116 Result(s)
within
Conference Paper
Dong-**ng Wang
English
Data Mining and Knowledge Discovery
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Chapter and Conference Paper
Transformer-Based Video Deinterlacing Method
Deinterlacing is a classical issue in video processing, aimed at generating progressive video from interlaced content. There are precious videos that are difficult to reshoot and still contain interlaced conte...
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Chapter and Conference Paper
A Fine-Grained Domain Adaptation Method for Cross-Session Vigilance Estimation in SSVEP-Based BCI
Brain-computer interface (BCI), a direct communication system between the human brain and external environment, can provide assistance for people with disabilities. Vigilance is an important cognitive state an...
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Chapter and Conference Paper
Triple-Path RNN Network: A Time-and-Frequency Joint Domain Speech Separation Model
Studies in speech separation have achieved significant success in recent years. To correctly separate the mixture signals, it is critical to encode the signals into an appropriate latent space. Existing speech...
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Chapter and Conference Paper
CSEC: A Chinese Semantic Error Correction Dataset for Written Correction
Existing research primarily focuses on spelling and grammatical errors in English, such as missing or wrongly adding characters. This kind of shallow error has been well-studied. Instead, there are many unsolv...
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Chapter and Conference Paper
CACL:Commonsense-Aware Contrastive Learning for Knowledge Graph Completion
Most knowledge graphs (KGs) are incomplete in the real world, so knowledge graph completion (KGC) is widely investigated to predict the most credible missing facts from given knowledge. However, existing KGC m...
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Chapter and Conference Paper
Graph Reinforcement Learning for Securing Critical Loads by E-Mobility
Inefficient scheduling of electric vehicles (EVs) is detrimental to not only the profitability of charging stations but also the experience of EV users and the stable operation of the grid. Regulating the char...
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Chapter and Conference Paper
An Effective Morphological Analysis Framework of Intracranial Artery in 3D Digital Subtraction Angiography
Acquiring accurate anatomy information of intracranial artery from 3D digital subtraction angiography (3D-DSA) is crucial for intracranial artery intervention surgery. However, this task often comes with chall...
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Chapter and Conference Paper
Adaptive Rounding Compensation for Post-training Quantization
Network quantization can compress and accelerate deep neural networks by reducing the bit-width of network parameters so that the quantized networks can be deployed to resource-limited devices. Post-Training Q...
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Chapter and Conference Paper
Rethinking Image Inpainting with Attention Feature Fusion
Recent image inpainting models have archived significant progress through learning from large-scale data. However, restoring images under complicated scenarios (e.g. large masks or complex textures) remains ch...
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Chapter and Conference Paper
Towards Accurate Alignment and Sufficient Context in Scene Text Recognition
Encoder-decoder framework has recently become cutting-edge in scene text recognition (STR), where most decoder networks consist of two parts: an attention model to align visual features from the encoder for ea...
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Chapter and Conference Paper
Training Noise Robust Deep Neural Networks with Self-supervised Learning
Training accurate deep neural networks (DNNs) on datasets with label noise is challenging for practical applications. The sample selection paradigm is a popular strategy that selects potentially clean data fro...
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Chapter and Conference Paper
Multi-modal Multi-emotion Emotional Support Conversation
This paper proposes a new task of Multi-modal Multi-emotion Emotional Support Conversation (MMESC), which has great value in various applications, such as counseling, daily chatting, and elderly company. This tas...
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Chapter and Conference Paper
User Interaction-Aware Knowledge Graphs for Recommender Systems
The performance of recommender systems can be improved effectively by using knowledge graphs as auxiliary information. However, most of the knowledge graph-based recommendations focus on learning item represen...
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Chapter and Conference Paper
LSN: Long-Term Spatio-Temporal Network for Video Recognition
Although recurrent neural networks (RNNs) are widely leveraged to process temporal or sequential data, they have attracted too little attention in current video action recognition applications. Therefore, this...
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Chapter and Conference Paper
GeoGTI: Towards a General, Transferable and Interpretable Site Recommendation
Lack of data and weak interpretability are the main problems faced by store site recommendations. This paper presents a unified site recommendation system called GeoGTI (General,Transferable and Interpretable), w...
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Chapter and Conference Paper
A Study of the Sustainable Energy Product for Bicycle Riding on Design Method
With the deepening of urbanization, the crisis of energy and environment is becoming more and more serious. Cycling, as an energy-saving and emission-reducing exercise, is highly praised by more and more peopl...
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Chapter and Conference Paper
Efficient Dual-Process Cognitive Recommender Balancing Accuracy and Diversity
In this paper, we propose a dual-process cognitive recommendation system for sequential recommendations. The framework includes an intuitive representation module (System 1) and an inference module (System 2)....
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Chapter and Conference Paper
Prediction of Element Distribution in Cement by CNN
Cement-based materials are widely used in today’s society. Their quality directly determines the quality of buildings. Therefore, it is urgent to improve the physical properties of cement. To study high-perfor...
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Chapter and Conference Paper
PhotoStylist: Altering the Style of Photos Based on the Connotations of Texts
The need to modify a photo to reflect the connotations of a text can arise due to multifarious reasons (e.g., a musician might modify a photo in the album cover to better reflect the connotations in her song l...
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Chapter and Conference Paper
AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life
Remaining Useful Life (RUL) prediction of equipment can estimate the time when equipment reaches the safe operating limit, which is essential for strategy formulation to reduce the possibility of loss due to u...
