By Professor Maurice Pagnucco

Book Chapters

Del Favero D; Thurow S; Pagnucco M; Frohne U, 2024, 'Reimagining Extreme Event Scenarios: The Aesthetic Visualisation of Climate Uncertainty to Enhance Preparedness', in Del Favero D; Thurow S; Ostwald M; Frohne U (ed.), Climate Disaster Preparedness Reimagining Extreme Events through Art and Technology, SpringerNature, Cham, pp. 7 - 24, http://dx.doi.org/10.1007/978-3-031-56114-6_2

Thurow S; Grehan H; Pagnucco M, 2024, 'Representing the Climate Crisis: Aesthetic Framings in Contemporary Performing and Visual Arts', in Del Favero D; Thurow S; Ostwald M; Frohne U (ed.), Climate Disaster Preparedness Reimagining Extreme Events through Art and Technology, SpringerNature, Cham, pp. 107 - 120, http://dx.doi.org/10.1007/978-3-031-56114-6_9

Song Y; Pagnucco M; Wu F; Asadipour A; Ostwald MJ, 2024, 'Intelligent Architectures for Extreme Event Visualisation', in Arts, Research, Innovation and Society, Springer Nature Switzerland, pp. 37 - 48, http://dx.doi.org/10.1007/978-3-031-56114-6_4

Fan L; Ding Y; Fan D; Wu Y; Pagnucco M; Song Y, 2023, 'Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection', in , pp. 660 - 667, http://dx.doi.org/10.3233/FAIA230329

Xu Y; Pagnucco M; Song Y, 2023, 'DHG-GAN: Diverse Image Outpainting via Decoupled High Frequency Semantics', in , pp. 168 - 184, http://dx.doi.org/10.1007/978-3-031-26293-7_11

Guo R; Pagnucco M; Song Y, 2021, 'Learning with Noise: Mask-Guided Attention Model for Weakly Supervised Nuclei Segmentation', in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, pp. 461 - 470, http://dx.doi.org/10.1007/978-3-030-87196-3_43

Cong C; Liu S; Di Ieva A; Pagnucco M; Berkovsky S; Song Y, 2021, 'Semi-supervised Adversarial Learning for Stain Normalisation in Histopathology Images', in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, pp. 581 - 591, http://dx.doi.org/10.1007/978-3-030-87237-3_56

Ashar J; Ashmore J; Hall B; Harris S; Hengst B; Liu R; Mei Z; Pagnucco M; Roy R; Sammut C; Sushkov O; Teh B; Tsekouras L, 2015, 'RoboCup SPL 2014 Champion Team Paper', in RoboCup 2014: Robot World Cup XVIII, Springer International Publishing, pp. 70 - 81, http://dx.doi.org/10.1007/978-3-319-18615-3_6

Heap B; Pagnucco M, 2013, 'Repeated Sequential Single-Cluster Auctions with Dynamic Tasks for Multi-Robot Task Allocation with Pickup and Delivery', in Multiagent System Technologies, Springer Berlin Heidelberg, pp. 87 - 100, http://dx.doi.org/10.1007/978-3-642-40776-5_10

Vongbunyong S; Kara S; Pagnucco M, 2012, 'A Framework for Using Cognitive Robotics in Disassembly Automation', in Leveraging Technology for a Sustainable World, Springer Berlin Heidelberg, pp. 173 - 178, http://dx.doi.org/10.1007/978-3-642-29069-5_30

Pagnucco M, 2006, 'Isaac Levi on abduction', in Knowledge and inquiry, Cambridge university press, New York, pp. 143 - 156

Pagnucco M; Foo N, 1993, 'Inverting resolution with conceptual graphs', in Conceptual Graphs for Knowledge Representation, Springer Berlin Heidelberg, pp. 238 - 253, http://dx.doi.org/10.1007/3-540-56979-0_13

Journal articles

Cong C; Liu S; Rana P; Pagnucco M; Di Ieva A; Berkovsky S; Song Y, 2024, 'Adaptive unified contrastive learning with graph-based feature aggregator for imbalanced medical image classification', Expert Systems with Applications, 251, http://dx.doi.org/10.1016/j.eswa.2024.123783

Wang J; Jiang Y; Long Y; Sun X; Pagnucco M; Song Y, 2024, 'Deconfounding Causal Inference for Zero-Shot Action Recognition', IEEE Transactions on Multimedia, 26, pp. 3976 - 3986, http://dx.doi.org/10.1109/TMM.2023.3318300

