66 citations · 275 across the 22 of their papers we have counts for
23 papers
Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction
Mingchen Li, Di Zhuang, Keyu Chen +2
Link prediction in graph data uses various algorithms and Graph Nerual Network (GNN) models to predict potential relationships between graph nodes. These techniques have found wide…
Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data
Hung Nguyen, Morris Chang
This study examines the impact of class-imbalanced data on deep learning models and proposes a technique for data balancing by generating synthetic data for the minority class. Unl…
Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation
Keyu Chen, Di Zhuang, Mingchen Li +1
Neural Machine Translation (NMT) models have become successful, but their performance remains poor when translating on new domains with a limited number of data. In this paper, we…
Towards Implementing Energy-aware Data-driven Intelligence for Smart Health Applications on Mobile Platforms
G. Dumindu Samaraweera, Hung Nguyen, Hadi Zanddizari +2
Recent breakthrough technological progressions of powerful mobile computing resources such as low-cost mobile GPUs along with cutting-edge, open-source software architectures have…
IMUNet: Efficient Regression Architecture for IMU Navigation and Positioning
Behnam Zeinali, Hadi Zandizari, J. Morris Chang
Data-driven based method for navigation and positioning has absorbed attention in recent years and it outperforms all its competitor methods in terms of accuracy and efficiency. Th…
MC-GEN:Multi-level Clustering for Private Synthetic Data Generation
Mingchen Li, Di Zhuang, J. Morris Chang
With the development of machine learning and data science, data sharing is very common between companies and research institutes to avoid data scarcity. However, sharing original d…
SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classification
Keyu Chen, Di Zhuang, J. Morris Chang
Convolutional neural networks (CNNs) have achieved great success in skin lesion classification. A balanced dataset is required to train a good model. However, due to the appearance…
Locally Differentially Private Distributed Deep Learning via Knowledge Distillation
Di Zhuang, Mingchen Li, J. Morris Chang
Deep learning often requires a large amount of data. In real-world applications, e.g., healthcare applications, the data collected by a single organization (e.g., hospital) is ofte…
Complementary Ensemble Learning
Hung Nguyen, Morris Chang
To achieve high performance of a machine learning (ML) task, a deep learning-based model must implicitly capture the entire distribution from data. Thus, it requires a huge amount…
COVID-19 Pneumonia Severity Prediction using Hybrid Convolution-Attention Neural Architectures
Nam Nguyen, J. Morris Chang
This study proposed a novel framework for COVID-19 severity prediction, which is a combination of data-centric and model-centric approaches. First, we propose a data-centric pre-tr…
SEC-NoSQL: Towards Implementing High Performance Security-as-a-Service for NoSQL Databases
G. Dumindu Samaraweera, J. Morris Chang
During the last few years, the explosion of Big Data has prompted cloud infrastructures to provide cloud-based database services as cost effective, efficient and scalable solutions…
ESAI: Efficient Split Artificial Intelligence via Early Exiting Using Neural Architecture Search
Behnam Zeinali, Di Zhuang, J. Morris Chang
Recently, deep neural networks have been outperforming conventional machine learning algorithms in many computer vision-related tasks. However, it is not computationally acceptable…
A compressive multi-kernel method for privacy-preserving machine learning
Thee Chanyaswad, J. Morris Chang, S. Y. Kung
As the analytic tools become more powerful, and more data are generated on a daily basis, the issue of data privacy arises. This leads to the study of the design of privacy-preserv…
Generating Black-Box Adversarial Examples in Sparse Domain
Hadi Zanddizari, Behnam Zeinali, J. Morris Chang
Applications of machine learning (ML) models and convolutional neural networks (CNNs) have been rapidly increased. Although state-of-the-art CNNs provide high accuracy in many appl…
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation
Keyu Chen, Di Zhuang, J. Morris Chang
The generalization capability of machine learning models, which refers to generalizing the knowledge for an "unseen" domain via learning from one or multiple seen domain(s), is of…
Utility-aware Privacy-preserving Data Releasing
Di Zhuang, J. Morris Chang
In the big data era, more and more cloud-based data-driven applications are developed that leverage individual data to provide certain valuable services (the utilities). On the oth…
SAIA: Split Artificial Intelligence Architecture for Mobile Healthcare System
Di Zhuang, Nam Nguyen, Keyu Chen +1
As the advancement of deep learning (DL), the Internet of Things and cloud computing techniques for biomedical and healthcare problems, mobile healthcare systems have received unpr…
CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification
Di Zhuang, Keyu Chen, J. Morris Chang
Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in skin lesion analysis. Compared with single CNN classifier, combining the results of multiple…
Locally Differentially Private Naive Bayes Classification
Emre Yilmaz, Mohammad Al-Rubaie, J. Morris Chang
In machine learning, classification models need to be trained in order to predict class labels. When the training data contains personal information about individuals, collecting t…
AutoGAN-based Dimension Reduction for Privacy Preservation
Hung Nguyen, Di Zhuang, Pei-Yuan Wu +1
Protecting sensitive information against data exploiting attacks is an emerging research area in data mining. Over the past, several different methods have been introduced to prote…
Enhanced PeerHunter: Detecting Peer-to-peer Botnets through Network-Flow Level Community Behavior Analysis
Di Zhuang, J. Morris Chang
Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the fundamental infrastructure for various cyber-crimes. More challenges are invo…
DynaMo: Dynamic Community Detection by Incrementally Maximizing Modularity
Di Zhuang, J. Morris Chang, Mingchen Li
Community detection is of great importance for online social network analysis. The volume, variety and velocity of data generated by today's online social networks are advancing th…
PeerHunter: Detecting Peer-to-Peer Botnets through Community Behavior Analysis
Di Zhuang, J. Morris Chang
Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few exi…