activity
20162023
most citedRepresentation Learning with Deconvolution for Multivariate Time Series Classification and Visualization

10 citations · 12 across the 5 of their papers we have counts for

collaborators

6 papers

cs.SE20241 cited

Cross-Inlining Binary Function Similarity Detection

Ang Jia, Ming Fan, Xi Xu +3

Binary function similarity detection plays an important role in a wide range of security applications. Existing works usually assume that the query function and target function sha…

cs.CR20231 cited

Do as You Say: Consistency Detection of Data Practice in Program Code and Privacy Policy in Mini-App

Yin Wang, Ming Fan, Junfeng Liu +6

Mini-app is an emerging form of mobile application that combines web technology with native capabilities. Its features, e.g., no need to download and no installation, have made it…

cs.CL20231 cited

BDMMT: Backdoor Sample Detection for Language Models through Model Mutation Testing

Jiali Wei, Ming Fan, Wenjing Jiao +2

Deep neural networks (DNNs) and natural language processing (NLP) systems have developed rapidly and have been widely used in various real-world fields. However, they have been sho…

physics.ao-ph2022

A Spatiotemporal-Aware Climate Model Ensembling Method for Improving Precipitation Predictability

Ming Fan, Dan Lu, Deeksha Rastogi +1

Multimodel ensembling has been widely used to improve climate model predictions, and the improvement strongly depends on the ensembling scheme. In this work, we propose a Bayesian…

cs.SE2021

1-to-1 or 1-to-n? Investigating the effect of function inlining on binary similarity analysis

Ang Jia, Ming Fan, Wuxia Jin +6

Binary similarity analysis is critical to many code-reuse-related issues and "1-to-1" mechanism is widely applied, where one function in a binary file is matched against one functi…

cs.LG201610 cited

Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization

Zhiguang Wang, Wei Song, Lu Liu +5

We propose a new model based on the deconvolutional networks and SAX discretization to learn the representation for multivariate time series. Deconvolutional networks fully exploit…