@article{ling_menzies_hazard_shu_beel_2024, title={Trading Off Scalability, Privacy, and Performance in Data Synthesis}, volume={12}, ISSN={["2169-3536"]}, url={https://doi.org/10.1109/ACCESS.2024.3366556}, DOI={10.1109/ACCESS.2024.3366556}, abstractNote={Synthetic data has been widely applied in the real world recently. One typical example is the creation of synthetic data for privacy concerned datasets. In this scenario, synthetic data substitute the real data which contains the privacy information, and is used to public testing for machine learning models. Another typical example is the unbalance data over-sampling which the synthetic data is generated in the region of minority samples to balance the positive and negative ratio when training the machine learning models. In this study, we concentrate on the first example, and introduce (a) the Howso engine, and (b) our proposed random projection based synthetic data generation framework. We evaluate these two algorithms on the aspects of privacy preservation and accuracy, and compare them to the two state-of-the-art synthetic data generation algorithms DataSynthesizer and Synthetic Data Vault. We show that the synthetic data generated by Howso engine has good privacy and accuracy, which results in the best overall score. On the other hand, our proposed random projection based framework can generate synthetic data with highest accuracy score, and has the fastest scalability.}, journal={IEEE ACCESS}, author={Ling, Xiao and Menzies, Tim and Hazard, Christopher and Shu, Jack and Beel, Jacob}, year={2024}, pages={26642–26654} }
 @article{ling_yan_alsallakh_pandey_bakshi_bhattacharya_2023, title={Learned Temporal Aggregations for Fraud Classification on E-Commerce Platforms}, DOI={10.1145/3543873.3587632}, abstractNote={Fraud and other types of adversarial behavior are serious problems on customer-to-customer (C2C) e-commerce platforms, where harmful behaviors by bad actors erode user trust and safety. Many modern e-commerce integrity systems utilize machine learning (ML) to detect fraud and bad actors. We discuss the practical problems faced by integrity systems which utilize data associated with user interactions with the platform. Specifically, we focus on the challenge of representing the user interaction events, and aggregating their features. We compare the performance of two paradigms to handle the feature temporality when training the ML models: hand-engineered temporal aggregation and a learned aggregation using a sequence encoder. We show 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.}, journal={COMPANION OF THE WORLD WIDE WEB CONFERENCE, WWW 2023}, author={Ling, Xiao and Yan, David and Alsallakh, Bilal and Pandey, Ashutosh and Bakshi, Manan and Bhattacharya, Pamela}, year={2023}, pages={1365–1372} }
 @article{ling_menzies_2023, title={What Not to Test (For Cyber-Physical Systems)}, volume={49}, ISSN={["1939-3520"]}, url={https://doi.org/10.1109/TSE.2023.3272309}, DOI={10.1109/TSE.2023.3272309}, abstractNote={For simulation-based systems, finding a set of test cases with the least cost by exploring multiple goals is a complex task. Domain-specific optimization goals (e.g., maximize output variance) are useful for guiding the rapid selection of test cases via mutation. But evaluating the selected test cases via mutation (that can distinguish the current program from) is a different goal to domain-specific optimizations. While the optimization goals can be used to guide the mutation analysis, that guidance should be viewed as a weak indicator since it can hurt the mutation effectiveness goals by focusing too much on the optimization goals. Based on the above, this paper proposes 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. DoLesS applies an inverted least squares approximation approach to find a minimal set of tests that can distinguish better from worse parts of the optimization goals. When tested on multiple simulation-based systems, DoLesS performs as well or even better as the prior state-of-the-art, while running 80-360 times faster on average (seconds instead of hours).}, number={7}, journal={IEEE TRANSACTIONS ON SOFTWARE ENGINEERING}, author={Ling, Xiao and Menzies, Tim}, year={2023}, month={Jul}, pages={3811–3826} }