79 citations · 167 across the 22 of their papers we have counts for
23 papers
POSIM: A Multi-Agent Simulation Framework for Social Media Public Opinion Evolution and Governance
Yongmao Zhang, Kai Qiao, Zhengyan Wang +5
Modeling social media public opinion evolution is essential for governance decision-making. Traditional epidemic models and rule-based agent-based models (ABMs) fail to capture the…
Efficient Pretraining Model based on Multi-Scale Local Visual Field Feature Reconstruction for PCB CT Image Element Segmentation
Chen Chen, Kai Qiao, Jie Yang +2
Element segmentation is a key step in nondestructive testing of Printed Circuit Boards (PCB) based on Computed Tomography (CT) technology. In recent years, the rapid development of…
Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective
Shuhao Shi, Kai Qiao, Zhengyan Wang +4
The presence of a large number of bots on social media has adverse effects. The graph neural network (GNN) can effectively leverage the social relationships between users and achie…
RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection
Shuhao Shi, Kai Qiao, Jie Yang +3
The presence of a large number of bots on social media leads to adverse effects. Although Random forest algorithm is widely used in bot detection and can significantly enhance the…
Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection
Shuhao Shi, Kai Qiao, Jie Yang +3
The presence of a large number of bots in Online Social Networks (OSN) leads to undesirable social effects. Graph neural networks (GNNs) are effective in detecting bots as they uti…
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
Shuhao Shi, Kai Qiao, Jian Chen +5
The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation qual…
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
Shuhao Shi, Jian Chen, Kai Qiao +3
The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory. Previo…
Adaptive Multi-layer Contrastive Graph Neural Networks
Shuhao Shi, Pengfei Xie, Xu Luo +4
We present Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-GNN), a self-supervised learning framework for Graph Neural Network, which learns feature representations of…
ShapeEditer: a StyleGAN Encoder for Face Swapping
Shuai Yang, Kai Qiao
In this paper, we propose a novel encoder, called ShapeEditor, for high-resolution, realistic and high-fidelity face exchange. First of all, in order to ensure sufficient clarity a…
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout
Pengfei Xie, Linyuan Wang, Ruoxi Qin +4
Deep neural networks(DNNs) is vulnerable to be attacked by adversarial examples. Black-box attack is the most threatening attack. At present, black-box attack methods mainly adopt…
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification
S. Shi, Kai Qiao, Shuai Yang +3
The Graph Neural Network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dat…
Defense-guided Transferable Adversarial Attacks
Zifei Zhang, Kai Qiao, Jian Chen +1
Though deep neural networks perform challenging tasks excellently, they are susceptible to adversarial examples, which mislead classifiers by applying human-imperceptible perturbat…
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features
Kai Qiao, Chi Zhang, Jian Chen +3
On basis of functional magnetic resonance imaging (fMRI), researchers are devoted to designing visual encoding models to predict the neuron activity of human in response to present…
BigGAN-based Bayesian reconstruction of natural images from human brain activity
Kai Qiao, Jian Chen, Linyuan Wang +3
In the visual decoding domain, visually reconstructing presented images given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI) is di…
AdvJND: Generating Adversarial Examples with Just Noticeable Difference
Zifei Zhang, Kai Qiao, Lingyun Jiang +2
Compared with traditional machine learning models, deep neural networks perform better, especially in image classification tasks. However, they are vulnerable to adversarial exampl…
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples
Wanting Yu, Hongyi Yu, Lingyun Jiang +2
Adversarial examples reveal the vulnerability and unexplained nature of neural networks. Studying the defense of adversarial examples is of considerable practical importance. Most…
Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization
Kai Qiao, Chi Zhang, Jian Chen +3
Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation. Visual encod…
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense
Lingyun Jiang, Kai Qiao, Ruoxi Qin +4
In image classification of deep learning, adversarial examples where inputs intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect resul…
Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices
Kai Qiao, Jian Chen, Linyuan Wang +4
Recently, visual encoding and decoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation.…
A visual encoding model based on deep neural networks and transfer learning
Chi Zhang, Kai Qiao, Linyuan Wang +4
Background: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve hi…
Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network
Chi Zhang, Kai Qiao, Linyuan Wang +3
In recent years, research on decoding brain activity based on functional magnetic resonance imaging (fMRI) has made remarkable achievements. However, constraint-free natural image…
Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture
Kai Qiao, Chi Zhang, Linyuan Wang +4
In neuroscience, all kinds of computation models were designed to answer the open question of how sensory stimuli are encoded by neurons and conversely, how sensory stimuli can be…
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
Hanming Zhang, Liang Li, Kai Qiao +4
Limited angle problem is a challenging issue in x-ray computed tomography (CT) field. Iterative reconstruction methods that utilize the additional prior can suppress artifacts and…