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Events for July 01, 2024
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PhD Dissertation Defense - Jessie Hoegen
Mon, Jul 01, 2024 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Decoding Situational Perspective: Incorporating Contextual Influences into Facial Expression Perception Modeling
Location: GFS 104 / Zoom: https://usc.zoom.us/j/6871678093?pwd=VWdZZjJMaW9PK2cyRmMvNUJpbVV6UT09
Date and time: July 1, 1pm - 3pm
Committee: Jonathan Gratch (chair), Emilio Ferrara, Giorgio Coricelli
Abstract:
The work that I performed during my doctoral studies relates to facial expressions within the field of Affective Computing. In this field, facial expressions are a popular subject of study. State-of-the-art facial expression models and emotion perception models have gone through leaps of improvements and nowadays match the accuracy of professionally trained expression coders. Because of these improvements, one might expect that automatic emotion recognition has become an indispensable tool for analyzing and predicting human social behavior. Indeed, psychological theories argue that emotional expressions serve crucial social functions such as revealing intentions and shaping partner behavior. Yet these theoretical benefits have largely failed to materialize within the field of affective computing.
There is now growing understanding that one of the obstacles to the advancement of affective computing is how the concept of emotion is typically represented within affective computing. Influenced by early theories from psychology, expressions are often treated as universal and context-independent signifiers of an underlying emotional state. This latent state is then assumed to shape subsequent human behavior. Yet more recent psychological theories argue that expressions should be seen more like words and function to coordinate social behavior. My dissertation embraces this latter view and explores its consequences for affective computing.
Following recent ``pragmatic'' theories of emotional expressions, I adopt the perspective that expressions in social settings should be best treated like words. Like words, the meaning of expressions must be seen as context dependent. Just as ``bank'' might refer to the side of a river or a financial institution, a smile might refer to pleasure or anger depending on the surrounding context. And like words, expressions can be examined from multiple perspectives. We can consider the ``author's'' perspective (why did this person produce this expression? what was their intent? what does it signal they will do next?) but also the ``reader's'' perspective (how does this expression shape the observer's emotions, intentions and actions?).
I illustrate the utility of this perspective for analyzing human social behavior. Focusing on a series of social tasks such as social dilemmas and negotiations, I show how the interpretation of facial expressions is shaped by context, and that expressions, when combined with context, can usefully predict the author's intentions and consequences for the reader. Together, this body of research makes several important contributions. First, I add to the growing body of research that questions the utility of context-free methods for automatically recognizing emotional expressions. Second, from the perspective of the author, I show how both expressions and context are necessary for predicting an author's subsequent actions in a face-to-face negotiation from their expressions. Thirdly, from the perspective of the reader, I show how emotional expressions shape the readers actions in a social dilemma. Finally, I show how these models could inform the behavior of interactive synthetic agents, for example allowing them to strategically select emotional expressions that will benefit a team task. More broadly, my dissertation illustrates the potential benefits of incorporating a pragmatic perspective on the meaning of emotional expressions into the field of affective computing.
Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 104
Audiences: Everyone Is Invited
Contact: Ellecia Williams
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Ph.D. Thesis Defense - Bingyi Zhang
Mon, Jul 01, 2024 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Bingyi Zhang
Committee Members: Prof. Paul Bogdan, Prof. Rajgopal Kannan, Prof. Viktor Prasanna (Chair), Prof. Weihang Wang
Title: Hardware-software Codesign for accelerating Graph Neural Networks on FPGA
Abstract: Graph Neural Networks (GNN) have revolutionized many real-world applications, such as recommendation systems, social networks, etc. However, current GNN libraries on general-purpose processors achieve sub-optimal performance due to several challenges: 1. Irregular data structure: the graphs in real-world applications are highly unstructured, with uneven degree distribution. Such irregularity leads to complex data access patterns. 2. Heterogeneous computation kernels: GNNs involve both sparse computation kernels and dense computation kernels. While general-purpose processors are efficient for dense computation, their data path and memory hierarchy are inefficient for sparse computation. 3. Dynamic data sparsity: In many applications, the graph connectivity and the data sparsity of vertex features are unknown before executing the GNN model. Such dynamic data sparsity makes it difficult for the compiler and runtime system to generate an optimal execution scheme for GNN. 4. Mixture of Models: Some GNN-based applications, such as GNN-based computer vision tasks, utilize a mixture of CNN and GNN models. Such a combination leads to complex data flow. In this dissertation, we address the above challenges through novel hardware-software codesign. First, to address the first two challenges, we develop an accelerator-compiler codesign on FPGA for GNN inference, named GraphAGILE, for the end-to-end acceleration of GNNs. Second, we propose Dynasparse, an efficient codesign of runtime system and hardware to exploit the dynamic sparsity in GNN inference. Third, we propose GCV-Turbo, a hardware-software codesign accelerating GNN-based computer vision (CV) tasks, which involves a mixture of GNN layers and CNN layers. Our codesigns achieve superior performance on various GNN-based applications compared with state-of-the-art graph machine learning libraries and hardware accelerators.
Bio: Bingyi Zhang is a fifth-year PhD candidate in Computer Engineering, advised by Professor Viktor K. Prasanna. He received the BS degree in microelectronics from Fudan University in 2017, and the MS degree in Integrated Circuit Engineering from Fudan University in 2019. His research interests include parallel computing, digital signal processing, digital circuit design.
Date: Monday, July 1st, 2024
Time: 2 pm
Location:
Zoom Link: https://usc.zoom.us/j/4520579668?pwd=eXNyaThLeFloNzBLVHlZQ0FYdzRGdz09Location: Zoom Only
Audiences: Everyone Is Invited
Contact: CS Events
