| Tawfik SA, Rashid M, Gupta S, Russo SP, Walsh TR and Venkatesh S (2023), "Machine learning-based discovery of vibrationally stable materials", npj Computational Materials. Vol. 9, pp. 5.
[BibTeX] |
BibTeX:
@article{tawfik2023machine,
author = {Tawfik, Sherif Abdulkader and Rashid, Mahad and Gupta, Sunil and Russo, Salvy P. and Walsh, Tiffany R. and Venkatesh, Svetha},
title = {Machine learning-based discovery of vibrationally stable materials},
journal = {npj Computational Materials},
year = {2023},
volume = {9},
pages = {5},
note = {(Q1 journal)}
}
|
| Zong W, Chow Y-W, Susilo W, Do K and Venkatesh S (2023), "TrojanModel: A Practical Trojan Attack against Automatic Speech Recognition Systems", In Symposium on Security and Privacy (SP). , pp. 906-922.
[BibTeX] |
BibTeX:
@inproceedings{zong2022trojanmodel,
author = {Zong, Wei and Chow, Yang-Wai and Susilo, Willy and Do, Kien and Venkatesh, Svetha},
title = {TrojanModel: A Practical Trojan Attack against Automatic Speech Recognition Systems},
booktitle = {Symposium on Security and Privacy (SP)},
year = {2023},
pages = {906--922},
note = {(A* conference)}
}
|
| Le TM, Le V, Gupta S, Venkatesh S and Tran T (2023), "Guiding Visual Question Answering with Attention Priors", In Winter Conference on Applications of Computer Vision (WACV). , pp. 4381-4390.
[BibTeX] |
BibTeX:
@inproceedings{le2023guiding,
author = {Le, Thao Minh and Le, Vuong and Gupta, Sunil and Venkatesh, Svetha and Tran, Truyen},
title = {Guiding Visual Question Answering with Attention Priors},
booktitle = {Winter Conference on Applications of Computer Vision (WACV)},
year = {2023},
pages = {4381--4390},
note = {(A conference)}
}
|
| Shilton A, Gupta S, Rana S and Venkatesh S (2023), "Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space", In International Conference on Machine Learning (ICML). , pp. 31435-31488.
[BibTeX] |
BibTeX:
@inproceedings{alistair2023gradient,
author = {Shilton, Alistair and Gupta, Sunil and Rana, Santu and Venkatesh, Svetha},
title = {Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2023},
pages = {31435-31488},
note = {(A* conference)}
}
|
| Tran H, Le V, Venkatesh S and Tran T (2023), "Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction", In International Conference on Computer Vision (ICCV). , pp. 9858-9867.
[BibTeX] |
BibTeX:
@inproceedings{tran2023persistent,
author = {Tran, Hung and Le, Vuong and Venkatesh, Svetha and Tran, Truyen},
title = {Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2023},
pages = {9858-9867},
note = {(A* conference)}
}
|
| Patil P, Gupta S, Rana S, Venkatesh S and Murala S (2023), "Multi-weather Image Restoration via Domain Translation", In International Conference on Computer Vision (ICCV). , pp. 21696-21705.
[BibTeX] |
BibTeX:
@inproceedings{DTRestoration2023,
author = {Patil, Prashant and Gupta, Sunil and Rana, Santu and Venkatesh, Svetha and Murala, Subrahmanyam},
title = {Multi-weather Image Restoration via Domain Translation},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2023},
pages = {21696-21705},
note = {(A* conference)}
}
|
| Nguyen D, Le H, Do K, Venkatesh S and Tran T (2023), "Social Motivation for Modelling Other Agent under Partial Observability in Decentralised Training", In International Joint Conferences on Artificial Intelligence (IJCAI). , pp. 4082-4090.
