Lecture Notes in Computer Science

Lecture Notes in Artificial Intelligence 14472

Founding Editor
Jörg Siekmann

Series Editors
Randy Goebel, University of Alberta, Edmonton, Canada
Wolfgang Wahlster, DFKI, Berlin, Germany
Zhi-Hua Zhou, Nanjing University, Nanjing, China
The series Lecture Notes in Artificial Intelligence (LNAI) was established in 1988 as a
topical subseries of LNCS devoted to artificial intelligence.
The series publishes state-of-the-art research results at a high level. As with the LNCS
mother series, the mission of the series is to serve the international R & D community
by providing an invaluable service, mainly focused on the publication of conference and
workshop proceedings and postproceedings.
Tongliang Liu · Geoff Webb · Lin Yue ·
Dadong Wang
Editors

AI 2023: Advances in
Artificial Intelligence
36th Australasian Joint Conference on Artificial Intelligence, AI 2023
Brisbane, QLD, Australia, November 28 – December 1, 2023
Proceedings, Part II
Editors
Tongliang Liu Geoff Webb
The University of Sydney Monash University
Darlington, NSW, Australia Clayton, VIC, Australia

Lin Yue Dadong Wang

The University of Newcastle CSIRO Data61
Callaghan, NSW, Australia Sydney, NSW, Australia

ISSN 0302-9743 ISSN 1611-3349 (electronic)

Lecture Notes in Artificial Intelligence
ISBN 978-981-99-8390-2 ISBN 978-981-99-8391-9 (eBook)
https://doi.org/10.1007/978-981-99-8391-9
LNCS Sublibrary: SL7 – Artificial Intelligence

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 Preface

This volume contains the papers presented at the 36th Australasian Joint Conference
on Artificial Intelligence, AJCAI 2023. The conference was held during November
28 – December 1, 2023, and was hosted by the University of Queensland in Brisbane,
Australia. This annual conference is one of the longest running conferences in artificial
intelligence, with the first conference held in Sydney in 1987. The conference remains the
premier event for artificial intelligence in Australasia, offering a forum for researchers
and practitioners across all subfields of artificial intelligence to meet and discuss recent
advances.
AJCAI 2023 received 213 submissions and each submission was reviewed by at
least two Program Committee (PC) members or external reviewers in a double-blind
process (over 90% of the submissions had three reviews). After a thorough discussion and
rigorous scrutiny by the reviewers, 24 papers were accepted for long oral presentation and
58 papers were accepted for oral presentation at the conference. In total, 82 submissions
were accepted for publication as full papers in these proceedings with an acceptance
rate of 38% (the acceptance rate of the long oral presentations was 11%). AJCAI 2023
had six keynote talks by the following distinguished scientists: Ling Chen from the
University of Technology Sydney, Australia; Manik Varma from Microsoft Research
India, India; Peter Soyer from the University of Queensland, Australia; Maria Garcia De
La Banda from Monash University, Australia; Mengjie Zhang from Victoria University
of Wellington, New Zealand; and Dadong Wang from Data61, Australia.
The following are notable aspects of the AJCAI 2023 conference:
• AJCAI 2023 was jointly held with the Defence Artificial Intelligence 2023 Sym-
posium (November 27, 2023). The Defence Artificial Intelligence Symposium is an
exciting opportunity for Defence and AI researchers to come together and explore
priorities, opportunities, and commonalities.
• AJCAI 2023 included a day with a special industry focus. Panel discussions allowed
industry and academia to share challenges and research directions.
• AJCAI 2023 included four workshops, held on November 28: Foundations for
Robust AI: Self-Supervised Learning, organised by Saimunur Rahman, David
Hall, Stephen Hausler, and Peyman Moghadam; Federated Learning in Australasia:
When FL Meets Foundation Models, organised by Guodong Long, Han Yu, and
Tao Shen; Artificial Intelligence Enabled Trustworthy Recommendations, organised
by Shoujin Wang, Rocky Tong Chen, Hongzhi Yin, Lina Yao, and Fang Chen; and
Machine Learning for Data-Driven Optimization, organised by Xilu Wang, Xiangyu
Wang, Shiqing Liu, and Yaochu Jin.
• AJCAI 2023 included three tutorials, held on November 28: Reinforcement Learning
for Automated Negotiation Supply Chain Management League as an Example, pre-
sented by Yasser Mohammad; Towards Communication-Efficient and Heterogeneity-
Robust Federated Learning, presented by Guodong Long and Yue Tan; and Decoding
vi Preface

