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Xiangping Zheng - IEEE Xplore Author Profile
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Xiangping Zheng

Publications
18
Citations
19
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (27)
Image of author Xiangping Zheng

Xiangping Zheng

Affiliation

College of Computer Science and Technology
Harbin Engineering University
Harbin, China
Modeling and Emulation in E-Government National Engineering Laboratory
Harbin Engineering University
Harbin, China

Publication Topics

Biography

Xiangping Zheng received the BS degree from the School of Intelligent Systems Engineering, Sun Yat-sen University, China, in 2022. She is currently working toward the PhD degree with the School of Computer Science and Engineering, Sun Yat-sen University, China. Her research interests include decoding algorithms for short codes, polar codes, joint source and channel coding.(Based on document published on 25 November 2024).
Publications
18
Citations
19
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (27)
Author's Published Works

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Self-Supervised Uncertainty-Guided Refinement for Robust Joint Optical Flow and Depth Estimation

Rokia Abdein;Wei Li;Chenghao Li;Xiangping Zheng;Rahul Yadav

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Jointly estimating the optical flow and depth tasks in real-world scenes presents considerable hurdles due to some phenomena, such as occlusion, ambiguous textures, and illumination variation. The lack of guidance from the labeled data makes these challenges harder to overcome. This paper presents a novel approach to learning the regions with high uncertainties in a self-supervised manner. Our met...Show More

Self-Supervised Uncertainty-Guided Refinement for Robust Joint Optical Flow and Depth Estimation

Rokia Abdein;Wei Li;Chenghao Li;Xiangping Zheng;Rahul Yadav

Year: 2025 | Conference Paper |

Dynamic Graph Convolutional Networks with Spatiotemporal Missing Pattern Awareness

Bingheng Pang;Zhuoxuan Liang;Wei Li;Xiangping Zheng;Rokia Abdein

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Missing data is ubiquitous phenomenon in the time series community, significantly challenging forecasting due to incomplete ground truth and sparse data. Most previous Multi-variate Time Series Forecasting with Missing Values (MTSFMV) approaches usually assume static missing patterns, neglecting the dynamic changes over time and space, leading to suboptimal forecasting results. To tackle these cha...Show More

Dynamic Graph Convolutional Networks with Spatiotemporal Missing Pattern Awareness

Bingheng Pang;Zhuoxuan Liang;Wei Li;Xiangping Zheng;Rokia Abdein

Year: 2025 | Conference Paper |

A Universal List Decoding Algorithm With Application to Decoding of Polar Codes

Xiangping Zheng;Xiao Ma

IEEE Transactions on Information Theory
Year: 2025 | Volume: 71, Issue: 2 | Journal Article |
Cited by: Papers (1)
This paper is concerned with a guessing codeword decoding (GCD) of linear block codes, which is optimal and typically requires a fewer number of searches than the naive exhaustive search decoding (ESD). Compared with the guessing random additive noise decoding (GRAND), which is only efficient for high-rate codes, the GCD is efficient for not only high-rate codes but also low-rate codes. We prove t...Show More

A Universal List Decoding Algorithm With Application to Decoding of Polar Codes

Xiangping Zheng;Xiao Ma

Year: 2025 | Volume: 71, Issue: 2 | Journal Article |

Participation-Dependent Privacy Preservation in Cross-Silo Federated Learning

Yanling Qin;Xiangping Zheng;Qian Ma;Guocheng Liao;Xu Chen

IEEE Transactions on Services Computing
Year: 2025 | Volume: 18, Issue: 1 | Journal Article |
In cross-silo federated learning (FL), clients of common interest cooperatively train a global model without sharing local sensitive data, but they still face potential privacy leakage due to privacy threats from malicious attackers. Although some articles have proposed effective privacy-preserving mechanisms for FL (such as differential privacy (DP)), clients in cross-silo FL are usually differen...Show More

Participation-Dependent Privacy Preservation in Cross-Silo Federated Learning

Yanling Qin;Xiangping Zheng;Qian Ma;Guocheng Liao;Xu Chen

Year: 2025 | Volume: 18, Issue: 1 | Journal Article |

SCL-GCD of Short Polar Codes

Xiangping Zheng;Qianfan Wang;Xiao Ma

GLOBECOM 2024 - 2024 IEEE Global Communications Conference
Year: 2024 | Conference Paper |
This paper is concerned with the SCL-GCD algorithm of polar codes, which performs the successive-cancellation list (SCL) decoding algorithm for a lower rate sub-code and the guessing codeword decoding (GCD) algorithm for a higher rate sub-code. We propose to implement the GCD algorithm in a parallel way and design early stopping criteria for reducing complexity and decoding latency without sacrifi...Show More

