Search Results for author:
Found 7 papers, 1 papers with code
Learning Behavior Recognition in Smart Classroom with Multiple Students Based on YOLOv5
The use of computer vision technology to identify students' learning behavior in the classroom can reduce the workload of traditional teachers in supervising students in the classroom, and ensure greater accuracy and comprehensiveness.
Graph Neural Networks Enhanced Smart Contract Vulnerability Detection of Educational Blockchain
With the development of blockchain technology, more and more attention has been paid to the intersection of blockchain and education, and various educational evaluation systems and E-learning systems are developed based on blockchain technology.
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing
Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction.
Image Compressed Sensing with Multi-scale Dilated Convolutional Neural Network
During the measurement period, we directly obtain all measurements from a trained measurement network, which employs fully convolutional structures and is jointly trained with the reconstruction network from the input image.
Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings
In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning.
Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks
However, when this method uses backpropagation to obtain gradients, it will cause noise in the saliency map, and even locate features that are irrelevant to decisions.
UIILD: A Unified Interpretable Intelligent Learning Diagnosis Framework for Intelligent Tutoring Systems
Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners' current knowledge mastery status and predict their future learning performance.
