Polygraph Research Articles

Intelligent Eye-Tracker-Based Methods for Detection of Deception: A Survey

Over the last few years, a large number of studies have been conducted on the monitoring of human behavior remaining beyond conscious control. One area of application for such monitoring systems is lie detection. The most popular method currently used for this purpose is polygraph examination, which has proven its usefulness in the field and in laboratories, but it is not without its drawbacks. Technological advances in data acquisition and automated analysis have ensured that contactless tools are in high demand in security fields like airport screening or pre-employment procedures. As a result, there has been a shift in interest away from traditional polygraph examinations toward the analysis of facial expressions, voice, and speech patterns, as well as eye-tracking signals to detect deceptive behavior. In this paper, we focus on the last aspect, offer a comprehensive overview of two distinct lie detection methodologies based on eye tracking, and examine the commonly used oculomotor feature analysis. Furthermore, we explore current research directions and their results within the context of their potential applications in the field of forensics. We also highlight future research prospects, suggesting the utilization of eye tracking and scan path interpretation methodologies as a potential fully functional alternative for the conventional polygraph in the future. These considerations refer to legal and ethical issues related to the use of new technology to detect lies.

Topic Audiolization: A Model for Rumor Detection Inspired by Lie Detection Technology

The psychological factors influence the frequency and intensity of the lie’s audio. In addition, the changes could be associated with each word in a segment of the liar’s output. We consider a single comment as the audio signal of a word and the entire propagation process as the audio signal of a paragraph. Thus, rumor detection based on propagation features is transformed into lie detection based on audio signals. Firstly, we propose a Topic2Audio method to transform the topic space into an audio-like signal. This method quantifies topic propagation features and maps them to the audio-like signal’s amplitude, frequency, and offset. Secondly, we retrieved audio features using Fourier transforms and Mel spectrum algorithms. Finally, we propose a rumor detection model based on topic audiolization (TARD). The model can detect ‘rumor’ signals using audio classification techniques by transforming the topic space into audio-like signals. Experiments conducted on three datasets, i.e., Weibo, Politifact, and Gossipcop, show performance improvements at +2.2%, +1.9%, and +1.3% when compared with the state-of-the-art method, respectively. Experiments show that our model can identify rumors effectively.

Deception/Truthful Prediction Based on Facial Feature and Machine Learning Analysis

The Automatic Deception detection refers to the investigative practices used to determine whether person is telling you Truth or lie. Automatic deception detection has been studied extensively as it can be useful in many real-life scenarios in health, justice, and security systems. Many psychological studies have been reported for deception detection. Polygraph testing is a current trending technique to detect deception, but it requires human intervention and training. In recent times, many machine learning based approaches have been applied to detect deceptions. Various modalities like Thermal Imaging, Brain Activity Mapping, Acoustic analysis, eye tracking. Facial Micro expression processing and linguistic analyses are used to detect deception. Machine learning techniques based on facial feature analysis look like a promising path for automatic deception detection. It also works without human intervention. So, it may give better results because it does not affect race or ethnicity. Moreover, one can do covert operation to find deceit using facial video recording. Covert Operation may capture the real personality of deceptive persons. By making combination of various facial features like Facial Emotion, Facial Micro Expressions and Eye blink rate, pupil size, Facial Action Units we can get better accuracy in Deception Detection.

O055 NIV titration: How to Match Ventilator Settings to the Patient?