SCL-GCD of Short Polar Codes

Xiangping Zheng;Qianfan Wang;Xiao Ma

Year: 2024 | Conference Paper |

Locally Constrained Guessing Codeword Decoding of Short Block Codes

Xiangping Zheng;Xiao Ma

2024 IEEE Information Theory Workshop (ITW)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
This paper is concerned with a universal guessing codeword decoding (GCD) of linear block codes, referred to as locally constrained GCD (LC-GCD), which does not require the online Gaussian elimination (GE). Distinguished from the GCD algorithm, the proposed LC-GCD queries the partial error patterns using the serial list Viterbi algorithm (SLVA) over a trellis specified by a local parity-check matr...Show More

Locally Constrained Guessing Codeword Decoding of Short Block Codes

Xiangping Zheng;Xiao Ma

Year: 2024 | Conference Paper |

Quasi-OSD of Binary Image of RS Codes with Applications to JSCC

Xiangping Zheng;Qianfan Wang;Baodian Wei;Xiao Ma

2024 IEEE International Symposium on Information Theory (ISIT)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
We propose quasi ordered statistic decoding (quasi-OSD) of binary image of Reed-Solomon (RS) codes and explore the application of RS codes to joint source-channel coding (JSCC). Unlike the conventional OSD algorithms that use Gaussian elimination to obtain the systematic matrix, the proposed quasi-OSD algorithm utilizes the parallel Lagrange interpolation at the symbol level, which achieves a lowe...Show More

Quasi-OSD of Binary Image of RS Codes with Applications to JSCC

Xiangping Zheng;Qianfan Wang;Baodian Wei;Xiao Ma

Year: 2024 | Conference Paper |

Trellis-Based Construction of Polar Codes for SCL Decoding

Xinyuanmeng Yao;Xiangping Zheng;Xiao Ma

2024 IEEE International Symposium on Information Theory (ISIT)
Year: 2024 | Conference Paper |
In this paper, we propose an approach to construction of polar codes for successive cancellation list (SCL) decoding with a preset list size. For a given code length, we construct a trellis through which a path corresponds to a polar code. Then, we employ a sequential search algorithm to find an expected path based on four path metrics and five path selection rules. Additionally, for polarization-...Show More

Trellis-Based Construction of Polar Codes for SCL Decoding

Xinyuanmeng Yao;Xiangping Zheng;Xiao Ma

Year: 2024 | Conference Paper |

Simultaneous Wireless Information and Power Transfer with Polar Coded Probabilistic Amplitude Shaping

Jiayi Yang;Qianfan Wang;Xiangping Zheng;Jie Hu;Xiao Ma

2024 IEEE International Conference on Communications Workshops (ICC Workshops)
Year: 2024 | Conference Paper |
In this paper, we propose a new simultaneous wireless information and power transfer (SWIPT) scheme by adopting the polar coded probabilistic amplitude shaping (PAS) framework. Here, the polar code is employed to support the reliable wireless information transfer (WIT), while the constant composition distribution matcher (CCDM) alters the distribution of channel inputs to implement the wireless po...Show More

Simultaneous Wireless Information and Power Transfer with Polar Coded Probabilistic Amplitude Shaping

Jiayi Yang;Qianfan Wang;Xiangping Zheng;Jie Hu;Xiao Ma

Year: 2024 | Conference Paper |

Select The Best: Enhancing Graph Representation with Adaptive Negative Sample Selection

Xiangping Zheng;Xun Liang;Bo Wu

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Graph contrastive learning (GCL) has emerged as a powerful tool to address real-world widespread label scarcity problems and has achieved impressive success in the graph learning domain. Albeit their remarkable performance, most current works mainly focus on designing sample augmentation methods, while the effect of negative sample selection strategy is largely ignored by previous works but rather...Show More

Select The Best: Enhancing Graph Representation with Adaptive Negative Sample Selection

Xiangping Zheng;Xun Liang;Bo Wu

Year: 2023 | Conference Paper |

Intent Does Matter! Propagating High-Order Relations for Exploring Interest Preferences

Xiangping Zheng;Xun Liang;Bo Wu;Junlan Feng;Yuhui Guo;Sensen Zhang

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Session-based recommendation (SBR) aims to predict the user’s action at the next timestamp according to an anonymous yet short interaction sequence (i.e., session). Almost all the existing SBR solutions for user preference are only based on the current session without exploiting the high-order relations among other sessions, which may restrict the SBR representation ability and even deteriorate th...Show More

Intent Does Matter! Propagating High-Order Relations for Exploring Interest Preferences

Xiangping Zheng;Xun Liang;Bo Wu;Junlan Feng;Yuhui Guo;Sensen Zhang

Year: 2023 | Conference Paper |

Cross-Modal Matching and Adaptive Graph Attention Network for RGB-D Scene Recognition

Yuhui Guo;Xun Liang;James T. Kwok;Xiangping Zheng;Bo Wu;Yuefeng Ma

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Despite the significant advances in RGB-D scene recognition, there are several major limitations that need further investigation. For example, simply extracting modal-specific features neglects the complex relationships among multiple modalities of features. Moreover, cross-modal features have not been considered in most existing methods. To address these concerns, we propose to integrate the task...Show More

