Differentially Private Generative Adversarial Networks with Model Inversion

To protect sensitive data in training a Generative Adversarial Network (GAN), the standard approach is to use differentially private (DP) stochastic gradient descent method in which controlled noise is added to the gradients. The quality of the output synthetic samples can be adversely affected and the training of the network may not even converge in the presence of these noises. We propose Differentially Private Model Inversion (DPMI) method where the private data is first mapped to the latent space via a public generator, followed by a lower-dimensional DP-GAN with better convergent properties. Experimental results on standard datasets CIFAR10 and SVHN as well as on a facial landmark dataset for Autism screening show that our approach outperforms the standard DP-GAN method based on Inception Score, Fr\'echet Inception Distance, and classification accuracy under the same privacy guarantee.Comment: Best Student Paper Award of 13th IEEE International Workshop on Information Forensics and Security (WIFS 2021), Montpellier, Franc

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Joint Optimization of Data Hiding and Video Compression

Abstract — From copyright protection to error concealment, video data hiding has found usage in a great number of applications. Recently proposed applications such as privacy data preservation require huge amount of information to be hidden inside a compressed video bitstream. Since data hiding disturbs the underlying statistical patterns of the source data, it adversely affects the performance of compression which are designed based on the statistical properties of the data. As such, it is imperative to design a data hiding scheme that is compatible with the compression algorithm and at the same time, introduces as little perceptual distortion as possible. In this paper, we propose a novel compression-domain video data-hiding algorithm that determines the optimal embedding strategy to minimize both the output perceptual distortion and the output bit rate. The hidden data is embedded into selective Discrete Cosine Transform (DCT) coefficients which are found in most video compression standards. The coefficients are selected based on minimizing a cost function that combines both distortion and bit rate via a usercontrolled weighting. Two methods are proposed – exhaustive search and fast Lagrangian approximation. While the former produces optimal results, the latter approach is significantly faster and amenable to real-time implementation. I

Computational Models for Biomedical Reasoning and Problem Solving

The results of computational model simulations allow researchers and clinicians to make predictions about what will happen in the biological systems that are being studied in response to changing conditions for a disease or disorder. With a well-developed computational model, researchers and clinicians can better understand the cause of a disease or a disorder and predict treatment results. Computational Models for Biomedical Reasoning and Problem Solving is a critical scholarly publication that provides insightful strategies to developing computational models that allow for the better understanding and treatment of various diseases and disorders. Featuring topics such as biomedicine, neuroscience, and artificial intelligence, this book is ideal for practitioners, clinicians, researchers, psychologists, and engineers. [From Amazon.com]https://digitalcommons.odu.edu/ece_books/1006/thumbnail.jp

Approximate Techniques in Solving Optimal Camera Placement Problems

While the theoretical foundation of the optimal camera placement problem has been studied for decades, its practical implementation has recently attracted significant research interest due to the increasing popularity of visual sensor networks. The most flexible formulation of finding the optimal camera placement is based on a binary integer programming (BIP) problem. Despite the flexibility, most of the resulting BIP problems are NP-hard and any such formulations of reasonable size are not amenable to exact solutions. There exists a myriad of approximate algorithms for BIP problems, but their applications, efficiency, and scalability in solving camera placement are poorly understood. Thus, we develop a comprehensive framework in comparing the merits of a wide variety of approximate algorithms in solving the optimal camera placement problems. We first present a general approach of adapting these problems into BIP formulations. Then, we demonstrate how they can be solved using different approximate algorithms including greedy heuristics, Markov-chain Monte Carlo, simulated annealing, and linear and semidefinite programming relaxations. The accuracy, efficiency, and scalability of each technique are analyzed and compared in depth. Extensive experimental results are provided to illustrate the strength and weakness of each method

Offline Generation of High Quality Background Subtraction Data

Ground truth is important not only for performance evaluation but also for a principled development of computer vision algorithms. Unfortunately obtaining ground truth data is difficult and often very labor intensive. This is particularly true of video analysis due to the immense cost of producing pixel-wise ground truth in potentially thousands of frames. In this paper, we propose a method to produce foreground/background segmentation for video sequences captured by a stationary camera, that requires very little human labor as compared to complete manual segmentation, while still producing high quality results. Given a sequence, we use a few hand labeled images and Adaboost to train a classifier that segments the rest of the sequence. We demonstrate the effectiveness of our approach on two sequences and discuss the new horizons opened by these encouraging results.