2023 Program

Professor David Abramson, President, CORE, Chair, 2023 ACSW

Professor David Abramson, President, CORE, Chair, 2023 ACSW

Associate Professor Lina Yao, Winner, CORE Research Award

HIKM Opening
Pari Delir Haghighi – Monash University, Australia
Klaus Veil FACHI FHL7 – Western Sydney University

1145 – 1230

Keynote: Health Informatics – 60 years of changing our environment – and more to come

Ed Hammond – Duke University, USA 

1230 – 1300

IR-ER- A Hybrid Pipeline for Classifying COVID-19 RNA Seq Data

Girija Rani Karetla, Daniel Catchpoole, Paul Kennedy, Simeon Simoff and Quang Vinh Nguyen – Western Sydney University 

Paul Denny, Nicole Herbert

Iqbal Gondal

Convener, Mitchell Hargreaves, Junior Deep Learning Engineer, Monash University

Note: Attendees need to bring their own laptop. 

This workshop is an introduction to how deep learning works and how you could create a neural network using TensorFlow v2. We start by learning the basics of deep learning including what a neural network is, how information passes through the network, and how the network learns from data through the automated process of gradient descent. You would build, train and evaluate your very own network using a cloud GPU (Google Colab).

This workshop is targeted at professionals with some data science knowledge who would like a theoretical and hands-on introduction to deep learning. The workshop assumes background knowledge in Python programming. A high level understanding of calculus and matrix operations is beneficial but not essential. Prior experience with machine learning is not required.

Andrew Luxton-Reilly, Ewan Tempero, Nalin Arachchilage, Angela Chang, Paul Denny, Allan Fowler, Nasser Giacaman, Igor Kontorovich, Danielle Lottridge, Sathiamoorthy Manoharan, Shyamli Sindhwani, Paramvir Singh, Ulrich Speidel, Sudeep Stephen, Valerio Terragni, Jacqueline Whalley, Burkhard Wuensche and Xinfeng Ye

Abeer Alalmaie, Priyadarsi Nanda and Xiangjian He

Filip Strömbäck, Linda Mannila and Mariam Kamkar

Mohan Anand Putrevu, Venkata Sai Charan Putrevu and Sandeep Kumar Shukla

Dr Yannan Li, Winner, CORE PhD Award

Sehajpreet Kaur, Shivansh Kumar and Hajar Homayouni – San Diego State University

Chamindi Samarasekara, Claudia Ott and Anthony Robins

Jacob Soper, Yue Xu Xu, Kien Nguyen, Ernest Foo and Zahra Jadidi

Muhammad Javed, Gerardo Luis Dimaguila, Sedigh Khademi Habibabadi, Chris Palmer and Jim Buttery – Murdoch Children’s Research Institute, Australia
 *** ACSW/HIKM 2023 Best Paper *** 

Vaishali Mahipal, Srija Ghosh, Ismaila Sanusi, Ruizhe Ma, Joseph Gonzales and Fred Martin

Mohamed Moetez Abdelhamid, Layth Sliman, Raoudha Ben Djemaa and Boussad Ait Salem

Health Informatics ‘Open Mic’

1600 – 1630

Improving syndromic detection capacity from emergency department notes using data augmentation

Sedigh Khademi, Christopher Palmer, Gerardo Luis Dimaguila, Muhammad Javed, Jim Buttery and Jim Black – Murdoch Children’s Research Institute, Australia

1630 – 1700

Stress Detection on Social Network: Public Mental Health Surveillance

Samaneh Madanian, Sunny Lam and Hamidreza Rasoulipanah – Auckland University of Technology, New Zealand

1700 – 1730

Electronic Master Problem List in Hong Kong Hospital Authority – its development, implementation and challenges

Maggie Lau, Vicky Fung and N T Cheung – Hong Kong Hospital Authority

Filip Strömbäck, Pontus Haglund, Aseel Berglund and Erik Berglund

Georgios Gkoktsis, Hagen Lauer and Lukas Jäger

Teemu Lehtinen, Lassi Haaranen and Juho Leinonen

Giang Nam Nguyen and Christian Bischof

Yulia Pechorina, Keith Anderson and Paul Denny

Salma Khan, Deborah Richards, Paul Formosa and Sarah Bankins

Professor Monica Whitty, Head of Department of Software and Cybersecurity, Monash University