[BibTeX] |
BibTeX:
@inproceedings{nguyen2023social,
author = {Nguyen, Dung and Le, Hung and Do, Kien and Venkatesh, Svetha and Tran, Truyen},
title = {Social Motivation for Modelling Other Agent under Partial Observability in Decentralised Training},
booktitle = {International Joint Conferences on Artificial Intelligence (IJCAI)},
year = {2023},
pages = {4082-4090},
note = {(A* conference)}
}
|
| Shvetcov A, Whitton A, Kasturi S, Zheng W-Y, Beames J, Ibrahim O, Han J, Hoon L, Mouzakis K, Gupta S and others (2023), "Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app", Internet Interventions. , pp. 100666.
[BibTeX] |
BibTeX:
@article{shvetcov2023machine,
author = {Shvetcov, Artur and Whitton, Alexis and Kasturi, Suranga and Zheng, Wu-Yi and Beames, Joanne and Ibrahim, Omar and Han, Jin and Hoon, Leonard and Mouzakis, Kon and Gupta, Sunil and others},
title = {Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app},
journal = {Internet Interventions},
year = {2023},
pages = {100666},
note = {(Q2 journal)}
}
|
| George Karimpanal T, Le H, Abdolshah M, Rana S, Gupta S, Tran T and Venkatesh S (2023), "Balanced Q-learning: Combining the influence of optimistic and pessimistic targets", Artificial Intelligence. Vol. 325(C), pp. 104021. |
BibTeX:
@article{thommen2023qlearning,
author = {George Karimpanal, Thommen and Le, Hung and Abdolshah, Majid and Rana, Santu and Gupta, Sunil and Tran, Truyen and Venkatesh, Svetha},
title = {Balanced Q-learning: Combining the influence of optimistic and pessimistic targets},
journal = {Artificial Intelligence},
year = {2023},
volume = {325},
number = {C},
pages = {104021},
doi = {10.1016/j.artint.2023.104021}
}
|
| Ryan S, Sushma NM, Le H, Arun Kumar A, Berk J, Nguyen T, Rana S, Kandanaarachchi S and Venkatesh S (2023), "The application of machine learning in micrometeoroid and orbital debris impact protection and risk assessment for spacecraft", International Journal of Impact Engineering. Vol. 181, pp. 104727. |
| Abstract: Current spacecraft micrometeoroid and orbital debris impact risk assessments utilize semi-empirical equations to describe the protection afforded by a spacecraft component (e.g., pressure hull, critical component, etc.). These equations demand fundamentally limiting assumptions, for example of projectile shape and material, to reduce the complexity of the mechanics and material response under such extreme conditions. Machine learning (ML) approaches, however, are well suited to such high dimensionality problems and have previously been shown to provide comparable classification accuracy to state-of-the-art empirical techniques in this domain. We demonstrate that ML models can readily incorporate additional complexity beyond that currently achievable with semi-empirical models, such as the effect of thermal insulation blankets, non-aluminium projectiles, and non-spherical projectiles on failure thresholds without any notable loss of performance, compared with baseline conditions. For future micrometeoroid and orbital debris (MMOD) risk assessment codes such ML models offer the potential to incorporate the true characteristics of the MMOD environment more accurately than existing approaches. |
BibTeX:
@article{RYAN2023104727,
author = {Shannon Ryan and Neeraj Mohan Sushma and Hung Le and A.V. Arun Kumar and Julian Berk and TM Nguyen and Santu Rana and Sevvandi Kandanaarachchi and Svetha Venkatesh},
title = {The application of machine learning in micrometeoroid and orbital debris impact protection and risk assessment for spacecraft},
journal = {International Journal of Impact Engineering},
year = {2023},
volume = {181},
pages = {104727},
url = {https://www.sciencedirect.com/science/article/pii/S0734743X23002373},
doi = {10.1016/j.ijimpeng.2023.104727}
}
|
| Werner-Seidler A, Maston K, Calear AL, Batterham PJ, Larsen ME, Torok M, O’Dea B, Huckvale K, Beames JR, Brown L, Fujimoto H, Bartholomew A, Bal D, Schweizer S, Skinner SR, Steinbeck K, Ratcliffe J, Oei J-L, Venkatesh S, Lingam R, Perry Y, Hudson JL, Boydell KM, Mackinnon A and Christensen H (2023), "The Future Proofing Study: Design, methods and baseline characteristics of a prospective cohort study of the mental health of Australian adolescents", International Journal of Methods in Psychiatric Research. Vol. 32(3), pp. e1954. |