Huang R; Ding J; Pagnucco M; Song Y, 2024, 'Fully Decoupling Trajectory and Scene Encoding for Lightweight Heatmap-oriented Trajectory Prediction', IEEE Robotics and Automation Letters, pp. 1 - 8, http://dx.doi.org/10.1109/lra.2024.3426376

Fan L; Ding Y; Fan D; Wu Y; Chu H; Pagnucco M; Song Y, 2023, 'An annotated grain kernel image database for visual quality inspection', Scientific Data, 10, http://dx.doi.org/10.1038/s41597-023-02660-8

Fan L; Fan D; Ding Y; Wu Y; Chu H; Pagnucco M; Song Y, 2023, 'AV4GAInsp: An Efficient Dual-Camera System for Identifying Defective Kernels of Cereal Grains', IEEE Robotics and Automation Letters, 9, pp. 851 - 858, http://dx.doi.org/10.1109/LRA.2023.3338517

Xu Y; Pagnucco M; Song Y, 2023, 'An edge guided coarse-to-fine generative network for image outpainting', Neurocomputing, 541, http://dx.doi.org/10.1016/j.neucom.2023.126254

Guo R; Xie K; Pagnucco M; Song Y, 2023, 'SAC-Net: Learning with weak and noisy labels in histopathology image segmentation', Medical Image Analysis, 86, http://dx.doi.org/10.1016/j.media.2023.102790

Foo G; Kara S; Pagnucco M, 2023, 'Artificial Learning for Part Identification in Robotic Disassembly Through Automatic Rule Generation in an Ontology', IEEE Transactions on Automation Science and Engineering, 20, pp. 296 - 309, http://dx.doi.org/10.1109/TASE.2022.3149242

Wang W; Pagnucco M; Xu C; Song Y, 2023, 'InterREC: An Interpretable Method for Referring Expression Comprehension', IEEE Transactions on Multimedia, 25, pp. 9330 - 9342, http://dx.doi.org/10.1109/TMM.2023.3251111

Yang Z; Cong C; Pagnucco M; Song Y, 2023, 'Multi-scale multi-reception attention network for bone age assessment in X-ray images', Neural Networks, 158, pp. 249 - 257, http://dx.doi.org/10.1016/j.neunet.2022.11.002

Cong C; Liu S; Di Ieva A; Pagnucco M; Berkovsky S; Song Y, 2022, 'Colour adaptive generative networks for stain normalisation of histopathology images', Medical Image Analysis, 82, http://dx.doi.org/10.1016/j.media.2022.102580

Chen WH; Foo G; Kara S; Pagnucco M, 2021, 'Automated generation and execution of disassembly actions', Robotics and Computer-Integrated Manufacturing, 68, http://dx.doi.org/10.1016/j.rcim.2020.102056

Foo G; Kara S; Pagnucco M, 2021, 'An ontology-based method for semi-automatic disassembly of lcd monitors and unexpected product types', International Journal of Automation Technology, 15, pp. 168 - 181, http://dx.doi.org/10.20965/IJAT.2021.P0168

Chen WH; Foo G; Kara S; Pagnucco M, 2020, 'Application of a multi-head tool for robotic disassembly', Procedia CIRP, 90, pp. 630 - 635, http://dx.doi.org/10.1016/j.procir.2020.02.047

Rezvani M; Rajaratnam D; Ignjatovic A; Pagnucco M; Jha S, 2019, 'Analyzing XACML policies using answer set programming', International Journal of Information Security, 18, pp. 465 - 479, http://dx.doi.org/10.1007/s10207-018-0421-5

Zhuang Z; Pagnucco M; Zhang Y, 2017, 'Inter-Definability of Horn Contraction and Horn Revision', Journal of Philosophical Logic, 46, pp. 299 - 332, http://dx.doi.org/10.1007/s10992-016-9401-2

Schwering C; Pagnucco M; Lakemeyer G, 2017, 'Belief revision and projection in the epistemic situation calculus', Artificial Intelligence, 251, pp. 62 - 97

Kara S; Vongbunyong S; Pagnucco M, 2016, 'Vision-based execution monitoring of state transition in disassembly automation', International Journal of Automation Technology, 10, pp. 708 - 716, http://dx.doi.org/10.20965/ijat.2016.p0708

Vongbunyong S; Kara S; Pagnucco M, 2015, 'Learning and revision in cognitive robotics disassembly automation', Robotics and Computer-Integrated Manufacturing, 34, pp. 79 - 94, http://dx.doi.org/10.1016/j.rcim.2014.11.003

