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Events for April 17, 2023
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PHD Thesis Proposal - Julie Jiang
Mon, Apr 17, 2023 @ 01:00 PM - 02:30 PM
Thomas Lord Department of Computer Science
University Calendar
PHD Thesis Proposal: Julie Jiang
Committee: Emilio Ferrara (Chair), Barath Raghavan, Su Jung Kim, Jesse Thomason, Kristina Lerman
Title: Socially-infused Content Mining of Online Human Behavior
Abstract:
The vast amount of data generated by human behavior online provides valuable insight into how people interact with one another and with digital environments. However, mining this data can be time-consuming and computationally intensive. This dissertation proposes a unified language and network model that leverages the concept of homophily to efficiently analyze large-scale human behavior. By identifying patterns in network interactions and linguistic styles, this model can characterize political polarization, detect hateful and toxic users, and quantify users based on their moral foundation leanings. The findings demonstrate how seemingly simple patterns in online behavior can offer a deeper understanding of human behavior in digital environments. I apply this model to a range of real-world problems, including characterizing political polarization, understanding social influence on networks of hateful users, and contextualizing user behavior based on their moral foundation leanings. The findings demonstrate how seemingly simple patterns in online behavior can offer a deeper understanding of human behavior in digital environments.
Location: https://usc.zoom.us/j/96953099505?pwd=MDhJVFFSbDhuNnBWNm9JZjRFRUVjZz09
Audiences: Everyone Is Invited
Contact: Asiroh Cham
Event Link: https://usc.zoom.us/j/96953099505?pwd=MDhJVFFSbDhuNnBWNm9JZjRFRUVjZz09
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PhD Thesis Proposal - Haidong Zhu
Mon, Apr 17, 2023 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Body Shape Reconstruction for Video-based Person Identification
Committee: Ram Nevatia (Chair), Ulrich Neumann, Mohammad Soleymani, Stefanos Nikolaidis, Antonio Ortega
Date: Monday April 17, 2pm PST
Abstract: The recognition of individuals in videos is a crucial task in connecting video clips of the same person captured by multiple non-overlapping cameras. While image-based person identification mainly relies on the person's appearance, video-based identification can exploit additional external information, such as walking poses and general body shape, leading to less biased identification results. However, changes in body shape due to clothing and camera views can negatively impact such biometrics. To address this limitation, we propose reconstructing 3-D human body shapes across frames to provide consistency and invariance to view changes, improving identification accuracy and robustness. Existing 3-D body shape reconstruction methods typically focus on frame-by-frame reconstruction and fail to leverage the consistency between frames. In this proposal, we aim to enhance body shape reconstruction and representation of objects and body. Additionally, we will extend these methods to reconstruct body shapes in videos and utilize this information to aid person identification. Our proposed approach leverages temporal information between frames and utilizes body shape information for identification assistance, with the goal of improving identification accuracy.
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/94346121440?pwd=S3JSVVRpTFc5WFB5THEvWE9TTEhSQT09
