scholarly journals Evaluating Deception Detection Model Robustness To Linguistic Variation

2021 ◽  
Author(s):  
Maria Glenski ◽  
Ellyn Ayton ◽  
Robin Cosbey ◽  
Dustin Arendt ◽  
Svitlana Volkova
Keyword(s):  
Deception Detection ◽  
Linguistic Variation ◽  
Detection Model ◽  
Model Robustness

Response-Specific Scalp Distributions in Deception Detection and ERP Correlates of Psychopathic Personality Traits

2004 ◽  
Vol 18 (1) ◽  
pp. 13-26 ◽  
Author(s):  
Antoinette R. Miller ◽  
J. Peter Rosenfeld
Keyword(s):  
Deception Detection ◽  
Event Related Potential ◽  
Psychopathic Personality ◽  
P300 Amplitude ◽  
Group Differences ◽  
Post Hoc Analysis ◽  
P300 Component ◽  
Psychopathic Personality Inventory ◽  
Post Hoc ◽  
First Time

Abstract University students were screened using items from the Psychopathic Personality Inventory and divided into high (n = 13) and low (n = 11) Psychopathic Personality Trait (PPT) groups. The P300 component of the event-related potential (ERP) was recorded as each group completed a two-block autobiographical oddball task, responding honestly during the first (Phone) block, in which oddball items were participants' home phone numbers, and then feigning amnesia in response to approximately 50% of items in the second (Birthday) block in which oddball items were participants' birthdates. Bootstrapping of peak-to-peak amplitudes correctly identified 100% of low PPT and 92% of high PPT participants as having intact recognition. Both groups demonstrated malingering-related P300 amplitude reduction. For the first time, P300 amplitude and topography differences were observed between honest and deceptive responses to Birthday items. No main between-group P300 effects resulted. Post-hoc analysis revealed between-group differences in a frontally located post-P300 component. Honest responses were associated with late frontal amplitudes larger than deceptive responses at frontal sites in the low PPT group only.

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

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.

Detection model of similar datasets

2019 ◽  
Author(s):  
Julio Acedo ◽  
Marcos Fernandez-Sellers ◽  
Adolfo Lozano-Tello
Keyword(s):  
Detection Model

New Directions in the Computational Analysis of Biblical Hebrew Grammar

2018 ◽  
Vol 27 (1) ◽  
Author(s):  
Cynthia Miller-Naudé ◽  
Jacobus A Naudé
Keyword(s):  
Southern Hemisphere ◽  
Hebrew Bible ◽  
North American ◽  
Computational Analysis ◽  
Biblical Hebrew ◽  
Linguistic Theory ◽  
Linguistic Variation ◽  
European Database ◽  
New Directions ◽  
African Context

The concern of the paper is to highlight how computational analysis of Biblical Hebrew grammar can now be done in very sophisticated ways and with insightful results for exegesis. Three databases, namely, the Eep Talstra Centre for Bible and Computer (ETCBC) Database, the Accordance Hebrew Syntactic Database, and the Andersen-Forbes Syntactic Database,are compared in terms of their relation to linguistic theory (or, theories), the nature and spectrum of retrieved data, and the representation of synchronic and diachronic linguistic variation. Interaction between different contexts, including the African context, are promoted namely between linguists working on Biblical Hebrew and exegetes working on the Hebrew Bible by illustrating how exegesis and language are intimately connected, as well as among geographical contexts by comparing a European database (ETCBC), a North American database (Accordance) and a Southern hemisphere database (Andersen-Forbes).

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