Search Results for author:
Found 9 papers, 0 papers with code
The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks
Spiking Neural Networks (SNNs), which offer exceptional energy efficiency for inference, and Federated Learning (FL), which offers privacy-preserving distributed training, is a rising area of interest that highly beneficial towards Internet of Things (IoT) devices.
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs).
Security Risks Concerns of Generative AI in the IoT
In an era where the Internet of Things (IoT) intersects increasingly with generative Artificial Intelligence (AI), this article scrutinizes the emergent security risks inherent in this integration.
CodeDSI: Differentiable Code Search
In an effort to improve the performance of code search, we have investigated docid representation strategies, impact of tokenization on docid structure, and dataset sizes on overall code search performance.
Automated Primary Hyperparathyroidism Screening with Neural Networks
Primary Hyperparathyroidism(PHPT) is a relatively common disease, affecting about one in every 1, 000 adults.
Security Vulnerability Detection Using Deep Learning Natural Language Processing
Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades.
Real Time 3D Indoor Human Image Capturing Based on FMCW Radar
Compared to traditional camera-based computer vision and imaging, radio imaging based on wireless sensing does not require lighting and is friendly to privacy.
Shared Multi-Task Imitation Learning for Indoor Self-Navigation
Deep imitation learning enables robots to learn from expert demonstrations to perform tasks such as lane following or obstacle avoidance.
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning
This solution includes a suboptimal sensor policy based on sensor fusion to automatically label states encountered by a robot to avoid human supervision during training.