the Grammar of DNA Using Natural Language Processing, presented by Tyrone Chen
and Sonika Tyagi.
• AJCAI 2023 included a PhD Forum, held on November 28, to mentor and assist post-
graduate students developing their research, with mentorship provided by research
leaders. Limited travel support was provided.
We especially appreciate the work of the members of the Program Committee and the
external reviewers for their expertise and tireless effort in assessing the papers within a
strict timeline. We are also very grateful to the members of the Organising Committee for
their efforts in the preparation, promotion, and organisation of the conference, especially
the General Chairs, Dacheng Tao, Sally Cripps, and Janet Wiles, for coordinating the
whole event.
Lastly, we thank the National Committee for Artificial Intelligence of the Australian
Computer Society; Springer, for the professional service provided by the Lecture Notes
in Artificial Intelligence editorial and publishing teams; and our conference sponsors:
the Australian Computer Society; the Defence Artificial Intelligence Research Network;
Pioneer Computers; the School of Computer Science at the University of Sydney; the
School of Electrical Engineering and Computer Science at the University of Queensland;
the Human Technology Institute at the University of Technology Sydney; the Adelaide
University; and the UNSW AI Institute.

October 2023 Tongliang Liu

Miao Xu
Geoff Webb
 Organization

General Chairs

Dacheng Tao The University of Sydney, Australia

Sally Cripps University of Technology Sydney, Australia
Janet Wiles The University of Queensland, Australia

Program Chairs

Tongliang Liu The University of Sydney, Australia

Miao Xu The University of Queensland, Australia
Geoff Webb Monash University, Australia

Proceedings Chairs

Weitong Chen The University of Adelaide, Australia

Lin Yue The University of Newcastle, Australia
Dadong Wang Data61, Australia

Senior Program Committee

Jing Jiang University of Technology Sydney, Australia

Mingyu Guo The University of Adelaide, Australia
Jonathan Kummerfeld University of Sydney, Australia
Hua Zuo University of Technology Sydney, Australia
Shuo Chen RIKEN, Japan
Zhongyi Han Mohamed Bin Zayed University of Artificial
Intelligence, United Arab Emirates
Runnan Chen The University of Hong Kong, China
Jingfeng Zhang University of Auckland, New Zealand
Feng Liu The University of Melbourne, Australia
Huong Ha RMIT University, Australia
Soyeon Han University of Western Australia, Australia
Zhanna Sarsenbayeva The University of Sydney, Australia
Mingming Gong The University of Melbourne, Australia
Yu Yao Usyd
Yuxuan Du The University of Sydney, Australia
viii Organization

Clément Canonne University of Sydney, Australia

Miaomiao Liu Australian National University, Australia
Dawei Zhou Xidian University, China
Yadan Luo University of Science and Technology of China,
China
Xiaobo Xia The University of Sydney, Australia
Guanfeng Liu Macquarie University, Australia
Zhen Fang University of Technology Sydney, Australia
Hien Nguyen University of Queensland, Australia

Program Committee

Ravneet Singh Arora Block Inc, USA

Adnan Mahmood Macquarie University, Australia
Yue Yuan Shandong University, China
Seyedamin Pouriyeh Kennesaw State University, USA
Yexiong Lin The University of Sydney, Australia
Jiahui Gao The University of Hong Kong, China
Xianzhi Wang University of Technology Sydney, Australia
Zhuo Huang Nanjing University of Science and Technology,
China
Alex Chu Beihang University, China
Ruihong Qiu The University of Queensland, Australia
Qingzheng Xu National University
Qiang Qu The University of Sydney, Australia
Lynn Miller Monash University, Australia
Zhuonan Liang The University of Sydney, Australia
Kun Han The University of Queensland, Australia
Tim Miller The University of Queensland, Australia
Zhuoxiao Chen The University of Queensland, Australia
Kun Wang University of Technology Sydney, Australia
Changqin Huang South China Normal University, China
Peng Yuwei Wuhan University, China
Brendon J. Woodford University of Otago, New Zealand
Weihua Li Auckland University of Technology, New Zealand
Mingzhe Zhang The University of Queensland, Australia
Peter Baumgartner CSIRO, Australia
Manolis Gergatsoulis Ionian University, Greece
Dianhui Wang La Trobe University, Australia
Jianan Fan University of Sydney, Australia
Xueping Peng University of Technology Sydney, Australia
Kairui Guo University of Technology Sydney, Australia
 Organization ix

