Kai Chen

APT Attack Analysis in SCADA Systems

2018 · MATEC Web of Conferences

SCADA (Supervisory Control and Data Acquisition) systems play a significant role in ICS (Industrial Control System). Safety, security and stable operation is crucial to the SCADA system. However, SCADA system currently faces many security threats, …

Zipper Stack: Shadow Stacks Without Shadow

2019 · arXiv (Cornell University)

Return-Oriented Programming (ROP) is a typical attack technique that exploits return addresses to abuse existing code repeatedly. Most of the current return address protecting mechanisms (also known as the Backward-Edge Control-Flow Integrity) work only in …

Value management in Blockchain technology related projects

2019 · Proceedings of the ... International Conference on Business Excellence

Abstract Because of the development of Bitcoin in recent years, the foundational technology behind it, the Blockchain technology, also begins to attract attention from international banks and companies, and entrepreneurs have developed Blockchain technology related …

Federated Singular Vector Decomposition.

2021

With the promulgation of data protection laws (e.g., GDPR in 2018), privacy preservation has become a general agreement in applications where cross-domain sensitive data are utilized. Out of many privacy-preserving techniques, federated learning (FL) has …

No Free Lunch Theorem for Security and Utility in Federated Learning

2022 · ACM Transactions on Intelligent Systems and Technology

In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data …

Research on Food Safety Data Sharing and Exchange Mechanism

2022

With the development of the economy and the improvement of people's quality of life, the public demand for food taste has gradually changed to the demand for food safety. In order to better facilitate the …

Trading Off Privacy, Utility and Efficiency in Federated Learning

2022 · arXiv (Cornell University)

Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the opposing requirements in preserving \textit{privacy} …

RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank

2023 · arXiv (Cornell University)

Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer …

Large Language Models Reveal Information Operation Goals, Tactics, and Narrative Frames

2024 · arXiv (Cornell University)

Adversarial information operations can destabilize societies by undermining fair elections, manipulating public opinions on policies, and promoting scams. Despite their widespread occurrence and potential impacts, our understanding of influence campaigns is limited by manual analysis …

Event-Based Intelligent Sensitivity Analysis and Performance Prediction With Application to Hydraulic System Monitoring

2024

This study proposes an intelligent sensitivity analysis and performance prediction approach for complex systems based on novel integration of event clustering and random forest regression techniques. The event clustering method is used for real-time sensitivity …

MixNet: A Runtime Reconfigurable Optical-Electrical Fabric for Distributed Mixture-of-Experts Training

2025

Mixture-of-Expert (MoE) models outperform conventional models by selectively activating different subnets, named experts, on a per-token basis. This gated computation generates dynamic communications that cannot be determined beforehand, challenging the existing GPU interconnects that remain …

Pre-Trained Policy Discriminators are General Reward Models

2025

We offer a novel perspective on reward modeling by formulating it as a policy discriminator, which quantifies the difference between two policies to generate a reward signal, guiding the training policy towards a target policy …

Towards Optimal Rack-scale µs-level CPU Scheduling through In-Network Workload Shaping

2025 · Rare & Special e-Zone (The Hong Kong University of Science and Technology)

>Rack-scale CPU scheduling has emerged as a promising direction to accommodate the increasing demands for microsecond-level services. However, prior work suffers from both inaccurate load balancing in the network and complex yet sub-optimal scheduling within …

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

2018 · arXiv (Cornell University)

Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the …