Dataset Information

You shall not pass: how facial variability and feedback affect the detection of low-prevalence fake IDs.

ABSTRACT: In many real-world settings, individuals rarely present another person's ID, which increases the likelihood that a screener will fail to detect it. Three experiments examined how within-person variability (i.e., differences between two images of the same person) and feedback may have influenced criterion shifting, thought to be one of the sources of the low-prevalence effect (LPE). Participants made identity judgments of a target face and an ID under either high, medium, or low mismatch prevalence. Feedback appeared after every trial, only error trials, or no trials. Experiment 1 used two controlled images taken on the same day. Experiment 2 used two controlled images taken at least 6 months apart. Experiment 3 used one controlled and one ambient image taken at least 1 year apart. Importantly, receiver operating characteristic curves revealed that feedback and greater within-person variability exacerbated the LPE by affecting both criterion and discriminability. These results carry implications for many real-world settings, such as border crossings and airports, where identity screening plays a major role in securing public safety.

SUBMITTER: Weatherford DR

PROVIDER: S-EPMC6987271 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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You shall not pass: how facial variability and feedback affect the detection of low-prevalence fake IDs.

Weatherford Dawn R DR Erickson William Blake WB Thomas Jasmyne J Walker Mary E ME Schein Barret B

Cognitive research: principles and implications 20200128 1

In many real-world settings, individuals rarely present another person's ID, which increases the likelihood that a screener will fail to detect it. Three experiments examined how within-person variability (i.e., differences between two images of the same person) and feedback may have influenced criterion shifting, thought to be one of the sources of the low-prevalence effect (LPE). Participants made identity judgments of a target face and an ID under either high, medium, or low mismatch prevalen ...[more]

PMID: 31993804

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Project description:BACKGROUND: We studied four extremely halophilic archaea by low-pass shotgun sequencing: (1) the metabolically versatile Haloarcula marismortui; (2) the non-pigmented Natrialba asiatica; (3) the psychrophile Halorubrum lacusprofundi and (4) the Dead Sea isolate Halobaculum gomorrense. Approximately one thousand single pass genomic sequences per genome were obtained. The data were analyzed by comparative genomic analyses using the completed Halobacterium sp. NRC-1 genome as a reference. Low-pass shotgun sequencing is a simple, inexpensive, and rapid approach that can readily be performed on any cultured microbe. RESULTS: As expected, the four archaeal halophiles analyzed exhibit both bacterial and eukaryotic characteristics as well as uniquely archaeal traits. All five halophiles exhibit greater than sixty percent GC content and low isoelectric points (pI) for their predicted proteins. Multiple insertion sequence (IS) elements, often involved in genome rearrangements, were identified in H. lacusprofundi and H. marismortui. The core biological functions that govern cellular and genetic mechanisms of H. sp. NRC-1 appear to be conserved in these four other halophiles. Multiple TATA box binding protein (TBP) and transcription factor IIB (TFB) homologs were identified from most of the four shotgunned halophiles. The reconstructed molecular tree of all five halophiles shows a large divergence between these species, but with the closest relationship being between H. sp. NRC-1 and H. lacusprofundi. CONCLUSION: Despite the diverse habitats of these species, all five halophiles share (1) high GC content and (2) low protein isoelectric points, which are characteristics associated with environmental exposure to UV radiation and hypersalinity, respectively. Identification of multiple IS elements in the genome of H. lacusprofundi and H. marismortui suggest that genome structure and dynamic genome reorganization might be similar to that previously observed in the IS-element rich genome of H. sp. NRC-1. Identification of multiple TBP and TFB homologs in these four halophiles are consistent with the hypothesis that different types of complex transcriptional regulation may occur through multiple TBP-TFB combinations in response to rapidly changing environmental conditions. Low-pass shotgun sequence analyses of genomes permit extensive and diverse analyses, and should be generally useful for comparative microbial genomics.

Project description:Intrusion detection systems (IDSs) analyze internet activities and traffic to detect potential attacks, thereby safeguarding computer systems. In this study, researchers focused on developing an advanced IDS that achieves high accuracy through the application of feature selection and ensemble learning methods. The utilization of the CIC-CSE-IDS2018 dataset for training and testing purposes adds relevance to the study. The study comprised two key stages, each contributing to its significance. In the first stage, the researchers reduced the dataset through strategic feature selection and carefully selected algorithms for ensemble learning. This process optimizes the IDS's performance by selecting the most informative features and leveraging the strengths of different classifiers. In the second stage, the ensemble learning approach was implemented, resulting in a powerful model that combines the benefits of multiple algorithms. The results of the study demonstrate its impact on improving attack detection and reducing detection time. By applying techniques such as Spearman's correlation analysis, recursive feature elimination (RFE), and chi-square test methods, the researchers identified key features that enhance the IDS's performance. Furthermore, the comparison of different classifiers showcased the effectiveness of models such as extra trees, decision trees, and logistic regression. These models not only achieved high accuracy rates but also considered the practical aspect of execution time. The study's overall significance lies in its contribution to advancing IDS capabilities and improving computer security. By adopting an ensemble learning approach and carefully selecting features and classifiers, the researchers created a model that outperforms individual classifier approaches. This model, with its high accuracy rate, further validates the effectiveness of ensemble learning in enhancing IDS performance. The findings of this study have the potential to drive future developments in intrusion detection systems and have a tangible impact on ensuring robust computer security in various domains.

Dataset Information

You shall not pass: how facial variability and feedback affect the detection of low-prevalence fake IDs.

Publications

You shall not pass: how facial variability and feedback affect the detection of low-prevalence fake IDs.

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