Oblivious Keyword Search with Authorization

2016 ◽  
pp. 173-190 ◽  
Author(s):  
Peng Jiang ◽  
Xiaofen Wang ◽  
Jianchang Lai ◽  
Fuchun Guo ◽  
Rongmao Chen
Keyword(s):  
Keyword Search

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

A study of Leakage Ratio for increasing performance of online keyword search advertising

2019 ◽  
Vol 118 (1) ◽  
pp. 36-41
Author(s):  
Jung-Woo Lee ◽  
Seung-Cheon Kim ◽  
Sung-Hoon Kim ◽  
Jin-Ho Lim
Keyword(s):  
Online Shopping ◽  
Keyword Search ◽  
Online Advertising ◽  
The Internet ◽  
Shopping Malls ◽  
Portal Site ◽  
Internet Portal ◽  
Search Advertising ◽  
Large Size ◽  
Low Efficiency

Background/Objectives: In this study, research to improve efficiency of online advertising market, we would like to propose a new performance index called "Leakage Ratio" which can increase the efficiency of advertisement. Methods/Statistical analysis: Naver, the Internet portal site in Korea, is the most influential medium for online keyword search advertising. In this study, Leakage Ratio management is applied to online keyword search ads for five medium and large size online shopping malls at Naver. Based on the performance trend of each search keyword, we tried to improve the efficiency of the whole advertisement by changing the bid of the low efficiency keyword.

A study of Leakage Ratio for increasing performance of online keyword search advertising

2019 ◽  
Vol 118 (8) ◽  
pp. 308-314
Author(s):  
Jung-Woo Lee ◽  
Seung- Cheon ◽  
Sung-Hoon Kim ◽  
Jin-Ho Lim
Keyword(s):  
Online Shopping ◽  
Keyword Search ◽  
Online Advertising ◽  
The Internet ◽  
Shopping Malls ◽  
Portal Site ◽  
Internet Portal ◽  
Search Advertising ◽  
Large Size ◽  
Low Efficiency

In this study, research to improve efficiency of online advertising market, we would like to propose a new performance index called "Leakage Ratio" which can increase the efficiency of advertisement. Methods/Statistical analysis: Naver, the Internet portal site in Korea, is the most influential medium for online keyword search advertising. In this study, Leakage Ratio management is applied to online keyword search ads for five medium and large size online shopping malls at Naver. Based on the performance trend of each search keyword, we tried to improve the efficiency of the whole advertisement by changing the bid of the low efficiency keyword.

Autobiographical Memory Impairments as a Transdiagnostic Feature of Mental Illness: A meta-analysis of autobiographical memory specificity and overgenerality amongst people with psychiatric diagnoses

2020 ◽  
Author(s):  
Tom Joseph Barry ◽  
David John Hallford ◽  
Keisuke Takano
Keyword(s):  
Autobiographical Memory ◽  
Memory Retrieval ◽  
Keyword Search ◽  
Meta Analysis ◽  
Future Research ◽  
Psychiatric Diagnoses ◽  
Schizophrenia Spectrum Disorders ◽  
Memory Impairments ◽  
Memory Specificity ◽  
Causal Mechanisms

Decades of research has examined the difficulty that people with psychiatric diagnoses, such as Major Depressive Disorder, Schizophrenia Spectrum Disorders, and Posttraumatic Stress Disorder, have in recalling specific autobiographical memories from events that lasted less than a day. Instead, they seem to retrieve general events that have occurred many times or which occurred over longer periods of time, termed overgeneral memory. We present the first transdiagnostic meta-analysis of memory specificity/overgenerality, and the first meta-regression of proposed causal mechanisms. A keyword search of Embase, PsycARTICLES and PsycINFO databases yielded 74 studies that compared people with and without psychiatric diagnoses on the retrieval of specific (k = 85) or general memories (k = 56). Multi-level meta-analysis confirmed that people with psychiatric diagnoses typically recall fewer specific (g = -0.864, 95% CI[-1.030, -0.698]) and more general (g = .712, 95% CI[0.524, 0.900]) memories than diagnoses-free people. The size of these effects did not differ between diagnostic groups. There were no consistent moderators; effect sizes were not explained by methodological factors such as cue valence, or demographic variables such as participants’ age. There was also no support for the contribution of underlying processes that are thought to be involved in specific/general memory retrieval (e.g., rumination). Our findings confirm that deficits in autobiographical memory retrieval are a transdiagnostic factor associated with a broad range of psychiatric problems, but future research should explore novel causal mechanisms such as encoding deficits and the social processes involved in memory sharing and rehearsal.

Top-k Fuzzy Spatial Keyword Search

2012 ◽  
Vol 35 (11) ◽  
pp. 2237 ◽  
Author(s):  
Jun HU ◽  
Ju FAN ◽  
Guo-Liang LI ◽  
Shan-Shan CHEN
Keyword(s):  
Keyword Search ◽  
Spatial Keyword Search

Multi-keyword search over P2P based on Bloom filter

2010 ◽  
Vol 30 (9) ◽  
pp. 2335-2338
Author(s):  
Hua-yun YAN ◽  
Ji-hong GUAN
Keyword(s):  
Keyword Search ◽  
Bloom Filter
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