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Events for November 15, 2022
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PhD Thesis Proposal - Ali Alotaibi
Tue, Nov 15, 2022 @ 08:00 AM - 10:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Ali Alotaibi
Title: Automated Repair of Layout Accessibility Issues in Mobile Applications
Time: Tuesday, November 15, 8:00 AM-10:00 AM PST
Committee: William GJ Halfond (chair), Murali Annavaram, Nenad Medvidovic, Mukund Raghothaman, and Chao Wang.
Abstract:
An increasing number of people are now dependent on mobile devices to access data and complete essential tasks. For people with disabilities, mobile apps that violate accessibility guidelines can prevent them from carrying out these activities. Layout accessibility issues are among the top accessibility issues in mobile applications. These issues impact the accessibility of mobile apps and make them difficult to use, especially for older people and people with disabilities. Unfortunately, existing techniques are limited in helping developers debug these issues. These techniques are only capable of detecting the issues. Therefore, the repair of layout accessibility issues remains a manual process.
Automated repair of layout accessibility issues is complicated by several challenges. First, a repair must account for multiple issues holistically in order to preserve the relative consistency of the original app design. Second, due to the complex relationship between UI components, there is no clear way of identifying the set of elements and properties that needs to be modified for a given issue. Third, assuming the relevant views and properties can be identified, the number of possible changes that need to be considered grows exponentially as more elements and properties need to be considered. Finally,
a change in one element can create cascading changes that lead to further problems in other areas of the UI. Together, these challenges make a seemingly simple repair difficult to achieve. In this thesis proposal, I propose an automated framework for repairing layout accessibility issues in mobile applications.
Zoom Link: https://usc.zoom.us/j/98863735277?pwd=MTVITkNqY2dQdmhKWWRkRElWeVppUT09
WebCast Link: https://usc.zoom.us/j/98863735277?pwd=MTVITkNqY2dQdmhKWWRkRElWeVppUT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Aaron Chan
Tue, Nov 15, 2022 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Aaron Chan
Title: Generating and Utilizing Machine Explanations for Trustworthy NLP
Time: Tuesday, November 15, 3:00PM-5:00PM PST
Committee: Xiang Ren (chair), Robin Jia, Jesse Thomason, Bistra Dilkina, Morteza Dehghani
Abstract:
Neural language models (LMs) have yielded remarkable success on a wide range of natural language processing (NLP) tasks. However, LMs sometimes exhibit undesirable behavior, which can be difficult to resolve due to LMs' opaque reasoning processes. This lack of transparency poses serious concerns about LMs' trustworthiness in high-stakes decision-making, thus motivating the use of machine explanations to automatically interpret how LMs make their predictions. In my thesis, I argue that building human trust in NLP systems requires being able to: (A) generate machine explanations for LM behavior faithfully and plausibly and (B) utilize machine explanations to improve LM generalization and decision-making. First, to address (A), I propose UNIREX, a unified learning framework for jointly optimizing machine explanations with respect to both faithfulness and plausibility, without compromising the LM's task performance. Second, for (B), I introduce ER-Test, a framework for evaluating the out-of-distribution generalization ability of LMs that are regularized via strongly-supervised machine explanations. Third, to further support (B), I present SalKG, an algorithm for improving LM generalization by regularizing LMs via weakly-supervised machine explanations. Finally, I discuss several future directions for achieving (A) and (B).
Zoom Link: https://usc.zoom.us/j/95606515253?pwd=QzBvaVVpcWtYSFhVYzVoUi9tdHBRdz09
WebCast Link: : https://usc.zoom.us/j/95606515253?pwd=QzBvaVVpcWtYSFhVYzVoUi9tdHBRdz09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