Cross-Modal Matching and Adaptive Graph Attention Network for RGB-D Scene Recognition

Yuhui Guo;Xun Liang;James T. Kwok;Xiangping Zheng;Bo Wu;Yuefeng Ma

Year: 2023 | Conference Paper |

Reinforced Sample Selection for Graph Neural Networks Transfer Learning

Bo Wu;Xun Liang;Xiangping Zheng;Jun Wang;Xiaoping Zhou

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Graph neural networks (GNNs) have become a practical paradigm for learning graph-structured data, which can generate node representations by recursively aggregating information from neighbor nodes. Recent works utilize self-supervised tasks to learn transferable knowledge from source domain graphs and improve the GNNs performance on target domain graphs. However, there are considerable low-quality...Show More

Reinforced Sample Selection for Graph Neural Networks Transfer Learning

Bo Wu;Xun Liang;Xiangping Zheng;Jun Wang;Xiaoping Zhou

Year: 2022 | Conference Paper |

Win-Win Cooperation: Semantic Encoding Learning and Saliency Selection for Weakly supervised Semantic Segmentation

Yuhui Guo;Xun Liang;Xiangping Zheng;Bo Wu;Xuan Zhang

IEEE Transactions on Cognitive and Developmental Systems
Year: 2022 | Early Access Article |
Cited by: Papers (1)
Image-level weakly supervised semantic segmentation methods have attracted increasing attention due to data labeling efficiency, but these methods mostly focus on utilizing the localization maps generated by the classification network to produce pseudo labels, leading to sparse object regions, object boundary mismatch and co-occurring pixels existing in the target objects. To address these issues,...Show More

Win-Win Cooperation: Semantic Encoding Learning and Saliency Selection for Weakly supervised Semantic Segmentation

Yuhui Guo;Xun Liang;Xiangping Zheng;Bo Wu;Xuan Zhang

Year: 2022 | Early Access Article |

CoNet: Co-Embedding by Reinforcing Graph Feature and Topology Information

Hui Tang;Xun Liang;Yuhui Guo;Bo Wu;Xiangping Zheng

2022 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Sparsity and smoothness are two main factors that affect the performance of Graph Convolutional Networks (GCNs). Sparsity ensures that models have the first-class generalization ability, while smoothness benefits to reduce noise and make edges reliable. As real-world graphs are often incom-plete and noisy, most GCNs learn node embeddings only acting them as ground-truth information, which unavoida...Show More

CoNet: Co-Embedding by Reinforcing Graph Feature and Topology Information

Hui Tang;Xun Liang;Yuhui Guo;Bo Wu;Xiangping Zheng

Year: 2022 | Conference Paper |

Adaptive Attention Graph Capsule Network

Xiangping Zheng;Xun Liang;Bo Wu;Yuhui Guo;Hui Tang

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
From the perspective of the spatial domain, Graph Convolutional Network (GCN) is essentially a process of iteratively aggregating neighbor nodes. However, the existing GCNs using simple average or sum aggregation may neglect the characteristics of each node and the topology between nodes, resulting in a large amount of early-stage information lost during the graph convolution step. To tackle the a...Show More

Adaptive Attention Graph Capsule Network

Xiangping Zheng;Xun Liang;Bo Wu;Yuhui Guo;Hui Tang

Year: 2022 | Conference Paper |

Graph Fine-Grained Contrastive Representation Learning

Hui Tang;Xun Liang;Yuhui Guo;Xiangping Zheng;Bo Wu

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Existing graph contrastive methods have benefited from ingenious data augmantations and mutual information estimation operations that are carefully designated to augment graph views and maximize the agreement between representations produced at the aftermost layer of two view networks. However, the design of graph CL schemes is coarse-grained and difficult to capture the universal and intrinsic pr...Show More

Graph Fine-Grained Contrastive Representation Learning

Hui Tang;Xun Liang;Yuhui Guo;Xiangping Zheng;Bo Wu

Year: 2022 | Conference Paper |

Improving Dynamic Graph Convolutional Network with Fine-Grained Attention Mechanism

Bo Wu;Xun Liang;Xiangping Zheng;Yuhui Guo;Hui Tang

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
Graph convolutional network (GCN) is a novel framework that utilizes a pre-defined Laplacian matrix to learn graph data effectively. With its powerful nonlinear fitting ability, GCN can produce high-quality node embedding. However, generalized GCN can only handle static graphs, whereas a large number of graphs are dynamic and evolve over time, which limits the application field of GCN. Facing the ...Show More

Improving Dynamic Graph Convolutional Network with Fine-Grained Attention Mechanism

Bo Wu;Xun Liang;Xiangping Zheng;Yuhui Guo;Hui Tang

Year: 2022 | Conference Paper |

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