| Abstract: Abstract Objectives The Future Proofing Study (FPS) was established to examine factors associated with the onset and course of mental health conditions during adolescence. This paper describes the design, methods, and baseline characteristics of the FPS cohort. Methods The FPS is an Australian school-based prospective cohort study with an embedded cluster-randomized controlled trial examining the effects of digital prevention programs on mental health. Data sources include self-report questionnaires, cognitive functioning, linkage to health and education records, and smartphone sensor data. Participants are assessed annually for 5 years. Results The baseline cohort (N = 6388, M = 13.9 years) is broadly representative of the Australian adolescent population. The clinical profile of participants is comparable to other population estimates. Overall, 15.1% of the cohort met the clinical threshold for depression, 18.6% for anxiety, 31.6% for psychological distress, and 4.9% for suicidal ideation. These rates were significantly higher in adolescents who identified as female, gender diverse, sexuality diverse, or Aboriginal and/or Torres Strait Islander (all ps < 0.05). Conclusions This paper provides current and comprehensive data about the status of adolescent mental health in Australia. The FPS cohort is expected to provide significant insights into the risk, protective, and mediating factors associated with development of mental health conditions during adolescence. |
BibTeX:
@article{https://doi.org/10.1002/mpr.1954,
author = {Werner-Seidler, Aliza and Maston, Kate and Calear, Alison L. and Batterham, Philip J. and Larsen, Mark E. and Torok, Michelle and O’Dea, Bridianne and Huckvale, Kit and Beames, Joanne R. and Brown, Lyndsay and Fujimoto, Hiroko and Bartholomew, Alexandra and Bal, Debopriyo and Schweizer, Susanne and Skinner, S. Rachel and Steinbeck, Katharine and Ratcliffe, Julie and Oei, Ju-Lee and Venkatesh, Svetha and Lingam, Raghu and Perry, Yael and Hudson, Jennifer L. and Boydell, Katherine M. and Mackinnon, Andrew and Christensen, Helen},
title = {The Future Proofing Study: Design, methods and baseline characteristics of a prospective cohort study of the mental health of Australian adolescents},
journal = {International Journal of Methods in Psychiatric Research},
year = {2023},
volume = {32},
number = {3},
pages = {e1954},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mpr.1954},
doi = {10.1002/mpr.1954}
}
|
| Morais R, Le V, Morgan C, Spittle A, Badawi N, Valentine J, Hurrion EM, Dawson PA, Tran T and Venkatesh S (2023), "Robust and Interpretable General Movement Assessment Using Fidgety Movement Detection", IEEE Journal of Biomedical and Health Informatics. Vol. 27(10), pp. 5042-5053. |
BibTeX:
@article{10195984,
author = {Morais, Romero and Le, Vuong and Morgan, Catherine and Spittle, Alicia and Badawi, Nadia and Valentine, Jane and Hurrion, Elizabeth M and Dawson, Paul A and Tran, Truyen and Venkatesh, Svetha},
title = {Robust and Interpretable General Movement Assessment Using Fidgety Movement Detection},
journal = {IEEE Journal of Biomedical and Health Informatics},
year = {2023},
volume = {27},
number = {10},
pages = {5042-5053},
doi = {10.1109/JBHI.2023.3299236}
}
|
| Ryan S, Sushma NM, Kumar AV A, Berk J, Hashem T, Rana S and Venkatesh S (2023), "Machine learning for predicting the outcome of terminal ballistics events", Defence Technology. , pp. 1-13. |
