8:00am - 9:30am
Presenter: Esraa Aldreabi, PhD Candidate
Advisor: Professor Jeremy Blackburn
When: Monday, April 29, 8 am
Where: EB N03
Zoom Link: https://binghamton.zoom.us/j/93499197651
Title: Ethical Algorithms: Safeguarding Freedom Of Speech In The Detection Of Islamophobic Content
Abstract: This dissertation examines how to identify Islamophobia on social media without infringing on free speech. It tackles the difficult task of differentiating harmful content from necessary discussions on religious and cultural issues, involving diverse groups like atheists, ex-Muslims, feminists, and the LGBTQ+ community.
The research investigates Islamophobia's presence across various social media platforms and how their specific features, user interactions, and moderation policies influence the conversation. It proposes tailored strategies for each platform to address hate speech effectively without applying a one-size-fits-all approach.
Leveraging machine learning and deep learning, the project aims to fine-tune the process of detecting Islamophobic content, balancing the removal of hate speech with the protection of free speech. The goal is to foster an online environment that encourages open and respectful exchanges, free from discrimination.
Additionally, the dissertation explores the ethical implications of automating content moderation. It advocates for a nuanced approach to ensure online dialogues remain rich and diverse without becoming diluted by excessive censorship. The vision is for a digital space where freedom of expression and safety from hate coexist harmoniously.
This contribution is significant for policymakers, platform designers, and the global community, shedding light on the complexities of digital moderation and its impact on inclusivity and safety online. It aims to enhance the dialogue around creating a more welcoming and secure digital landscape for all.
Advisor: Professor Jeremy Blackburn
When: Monday, April 29, 8 am
Where: EB N03
Zoom Link: https://binghamton.zoom.us/j/93499197651
Title: Ethical Algorithms: Safeguarding Freedom Of Speech In The Detection Of Islamophobic Content
Abstract: This dissertation examines how to identify Islamophobia on social media without infringing on free speech. It tackles the difficult task of differentiating harmful content from necessary discussions on religious and cultural issues, involving diverse groups like atheists, ex-Muslims, feminists, and the LGBTQ+ community.
The research investigates Islamophobia's presence across various social media platforms and how their specific features, user interactions, and moderation policies influence the conversation. It proposes tailored strategies for each platform to address hate speech effectively without applying a one-size-fits-all approach.
Leveraging machine learning and deep learning, the project aims to fine-tune the process of detecting Islamophobic content, balancing the removal of hate speech with the protection of free speech. The goal is to foster an online environment that encourages open and respectful exchanges, free from discrimination.
Additionally, the dissertation explores the ethical implications of automating content moderation. It advocates for a nuanced approach to ensure online dialogues remain rich and diverse without becoming diluted by excessive censorship. The vision is for a digital space where freedom of expression and safety from hate coexist harmoniously.
This contribution is significant for policymakers, platform designers, and the global community, shedding light on the complexities of digital moderation and its impact on inclusivity and safety online. It aims to enhance the dialogue around creating a more welcoming and secure digital landscape for all.
1:00pm - 2:30pm
Presenter: Satrio Yudhoatmojo, PhD Candidate
Advisor: Professor Jeremy Blackburn
When: Monday, April 29, 1 pm
Where: EB N03
Zoom Link: https://binghamton.zoom.us/j/96439016688
Title: Quantitative Insights on the Use and Mis-use of Social Media in Scientific, Extremist, and Political Contexts
Abstract: The Internet has fundamentally changed how we communicate, share information, and interact with one another, giving rise to an array of Web communities and social media platforms. This explosion of user-generated content has created a vast landscape for exploration and analysis. In this dissertation, I explore online discussions across various platforms, aiming to unravel the complexities of human behavior and interaction.
