Entity Hierarchy Construction for Repair Request Records

2016 ◽  
Author(s):  
Jingyu Sun ◽  
Xiaomei Zhang ◽  
Quan Liu ◽  
Liang Ju ◽  
Zhengying Li
Keyword(s):  
Hierarchy Construction

Session 2

2007 ◽  
pp. 25-44
Author(s):  
Barbara Olasov Rothbaum ◽  
Edna B. Foa ◽  
Elizabeth A. Hembree
Keyword(s):  
Drug Use ◽  
Treatment Program ◽  
Construction Safety ◽  
Self Image ◽  
Safety Considerations ◽  
Fear And Anxiety ◽  
Hierarchy Construction

Chapter 4 details the second session of the treatment program, including a discussion about common reactions to trauma (fear and anxiety, reexperiencing the trauma, increased arousal, avoidance, anger, guilt, grief and depression, negative self-image, suffering relationships, and alcohol or drug use), examples of in vivo exposure, an introduction to Subjective Units of Discomfort (SUDS), in vivo hierarchy construction, safety considerations, in vivo assignments, and the model of gradual in vivo exposure.

scholarly journals Novel Trusted Hierarchy Construction for RFID Sensor–Based MANETs Using ECCs

ETRI Journal ◽  
2015 ◽  
Vol 37 (1) ◽  
pp. 186-196 ◽  
Author(s):  
Adarsh Kumar ◽  
Krishna Gopal ◽  
Alok Aggarwal
Keyword(s):  
Hierarchy Construction

scholarly journals Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 36
Author(s):  
Weiping Zheng ◽  
Zhenyao Mo ◽  
Gansen Zhao
Keyword(s):  
Multitask Learning ◽  
Classification Error ◽  
Learning Method ◽  
Scene Classification ◽  
Fine Grained ◽  
Learning Framework ◽  
Self Organized ◽  
Similarity Relations ◽  
Hierarchy Construction ◽  
Better Than

Acoustic scene classification (ASC) tries to inference information about the environment using audio segments. The inter-class similarity is a significant issue in ASC as acoustic scenes with different labels may sound quite similar. In this paper, the similarity relations amongst scenes are correlated with the classification error. A class hierarchy construction method by using classification error is then proposed and integrated into a multitask learning framework. The experiments have shown that the proposed multitask learning method improves the performance of ASC. On the TUT Acoustic Scene 2017 dataset, we obtain the ensemble fine-grained accuracy of 81.4%, which is better than the state-of-the-art. By using multitask learning, the basic Convolutional Neural Network (CNN) model can be improved by about 2.0 to 3.5 percent according to different spectrograms. The coarse category accuracies (for two to six super-classes) range from 77.0% to 96.2% by single models. On the revised version of the LITIS Rouen dataset, we achieve the ensemble fine-grained accuracy of 83.9%. The multitask learning models obtain an improvement of 1.6% to 1.8% compared to their basic models. The coarse category accuracies range from 94.9% to 97.9% for two to six super-classes with single models.

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