Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of hepatocellular carcinoma (HCC).

Medicine·2021
Same author

Dual Optical Path Based Adaptive Compressive Sensing Imaging System.

Sensors (Basel, Switzerland)·2021
Same author

Pharmacological inhibition of arachidonate 12-lipoxygenase ameliorates myocardial ischemia-reperfusion injury in multiple species.

Cell metabolism·2021
Same author

Copper-catalysed exclusive CO<sub>2</sub> to pure formic acid conversion via single-atom alloying.

Nature nanotechnology·2021
Same author

Genetic association between bone mineral density and the fracture of distal radius: A case-control study.

Medicine·2021
Same author

Optical Fiber Based Mach-Zehnder Interferometer for APES Detection.

Sensors (Basel, Switzerland)·2021
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: May 12, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.0K

Privacy-Preserving Biometric Verification With Handwritten Random Digit String.

Peirong Zhang, Yuliang Liu, Songxuan Lai

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Privacy concerns in handwriting verification are addressed using Random Digit Strings (RDS). Our new model, PAVENet with DPM, effectively verifies handwriting while protecting personal information.

    More Related Videos

    Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
    05:58

    Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

    Published on: March 11, 2021

    4.4K
    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
    08:15

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

    Published on: March 28, 2025

    361

    Related Experiment Videos

    Last Updated: May 12, 2026

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
    10:28

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

    Published on: July 24, 2019

    15.0K
    Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
    05:58

    Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

    Published on: March 11, 2021

    4.4K
    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
    08:15

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

    Published on: March 28, 2025

    361

    Area of Science:

    • Biometrics
    • Computer Science
    • Cybersecurity

    Background:

    • Traditional handwriting verification methods pose privacy risks due to personal data in signatures.
    • Biometric authentication requires robust privacy-preserving solutions.

    Purpose of the Study:

    • To introduce Random Digit String (RDS) as a privacy-preserving method for handwriting verification.
    • To develop and evaluate a novel deep learning model for RDS-based authentication.

    Main Methods:

    • Construction of the HRDS4BV dataset for online handwritten RDS.
    • Proposal of the Pattern Attentive VErification Network (PAVENet) with Discriminative Pattern Mining (DPM).
    • DPM module enhances recognition of consistent and discriminative writing patterns for improved style representation.

    Main Results:

    • PAVENet demonstrated superior performance over existing methods in online RDS verification.
    • A novel forgery phenomenon was identified, offering enhanced defense against impostor attacks.
    • The study validates the effectiveness of RDS for privacy-preserving biometric verification.

    Conclusions:

    • Random Digit String (RDS) offers a viable and private alternative for handwriting verification.
    • PAVENet and DPM significantly advance the accuracy and privacy of biometric authentication.
    • This research paves the way for wider adoption of privacy-preserving biometric technologies.