Adversarial attacks against profile HMM website fingerprinting detection model

Cognitive Systems Research ◽

10.1016/j.cogsys.2018.12.005 ◽

2019 ◽

Vol 54 ◽

pp. 83-89

Author(s):

Xiaolei Liu ◽

Zhongliu Zhuo ◽

Xiaojiang Du ◽

Xiaosong Zhang ◽

Qingxin Zhu ◽

...

Keyword(s):

Detection Model ◽

Profile Hmm

Download Full-text

A Signal Detection Model of Moral Decision Making

PsycEXTRA Dataset ◽

10.1037/e527342012-637 ◽

2007 ◽

Author(s):

Jasper Jancik ◽

Dale J. Cohen

Keyword(s):

Decision Making ◽

Signal Detection ◽

Moral Decision ◽

Moral Decision Making ◽

Detection Model

Download Full-text

Frequency effects in recognition memory: Evidence for a multiprocess detection model?

PsycEXTRA Dataset ◽

10.1037/e537102012-320 ◽

2001 ◽

Author(s):

David G. Smith ◽

Marty W. Niewiadomski

Keyword(s):

Recognition Memory ◽

Frequency Effects ◽

Detection Model

Download Full-text

Recognition of classroom student state features based on deep learning algorithms and machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189232 ◽

2020 ◽

pp. 1-12

Author(s):

Hu Jingchao ◽

Haiying Zhang

Keyword(s):

Deep Learning ◽

Feature Recognition ◽

Subjective Evaluation ◽

Recognition Algorithm ◽

Video Data ◽

State Recognition ◽

Detection Model ◽

State Classification ◽

Intelligent Models ◽

Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Download Full-text