Event Category: In-house Presentation

Machine Learning for FPGAs accelerators

Machine learning, and especially deep learning, are techniques that are truly computationally demanding. This issue becomes even more critical when...

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Setchain Algorithms for Blockchain Scalability

In this talk, I will discuss our current research on Blockchain scalability using Setchain Data-type. Blockchain scalability has been a...

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Network-Layer attacks in Blockchain Systems

In this talk, I will introduce some topics from my current research on the design of cryptocurrency networks and the...

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From Source to Fragmentation: Unveiling Supply Chain Dynamics and OEM Customizations in Android TLS Stack

The Android ecosystem’s open-source nature allows Original Equipment Manufacturers (OEMs) to tailor the platform to their specific needs, resulting in...

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Exploring and Building Digital Twins for Quantum Key Distribution Networks

Quantum Key Distribution (QKD) networks, which use the principles of Quantum Mechanics to securely distribute cryptographic keys to remote entities,...

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Caught Between Feeling Lucky and Abusing Free Returns: How to Bootstrap Data Marketplaces

Data is the fuel of AI systems, meant to have a profound impact on society and the economy. However, bootstrapping...

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A Study of Malicious Source Code Reuse Among GitHub, StackOverflow and Underground Forums

To date, most analysis of collaboration between malware authors has been performed on meta-data and compiled binaries, while ignoring artifacts...

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I love pinapple on pizza! = I hate pinapple on pizza: Stance-Aware Sentence Transformers for Opinion Mining

Sentence transformers’ job is to convert pieces of text into embedded vectors, where similar (dissimilar) pieces of text have spatially...

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Characterizing, Modeling and Exploiting the Mobile Demand Footprint of Large Public Protests

Smartphones and mobile applications are staple tools in the operation of current-age public demonstrations, where they support organizers and participants...

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FedQV: Leveraging Quadratic Voting in Federated Learning

Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels. A crucial...

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