[cs-talks] REMINDER: PhD Defense: Mehrnoosh Sameki TOMORROW @ 1pm, MCS 180

Harrington, Jacob Walter jwharrin at bu.edu
Tue Aug 1 08:54:22 EDT 2017


Mehrnoosh Sameki: PhD Thesis Defense
Accurate and Budget-Efficient Text, Image, and Video Analysis Systems Powered by the Crowd
Wednesday, August 2nd, 2017 at 1:00pm, Hariri Seminar Room, 111 Cummington Mall, Room 180




Abstract:

Crowdsourcing systems empower individuals and companies to outsource labor-intensive tasks that cannot currently be solved by automated methods. They distribute such tasks among internet workers who typically have a range of skills and knowledge, differing previous exposure to the task at hand, and biases that may influence their work. This inhomogeneity of the workforce makes the design of accurate and efficient crowdsourcing systems challenging. This dissertation presents solutions to improve existing crowdsourcing systems in terms of accuracy and efficiency. It explores crowdsourcing tasks in two application areas, political discourse and annotation of biomedical and everyday images. The first part of the dissertation makes three contributions to the problem of how to design accurate, budget-efficient crowdsourcing systems. First, the thesis proposes an image analysis system called ICORD that utilizes behavioral cues of the crowd worker, augmented by automated evaluation of image features, to infer the quality of a worker-drawn outline of a cell in a microscope image dynamically. ICORD determines the need to seek additional annotations from other workers in a budget-efficient manner. Second, the thesis describes an interactive system for cell tracking in time-lapse microscopy videos, based on a prediction model that determines when automated cell tracking algorithms fail and human interaction is needed to ensure accurate tracking. Third, the thesis proposes a budget-efficient machine learning system that uses fewer workers to analyze easy-to-label data and more workers for data that require extra scrutiny. The system learns a mapping from data features to number of allocated crowd workers for two case studies, sentiment analysis of twitter messages and segmentation of biomedical images. The second part of the dissertation investigates how workers' behavioral factors and their unfamiliarity with data can be leveraged by crowdsourcing systems to control quality. The thesis presents Crowd-O-Meter, a system that automatically predicts the vulnerability of crowd workers to believe "fake news" in text and video. Finally, through studies that involve familiar and unfamiliar image content, the thesis demonstrates the benefit of explicitly accounting for a worker's familiarity with the data when designing annotation systems powered by the crowd.



Committee:
Prof. Margrit Betke
Prof. Danna Gurari
Prof. Prakash Ishwar
Prof. Lei Guo
Prof. Evimaria Terzi (Chair)


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