In data-driven systems, data exploration is imperative for making real-time decisions.
However, big data are stored in massive databases that are difficult to retrieve. Approximate …

Accompanying the successes of learning-based defensive software vulnerability analyses is
the lack of large and quality sets of labeled vulnerable program samples, which impedes …

Most machine learning algorithms only have a good recognition rate on balanced datasets.
However, in the field of malicious traffic identification, benign traffic on the network is far …

The generation of synthetic households is challenging due to the necessity of maintaining
consistency between the two layers of interest: the household itself, and the individuals …

Synthetic data generation has been a growing area of research in recent years. However, its
potential applications in serious games have yet to be thoroughly explored. Advances in this …

Census and Household Travel Survey datasets are regularly collected from households and
individuals and provide information on their daily travel behavior with demographic and …

Transportation science today is tasked with predicting the complex mobility needs of
individuals, which necessitates the use of advanced mobility and travel demand models …

This paper presents a novel hybrid framework for generating and updating a synthetic
population. We call it hybrid because it combines model-based and data-driven approaches …

Randomized clinical trials (RCTs) are essential to guide medical practice; however, their
generalizability to a given population is often uncertain. We developed a statistically …

Conditionality has become a core component for Generative Adversarial Networks (GANs)
for generating synthetic images. GANs are usually using latent conditionality to control the …