@inproceedings {43347,
	title = {Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models},
	journal = { ACM International Conference on Information and Knowledge Management},
	year = {2023},
	abstract = {Causal Neural Network models have shown high levels of robustness to adversarial attacks as well as an increased capacity for generalisation tasks such as few-shot learning and rare-context classification compared to traditional Neural Networks. This robustness is argued to stem from the disentanglement of causal and confounder input signals. However, no quantitative study has yet measured the level of disentanglement achieved by these types of causal models or assessed how this relates to their adversarial robustness.\&nbsp;\&nbsp; Existing causal disentanglement metrics are not applicable to deterministic models trained on real-world datasets. We, therefore, utilise metrics of content/style disentanglement from the field of Computer Vision to measure different aspects of the causal disentanglement for four state-of-the-art causal Neural Network models. By re-implementing these models with a common ResNet18 architecture we are able to fairly measure their adversarial robustness on three standard image classification benchmarking datasets under seven common white-box attacks. We find a strong association (r=0.820, p=0.001) between the degree to which models decorrelate causal and confounder signals and their adversarial robustness. Additionally, we find a moderate negative association between the pixel-level information content of the confounder signal and adversarial robustness (r=-0.597, p=0.040).},
	author = {Preben Monteiro {Ness} and Marijan, Dusica and Bose, Sunanda}
}
@article {43349,
	title = {SenSchedule: Scheduling Heterogeneous Resources in Sensor-Cloud Infrastructuresupp1-3022679.pdf},
	journal = {IEEE Transactions on Services Computing},
	volume = {1595},
	year = {2022},
	month = {Jan-07-2022},
	pages = {1825 - 1840},
	publisher = { IEEE},
	doi = {10.1109/TSC.2020.302267910.1109/TSC.2020.3022679/mm1},
	url = {https://ieeexplore.ieee.org/document/9187904/http://xplorestaging.ieee.org/ielx7/4629386/9851490/09187904.pdf?arnumber=9187904https://ieeexplore.ieee.org/ielx7/4629386/9851490/9187904/supp1-3022679.pdf?arnumber=9187904},
	author = {Bose, Sunanda and Mukherjee, Nandini}
}
@article {43348,
	title = {Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure},
	journal = {ACM Transactions on Spatial Algorithms and Systems},
	volume = {7},
	year = {2021},
	month = {Jun-09-2023},
	pages = {1 - 38},
	publisher = {ACM},
	issn = {2374-0353},
	doi = {10.1145/3446936},
	url = {https://dl.acm.org/doi/10.1145/3446936https://dl.acm.org/doi/pdf/10.1145/3446936},
	author = {Bose, Sunanda and Sumit Kumar {Paul} and Mukherjee, Nandini}
}
@article {43350,
	title = {A Framework for Heterogeneous Resource Allocation in Sensor-Cloud Environment},
	journal = {Wireless Personal Communications},
	volume = {1082012149731114911243532},
	year = {2019},
	month = {Jan-09-2019},
	pages = {19 - 36},
	publisher = { IEEE},
	issn = {0929-6212},
	doi = {10.1007/s11277-019-06383-1},
	url = {https://link.springer.com/article/10.1007/s11277-019-06383-1},
	author = {Bose, Sunanda and Sarkar, Debangana and Mukherjee, Nandini}
}
