8,357 Result(s)
within
Chapter
**ang-**g Wang
Computer Science
Artificial Intelligence
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Chapter and Conference Paper
A Probability Map**-Based Privacy Preservation Method for Social Networks
The mining and analysis of social networks can bring significant economic and social benefits. However, it also poses a risk of privacy leakages. Differential privacy is a de facto standard to prevent such lea...
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Chapter and Conference Paper
Decoupled Contrastive Learning for Long-Tailed Distribution
Self-supervised contrastive learning is popularly used to obtain powerful representation models. However, unlabeled data in the real world naturally exhibits a long-tailed distribution, making the traditional ...
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Chapter and Conference Paper
Mitigating the Adverse Effects of Long-Tailed Data on Deep Learning Models
When the data distribution in a dataset is highly imbalanced or long-tailed, it can severely affect the effectiveness of a deep network model. This drop in performance is caused due to the biased classifier, w...
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Chapter and Conference Paper
DeepChrom: A Diffusion-Based Framework for Long-Tailed Chromatin State Prediction
Chromatin state reflects distinct biological roles of the genome that can systematically characterize regulatory elements and their functional interaction. Despite extensive computational studies, accurate pre...
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Chapter and Conference Paper
Parameters Efficient Fine-Tuning for Long-Tailed Sequential Recommendation
In an era of information explosion, recommendation systems play an important role in people’s daily life by facilitating content exploration. It is known that user activeness, i.e., number of behaviors, tends to ...
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Chapter and Conference Paper
Robust Object Recognition and Command Understanding for a House Tidying-Up Robot
In this study, a robust object recognition and command understanding system for a house tidying-up robot is proposed. The robot can understand the user’s intentions by using a speech recognition system. When t...
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Chapter and Conference Paper
DLVS4Audio2Sheet: Deep Learning-Based Vocal Separation for Audio into Music Sheet Conversion
While manual transcription tools exist, music enthusiasts, including amateur singers, still encounter challenges when transcribing performances into sheet music. This paper addresses the complex task of transl...
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Chapter and Conference Paper
Blendshape-Based Migratable Speech-Driven 3D Facial Animation with Overlap** Chunking-Transformer
Speech-driven 3D facial animation has attracted an amount of research and has been widely used in games and virtual reality. Most of the latest state-of-the-art methods employ Transformer-based architecture wi...
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Chapter and Conference Paper
Autoencoder and Masked Image Encoding-Based Attentional Pose Network
Despite recent advances in single-image-based 3D human pose and shape estimation, partial occlusion remains a major challenge for many methods, leading to significant prediction errors. Some existing methods ...
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Chapter and Conference Paper
STFM: Enhancing Autism Spectrum Disorder Classification Through Ensemble Learning-Based Fusion of Temporal and Spatial fMRI Patterns
This paper presents the novel spatial and temporal fusion model (STFM), an effective approach for Autism Spectrum Disorder (ASD) detection and classification tasks using foundational machine learning models. U...
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Chapter and Conference Paper
Advancing Short-Term Traffic Congestion Prediction: Navigating Challenges in Learning-Based Approaches
Traffic congestion prediction has already become a significant aspect of modern transportation systems. By predicting traffic congestion, transportation planners and traffic management agencies can take steps ...
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Chapter and Conference Paper
Deep Learning-Based Analysis and Dynamic Forecasting of Multidimensional Factors Affecting Educational Quality: An Empirical Study
As deep learning technology continues to evolve, its implementation within the field of education is broadening. This research is dedicated to the construction of a deep learning-based multidimensional analysi...
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Chapter and Conference Paper
Review of Traveling Salesman Problem Solution Methods
The Traveling Salesman Problem (TSP) is a key focus in the fields of computer science and operations research, widely applied in areas such as data collection, search and rescue, robot task allocation and sche...
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Chapter and Conference Paper
Rethinking Personalized Federated Learning with Clustering-Based Dynamic Graph Propagation
Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation, we propose a simple yet effe...
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Chapter and Conference Paper
LBKT: A LSTM BERT-Based Knowledge Tracing Model for Long-Sequence Data
The field of Knowledge Tracing (KT) aims to understand how students learn and master knowledge over time by analyzing their historical behaviour data. To achieve this goal, many researchers have proposed KT mo...
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Chapter and Conference Paper
CCA-MTFCN: A Robotic Pushing-Gras** Collaborative Method Based on Deep Reinforcement Learning
Robotic gras** in dense clutter is often infeasible because of the occlusion and stacking of objects. Directly gras** the stacked objects may cause collisions and result in low efficiency and high failure ...
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Chapter and Conference Paper
SmartBuoy: A Machine Learning-Based Detection Method for Interest Flooding Attacks in VNDN
Due to the advantages of multi-source, multi-path, and in-network caching, Named Data Networking (NDN) can improve the efficiency of data exchange in Vehicular Ad-hoc NETworks (VANETs). As Vehicular Named Data...
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Chapter and Conference Paper
Spatial-Angular Decoupling Interaction Networks for Light Field Angular Super-Resolution
The light field image simultaneously captures both the intensity and directional information of light rays, making it a valuable tool for acquiring four-dimensional spatial data. However, the restriction of se...
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Chapter and Conference Paper
Genetic Algorithm Driven by Translational Mutation Operator for the Scheduling Optimization in the Steelmaking-Continuous Casting Production
The scheduling optimization of industrial processes is crucial for enhancing production capacity and minimizing energy consumption. In the realm of continuous casting, the expansion of the scheduling scale and...
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Chapter and Conference Paper
Consistency Guided Multiview Hypergraph Embedding Learning with Multiatlas-Based Functional Connectivity Networks Using Resting-State fMRI
Recently, resting-state functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has greatly boosted diagnostic performance of brain diseases in a manner that can refine FCN embed...
