Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Types of Errors: Detection and Minimization

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...

Machines: Problem Solving II

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

Sign Test for Matched Pairs

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...

Machines: Problem Solving I

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

You might also read

Related Articles

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

Sort by

Same author

Integrative Multi-Omics Analysis Identifies an <i>SPP1</i>-Associated Spatial Mesenchymal-Myeloid Program in Glioblastoma.

Genes·2026

Same author

Light Induced Transformations of the NO<sub>2</sub> Group in Nitroarenes.

Chemistry (Weinheim an der Bergstrasse, Germany)·2026

Same author

Individual and joint association between per- and polyfluoroalkyl substances and skeletal muscle among 6-9 years old children in China.

International journal of hygiene and environmental health·2026

Same author

Restoration of circadian rhythm as novel targets against sarcopenia.

Chinese medical journal·2026

Same author

Carbon dioxide laser combined with photodynamic therapy versus photodynamic therapy alone for vaginal intraepithelial neoplasia: a multicenter retrospective comparative cohort study.

BMC women's health·2026

Same author

Effect of CNTs and GO Additives on Mechanical and Electrochemical Properties of Cement Structural Supercapacitors.

Materials (Basel, Switzerland)·2026

Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026

Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026

Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026

Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026

Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026

Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Semantic aware-based instruction embedding for binary code similarity detection.

Yuhao Jia¹, Zhicheng Yu¹, Zhen Hong¹

¹College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China.

|June 11, 2024

Summary

This summary is machine-generated.

This study introduces BinBcla, a new method for binary code similarity detection. BinBcla enhances binary embedding representations and extracts multi-level semantic features for improved accuracy in vulnerability detection and malware analysis.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Related Experiment Videos

Last Updated: Jun 24, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

Computer Science
Cybersecurity
Software Engineering

Background:

Existing binary code similarity detection methods struggle with limited differentiation across compilation environments and lack dynamic semantics.
Current approaches often fail to extract multi-level semantic features, hindering precise semantic information acquisition.

Purpose of the Study:

To introduce BinBcla, a novel solution for binary code similarity detection.
To address limitations in existing methods by enhancing binary embedding representations and incorporating multi-level semantic feature extraction.

Main Methods:

Employs an enhanced pre-training model for instruction embeddings with dynamic semantics.
Utilizes multi-feature fusion and self-attention for local and global feature extraction and structural comprehension.
Applies an improved cosine similarity method for robust relationship learning among distance vectors.

Main Results:

BinBcla achieves higher accuracy, precision, and F1 score compared to existing methods.
Demonstrates improved robustness to new sample functions across different architectures, compilers, and optimization levels.

Conclusions:

BinBcla offers a more effective approach to binary code similarity detection.
The method shows significant potential for applications in binary security, including vulnerability detection and malicious software analysis.