Abstract Introduction Non-invasive ventilation (NIV) is a treatment that improves mortality and quality of life in chronic respiratory failure. The monitoring used to titrate NIV varies substantially; however, treatment is directed towards improving pulmonary gas exchange and sleep quality. The aim was to evaluate whether different montages of signals, influenced identification of respiratory and patient ventilator asynchrony (PVA) events and ventilation titration. Method 50 laboratory NIV polysomnography (PSG) studies were reviewed under 3 different signal conditions: NIV PSG, ventilator derived signals (V) and polygraphy (PG): ventilator signals, respiratory bands, transcutaneous carbon dioxide and oximetry. Two respiratory and sleep physicians reviewed and scored respiratory and PVA events for each individual study with agreement quantified with Cohen’s kappa statistic. Physicians’ confidence and satisfaction of ventilator settings and titration recommendations were ranked on a 5-point Likert scale and compared with ordinal logistic regression. Results There were marked differences in the identification of respiratory events including upper airway obstruction with reduced respiratory drive; PSG vs V kappa 0.1 (CI -0.12, 0.32) and PSG vs V kappa 0.43(CI 0.2, 0.67). Ineffective effort, a measure of PVA, PSG vs V studies demonstrated kappa 0.02 (CI 0, 0.05) compared with PSG vs PG kappa 0.34 (CI 0.16, 0.53). Physician confidence for ventilation prescription was lower in PG and V studies at -1.3 and -3.5 points respectively when compared with PSG. Conclusion Ventilator only derived signals resulted in key differences in respiratory event, PVA identification and physician confidence. This may help inform future models of NIV care.

Neural Network Applications in Polygraph Scoring—A Scoping Review

Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions.

Can Polygraph Test Results Be Used as Evidence?

Can Polygraph Test Results Be Used as Evidence?

Use of Virtual Reality in Polygraph Testing

Use of Virtual Reality in Polygraph Testing

Explainable Enhanced Recurrent Neural Network for lie detection using voice stress analysis

Lie detection is a crucial aspect of human interactions that affects everyone in their daily lives. Individuals often rely on various cues, such as verbal and nonverbal communication, particularly facial expressions, to determine if someone is truthful. While automated lie detection systems can assist in identifying these cues, current approaches are limited due to a lack of suitable datasets for testing their performance in real-world scenarios. Despite ongoing research efforts to develop effective and reliable lie detection methods, this remains a work in progress. The polygraph, voice stress analysis, and pupil dilation analysis are some of the methods currently used for this task. In this study, we propose a new detection algorithm based on an Enhanced Recurrent Neural Network (ERNN) with Explainable AI capabilities. The ERNN, based on long short-term memory (LSTM) architecture, was optimized using fuzzy logic to determine the hyperparameters. The LSTM model was then created and trained using a dataset of audio recordings from interviews with a randomly selected group. The proposed ERNN achieved an accuracy of 97.3%, which is statistically significant for the problem of voice stress analysis. These results suggest that it is possible to detect patterns in the voices of individuals experiencing stress in an explainable manner.

Signal quality of home polygraphy in children and adolescents.

The aims of the study were to examine the signal quality (SQ) of home polygraphy (PG) in children and adolescents and to compare automatic and manual scoring of the PGs. Clinical Trials Registration: NCT04964830. Participants and caregivers were instructed to set up the equipment and perform home PGs themselves. The PGs were analysed according to SQ and their interpretability and differences in automatic vs. manual scoring regarding apnoea-hypopnoea index (AHI), apnoea index (AI), hypopnoea index (HI) and oxygen desaturation index (ODI) were examined. 54 healthy children aged 9-14 years participated in the study. 86% of the PGs were interpretable with mean SQ of 79.1% (CI 95%: 73.5%; 84.8%). Significant differences between the automatic and manual scoring were found for AHI, AI, HI and ODI (p < 0.0001). Home PGs of children and adolescents are feasible to be performed with good SQ. Significantly higher markers of sleep-disordered breathing were achieved in the automatic scoring in comparison with the manual scoring.

The Offender Recognizes the Victim – About the Advantages of Using the Visual Version of Polygraph Test in Serious Criminal Case

Abstract The authors of this article consider the advantages of using a visual version of CIT during polygraph examinations. The presentation of the rarely used CIT encourages to discussion about the possibility of implementation such tests in serious criminal cases. Their application takes a form similar to the police lineup. However, the main difference is that the suspect reviews photographs of possible crime victim. In the described case we are dealing with a so-called “reverse police lineup”. As the result, the visual CIT proved that the examinee knew the victim of the crime, despite the fact that he had previously denied this. Thanks to the examinee’s arousal recorded on the key question, it was also possible to obtain the desired psychological effect in the form of the perpetrator’s confession and the indication of other evidence proving his guilt.

