Publications
Improving long-text authorship verification via model selection and data tuning
Summary
Summary
Authorship verification is used to link texts written by the same author without needing a model per author, making it useful for deanonymizing users spreading text with malicious intent. Recent advances in Transformer-based language models hold great promise for author verification, though short context lengths and non-diverse training regimes present...
Automated exposure notification for COVID-19
Summary
Summary
Private Automated Contact Tracing (PACT) was a collaborative team and effort formed during the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic. PACT's mission was to enhance contact tracing in pandemic response by designing exposure-detection functions in personal digital communication devices that have maximal public health utility while preserving privacy...
A generative approach to condition-aware score calibration for speaker verification
Summary
Summary
In speaker verification, score calibration is employed to transform verification scores to log-likelihood ratios (LLRs) which are statistically interpretable. Conventional calibration techniques apply a global score transform. However, in condition-aware (CA) calibration, information conveying signal conditions is provided as input, allowing calibration to be adaptive. This paper explores a generative...
Backdoor poisoning of encrypted traffic classifiers
Summary
Summary
Significant recent research has focused on applying deep neural network models to the problem of network traffic classification. At the same time, much has been written about the vulnerability of deep neural networks to adversarial inputs, both during training and inference. In this work, we consider launching backdoor poisoning attacks...
Automated contact tracing assessment
Summary
Summary
The COVID-19 pandemic placed unprecedented demands on the global public health systems for disease surveillance and contact tracing. Engineers and scientists recognized that it might be possible to augment the efforts of public health teams, if a system for automated digital contact tracing could be quickly devised and deployed to...
Predicting ankle moment trajectory with adaptive weighted ensemble of LSTM network
Summary
Summary
Estimations of ankle moments can provide clinically helpful information on the function of lower extremities and further lead to insight on patient rehabilitation and assistive wearable exoskeleton design. Current methods for estimating ankle moments leave room for improvement, with most recent cutting-edge methods relying on machine learning models trained on...
Science applications of phased array radars
Summary
Summary
Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to...
Multimodal physiological monitoring during virtual reality piloting tasks
Summary
Summary
This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were...
Development and validation of the public-facing SimAEN web application
Summary
Summary
During a pandemic such as COVID-19, non-pharmaceutical interventions (NPIs) can help protect public health; however, it is not always clear which actions will have the greatest positive impact, or what the trade-offs are between different options. Exposure Notification (EN) was introduced as a prevention measure during the COVID-19 pandemic to...
Self-supervised contrastive pre-training for time series via time-frequency consistency
Summary
Summary
Pre-training on time series poses a unique challenge due to the potential mismatch between pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends, and long-range and short cyclic effects, which can lead to poor downstream performance. While domain adaptation methods can mitigate these shifts, most methods need...