Nakata NM; Hamacher DW; Warren J; Byrne A; Pagnucco M; Harley R; Venugopal S; Thorpe K; Neville R; Bolt R, 2014, 'Using Modern Technologies to Capture and Share Indigenous Astronomical Knowledge', Australian Academic & Research Libraries, 45, pp. 101 - 110, http://dx.doi.org/10.1080/00048623.2014.917786

Zhuang Z; Pagnucco M, 2014, 'Entrenchment-Based Horn Contraction', JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 51, pp. 227 - 254, http://dx.doi.org/10.1613/jair.4430

Vongbunyong S; Kara S; Pagnucco M, 2013, 'Application of cognitive robotics in disassembly of products', CIRP Annals - Manufacturing Technology, 62, pp. 31 - 34, http://dx.doi.org/10.1016/j.cirp.2013.03.037

Vongbunyong S; Kara S; Pagnucco M, 2013, 'Basic behaviour control of the vision-based cognitive robotic disassembly automation', Assembly Automation, 33, pp. 38 - 56, http://dx.doi.org/10.1108/01445151311294694

Burgard W; Konolige K; Pagnucco M; Vassos S, 2012, 'AAAI Workshop - Technical Report: Preface', AAAI Workshop - Technical Report, WS-12-06

Shapiro S; Pagnucco M; Lesperance Y; Levesque HJ, 2011, 'Iterated belief change in the situation calculus', Artificial Intelligence, 175, pp. 165 - 192, http://dx.doi.org/10.1016/j.artint.2010.04.003

Delgrande J; Nayak A; Pagnucco M, 2005, 'Gricean belief change', Studia Logica, 79(1), pp. 97 - 113

Nayak A; Pagnucco M; Peppas P, 2003, 'Dynamic Belief Revision Operators', Artificial Intelligence, 146(2), pp. 193 - 228

Pagnucco M; Peppas P, 2001, 'Causality and minimal change demystified', IJCAI International Joint Conference on Artificial Intelligence, pp. 125 - 130

Pagnucco M; Jauregui V; Foo N, 2001, 'A Trajectory Approach to Causality', Studia Logica, pp. 385 - 401

Rott H; Pagnucco M, 1999, 'Severe withdrawal (and recovery)', Journal of Philosophical Logic, 28, pp. 501 - 547, http://dx.doi.org/10.1023/A:1004344003217

Conference Papers

Fan L; Ding Y; Pagnucco M; Song Y, 2024, 'Patch-Wise Augmentation for Anomaly Detection and Localization', in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, presented at ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 14 April 2024 - 19 April 2024, http://dx.doi.org/10.1109/icassp48485.2024.10446994

Cong C; Xuan S; Liu S; Zhang S; Pagnucco M; Song Y, 2024, 'Decoupled Optimisation for Long-Tailed Visual Recognition', in Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1380 - 1388, http://dx.doi.org/10.1609/aaai.v38i2.27902

Limarga R; Song Y; Pagnucco M; Rajaratnam D, 2024, 'Epistemic Reasoning in Computational Machine Ethics', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 82 - 94, http://dx.doi.org/10.1007/978-981-99-8391-9_7

Wang Y; Pagnucco M; Song Y, 2024, 'Self-training with Domain-Mixed Data for Few-Shot Domain Adaptation in Medical Image Segmentation Tasks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 299 - 309, http://dx.doi.org/10.1007/978-3-031-45673-2_30

Xu Y; Guo R; Pagnucco M; Song Y, 2023, 'Draw2Edit: Mask-Free Sketch-Guided Image Manipulation', in MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia, pp. 7205 - 7215, http://dx.doi.org/10.1145/3581783.3612398

Huang R; Tompkins A; Pagnucco M; Song Y, 2023, 'Towards Single Source Domain Generalisation in Trajectory Prediction: A Motion Prior based Approach', in Proceedings of Machine Learning Research, McGill University, Montréal, Québec, Canada, pp. 227 - 243, presented at Conference on Lifelong Learning Agents, 22-25 August 2023,, McGill University, Montréal, Québec, Canada, 22 August 2023 - 25 August 2023, https://proceedings.mlr.press/v232/huang23a.html

Gao J; Blair A; Pagnucco M, 2023, 'A Symbolic-Neural Reasoning Model for Visual Question Answering', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN54540.2023.10191538

Qian S; Pagnucco M; Song Y, 2023, 'Adaptive Local Prototype and Cycle Attention for Few-shot Medical Image Segmentation', in 2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023, pp. 49 - 56, http://dx.doi.org/10.1109/DICTA60407.2023.00016

Back to profile page