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ISTD PhD Oral Defense Seminar by Gionnieve Lim – Human-centred design of automated labelling interventions to mitigate misinformation on social media
ISTD PhD Oral Defense Seminar by Gionnieve Lim – The thesis investigates the use of various labelling interventions that incorporate automated fact-checking elements for humans, examining people’s perceptions of the labels and their attitudes to the labelled content.
ISTD PhD Oral Defense presented by Rulin Chen – Modeling and Design of Assemblies with Discrete Equivalence Classes
An assembly comprises parts joined together to achieve a specific form or functionality. Compared to monolithic objects, assemblies have many benefits in terms of fabrication, transportation, and adaptability. Parts of assemblies are always geometrically simple to fabricate with digital techniques, can be efficiently packed for transportation, and offer adaptability through flexible replacement or modification. Hence, assemblies are widely used in our daily lives that most of our consumer products, industry machines, and architectural structures are assemblies. […]
ISTD PhD Oral Defense Seminar by Bhardwaj Rishabh – AI metrics beyond performance: safety and trustworthiness of AI systems
ISTD PhD Oral Defense Seminar by Bhardwaj Rishabh – This thesis investigates critical non-idealities in AI systems, focusing on safety behaviour post-training and alignment.
Congratulations to Assistant Professor Soujanya Poria and his PhD Student Bhardwaj Rishabh for winning Gemma Academic Program GCP credit award
Congratulations to Assistant Professor Soujanya Poria and his PhD Student Bhardwaj Rishabh for winning Gemma Academic Program GCP credit award
Congratulations to Assistant Professor Simon Perrault and his PhD student Gionnieve Lim for winning the Steve Howard Best Student Paper Award
OzCHI is the annual non-profit conference for the Computer-Human Interaction Special Interest Group (CHISIG) and Australia’s leading forum for the latest in HCI research and practice. OzCHI attracts a broad international community of researchers, industry practitioners, academics and students.
ISTD PhD Oral Defense Seminar by Ho Ngai Lam – Utilizing Large Language Models for Tour Itinerary Recommendation
Planning a tour Itinerary poses a significant challenge for tourists, especially when navigating unfamiliar territories. […]
Congratulations to ISTD PhD student Thilini Cooray and Associate Professor Cheung Ngai-Man for their AAAI-2022 paper selected for Oral Presentation
Congratulations to ISTD PhD student Thilini Cooray and Associate Professor Cheung Ngai-Man for their AAAI-2022 paper selected for Oral Presentation – Information Systems Technology and Design (ISTD)
ISTD PhD Oral Defense Seminar by Ong Kian Eng – Towards Intelligent Analytics for Smarter Animal Behavioral Analysis
Understanding and analyzing animal behaviors is crucial for gaining profound insights into the health, needs, and overall well-being of the animal. This involves measuring and monitoring factors such as size, growth, poses, and actions. The analysis of animal behavior holds significant importance in a wide range of domains and industries, such as livestock farming, veterinary sciences, scientific research, ecological and conservation studies. […]
ISTD PhD Oral Defense presented by Li Xu – Towards Effective, Robust, and Continual Multi-modal Learning
In the ever-evolving field of artificial intelligence (AI), deep learning has emerged as a pivotal technique driving remarkable advancements across various domains. Among its many branches, multi-modal learning stands out as a particularly significant approach, which involves integrating and processing information from multiple modalities of data, such as visual content and language information, to enhance the capabilities of AI systems. The primary objective of multi-modal learning is to leverage the complementary information present in different modalities to achieve better performance than using any single modality alone, mimicking the way humans perceive and understand the world. […]
ISTD PhD Oral Defense presented by Haoran Li – Overcoming the Limitations of Autoregressive and Non-Autoregressive Neural Models
Language models are critical to the advancement of natural language processing and general artificial intelligence. In this thesis, we aim to address the limitations of language models, particularly focusing on the exposure bias in Autoregressive (AR) models and the label bias in Non-Autoregressive (NAR) models. […]