The first part of this study explores human behavior on platforms like Reddit and 4chan’s Politically Incorrect board, where users often twist scientific e-prints to fit their own narratives. This highlights the importance of communicating scientific findings to the general public in accessible language to prevent misinformation. Next, I explore interactions within Telegram channels linked to the extremist group Sovereign Citizens, revealing the dangers of their narratives in challenging legitimate government institutions. Finally, I turn to Twitter discussions surrounding the 2024 Indonesian general elections, uncovering the importance of public opinion in countering election conspiracies and shaping support for potential presidential candidates. Through these analyses, this dissertation seeks to shed light on the intricate dynamics of online human behavior and interaction.
Advisor: Professor Jeremy Blackburn
When: Monday, April 29, 1 pm
Where: EB N03
Zoom Link: https://binghamton.zoom.us/j/96439016688
Title: Quantitative Insights on the Use and Mis-use of Social Media in Scientific, Extremist, and Political Contexts
Abstract: The Internet has fundamentally changed how we communicate, share information, and interact with one another, giving rise to an array of Web communities and social media platforms. This explosion of user-generated content has created a vast landscape for exploration and analysis. In this dissertation, I explore online discussions across various platforms, aiming to unravel the complexities of human behavior and interaction.
The first part of this study explores human behavior on platforms like Reddit and 4chan’s Politically Incorrect board, where users often twist scientific e-prints to fit their own narratives. This highlights the importance of communicating scientific findings to the general public in accessible language to prevent misinformation. Next, I explore interactions within Telegram channels linked to the extremist group Sovereign Citizens, revealing the dangers of their narratives in challenging legitimate government institutions. Finally, I turn to Twitter discussions surrounding the 2024 Indonesian general elections, uncovering the importance of public opinion in countering election conspiracies and shaping support for potential presidential candidates. Through these analyses, this dissertation seeks to shed light on the intricate dynamics of online human behavior and interaction.
10:00am - 11:00am
Presenter: Mohammad Shadab
Advisor: Professor Dmitry Ponomarev
When: Tuesday, April 30, 2024 10 am
Where: PO3
Title: Timing Attacks in GPU Systems
Abstract: GPUs enhance the performance of computationally intensive applications. As GPUs are increasingly used in deep learning workloads, a number of vulnerabilities have emerged. These vulnerabilities can be exploited to extract information from a victim application by employing side channels and covert channels through shared resources. In this presentation, we will discuss hardware security in GPUs, particularly their vulnerabilities to timing attacks. There are several prerequisites to make attacks possible on GPUs, such as co-location and concurrency between two applications. To establish attacks on GPUs, we need some information which is not published by manufacturers. Therefore, one of the critical parts of designing attacks on GPUs is reverse engineering to obtain necessary information, such as scheduling policies or co-location algorithms. After conducting several practical reverse engineering on GPUs, the next step is extracting information from a victim model such as DNN model characteristics. In this presentation, we will discuss related works that establish covert and side channels and use them to design attacks on GPUs. We will also introduce a new attack surface on the GPUs to demonstrate a possible channel to communicate between two malicious applications or extract information from a victim model.
Advisor: Professor Dmitry Ponomarev
When: Tuesday, April 30, 2024 10 am
Where: PO3
Title: Timing Attacks in GPU Systems
Abstract: GPUs enhance the performance of computationally intensive applications. As GPUs are increasingly used in deep learning workloads, a number of vulnerabilities have emerged. These vulnerabilities can be exploited to extract information from a victim application by employing side channels and covert channels through shared resources. In this presentation, we will discuss hardware security in GPUs, particularly their vulnerabilities to timing attacks. There are several prerequisites to make attacks possible on GPUs, such as co-location and concurrency between two applications. To establish attacks on GPUs, we need some information which is not published by manufacturers. Therefore, one of the critical parts of designing attacks on GPUs is reverse engineering to obtain necessary information, such as scheduling policies or co-location algorithms. After conducting several practical reverse engineering on GPUs, the next step is extracting information from a victim model such as DNN model characteristics. In this presentation, we will discuss related works that establish covert and side channels and use them to design attacks on GPUs. We will also introduce a new attack surface on the GPUs to demonstrate a possible channel to communicate between two malicious applications or extract information from a victim model.
9:30am - 10:15am
EB T1 and Zoom. Link below.
