
Massive Data ◽  
Discrimination Accuracy ◽  
Mobile Business ◽  
Original Discrimination ◽  
Fingerprint Algorithm

The problem of effectively identifying of joining the network again is able to obtain the actual interest in the mobile business. The biggest problem of identifying the joining the network again is that even if a small amount of data will also require a lot of computing resources for comparison algorithm. This article using Hadoop technology proposed Hadoop's MapReduce technology heavy network fingerprint discriminant algorithm, which greatly improves the efficiency of heavy network fingerprint algorithm discrimination, the the discriminant algorithm proposed new higher discrimination accuracy than the original discrimination algorithm.

Stimulus Ratio Effects on Speech Discrimination by Children and Adults

1991 ◽  
Vol 34 (3) ◽  
pp. 671-678 ◽  
Author(s):  
Joan E. Sussman
Keyword(s):  
Discrimination Task ◽  
Response Pattern ◽  
Response Patterns ◽  
Speech Discrimination ◽  
Adult Group ◽  
Discrimination Accuracy ◽  
Formant Frequency ◽  
Response Strategies ◽  
Place Of Articulation ◽  
Adults And Children

This investigation examined the response strategies and discrimination accuracy of adults and children aged 5–10 as the ratio of same to different trials was varied across three conditions of a “change/no-change” discrimination task. The conditions varied as follows: (a) a ratio of one-third same to two-thirds different trials (33% same), (b) an equal ratio of same to different trials (50% same), and (c) a ratio of two-thirds same to one-third different trials (67% same). Stimuli were synthetic consonant-vowel syllables that changed along a place of articulation dimension by formant frequency transition. Results showed that all subjects changed their response strategies depending on the ratio of same-to-different trials. The most lax response pattern was observed for the 50% same condition, and the most conservative pattern was observed for the 67% same condition. Adult response patterns were most conservative across condition. Differences in discrimination accuracy as measured by P(C) were found, with the largest difference in the 5- to 6-year-old group and the smallest change in the adult group. These findings suggest that children’s response strategies, like those of adults, can be manipulated by changing the ratio of same-to-different trials. Furthermore, interpretation of sensitivity measures must be referenced to task variables such as the ratio of same-to-different trials.

Hearing words changes color perception: Facilitation of color discrimination by verbal and visual cues

2017 ◽  
Author(s):  
Lewis Forder ◽  
Gary Lupyan
Keyword(s):  
Visual Cues ◽  
Color Word ◽  
Color Perception ◽  
Color Discrimination ◽  
Target Color ◽  
Discrimination Accuracy ◽  
Verbal Cues ◽  
The Moment

As part of learning some languages, people learn to name colors using categorical labels such as “red”, “yellow”, and “green”. Such labeling clearly facilitates communicating about colors, but does it also impact color perception? We demonstrate that simply hearing color words enhances categorical color perception, improving people’s accuracy in discriminating between simultaneously presented colors in an untimed task. Immediately after hearing a color word participants were better able to distinguish between colors from the named category and colors from nearby categories. Discrimination was also enhanced between typical and atypical category members. Verbal cues slightly decreased discrimination accuracy between two typical shades of the named color. In contrast to verbal cues, a preview of the target color, an arguably more informative cue, failed to yield any changes to discrimination accuracy. The finding that color words strongly affect color discrimination accuracy suggests that categorical color perception may be due to color representations being augmented in-the-moment by language.

Approximate Join Aggregate on Massive Data

2010 ◽  
Vol 33 (10) ◽  
pp. 1919-1933
Author(s):  
Xi-Xian HAN ◽  
Dong-Hua YANG ◽  
Jian-Zhong LI
Keyword(s):  
Massive Data

Parallel massive data oriented astronomical cross-match

2010 ◽  
Vol 30 (8) ◽  
pp. 2056-2059 ◽  
Author(s):  
Qing ZHAO ◽  
Ji-zhou SUN ◽  
Ce YU ◽  
Chen-zhou CUI ◽  
Jian XIAO
Keyword(s):  
Massive Data ◽  
Cross Match
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