Zehong Cao University of South Australia, Australia

Wenhao Yang Nanjing University, China
Yi Gao Southeast University, China
Yi Mei Victoria University of Wellington, New Zealand
Chenhao Zhang University of Queensland, Australia
Youquan Liu Hochschule Bremerhaven, Germany
Wenhua Zhang Shanghai University, China
Yu Yao MBZUAI, UAE & CMU, USA
Hao Hou Nanjing University of Science and Technology,
China
Yuan Liu The University of Hong Kong, China
Jianlong Zhou University of Technology Sydney, Australia
Ran Wang University of Technology Sydney, Australia
Jun Wang The University of Sydney, Australia
Weijia Zhang The University of Newcastle, Australia
Zhuoyun Ao Defence Science and Technology Organisation
Xinheng Wu University of Technology Sydney, Australia
Abdul Sattar Griffith University, Australia
Daokun Zhang Monash University, Australia
Ge-Peng Ji Wuhan University, China
Dongting Hu The University of Melbourne, Australia
Chengbin Du The University of Sydney, Australia
Ying Bi Victoria University of Wellington, New Zealand
Rafal Rzepka Hokkaido University, Japan
Cong Lei The University of Sydney, Australia
Yue Tan University of Technology Sydney, Australia
Hongwei Sheng The University of Queensland, Australia
M. A. Hakim Newton University of Newcastle, Australia
Shaokun Zhang Penn State University, USA
Pengqian Lu The University of Sydney, Australia
Peng Yan Nanjing University of Post and
Telecommunication, China
Weidong Cai The University of Sydney, Australia
Huan Huo University of Technology Sydney, Australia
Yuhao Wu The University of Sydney, Australia
Rui Dai University of Science and Technology of China,
China
Fangfang Zhang Victoria University of Wellington, New Zealand
Xiaobo Xia The University of Sydney, Australia
Giorgio Gnecco IMT - School for Advanced Studies, Lucca, Italy
Yu Zheng The Chinese University of Hong Kong, China
Ickjai Lee James Cook University, Australia
x Organization

Jiepeng Wang The University of Hong Kong, China

Qizhou Wang Hong Kong Baptist University, China
Chen Liu University of Technology Sydney, Australia
Yuanyuan Wang The University of Melbourne, Australia
Wei Duan The Australian Artificial Intelligence Institute
(AAII), and University of Technology Sydney,
Australia
Aoqi Zuo The University of Melbourne, Australia
Yiming Ren ShanghaiTech University, China
Stephen Chen York University, Canada
Wenjie Wang The University of Melbourne, Australia
Zhiyuan Li University of Sydney, Australia
Tao Shen Microsoft, China
Guangzhi Ma University of Technology Sydney, Australia
Haodong Chen The University of Sydney, Australia
Yu Lu University of Technology Sydney, Australia
Angus Dempster Monash University, Australia
Jing Teng North China Electric Power University, China
Yawen Zhao The University of Queensland, Australia
Harith Al-Sahaf Victoria University of Wellington, New Zealand
Pengxin Zeng Sichuan University, China
Hangyu Li Xidian University, China
Huaxi Huang CSIRO, Australia
Bernhard Pfahringer University of Waikato, New Zealand
Huiqiang Chen University of Technology Sydney, Australia
Xin Yu University of Technology Sydney, Australia
Yanjun Zhang University of Technology Sydney, Australia
Bach Nguyen Victoria University of Wellington, New Zealand
Peng Mi Xiamen University, China
Jiyang Zheng University of Sydney, Australia
Rundong He Shandong University, China
Shikun Li Chinese Academy of Sciences, China
Kevin Wong Murdoch University, Australia
Xiu-Chuan Li Chinese Academy of Science, China
Jianglin Qiao Western Sydney University, Australia
Maurice Pagnucco The University of New South Wales, Australia
Bing Wang The University of New South Wales, Australia
Zhaoqing Wang The University of Sydney, Australia
Mark Reynolds The University of Western Australia, Australia
Xuyun Zhang Macquarie University, Australia
Zige Wang Peking University, China
Chang Wei Tan Monash University, Australia
 Organization xi

