Xiao Ling

College of Engineering

Works (3)

Updated: April 6th, 2024 05:02

2024 journal article

Trading Off Scalability, Privacy, and Performance in Data Synthesis

IEEE ACCESS, 12, 26642–26654.

By: X. Ling n, T. Menzies n , C. Hazard*, J. Shu & J. Beel

author keywords: Synthetic data; Clustering algorithms; Data models; Engines; Biomedical imaging; Generative adversarial networks; Data privacy; Regression analysis; Classification algorithms; Scalability; Homomorphic encryption; Synthetic data generation; privacy preservation; regression; classification
TL;DR: It is shown that the synthetic data generated by Howso engine has good privacy and accuracy, which results in the best overall score, and the proposed random projection based synthetic data generation framework can generate synthetic data with highest accuracy score, and has the fastest scalability. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: February 22, 2024

2023 article

Learned Temporal Aggregations for Fraud Classification on E-Commerce Platforms

COMPANION OF THE WORLD WIDE WEB CONFERENCE, WWW 2023, pp. 1365–1372.

By: X. Ling n, D. Yan, B. Alsallakh *, A. Pandey, M. Bakshi & P. Bhattacharya

author keywords: machine learning; integrity; fraud; e-commerce; recurrent neural networks
TL;DR: It is shown that a model which learns a time-aggregation using a sequence encoder outperforms models trained on handcrafted aggregations on the fraud classification task with a real-world dataset. (via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: February 19, 2024

2023 journal article

What Not to Test (For Cyber-Physical Systems)

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(7), 3811–3826.

By: X. Ling n & T. Menzies n 

author keywords: Search-based software engineering; modeling and model-driven engineering; validation and verification; software testing; simulation-based testing; multi-goal optimization
TL;DR: DoLesS (Domination with Least Squares Approximation) that selects the minimal and effective test cases by averaging over a coarse-grained grid of the information gained from multiple optimizations goals to find a minimal set of tests that can distinguish better from worse parts of the optimization goals. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 28, 2023

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