Get Program details as PDF
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Time (AEDT) |
Paper ID |
Title |
Session Chair |
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Day 1, 14 Dec : Research track papers |
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11:00-12:00 |
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Keynote: Prof Shama Chakravarthy "Why Multilayer Networks are Needed for Complex Data Analysis" |
Richi |
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12:00-13:00 |
Break |
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13:00 – 14:20 |
Session 1 |
Thiru |
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13:00 |
10 |
Deep Learning for Bias Detection: From Inception to Deployment |
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13:20 |
12 |
Exploring Fusion Strategies in Deep Learning Models for Multi-modal Classification |
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13:40 |
32 |
A Novel Deep Learning based Factorization Machine Model for Collaborative Filtering |
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14:00 |
24 |
Hospital Readmission Prediction using Semantic relations between medical codes |
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14:20 – 14:50 |
Break |
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14:50 -16:10 |
Session 2 |
Khanh |
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14:50 |
5 |
Parallel Nonlinear Dimensionality Reduction Using GPU Acceleration |
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15:10 |
25 |
A Drift Aware Hierarchical Test based Approach for Combating Social Spammers in Online Social Networks |
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15:30 |
7 |
Taking the confusion out of multinomial confusion matrices and imbalanced classes |
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15:50 |
18 |
Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs |
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Day 2 , 15 Dec : Application track papers |
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10:00 – 11:30 |
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industrial showcase presentations (3 speakers, 30min each) |
Warrick |
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10:00 |
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Topic: How Cybercriminals Use Our Brains
Against Us: What behavioural economics can teach us about cybersecurity |
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10:30 |
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Topic: Applying CNNs for identifying and
ingesting invoices to facilitate automated processing |
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11:00 |
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Topic: Assuring your information before
analytics |
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11:30 – 12:00 |
Break |
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12:00 – 13:00 |
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Keynote: Jeremy Howard "An introduction to self-supervised learning and contrastive loss" |
Graham |
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13:00 – 13:15 |
Break |
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13:15 – 14:35 |
Session 1 |
Rosalind |
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13:15 |
14 |
How to Read the News: A Study of How Sentiment Effects Financial Markets |
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13:35 |
15 |
PostMatch: a Framework for Efficient Address Matching |
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13:55 |
20 |
A Semi-automatic Data Extraction System for Heterogeneous Data Sources: A Case Study from Cotton Industry |
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14:15 |
30 |
Nonnegative Matrix Factorization to understand Spatio-Temporal Traffic Pattern Variations during COVID-19: A Case Study |
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14:35 – 15:00 |
Break |
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15:00 – 16:20 |
Session 2 |
Yeeling |
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15:00 |
23 |
Chameleon: A Python Workflow Toolkit for Feature Selection |
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15:20 |
33 |
Investigation of Topic Modelling Methods for Understanding the Reports of the Mining Projects in Queensland |
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15:40 |
13 |
SOMPS-Net : Attention based social graph framework for early detection of fake health news |
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16:00 |
6 |
Detection of Classical Cipher Types with Feature-Learning Approaches |
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AusDM'21 