Muyang Li The University of Sydney, Australia

Guangyan Huang Deakin University, Australia
Liangchen Liu Xidian University, China
Nayyar Zaidi Deakin University, Australia
Erdun Gao The University of Melbourne, Australia
Chuyang Zhou The University of Sydney, Australia
Shaofei Shen The University of Queensland, Australia
Yixuan Qiu The University of Queensland, Australia
Jianhua Yang UWS, Australia
Keqiuyin Li University of Technology Sydney, Australia
Yanjun Shu Harbin Institute of Technology, China
Lingdong Kong National University of Singapore, Singapore
Jingyu Zhang City University of Hong Kong, China
Sung-Bae Cho Yonsei University, South Korea
Shuxiang Xu University of Tasmania, Australia
Wan Su Shandong University, China
Markus Wagner The University of Adelaide, Australia
Xiaoying Gao Victoria University of Wellington, New Zealand
William Bingley The University of Queensland, Australia
Sishuo Chen Peking University, China
Hao Sun Shandong University, China
Ming Zhou Hefei University of Technology, China

Sponsors
xii Organization
 Contents – Part II

Knowledge Representation and NLP

Collaborative Qualitative Environment Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Adeline Secolo, Paulo E. Santos, Patrick Doherty, and Zoran Sjanic

Towards Learning Action Models from Narrative Text Through Extraction

and Ordering of Structured Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Ruiqi Li, Patrik Haslum, and Leyang Cui

The Difficulty of Novelty Detection and Adaptation in Physical

Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Vimukthini Pinto, Chathura Gamage, Matthew Stephenson,
and Jochen Renz

Lateral AI: Simulating Diversity in Virtual Communities . . . . . . . . . . . . . . . . . . . . 41

Fedja Hadzic and Maya Krayneva

Reports, Observations, and Belief Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Aaron Hunter

A Prompting Framework to Enhance Language Model Output . . . . . . . . . . . . . . . 66

Himath Ratnayake and Can Wang

Epistemic Reasoning in Computational Machine Ethics . . . . . . . . . . . . . . . . . . . . . 82

Raynaldio Limarga, Yang Song, Maurice Pagnucco,
and David Rajaratnam

Using Social Sensing to Validate Flood Risk Modelling in England . . . . . . . . . . . 95

Joshua Joyce, Rudy Arthur, Guangtao Fu, Alina Bialkowski,
and Hywel Williams

Symbolic Data Analysis to Improve Completeness of Model Combination

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Pedro Strecht, João Mendes-Moreira, and Carlos Soares

CySpider: A Neural Semantic Parsing Corpus with Baseline Models

for Property Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Ziyu Zhao, Wei Liu, Tim French, and Michael Stewart
xiv Contents – Part II

S5TR: Simple Single Stage Sequencer for Scene Text Recognition . . . . . . . . . . . 133
Zhijian Wu, Jun Li, and Jianhua Xu

Explainable AI

Coping with Data Distribution Shifts: XAI-Based Adaptive Learning

with SHAP Clustering for Energy Consumption Prediction . . . . . . . . . . . . . . . . . . 147
Tobias Clement, Hung Truong Thanh Nguyen, Nils Kemmerzell,
Mohamed Abdelaal, and Davor Stjelja

Concept-Guided Interpretable Federated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 160

Jianan Yang and Guodong Long

Systematic Analysis of the Impact of Label Noise Correction on ML

Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Inês Oliveira e Silva, Carlos Soares, Inês Sousa, and Rayid Ghani

Part-Aware Prototype-Aligned Interpretable Image Classification

with Basic Feature Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Liangping Li, Xun Gong, Chenzhong Wang, and Weiji Kong

Hybrid CNN-Interpreter: Interprete Local and Global Contexts

for CNN-Based Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
Wenli Yang, Guan Huang, Renjie Li, Jiahao Yu, Yanyu Chen,
and Quan Bai

