Engineering Commons^™

Improving Pure-Tone Audiometry Using Probabilistic Machine Learning Classification, Xinyu Song

McKelvey School of Engineering Theses & Dissertations

Hearing loss is a critical public health concern, affecting hundreds millions of people worldwide and dramatically impacting quality of life for affected individuals. While treatment techniques have evolved in recent years, methods for assessing hearing ability have remained relatively unchanged for decades. The standard clinical procedure is the modified Hughson-Westlake procedure, an adaptive pure-tone detection task that is typically performed manually by audiologists, costing millions of collective hours annually among healthcare professionals. In addition to the high burden of labor, the technique provides limited detail about an individual’s hearing ability, estimating only detection thresholds at a handful of pre-defined pure-tone …

Decoupling Information And Connectivity In Information-Centric Networking, Hila Ben Abraham, Jyoti Parwatikar, John Dehart, Adam Drescher, Patrick Crowley

All Computer Science and Engineering Research

This paper introduces and demonstrates the concept of Information-Centric Transport as a mechanism for cleanly decoupling the information plane from the connectivity plane in Information-Centric Networking (ICN) architectures, such as NDN and CICN. These are coupled in today's incarnations of NDN and CICN through the use of forwarding strategy, which is the architectural component for deciding how to forward packets in the presence of either multiple next-hop options or dynamic feedback. As presently designed, forwarding strategy is not sustainable: application developers can only confidently specify strategy if they understand connectivity details, while network node operators can only confidently assign strategies …

An Improved Algorithm For Learning To Perform Exception-Tolerant Abduction, Mengxue Zhang

McKelvey School of Engineering Theses & Dissertations

Abstract

Inference from an observed or hypothesized condition to a plausible cause or explanation for this condition is known as abduction. For many tasks, the acquisition of the necessary knowledge by machine learning has been widely found to be highly effective. However, the semantics of learned knowledge are weaker than the usual classical semantics, and this necessitates new formulations of many tasks. We focus on a recently introduced formulation of the abductive inference task that is thus adapted to the semantics of machine learning. A key problem is that we cannot expect that our causes or explanations will be perfect, …