2:00pm - 4:00pm
Presenter: Xiaohan Zhang
Advisor: Shiqi Zhang
Date: Thursday, May 2, 2-4 pm
Location: EB T1
Zoom link: https://binghamton.zoom.us/j/9953067186?omn=97023042413
Title: Symbol Grounding for Task and Motion Planning in Robotics
Abstract: For modern robots that are equipped with a set of skills, such as manipulation and navigation, it's crucial that they can autonomously make decisions to complete tasks over extended periods of time. To this end, researchers have been developing planning algorithms that allow robots to effectively sequence and use their skills to reach specific task-level goals. Existing planning systems have a strong reliance on structured and predictive world models which are often represented as symbol tokens. Symbol grounding is thus required to establish a meaningful connection between these abstract symbols and their real-world interpretations that robots can understand. A fundamental approach to symbol grounding for robots involves utilizing their perception capabilities, allowing the mapping of sensory readings to symbol tokens and thereby assigning perceptual meanings to symbols. Additionally, robots frequently need to plan their actions in the process of grounding, given robot actions being tightly coupled with perception. Motivated by the above observations, this dissertation focuses on 1) symbol grounding using perception and action, and 2) robot planning with grounded symbols. Our research draws on theories and methods from computer vision and machine learning with both being integrated with AI planning and continuous motion planning towards building long-horizon robot autonomy.
Advisor: Shiqi Zhang
Date: Thursday, May 2, 2-4 pm
Location: EB T1
Zoom link: https://binghamton.zoom.us/j/9953067186?omn=97023042413
Title: Symbol Grounding for Task and Motion Planning in Robotics
Abstract: For modern robots that are equipped with a set of skills, such as manipulation and navigation, it's crucial that they can autonomously make decisions to complete tasks over extended periods of time. To this end, researchers have been developing planning algorithms that allow robots to effectively sequence and use their skills to reach specific task-level goals. Existing planning systems have a strong reliance on structured and predictive world models which are often represented as symbol tokens. Symbol grounding is thus required to establish a meaningful connection between these abstract symbols and their real-world interpretations that robots can understand. A fundamental approach to symbol grounding for robots involves utilizing their perception capabilities, allowing the mapping of sensory readings to symbol tokens and thereby assigning perceptual meanings to symbols. Additionally, robots frequently need to plan their actions in the process of grounding, given robot actions being tightly coupled with perception. Motivated by the above observations, this dissertation focuses on 1) symbol grounding using perception and action, and 2) robot planning with grounded symbols. Our research draws on theories and methods from computer vision and machine learning with both being integrated with AI planning and continuous motion planning towards building long-horizon robot autonomy.
10:30am - 4:00pm
UU 111
Agenda to follow as the date draws nearer.
1:00pm - 2:00pm
Presenter: Williams Zhang Cen
Advisor: Professor Dmitry Ponomarev
When: Wednesday, May 8, 2024, 1 pm
Where: EB PO3
Title: Program Verification Using Side-Channels
Abstract: In this talk, we will discuss recent research related to ensuring the integrity of program execution to protect against a range of attacks. Specifically, we will focus on control flow integrity solutions, both software-only and with support of hardware. After that, we will review recent literature that verifies program execution remotely using analog side-channel signatures produced as a result of execution. Finally, we will articulate some initial ideas on how to use microarchitectural side-channels to remotely verify correct execution of programs, effectively putting side-channels to good use.
Advisor: Professor Dmitry Ponomarev
When: Wednesday, May 8, 2024, 1 pm
Where: EB PO3
Title: Program Verification Using Side-Channels
Abstract: In this talk, we will discuss recent research related to ensuring the integrity of program execution to protect against a range of attacks. Specifically, we will focus on control flow integrity solutions, both software-only and with support of hardware. After that, we will review recent literature that verifies program execution remotely using analog side-channel signatures produced as a result of execution. Finally, we will articulate some initial ideas on how to use microarchitectural side-channels to remotely verify correct execution of programs, effectively putting side-channels to good use.
10:30am - 1:30pm
University Union, Old Union Hall
10:30am - 11:30am