Impact of Fidelity and Robustness of Machine Learning Explanations

on User Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Bo Wang, Jianlong Zhou, Yiqiao Li, and Fang Chen

Interpretable Drawing Psychoanalysis via House-Tree-Person Test . . . . . . . . . . . 221

Yaowu Xie, Ting Pan, Baodi Liu, Honglong Chen, and Weifeng Liu

A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional

Mixture of Experts via Joint Rank and Variable Selection . . . . . . . . . . . . . . . . . . . 234
TrungTin Nguyen, Dung Ngoc Nguyen, Hien Duy Nguyen,
and Faicel Chamroukhi

Reinforcement Learning

Auction-Based Allocation of Location-Specific Tasks . . . . . . . . . . . . . . . . . . . . . . . 249

Fahimeh Ramezani, Brendan Sims, and Haris Aziz
 Contents – Part II xv

Generalized Bargaining Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

Yasser Mohammad

SAGE: Generating Symbolic Goals for Myopic Models in Deep

Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
Andrew Chester, Michael Dann, Fabio Zambetta, and John Thangarajah

Leaving the NavMesh: An Ablative Analysis of Deep Reinforcement

Learning for Complex Navigation in 3D Virtual Environments . . . . . . . . . . . . . . . 286
Dale Grant, Jaime Garcia, and William Raffe

Transformed Successor Features for Transfer Reinforcement Learning . . . . . . . . 298

Kiyoshige Garces, Junyu Xuan, and Hua Zuo

Cooperative Multi-Agent Reinforcement Learning with Dynamic Target

Localization: A Reward Sharing Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
Helani Wickramaarachchi, Michael Kirley, and Nicholas Geard

Competitive Collaboration for Complex Task Learning in Agent Systems . . . . . . 325

Dilini Samarasinghe, Michael Barlow, and Erandi Lakshika

Limiting Inequalities in Repeated House and Task Allocation . . . . . . . . . . . . . . . . 338

Martin Aleksandrov

Non-stationarity Detection in Model-Free Reinforcement Learning

via Value Function Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
Maryem Hussein, Marwa Keshk, and Aya Hussein

Toward a Unified Framework for RGB and RGB-D Visual Navigation . . . . . . . . 363
Heming Du, Zi Huang, Scott Chapman, and Xin Yu

Improving CCA Algorithms on SSVEP Classification with Reinforcement

Learning Based Temporal Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376
Liang Ou, Thomas Do, Xuan-The Tran, Daniel Leong,
Yu-Cheng Chang, Yu-Kai Wang, and Chin-Teng Lin

Evolving Epidemic Management Rules Using Deep Neuroevolution:

A Novel Approach to Inspection Scheduling and Outbreak Minimization . . . . . . 387
Victoria Huang, Chen Wang, Samik Datta, Bryce Chen, Gang Chen,
and Hui Ma
xvi Contents – Part II

Genetic Algorithm

A Semantic Genetic Programming Approach to Evolving Heuristics

for Multi-objective Dynamic Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
Meng Xu, Yi Mei, Fangfang Zhang, and Mengjie Zhang

XC-NAS: A New Cellular Encoding Approach for Neural Architecture

Search of Multi-path Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . 416
Trevor Londt, Xiaoying Gao, Peter Andreae, and Yi Mei

Bloating Reduction in Symbolic Regression Through Function

Frequency-Based Tree Substitution in Genetic Programming . . . . . . . . . . . . . . . . 429
Mohamad Rimas, Qi Chen, and Mengjie Zhang

Generating Collective Motion Behaviour Libraries Using Developmental

Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
Md Khan, Kathryn Kasmarik, Michael Barlow, Shadi Abpeikar,
Huanneng Qiu, Essam Debie, and Matt Garratt

A Group Genetic Algorithm for Energy-Efficient Resource Allocation

in Container-Based Clouds with Heterogeneous Physical Machines . . . . . . . . . . . 453
Zhengxin Fang, Hui Ma, Gang Chen, and Sven Hartmann

Genetic Programming with Adaptive Reference Points for Pareto Local

Search in Many-Objective Job Shop Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . 466
Atiya Masood, Gang Chen, Yi Mei, Harith Al-Sahaf, and Mengjie Zhang

A Study of Fitness Gains in Evolving Finite State Machines . . . . . . . . . . . . . . . . . 479