James Curran, CEO, Grok Academy

• Fast Effective Clustering Technologies for Highly Dynamic Massive Networks
  Junhao Gan, University of Melbourne
• Co-designing and Co-evaluating Technology Experiences in Residential Care
  Leon Stirling, Swinburne University
• Blockchain-Enabled Federated Learning for Secure and Decentralised Learning
  Yong Xiang, Deakin University
• Driving Towards Greener and Safer Roads using Big Spatiotemporal Data
  Adel N. Toosi, Monash University
• Next Generation Spatial Data Management for Virtual Spatial Systems
  Jianzhong Qi, University of Melbourne
• Three-dimensional Bayesian Modelling of Geological and Geophysical data
  Lachlan Grose, Monash University
• Making eMaking Accessible for People with Intellectual Disabilities
  Kirsten Ellis, Monash University
• Congestion-aware Routing based on Traffic Assignment
  Ali Davoodi, Monash University
• Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment
  Jinli Cao, La Trobe University

Alex Shaw, Burkhard Wuensche, Christof Lutteroth, Jude Buckley and Paul Corballis – University of Auckland

Elliot Varoy, Andrew Luxton-Reilly, Kerry Lee and Nasser Giacaman

Leilei Xu, Xiao Liu, Frank Jiang, Yi Xu, Aiting Yao, Jia Xu and Xuejun Li

Data-Efficient Learning of Multimodal Sensor Data

Professor Flora Salim

Corban Draper, Joe Ee Cheung, Philip J. Sanders and Burkhard Wuensche – University of Auckland

Nicole Herbert, Zhixi Lin and Matthew Springer

Fariza Sabrina, Tony Sahama, Md Mamunur Rashid and Steven Gordon

Yashi Lin, Jiaxuan Wang, Zihao Luo, Shaojun Li, Yidan Zhang and Burkhard Wuensche – University of Auckland

Shamima Nasrin Runa, Anna Markella Antoniadi, Brett Becker and Catherine Mooney

Minh Hoang Nguyen, Phuong Duy Huynh, Son Hoang Dau and Xiaodong Li

Professor Gill Dobbie, Winner, CORE Distinguished Service Award

Investigating Changes in Hand-Eye Coordination and Reaction Time Using a VR Squash Simulation

Akash Ashok, Mazen Darwish and Burkhard Wünsche – University of Auckland

1455 – 1515

Sleep Patterns of Adults With Chronic Insomnia and Their Bed Partners During the Weekdays and on Weekends Using Actigraphy Data

Sunita Rani, Dr Sergiy Shelyag and Dr Maia Angelova – Deakin University, Australia

1515 – 1535

Mining adverse drug events from patients’ disease histories via a GNN-based subgraph prediction method

Fangyu Zhou and Shahadat Uddin – University of Sydney

James Finnie-Ansley, Paul Denny, Andrew Luxton-Reilly, Eddie Antonio Santos, James Prather and Brett Becker

Akash Patel, Nitesh Kumar, Anand Handa and Sandeep K Shukla

Yanli Li, Laicheng Zhong, Dong Yuan, Huaming Chen and Wei Bao

Ewan Tempero, Kelly Blincoe and Danielle Lottridge

Hania Mohamed, Nicholaos Koroniotis and Nour Moustafa

Yuqi Zhang, Jian Yu, Ji Ruan and Nancy Wang

Health Informatics ‘Open Mic’

1600 – 1620

How could a weighted drug-drug network help improve adverse drug reaction predictions? Machine learning reveals the importance of edge weights

Fangyu Zhou and Shahadat Uddin

1620 – 1640

Developing an anomaly detection framework for Medicare claims

James Kemp, Christopher Barker, Norm Good and Michael Bain

1640 – 1700

Using AI-Driven Triaging to Optimise Clinical Workflows in Non-Emergency Outpatient Settings: A Real-World Case Study Concerning the Screening of Tuberculosis

David Hua, Neysa Petrina and Simon Poon

1700 – 1745

Keynote: A Knowledge-Based Approach to Integration and Interoperability of Sustainable Health Ecosystems in Transformation

Bernd Blobel

Flynn Fromont, Hiruna Jayamanne and Paul Denny

Junwei Luo, Xuechao Yang, Xun Yi, Fengling Han, Iqbal Gondal and Guang-Bin Huang

Sonal Gupta and Subrat Kar

Nea Pirttinen, Arto Hellas and Juho Leinonen

Bachar Kachouh, Layth Sliman, Abed Ellatif Samhat and Kamel Barkaoui

The Rippon Lea Estate Mansion will be open for dinner attendees to take a self-guided tour. Estate staff will be on hand to answer questions and talk with attendees about the history of the Estate. Pre-dinner drinks will be served on the pool deck from 1830. 

192 Hotham Street, Elsternwick, Victoria

Rippon Lea Estate is a 10 minute drive from the Monash University Caulfield Campus, click here for information on getting to the venue. 

