activity
20082022
most citedFinger Texture Biometric Characteristic: a Survey

17 citations · 27 across the 10 of their papers we have counts for

collaborators

13 papers

cs.PL2022

Preventing Timing Side-Channels via Security-Aware Just-In-Time Compilation

Qi Qin, JulianAndres JiYang, Fu Song +2

Recent work has shown that Just-In-Time (JIT) compilation can introduce timing side-channels to constant-time programs, which would otherwise be a principled and effective means to…

cs.LG20211 cited

BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks

Yedi Zhang, Zhe Zhao, Guangke Chen +2

Verifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications. In this paper, we st…

eess.SY20204 cited

Learning Safe Neural Network Controllers with Barrier Certificates

Hengjun Zhao, Xia Zeng, Taolue Chen +2

We provide a novel approach to synthesize controllers for nonlinear continuous dynamical systems with control against safety properties. The controllers are based on neural network…

cs.LO2020

A Decision Procedure for Path Feasibility of String Manipulating Programs with Integer Data Type

Taolue Chen, Matthew Hague, Jinlong He +4

Strings are widely used in programs, especially in web applications. Integer data type occurs naturally in string-manipulating programs, and is frequently used to refer to lengths…

cs.CR20202 cited

A Hybrid Approach to Formal Verification of Higher-Order Masked Arithmetic Programs

Pengfei Gao, Hongyi Xie, Fu Song +1

Side-channel attacks, which are capable of breaking secrecy via side-channel information, pose a growing threat to the implementation of cryptographic algorithms. Masking is an eff…

cs.CV202017 cited

Finger Texture Biometric Characteristic: a Survey

Raid R. O. Al-Nima, Tingting Han, Taolue Chen +2

\begin{abstract} In recent years, the Finger Texture (FT) has attracted considerable attention as a biometric characteristic. It can provide efficient human recognition performance…