| Abstract: Machine learning (ML) is well suited for the prediction of high-complexity, high-dimensional problems such as those encountered in terminal ballistics. We evaluate the performance of four popular ML-based regression models, extreme gradient boosting (XGBoost), artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GP), on two common terminal ballistics’ problems: (a) predicting the V50 ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments, and (b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness. To achieve this we utilise two datasets, each consisting of approximately 1000 samples, collated from public release sources. We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range. Although extrapolation is not advisable for ML-based regression models, for applications such as lethality/survivability analysis, such capability is required. To circumvent this, we implement expert knowledge and physics-based models via enforced monotonicity, as a Gaussian prior mean, and through a modified loss function. The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models, providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not. The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types, target materials and thicknesses, and impact conditions significantly more diverse than that achievable from any existing analytical approach. Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster. We provide some general guidelines throughout for the development, application, and reporting of ML models in terminal ballistics problems. |
BibTeX:
@article{RYAN2023,
author = {Shannon Ryan and Neeraj Mohan Sushma and Arun Kumar AV and Julian Berk and Tahrima Hashem and Santu Rana and Svetha Venkatesh},
title = {Machine learning for predicting the outcome of terminal ballistics events},
journal = {Defence Technology},
year = {2023},
pages = {1--13},
url = {https://www.sciencedirect.com/science/article/pii/S2214914723001940},
doi = {10.1016/j.dt.2023.07.010}
}
|
| Nguyen-Tang T, Yin M, Gupta S, Venkatesh S and Arora R (2023), "On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation", In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37(8), pp. 9310-9318. |
BibTeX:
@inproceedings{Thanh2023AAAI,
author = {Nguyen-Tang, Thanh and Yin, Ming and Gupta, Sunil and Venkatesh, Svetha and Arora, Raman},
title = {On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
year = {2023},
volume = {37},
number = {8},
pages = {9310-9318},
note = {(A* conference)},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/26116}
}
|
| Huckvale K, Hoon L, Stech E, Newby JM, Zheng WY, Han J, Vasa R, Gupta S, Barnett S, Senadeera M, Cameron S, Kurniawan S, Agarwal A, Kupper JF, Asbury J, Willie D, Grant A, Cutler H, Parkinson B, Ahumada-Canale A, Beames JR, Logothetis R, Bautista M, Rosenberg J, Shvetcov A, Quinn T, Mackinnon A, Rana S, Tran T, Rosenbaum S, Mouzakis K, Werner-Seidler A, Whitton A, Venkatesh S and Christensen H (2023), "Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up study", BMJ Open. Vol. 13(4), pp. e066249. British Medical Journal Publishing Group. |
| Abstract: Introduction Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity.Methods and analysis The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions.Ethics and dissemination Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP).Trial registration number ACTRN12621001223820. |
BibTeX:
@article{Huckvalee066249,
author = {Kit Huckvale and Leonard Hoon and Eileen Stech and Jill M Newby and Wu Yi Zheng and Jin Han and Rajesh Vasa and Sunil Gupta and Scott Barnett and Manisha Senadeera and Stuart Cameron and Stefanus Kurniawan and Akash Agarwal and Joost Funke Kupper and Joshua Asbury and David Willie and Alasdair Grant and Henry Cutler and Bonny Parkinson and Antonio Ahumada-Canale and Joanne R Beames and Rena Logothetis and Marya Bautista and Jodie Rosenberg and Artur Shvetcov and Thomas Quinn and Andrew Mackinnon and Santu Rana and Truyen Tran and Simon Rosenbaum and Kon Mouzakis and Aliza Werner-Seidler and Alexis Whitton and Svetha Venkatesh and Helen Christensen},
title = {Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up study},
journal = {BMJ Open},
publisher = {British Medical Journal Publishing Group},
year = {2023},
volume = {13},
number = {4},
pages = {e066249},
url = {https://bmjopen.bmj.com/content/13/4/e066249},
doi = {10.1136/bmjopen-2022-066249}
}
|