Ivoline Ngong

Ivoline Ngong

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OLYMPIA: A Simulation Framework for Evaluating the Concrete Scalability of Secure Aggregation Protocols

Ngong, I. C., Gibson, N., & Near, J. P.  
Arxiv, 2023

“OLYMPIA is a framework that allows for empirical evaluation of secure protocols via simulation. It provides an embedded language for defining protocols and a simulation framework for performance evaluation. OLYMPIA has been used to implement several recent secure aggregation protocols, and the first empirical comparison of their end-to-end running times has been performed. OLYMPIA is available as an open-source resource.”
Ngong, I. C., Gibson, N., & Near, J. P. (2023). OLYMPIA: A Simulation Framework for Evaluating the Concrete Scalability of Secure Aggregation Protocols. arXiv preprint arXiv:2302.10084.

Different Deep Learning Based Classification Models for COVID-19 CT-Scans and Lesion Segmentation Through the cGAN-UNet Hybrid Method

Ngong, I. C., & Baykan, N. A.
Traitement du Signal, 2023

“A two-stage system is proposed to assist radiologists in detecting COVID-19 from CT scans. The first stage uses a hybrid method to segment the infected regions from the images, with the cGAN-UNet hybrid system proving most successful with a dice score of 92.32% and IoU score of 86.41%. The second stage classifies the segmented images as either COVID-19 or not, using a Convolutional Neural Network (CNN), a PatchCNN and a Capsule Neural Network (CapsNet), with the CNN achieving the highest success rate of 99.20%.”
Ngong, I. C., & Baykan, N. A. (2023). Different Deep Learning Based Classification Models for COVID-19 CT-Scans and Lesion Segmentation Through the cGAN-UNet Hybrid Method. Traitement du Signal, 40(1), 1.

Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers

K Maughan, IC Ngong and JP Near
Arxiv, 2022

“The paper presents prediction sensitivity, an approach for ongoing evaluation of counterfactual fairness in deployed AI classifiers. Prediction sensitivity is effective in identifying discrimination against individuals and does not require sensitive information at prediction time. It can detect violations of counterfactual fairness by answering the question of whether a prediction would have been different for an individual in a different demographic group. The empirical results demonstrate the effectiveness of prediction sensitivity in detecting violations of counterfactual fairness.”
Maughan, K., Ngong, I. C., & Near, J. P. (2022). Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers. arXiv preprint arXiv:2202.04504.

Feature Extraction Methods for Predicting the Prevalence of Heart Disease

IC Ngong, NA Baykan
Springer, Cham, 2021.

  • TLDR
  • Paper
  • Citation
    • “This paper presents an automatic classification technique using Support Vector Machine (SVM) kernel classifiers and feature extraction methods to accurately detect cardiac arrhythmias from ECG signals. The study shows that the CNN-SVM classifier with a polynomial kernel achieved the highest accuracy of 99.2% and compared favorably with other approaches in literature. The technique has the potential to significantly reduce mortality rates by providing accurate and early detection of beat abnormalities..”
    Ngong, I. C., & Baykan, N. A. (2021, November). Feature Extraction Methods for Predicting the Prevalence of Heart Disease. In The Proceedings of the International Conference on Smart City Applications (pp. 481-494). Springer, Cham.

Towards auditability for fairness in deep learning

IC Ngong, K Maughan, JP NearACL Arxiv 2020

“The paper presents a new method called smooth prediction sensitivity for detecting individual fairness in deep learning models. The method is designed to complement group fairness metrics, which can sometimes miss blatantly unfair predictions. Smooth prediction sensitivity is an efficient measure that allows individual predictions to be audited for fairness, and preliminary experimental results suggest that it can help distinguish between fair and unfair predictions even from "group-fair" models.”
Ngong, I. C., Maughan, K., & Near, J. P. (2020). Towards auditability for fairness in deep learning. arXiv preprint arXiv:2012.00106.