A Metatheoretical Review of Cognitive Load Lie Detection

This article examines the idea that cognitive load interventions can expose lies—because lying is more demanding than truth-telling. I discuss the limitations of that hypothesis by reviewing seven of its justifications. For example, liars must suppress the truth while lying, and this handicap makes lying challenging such that one can exploit the challenge to expose lies. The theoretical fitness of each justification is variable and unknown. Those ambiguities prevent analysts from ascertaining the verisimilitude of the hypothesis. I propose research questions whose answers could assist in specifying the justifications and making cognitive load lie detection amenable to severe testing.

On the Issue of Compliance with the Legislation on Personal Data by the Employer During Video Recording and Psychophysiological Study of Workers on the Polygraph

The article is devoted to the issues of compliance by the employer with the legislation of the Russian Federation on the collection of personal data of an employee through technical means of control over him and his workplace, as well as the fact and limits of interference in his private life. In this case, the need for the legitimacy of ensuring the rights and obligations of the parties to labor relations and the consistency of their interests is indicated. The authors note that the workplace is under the control of the employer, who is free to choose the means of control over the employee. The article discusses such means of control as video surveillance of employees and their testing on a polygraph (lie detector). An analysis of the current labor and other legislation of the Russian Federation, which regulates these legal relations of participants in the labor process, as well as the current judicial practice, which is ambiguous, has been undertaken. It is concluded that video surveillance and video recording of the workplace, subject to a number of conditions specified in the article, do not aim to interfere with the private life of the employee, do not violate the rules on the protection of personal data, since they are aimed at ensuring the safety of employees, their control, the safety of property on the territory of the employer, fixing and proving non-fulfillment by the employee of his labor duties. The article formulates a number of requirements, the observance of which will ensure the legality of video monitoring and the inadmissibility of violating the rights and freedoms of employees. When testing an employee on a polygraph, taking into account the provisions of the Labor Code of the Russian Federation, a polygraph examiner acting in the interests of the employer should also comply with the requirements of legislation on personal data. Compliance by the employer with certain conditions for the participation of an employee in a psychophysiological study using a polygraph, including bringing to the attention of the employee the subject and purpose of the study that are of interest to the employer, makes this event voluntary and legal, and the processing of personal data in the process of its implementation – in line with current legislation in the field of personal data and labor relations.

CONSTRUCTION OF EXAMPLES AND COUNTEREXAMPLES IN PRIMARY SCHOOL TO VERIFY THE TRUTH OF STATEMENTS

In the modern mathematics learning process in school, the skill of creating examples and counterexamples in both familiar and new situations is emphasized. In the context of critical thinking, the construction of examples and counterexamples is an effective technique for evaluating statements and justifying arguments. This technique is suitable for students before other methods of proving general statements in mathematics are introduced.The aim of the study is to highlight the topics of the study course "Elements of Mathematical Logic and Set Theory" for the Professional Bachelor's degree in Teacher Education, which are relevant for constructing examples and counterexamples. In the study, the qualitative and quantitative analysis of the answers of the students in teacher programs to the questions of the questionnaire about the student experience related to the ability to formulate examples and counterexamples was carried out. The research shows that the creation of examples and counterexamples to the given statements does not cause great difficulties. It is much more difficult to create statements with mathematical content for grades 1-6, the truth verification of which, using the technique of constructing examples and counterexamples, would activate the formation of understanding of mathematical concepts and relationships. In order to connect the topics of the "Elements of Mathematical Logic and Set Theory" course with the planned results of primary education, a qualitative content analysis of the Latvian primary education standard and the primary education program was carried out. Based on the results obtained in the study, the authors identified several topics that are relevant in the course "Elements of Mathematical Logic and Set Theory".

Особенности организации обучения сотрудников органов внутренних дел Российской Федерации в рамках общей физической подготовки

Особенности организации обучения сотрудников органов внутренних дел Российской Федерации в рамках общей физической подготовки

Promoting Honesty in Children, or Fostering Pathological Behaviour?