Gábor Zoltai, Yue Xie, and Frank Neumann

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491

 Contents – Part I

Computer Vision

Multi-graph Laplacian Feature Mapping Incorporating Tag Information

for Image Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Yan Liu, Qianqian Shao, Rui Cheng, Weifeng Liu, and Baodi Liu

Short-Term Solar Irradiance Forecasting from Future Sky Images

Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Hoang Chuong Nguyen and Miaomiao Liu

No Token Left Behind: Efficient Vision Transformer via Dynamic Token

Idling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Xuwei Xu, Changlin Li, Yudong Chen, Xiaojun Chang, Jiajun Liu,
and Sen Wang

Story Sifting Using Object Detection Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Wilkins Leong, Julie Porteous, and Jonathan Thangarajah

SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining

Environments: A Novel Dataset and ROS-Based Automatic Annotation
Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Mehala Balamurali and Ehsan Mihankhah

Oyster Mushroom Growth Stage Identification: An Exploration

of Computer Vision Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Lipin Guo, Wei Emma Zhang, Weitong Chen, Ni Yang, Queen Nguyen,
and Trung Duc Vo

Handling Heavy Occlusion in Dense Crowd Tracking by Focusing

on the Heads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Yu Zhang, Huaming Chen, Zhongzheng Lai, Zao Zhang, and Dong Yuan

SAR2EO: A High-Resolution Image Translation Framework

with Denoising Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Shenshen Du, Jun Yu, Guochen Xie, Renjie Lu, Pengwei Li,
Zhongpeng Cai, and Keda Lu

A New Perspective of Weakly Supervised 3D Instance Segmentation

via Bounding Boxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Qingtao Yu, Heming Du, and Xin Yu
xviii Contents – Part I

Large-Kernel Attention Network with Distance Regression and Topological

Self-correction for Airway Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Yan Hu, Erik Meijering, and Yang Song

Deep Learning

WeightRelay: Efficient Heterogeneous Federated Learning on Time Series . . . . . 129

Wensi Tang and Guodong Long

Superpixel Attack: Enhancing Black-Box Adversarial Attack

with Image-Driven Division Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Issa Oe, Keiichiro Yamamura, Hiroki Ishikura, Ryo Hamahira,
and Katsuki Fujisawa

Cross Domain Pulmonary Nodule Detection Without Source Data . . . . . . . . . . . . 153

Rui Xu, Yong Luo, and Yan Xu

3RE-Net: Joint Loss-REcovery and Super-REsolution Neural Network

for REal-Time Video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
Liming Ge, David Zhaochen Jiang, and Wei Bao

Neural Networks in Forecasting Financial Volatility . . . . . . . . . . . . . . . . . . . . . . . . 178

Wenbo Ge, Pooia Lalbakhsh, Leigh Isai, and Hanna Suominen

CLIP-Based Composed Image Retrieval with Comprehensive Fusion

and Data Augmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
Haoqiang Lin, Haokun Wen, Xiaolin Chen, and Xuemeng Song

LiDAR Inpainting of UAV Based 3D Point Cloud Using Supervised

Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Muhammad Talha, Aya Hussein, and Mohammed Hossny

A Sampling Method for Performance Predictor Based on Contrastive

Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Jingrong Xie, Yuqi Feng, and Yanan Sun

AdaptMatch: Adaptive Consistency Regularization for Semi-supervised

Learning with Top-k Pseudo-labeling and Contrastive Learning . . . . . . . . . . . . . . 227
Nan Yang, Fan Huang, and Dong Yuan

Estimation of Unmasked Face Images Based on Voice and 3DMM . . . . . . . . . . . 239

Tetsumaru Akatsuka, Ryohei Orihara, Yuichi Sei, Yasuyuki Tahara,
and Akihiko Ohsuga
 Contents – Part I xix

Aging Contrast: A Contrastive Learning Framework for Fish

Re-identification Across Seasons and Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
Weili Shi, Zhongliang Zhou, Benjamin H. Letcher, Nathaniel Hitt,
Yoichiro Kanno, Ryo Futamura, Osamu Kishida, Kentaro Morita,
and Sheng Li

Spatial Bottleneck Transformer for Cellular Traffic Prediction in the Urban

City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
Hexuan Weng, Yanbin Liu, and Ling Chen