Dr Chris McDonald, Winner, CORE Teaching Award

• Democratisation of Deep Learning: Neural Architecture Search at Low Cost
  Saman Halgamuge, University of Melbourne
• Privacy Preserving and Data Utility in Outsourced Systems
  Hua Wang, Victoria University
• Low-cost Sensing Methods and Hybrid Learning Models
  Wei Xiang, Latrobe University
• Accessible Data Exploration and Analysis for Blind People
  Kimbal Marriott, Monash University
• Mapping the Effectiveness of Automated Software Testing
  Aldeida Aleti, Monash University
• Snapshot Optimal Real-time Ride Sharing System
  Afzaal Hassan, Swinburne University
• Machine learning techniques for fuel loss detection at service stations
  Dr Li Chik, RMIT University
• Tuning parallel applications on software-defined supercomputers
  David Abramson, University of Queensland
• Optimised Routing for Personalised Public Transport
  Abdallah Abuaisha, Monash University

A Case for Causal Loop Diagrams to Model Electronic Health Records Ecosystems

Mustafa Hashmi, Angelique McInnes, Andrew Stranieri and Tony Sahama

1150 – 1210

A Data-driven Framework to Test Validity of The Discovered Clinical Process Based on Patient Outcomes

Qifan Chen, Yang Lu, Charmaine Tam and Simon Poon 

1210 – 1230

A study into the impact of data breaches of Electronic Health Records

Ravi Pilla, Taiwo Oseni and Andrew Stranieri

1230 – 1250

Improving the inclusion of people with disabilities in the remote workplace: Digital assistive technologies

Nicola Shaw and Kevin Vinsen – Algoma University, Canada & University of Western Australia

1250 – 1310

Exploring the Relationship between Testosterone and Diabetes within the UK Biobank Data

Giles Oatley – Federation University, Australia

Robbie Dumitru, Mark Beaumont, Bradley Hopkins and Simon Windows

Convener, Professor Peter Stuckey, Department of Data Science and Artificial Intelligences, Faculty of Information Technology, Monash University

Note: Attendees need to bring their own laptop. Attendees should be able to run the MiniZinc workshop problems from the browser, though they may want to preinstall MiniZinc on their laptop.

The world is full of hard discrete optimisation problems. You have experienced them already if you have ever solved a sudoku puzzle or organised the seating at a wedding banquet. These problems underpin much of our daily lives and are part of determining daily delivery routes for packages, making school timetables, and delivering power to our homes. Despite their fundamental importance, all of these problems are a nightmare to solve using traditional computer science methods.

In this short tutorial, you will be introduced to an entirely new way to think about solving these challenging discrete optimisation problems by stating the problem in the high-level modelling language, MiniZinc, and letting constraint solving software do

the rest. This will allow you to unlock the power of industrial solving technologies, which have been perfected over decades by hundreds of researchers.

Conveners

Prof. Kate Smith-Miles
OPTIMA, School of Mathematics and Statistics
The University of Melbourne
Australia

Dr. Mario Andrés Muñoz
OPTIMA, School of Computer and Information Systems
The University of Melbourne
Australia

A common question faced by practitioners when developing an algorithmic solution is “Which algorithm is best for my problem instance?” Answering this question requires extensive testing, as to understand the strengths and weaknesses of an algorithm, and their relationship with the structure of the problem instance.

This tutorial introduces Instance Space Analysis (ISA), a visual methodology for algorithm evaluation that focuses on understanding this relationship. Moreover, ISA provides opportunities to gain nuanced insights into the relative power of algorithms, and whether the test instances have been chosen without bias. A space is constructed whereby an instance is represented a point in a 2d plane, with algorithm footprints being the regions of predicted good performance of an algorithm, based on statistical evidence. From this broad instance space, we can assess the diversity of a chosen test set. Moreover, through ISA we can identify regions where additional test instances would be valuable to support greater insights. By generating new instances with controllable properties, an algorithm can be “stress-tested” under all possible conditions. The tutorial makes use of the on-line tools available at the Melbourne Algorithm Test Instance Library with Data Analytics (MATILDA) and provides access to its MATLAB computational engine. MATILDA also provides a collection of ISA results and other meta-data available for downloading for several well-studied problems from optimization and machine learning, from previously published studies.

Nanda Rani, Bikash Saha, Vikas Maurya and Sandeep Kumar Shukla

Posters

1510 – 1700

Colloquium

Hosted by Grok Academy, this workshop will focus on reviewing the Australian Curriculum: Digital Technologies and how the design of undergraduate curricula does (and might) respond to the capabilities of students who have been exposed to the full curricula.

Cost: The workshop is free to attend, however space is limited.

For more information and to register visit grok.ac/acsw

Who should attend?

• Academics with responsibility for teaching computer science/software engineering across our tertiary institutions – ie Deputy Head of School/Department (Teaching/Education), Undergraduate Directors or first year coordinators.
• University staff who are engaged in outreach to schools or high school students within the school/dept/faculty.
• Secondary teachers