Promoting Honesty in Children, or Fostering Pathological Behaviour?

Оценка вклада физиологических параметров в определение значимости фактора риска в ситуации скринингового тестирования на полиграфе

&lt;p style="text-align: justify;"&gt;The scientific validity of polygraph testing is regularly criticized by the scientific community in view of the subjectivity of data evaluation and the unclear predictive value of the parameters obtained with the help of a polygraph. In order to clarify the contribution of the physiological cues used in identifying the salience of the topic during polygraph screening, the materials of an empirical study obtained from a sample of 67 volunteers who have a risk factor associated with forgery of documents and do not have it are presented. A psychophysiological method was used with a preliminary questionnaire about the well-being at the time of the experiment. During the paid experiment, it was proposed to forge a financial document and lie about it to a polygraph examiner, or answer questions honestly. In order to eliminate subjectivity in data evaluation, all registered signals were digitized, standardized and processed using the discriminate analysis method. At the same time, a high accuracy of the discriminant model was found (92.5%). The parameters of a short-term rise in blood pressure and changes in the electrical activity of the skin were the main variables for the classification of persons with and without a risk factor. The data obtained complement previous studies on the accuracy of the polygraph screening test, expanding the prognostic value of the length of the respiratory signal line.&lt;/p&gt;

Blocks

Blocks

Polygraph Examinations as Preventative Measure of Corruptive Behavior – A Case Study

Polygraph Examinations as Preventative Measure of Corruptive Behavior – A Case Study

Impact of Obstructive Sleep Apnea on Health-Related Quality of Life in Patients With Partial Laryngectomy for Laryngeal Cancer.

Objectives: To evaluate the possible risk factors of obstructive sleep apnea (OSA) and its impact on the health-related quality of life (HRQoL) in patients with partial laryngectomy for laryngeal cancer. Methods: A cross-sectional method was used to carry out this study. Patients who underwent partial laryngectomy for laryngeal cancer completed overnight polygraphy (PG) home sleep tests and quality of life questionnaires. The Medical Outcome Study 36-item Short-Form Health Survey (SF-36) questionnaire was used to investigate the factors influencing HRQoL. Results: A total of 59 patients completed the PG tests and quality of life questionnaires, with 74.6% demonstrating evidence of OSA. There were significant differences in tumor area and neck dissection between OSA group and non-OSA groups. Based on sleep-related parameters, patients were divided into cluster 1 (n = 14) and cluster 2 (n = 45) using principal component analysis combined with K-means clustering. Two clusters had significantly different scores of body pain, general health, and health transition in SF-36 domains. Independent factors associated with general health were identified as tobacco use (OR = 4.716), alcohol use (OR = 3.193), and OSA-related condition (OR = 11.336). Conclusions: Larger tumor area and neck dissection might be associated with an increased risk for developing OSA in patients with partial laryngectomy for laryngeal cancer. OSA partially mediated the effect on physical health, including body pain, general health, and health transition. It is important to be aware of the potential impact of OSA on diminished HRQoL of these patients.

Outlier Detection with Reinforcement Learning for Costly to Verify Data.

Outliers are often present in data and many algorithms exist to find these outliers. Often we can verify these outliers to determine whether they are data errors or not. Unfortunately, checking such points is time-consuming and the underlying issues leading to the data error can change over time. An outlier detection approach should therefore be able to optimally use the knowledge gained from the verification of the ground truth and adjust accordingly. With advances in machine learning, this can be achieved by applying reinforcement learning on a statistical outlier detection approach. The approach uses an ensemble of proven outlier detection methods in combination with a reinforcement learning approach to tune the coefficients of the ensemble with every additional bit of data. The performance and the applicability of the reinforcement learning outlier detection approach are illustrated using granular data reported by Dutch insurers and pension funds under the Solvency II and FTK frameworks. The application shows that outliers can be identified by the ensemble learner. Moreover, applying the reinforcement learner on top of the ensemble model can further improve the results by optimising the coefficients of the ensemble learner.