MIDGET: Music Conditioned 3D Dance Generation . . . . . . . . . . . . . . . . . . . . . . . 277

Jinwu Wang, Wei Mao, and Miaomiao Liu

Machine Learning and Data Mining

Minimum Message Length Inference of the Weibull Distribution

with Complete and Censored Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
Enes Makalic and Daniel F. Schmidt

Multiple Teacher Model for Continual Test-Time Domain Adaptation . . . . . . . . . 304

Ran Wang, Hua Zuo, Zhen Fang, and Jie Lu

Causal Disentanglement for Adversarial Defense . . . . . . . . . . . . . . . . . . . . . . . . . . . 315

Ji-Young Park, Lin Liu, Jixue Liu, and Jiuyong Li

Gemini: A Dual-Task Co-training Model for Partial Label Learning . . . . . . . . . . . 328

Beibei Li, Senlin Shu, Beihong Jin, Tao Xiang, and Yiyuan Zheng

Detecting Stress from Multivariate Time Series Data Using Topological

Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341
Hieu Vu Tran, Carolyn McGregor, and Paul J. Kennedy

Mining Label Distribution Drift in Unsupervised Domain Adaptation . . . . . . . . . 354

Peizhao Li, Zhengming Ding, and Hongfu Liu

Automatic Classification of Sensors in Buildings: Learning from Time

Series Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367
Mashud Rana, Ashfaqur Rahman, Mahathir Almashor,
John McCulloch, and Subbu Sethuvenkatraman

An Integrated Federated Learning and Meta-Learning Approach

for Mining Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
Venkat Munagala, Sankhya Singh, Srikanth Thudumu, Irini Logothetis,
Sushil Bhandari, Amit Bhandari, Kon Mouzakis, and Rajesh Vasa
xx Contents – Part I

An Augmented Learning Approach for Multiple Data Streams Under

Concept Drift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
Kun Wang, Jie Lu, Anjin Liu, and Guangquan Zhang

Sequence Unlearning for Sequential Recommender Systems . . . . . . . . . . . . . . . . . 403

Shanshan Ye and Jie Lu

MPANet: Multi-scale Pyramid Attention Network for Collaborative

Modeling Spatio-Temporal Patterns of Default Mode Network . . . . . . . . . . . . . . . 416
Hang Yuan, Xiang Li, and Benzheng Wei

Optimization

Dynamic Landscape Analysis for Constrained Multiobjective Optimization

Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
Hanan Alsouly, Michael Kirley, and Mario Andrés Muñoz

Finding Maximum Weakly Stable Matchings for Hospitals/Residents

with Ties Problem via Heuristic Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442
Son Thanh Cao, Le Van Thanh, and Hoang Huu Viet

Approximating Solutions to the Knapsack Problem Using the Lagrangian

Dual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455
Mitchell Keegan and Mahdi Abolghasemi

An Optimised Grid Search Based Framework for Robust Large-Scale

Natural Soundscape Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468
Thomas Napier, Euijoon Ahn, Slade Allen-Ankins, and Ickjai Lee

Medical AI

Interpretable 3D Multi-modal Residual Convolutional Neural Network

for Mild Traumatic Brain Injury Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483
Hanem Ellethy, Viktor Vegh, and Shekhar S. Chandra

Comparative Assessment of Machine Learning Strategies

for Electrocardiogram Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495
Brenda Wang, Chirath Hettiarachchi, Hanna Suominen,
and Elena Daskalaki

COVID-19 Fake News Detection Using Cross-Domain Classification

Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507
Arnav Sharma, Subhanjali Sharma, Utkarsh Bhardwaj, Sajib Mistry,
Novarun Deb, and Aneesh Krishna
 Contents – Part I xxi

Context-Based Masking for Spontaneous Venous Pulsations Detection . . . . . . . . 520

Hongwei Sheng, Xin Yu, Xue Li, and Mojtaba Golzan

Beyond Model Accuracy: Identifying Hidden Underlying Issues in Chest

X-ray Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533
Richard Wainwright, Danny Wang, Harrison Layton,
and Alina Bialkowski

Enhance Reading Comprehension from EEG-Based Brain-Computer

Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545
Xinping Liu and Zehong Cao

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557