52 Result(s)
within
**aoDong Yang
Computer Science
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Article
Open AccessParallel processing model for low-dose computed tomography image denoising
Low-dose computed tomography (LDCT) has gained increasing attention owing to its crucial role in reducing radiation exposure in patients. However, LDCT-reconstructed images often suffer from significant noise ...
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Chapter and Conference Paper
TFAugment: A Key Frequency-Driven Data Augmentation Method for Human Activity Recognition
Data augmentation enhances Human Activity Recognition (HAR) models by diversifying training data through transformations, improving their robustness. However, traditional techniques with random masking pose ch...
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Chapter and Conference Paper
A New Combination Model for Offshore Wind Power Prediction Considering the Number of Climbing Features
The accurate identification of offshore wind power ramp events has great effects on wind power forecast. In order to improve the prediction accuracy of offshore wind power, this paper proposes an XGBoost-GRU c...
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Article
Large sequence models for sequential decision-making: a survey
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e.g., GPT-3 and Swin Trans...
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Article
Open AccessConvergence rate of multiple-try Metropolis independent sampler
The multiple-try Metropolis method is an interesting extension of the classical Metropolis–Hastings algorithm. However, theoretical understanding about its usefulness and convergence behavior is still lacking....
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Article
Open AccessOffline Pre-trained Multi-agent Decision Transformer
Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent rein...
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Article
Online Markov decision processes with non-oblivious strategic adversary
We study a novel setting in Online Markov Decision Processes (OMDPs) where the loss function is chosen by a non-oblivious strategic adversary who follows a no-external regret algorithm. In this setting, we first ...
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Chapter and Conference Paper
A Game-Theoretic Approach to Multi-agent Trust Region Optimization
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration. Nonetheless, when applied in multi-agent set...
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Article
Security analysis and improvement of a privacy-preserving authentication scheme in VANET
The vehicular ad-hoc network (VANET) is a critical component of intelligent transportation, which can improve transportation efficiency and promote road safety. To address security and privacy concerns in VANE...
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Article
Dual layer transfer learning for sEMG-based user-independent gesture recognition
During the last few years, significant attention has been paid to surface electromyographic (sEMG) signal–based gesture recognition. Nevertheless, sEMG signal is sensitive to various user-dependent factors, li...
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Chapter and Conference Paper
Debias the Black-Box: A Fair Ranking Framework via Knowledge Distillation
Deep neural networks can capture the intricate interaction history information between queries and documents, because of their many complicated nonlinear units, allowing them to provide correct search recommen...
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Chapter and Conference Paper
Neural Network Repair with Reachability Analysis
Safety is a critical concern for the next generation of autonomy that is likely to rely heavily on deep neural networks for perception and control. This paper proposes a method to repair unsafe ReLU DNNs in sa...
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Article
Verification of piecewise deep neural networks: a star set approach with zonotope pre-filter
Verification has emerged as a means to provide formal guarantees on learning-based systems incorporating neural network before using them in safety-critical applications. This paper proposes a new verification...
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Chapter and Conference Paper
Hyperbolic Tangent Polynomial Parity Cyclic Learning Rate for Deep Neural Network
With the development of artificial intelligence technology, optimizing the performance of deep neural network model has become a hot issue in the field of scientific research. Learning rate is one of the most ...
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Chapter and Conference Paper
Robustness Verification of Semantic Segmentation Neural Networks Using Relaxed Reachability
This paper introduces robustness verification for semantic segmentation neural networks (in short, semantic segmentation networks [SSNs]), building on and extending recent approaches for robustness verificatio...
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Article
Open AccessTransient ischemic attack analysis through non-contact approaches
The transient ischemic attack (TIA) is a kind of sudden disease, which has the characteristics of short duration and high frequency. Since most patients can return to normal after the onset of the disease, it ...
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Article
Diagnosis of the Hypopnea syndrome in the early stage
Hypopnea syndrome is a chronic respiratory disease that is characterized by repetitive episodes of breathing disruptions during sleep. Hypopnea syndrome is a systemic disease that manifests respiratory proble...
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Article
A neighbour scale fixed approach for influence maximization in social networks
Influence maximization is currently a most extensively researched topic in social network analysis. Existing approaches tackle this task by either pursuing the real influence strength of a node or designing pr...
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Chapter and Conference Paper
Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge domain gaps. Recently, there ...
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Chapter
Dealing with Label Quality Disparity in Federated Learning
Federated Learning (FL) is highly useful for the applications which suffer silo effect and privacy preserving, such as healthcare, finance, education, etc. Existing FL approaches generally do not account for d...
