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Found 9 papers, 0 papers with code
RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory
Different from existing methods that predict human poses from RF signals on the signal level directly, we consider the structure difference between the RF signals and the human poses, propose to transform the RF signals to the pose domain on the feature level based on Optimal Transport (OT) theory, and generate human poses from the transformed features.
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection
Cyber intrusion attacks that compromise the users' critical and sensitive data are escalating in volume and intensity, especially with the growing connections between our daily life and the Internet.
Intrusion Detection in Internet of Things using Convolutional Neural Networks
As security mechanisms are often neglected during the deployment of IoT devices, they are more easily attacked by complicated and large volume intrusion attacks using advanced techniques.
PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection
It is especially so when the model's incorrect prediction can lead to severe damages or even losses to lives and critical assets.
RFGAN: RF-Based Human Synthesis
To the best of our knowledge, this is the first work to generate optical images based on RF signals.
"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector
In the following experiments, we compare the explainability and fidelity of our proposed method with state-of-the-arts, respectively.
Personalized Outfit Recommendation With Learnable Anchors
To accommodate the variety of users' preferences, we characterize each user with a set of anchors, i. e. a group of learnable latent vectors in the outfit space that are the representatives of the outfits the user likes.
Learning Binary Code for Personalized Fashion Recommendation
In this paper, we propose to learn binary code for efficient personalized fashion outfits recommendation.
Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach
In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of micro-calcifications ($\mu$C).
