arXiv subjects

Dung T. Nguyen

Publications and source records attributed to Dung T. Nguyen.

4 recordsLinked to original sources

arXiv2026

Bivariate Frank Copula: Some More Results on Point Estimation of the Association Parameter from a Bayesian Perspective and Revisiting the Goodness of Fit Tests with an Application to Model Groundwater Data from Dong Thap, Vietnam

This work consists of two major parts. (i) The first part extends the recent comprehensive study of Pham et al. (2025), where three classical point estimators of the association parameter of a bivariate Frank Copula were compared and the maximum likelihood estimator (MLE) was established to have the best overall performance in terms of bias and mean squared error (MSE). We investigate two Bayes estimators under two natural priors, namely the noninformative (generalized) flat prior and the invariant Jeffreys prior, and find that the latter uniformly dominates the former as well as the MLE in terms of MSE for small sample sizes (n <= 25). For moderate to large sample sizes (n > 25), all three estimators have almost identical performances in terms of bias and MSE. We also point out computational aspects in R that may have important implications for computing the MLE and its bias and/or MSE for very small samples. (ii) The second part uses a recent dataset from Vietnam and applies the Frank Copula to analyze the association between groundwater arsenic concentration and three other benign elements that are easy to monitor. In this application, we revisit the two goodness-of-fit (GoF) tests introduced by Genest et al. (2006), explore non-intuitive behavior of the two test statistics, and provide extensive tables of their critical values through a comprehensive simulation study. This complements and refines the computational results of Genest et al. (2006). Using the groundwater data, we also demonstrate that nonparametric marginals can offer benefits over parametric marginals in predicting arsenic concentration from a benign element using the Frank Copula-based regression model.

stat.ME↗

arXiv2024

Trigger-Based Fragile Model Watermarking for Image Transformation Networks

In fragile watermarking, a sensitive watermark is embedded in an object in a manner such that the watermark breaks upon tampering. This fragile process can be used to ensure the integrity and source of watermarked objects. While fragile watermarking for model integrity has been studied in classification models, image transformation/generation models have yet to be explored. We introduce a novel, trigger-based fragile model watermarking system for image transformation/generation networks that takes advantage of properties inherent to image outputs. For example, manifesting watermarks as specific visual patterns, styles, or anomalies in the generated content when particular trigger inputs are used. Our approach, distinct from robust watermarking, effectively verifies the model's source and integrity across various datasets and attacks, outperforming baselines by 94%. We conduct additional experiments to analyze the security of this approach, the flexibility of the trigger and resulting watermark, and the sensitivity of the watermarking loss on performance. We also demonstrate the applicability of this approach on two different tasks (1 immediate task and 1 downstream task). This is the first work to consider fragile model watermarking for image transformation/generation networks.

cs.CR↗

arXiv2016

Least Cost Influence Maximization Across Multiple Social Networks

Recently in Online Social Networks (OSNs), the Least Cost Influence (LCI) problem has become one of the central research topics. It aims at identifying a minimum number of seed users who can trigger a wide cascade of information propagation. Most of existing literature investigated the LCI problem only based on an individual network. However, nowadays users often join several OSNs such that information could be spread across different networks simultaneously. Therefore, in order to obtain the best set of seed users, it is crucial to consider the role of overlapping users under this circumstances. In this article, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem by mapping a set of networks into a single one via lossless and lossy coupling schemes. The lossless coupling scheme preserves all properties of original networks to achieve high quality solutions, while the lossy coupling scheme offers an attractive alternative when the running time and memory consumption are of primary concern. Various experiments conducted on both real and synthesized datasets have validated the effectiveness of the coupling schemes, which also provide some interesting insights into the process of influence propagation in multiplex networks.

cs.SI↗

arXiv2014

Beyond Worst-Case Analysis for Joins with Minesweeper

We describe a new algorithm, Minesweeper, that is able to satisfy stronger runtime guarantees than previous join algorithms (colloquially, `beyond worst-case guarantees') for data in indexed search trees. Our first contribution is developing a framework to measure this stronger notion of complexity, which we call {\it certificate complexity}, that extends notions of Barbay et al. and Demaine et al.; a certificate is a set of propositional formulae that certifies that the output is correct. This notion captures a natural class of join algorithms. In addition, the certificate allows us to define a strictly stronger notion of runtime complexity than traditional worst-case guarantees. Our second contribution is to develop a dichotomy theorem for the certificate-based notion of complexity. Roughly, we show that Minesweeper evaluates $β$-acyclic queries in time linear in the certificate plus the output size, while for any $β$-cyclic query there is some instance that takes superlinear time in the certificate (and for which the output is no larger than the certificate size). We also extend our certificate-complexity analysis to queries with bounded treewidth and the triangle query.

cs.DB↗