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Events for October
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Ph.D. Defense - Eli Pincus
Mon, Oct 05, 2020 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Eli Pincus 10/05 2:00 pm "An Investigation of Fully Interactive Multi-Role Dialogue Agents"
Ph.D. Candidate: Eli Pincus
Date: Monday, October 5, 2020
Time: 2:00 pm - 4:00 pm
Committee: David Traum (chair), Maja Mataric, Peter Kim
Title: An Investigation of Fully Interactive Multi-Role Dialogue Agents
Abstract:
In the course of their lives human perform multiple roles such as work and social roles. However, current research in human-computer dialogue has focused on dialogue agents that perform only one role of an interaction. For example, Apple's Siri acts mainly as an assistant. In this thesis we helps fill the gap in multi-role dialogue agent research.
We describe an architecture that endows a test-bed agent with core dialogue management capabilities for both roles of a word-guessing game but can be adapted for different embodiments including virtual human, robot, and a non-embodied web-platform that enables use of the test-bed agent in "in the wild" experiments. We incrementally evaluate design decisions for the test-bed agent that decrease the chance that our later experiments, that more directly evaluate the agent's multi-role capabilities, failed to find effects due to confounds stemming from poor design decisions. We establish that multi-role agents, when compared to single-role versions of the same agent, are able to elicit enjoyment from users without negatively impacting user's perceptions. We also use an "in the wild" experiment to prove that a multi-role content sourcing strategy can be superior to other scalable content sourcing strategies.
Meeting Links:
Join Zoom Meeting
https://usc.zoom.us/j/92791499440?pwd=djNKQzMxalJXZTVUR3dTQUp6Ykw2dz09
Meeting ID: 927 9149 9440
Passcode: 281112WebCast Link: https://usc.zoom.us/j/92791499440?pwd=djNKQzMxalJXZTVUR3dTQUp6Ykw2dz09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense -Yixue Zhao
Fri, Oct 16, 2020 @ 09:00 AM - 11:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Yixue Zhao
Committee:
Nenad Medvidovic (Chair)
Chao Wang
Bhaskar Krishnamachari
Date: 10/16/2020
Time: 9am
Zoom: https://usc.zoom.us/j/96796759326?pwd=aTF3SnJlS3ljM1pjMkhZNzIyVGttdz09
Meeting ID: 967 9675 9326
Passcode: 149878
Title: Reducing User-Perceived Latency in Mobile Applications via Prefetching and Caching
Prefetching and caching is a fundamental approach to reduce user-perceived latency, and has been shown effective in various domains for decades. However, its application on today's mobile apps remains largely under-explored. This is an important but overlooked research area since mobile devices have become the dominant platform, and this trend is reflected in the billions of mobile devices and millions of mobile apps in use today. At the same time, user-perceived latency has been shown to have a large impact on mobile-user experience and can cause significant economic consequences.
In my dissertation, I aim to fill this gap by providing a multifaceted solution to establish the foundation for exploring various aspects of prefetching and caching techniques in the mobile-app domain. To that end, my dissertation consists of four major elements. As a first step, I conducted an extensive study to investigate the opportunities for applying prefetching and caching techniques in mobile apps, providing empirical evidence on their applicability and showing insights to guide future techniques. Second, I developed PALOMA, the first content-based prefetching technique for mobile apps using program analysis, which has achieved significant latency reduction with high accuracy and negligible overhead. Third, I constructed HiPHarness, a tailorable framework for investigating history-based prefetching in a wide range of scenarios. Guided by today's stringent privacy regulations that have limited the access to mobile-user data, I further leveraged HiPHarness to conduct the first study on history-based prefetching with "small" prediction models, demonstrating its feasibility on mobile platforms and in turn, opening up a new research area. Finally, to reduce the manual effort required in evaluating prefetching and caching techniques, I have devised FrUITeR, a framework for assessing test-reuse techniques in order to automatically select suitable test cases to evaluate prefetching and caching techniques, without real users' engagement as required previously.
WebCast Link: https://usc.zoom.us/j/96796759326?pwd=aTF3SnJlS3ljM1pjMkhZNzIyVGttdz09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
