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Found 28 papers, 5 papers with code
Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision
The evolution of deep learning and artificial intelligence has significantly reshaped technological landscapes.
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation
In the field of Medical Imaging, extensive research has been dedicated to leveraging its potential in uncovering critical diagnostic features in patients.
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning
In the ambitious realm of space AI, the integration of federated learning (FL) with low Earth orbit (LEO) satellite constellations holds immense promise.
Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM
Space AI has become increasingly important and sometimes even necessary for government, businesses, and society.
CR-SAM: Curvature Regularized Sharpness-Aware Minimization
However, as training progresses, the non-linearity of the loss landscape increases, rendering one-step gradient ascent less effective.
LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate
The transferability of adversarial examples is of central importance to transfer-based black-box adversarial attacks.
Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps
Additionally, we introduce a robust preprocessing pipeline that adapts to the unique characteristics of individual subjects and makes use of whole brain images rather than slices or patches.
Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation
Our analysis and results show that FedSecure preserves the privacy of each satellite's data against eavesdroppers, a curious server, or curious satellites.
A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition
In this paper, we (1) propose a simple adaptive ensemble classification system that handles the inter-subject variability, (2) present a simple BCI-AR framework that supports the development of a wide range of SSVEP-based BCI-AR applications, and (3) evaluate the performance of our ensemble algorithm in an SSVEP-based BCI-AR application with head rotations which has demonstrated robustness to the movement interference.
Catching Elusive Depression via Facial Micro-Expression Recognition
Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood that leads to emotional distress.
Micro Expression Recognition
Micro-Expression Recognition
+1
YOGA: Deep Object Detection in the Wild with Lightweight Feature Learning and Multiscale Attention
In addition, it performs multi-scale feature fusion in its neck using an attention mechanism instead of the naive concatenation used by conventional detectors.
GNP Attack: Transferable Adversarial Examples via Gradient Norm Penalty
Adversarial examples (AE) with good transferability enable practical black-box attacks on diverse target models, where insider knowledge about the target models is not required.
One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes
A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft tracking.
LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing
However, deep learning models are typically iteratively optimized in a central server with input data gathered from edge devices, and such data transfer between edge devices and the central server impose substantial overhead on the network and incur additional latency and energy consumption.
Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World
With the advances of IoT developments, copious sensor data are communicated through wireless networks and create the opportunity of building Digital Twins to mirror and simulate the complex physical world.
TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks
To achieve these goals, we convert each input time-series into a sequence of 2D images using two encoding techniques with the intent of capturing temporal patterns and various types of deviance.
Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling
The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS.
AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms
Not only does AsynFLEO address the bottleneck (idle waiting) in synchronous FL, but it also solves the issue of model staleness caused by straggler satellites.
Learning Deep Representations via Contrastive Learning for Instance Retrieval
To begin with, we investigate the efficacy of transfer learning in IIR, by comparing off-the-shelf features learned by a pre-trained deep neural network (DNN) classifier with features learned by a CL model.
Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning
Not only this, but we can also distinguish between targeted and untargeted attacks among malicious clients, unlike most prior works which only consider one type of the attacks.
No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects
Convolutional neural networks (CNNs) have made resounding success in many computer vision tasks such as image classification and object detection.
FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs
Low Earth Orbit (LEO) satellite constellations have seen a surge in deployment over the past few years by virtue of their ability to provide broadband Internet access as well as to collect vast amounts of Earth observational data that can be utilized to develop AI on a global scale.
Data-Free Evaluation of User Contributions in Federated Learning
We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset.
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach
In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset, and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing
We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets.
Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing
We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
COBRA: Context-aware Bernoulli Neural Networks for Reputation Assessment
Trust and reputation management (TRM) plays an increasingly important role in large-scale online environments such as multi-agent systems (MAS) and the Internet of Things (IoT).
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT
Wireless sensor networks (WSN) are fundamental to the Internet of Things (IoT) by bridging the gap between the physical and the cyber worlds.
