jie jack yang

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Lecturer/Dr

University of Wollongong

Australia

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About

Dr. Jie Jack Yang is a Lecturer at the University of Wollongong in Australia. His research interests encompass areas such as blended learning, technology acceptance in education, real-time monitoring in distributed systems, and action recognition through advanced machine learning techniques. He has published several recent articles addressing topics including digital competency development for preservice teachers and industrial IoT intrusion detection.

Recent Grants

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Building on rural knowledges to unlock the potential of rural students

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Mobile Webtoons: Creative Innovation in a New Digital Economy

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Efficient Multi-key Homomorphic Encryption and Its Applications

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Automatic and Modularised toolbox for Knowledge Discovery

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Articles (15)

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2025

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2025

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2025

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2024

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2024

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2022

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2022

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2022

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2021

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2021

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2021

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2021

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2020

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AUSTRALIA

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Wanqing Li

Associate Professor

University of Wollongong

AUSTRALIA

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jie jack yang

Research Interests

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About

Recent Grants

Articles (15)

How do they like it? Higher education teachers’ professional development preferences for blended learning and technology acceptance profiles

Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition

Real-Time Monitoring of LTL Properties in Distributed Stream Processing Applications

An attention-based CNN for automatic whole-body postural assessment

Asynchronous Joint-based Temporal Pooling for Skeleton-based Action Recognition

Sparse fuzzy classification for profiling online users and relevant user-generated content

Industrial IoT Intrusion Detection via Evolutionary Cost-Sensitive Learning and Fog Computing

Low-rank and sparse representation based learning for cancer survivability prediction

What to teach? Strategies for developing digital competency in preservice teacher training

One-size does not fit all: Towards an adaptive model to develop preservice teachers’ digital competencies

A graph convolutional network-based sensitive information detection algorithm

AEMF: An Attention-Based Efficient and Multifeature Fast Text Detector

Cancer Risk Analysis Based on Improved Probabilistic Neural Network

Necessary and Sufficient Conditions for Expressing Quadratic Rational Bézier Curves

Usage profiling from mobile applications: A case study of online activity for Australian primary schools

Collaborators (7)

Guoxin Su

Katrien Struyven

Brian Yecies

Philip O. Ogunbona

Sarah Howard

Peng Wang

Wanqing Li

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