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学者姓名：刘漳辉
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Graph convolutional networks have achieved remarkable success in the field of multi-view learning. Unfortunately, most graph convolutional network-based multi-view learning methods fail to capture long-range dependencies due to the over-smoothing problem. Many studies have attempted to mitigate this issue by decoupling graph convolution operations. However, these decoupled architectures lead to the absence of feature transformation module, thus limiting the expressive power of the model. To this end, we propose an information-controlled graph convolutional network for multi-view semi-supervised classification. In the proposed method, we maintain the paradigm of node embeddings during propagation by imposing orthogonality constraints on the feature transformation module. By further introducing a damping factor based on residual connections, we theoretically demonstrate that the proposed method can alleviate the over-smoothing problem while retaining the feature transformation module. Furthermore, we prove that the proposed model can stabilize both forward inference and backward propagation in graph convolutional networks. Extensive experimental results on benchmark datasets demonstrate the effectiveness of the proposed method.
Keyword ：
Graph convolutional network Graph convolutional network Layer normalization Layer normalization Multi-view learning Multi-view learning Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Shi, Yongquan , Pi, Yueyang , Liu, Zhanghui et al. Information-controlled graph convolutional network for multi-view semi-supervised classification [J]. | NEURAL NETWORKS , 2025 , 184 . |
| MLA | Shi, Yongquan et al. "Information-controlled graph convolutional network for multi-view semi-supervised classification" . | NEURAL NETWORKS 184 (2025) . |
| APA | Shi, Yongquan , Pi, Yueyang , Liu, Zhanghui , Zhao, Hong , Wang, Shiping . Information-controlled graph convolutional network for multi-view semi-supervised classification . | NEURAL NETWORKS , 2025 , 184 . |
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Multimodal aspect-based sentiment analysis (MABSA) is an essential task in the field of sentiment analysis, which still confronts several critical challenges. The first challenge is how to effectively capture key information within both image and text features to enhance the recognition and understanding of complex sentiment expressions. The second challenge is how to achieve cross-modal alignment of multi-granularity text features and image features. The third challenge is how to narrow the semantic gap between image modality and text modality through effective cross-modal feature fusion. To address these issues, a framework that leverages external knowledge and multi-granularity image and text features (EKMG) is proposed. Firstly, an external knowledge enhanced semantic extraction module is introduced to fuse external knowledge with image features and text features, thereby capturing the key information from texts and images. Secondly, we design a multi-granularity image-text contrastive learning module. This module initially introduces a graph attention network and a novel cross-modal fusion mechanism to align image features and text features at multiple granularities. Additionally, the module employs an image-text contrastive learning strategy to narrow the semantic gap between different modalities. Experimental results on two public benchmark datasets demonstrate that EKMG achieves significant performance improvements compared to state-of-the-art baseline models.
Keyword ：
Contrastive learning Contrastive learning Cross-modal fusion Cross-modal fusion External knowledge External knowledge Multi-granularity Multi-granularity Multimodal aspect-based sentiment analysis Multimodal aspect-based sentiment analysis
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| GB/T 7714 | Liu, Zhanghui , Lin, Jiali , Chen, Yuzhong et al. Multimodal Aspect-Based Sentiment Analysis with External Knowledge and Multi-granularity Image-Text Features [J]. | NEURAL PROCESSING LETTERS , 2025 , 57 (2) . |
| MLA | Liu, Zhanghui et al. "Multimodal Aspect-Based Sentiment Analysis with External Knowledge and Multi-granularity Image-Text Features" . | NEURAL PROCESSING LETTERS 57 . 2 (2025) . |
| APA | Liu, Zhanghui , Lin, Jiali , Chen, Yuzhong , Dong, Yu . Multimodal Aspect-Based Sentiment Analysis with External Knowledge and Multi-granularity Image-Text Features . | NEURAL PROCESSING LETTERS , 2025 , 57 (2) . |
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现今社会上数据的规模和种类变得越来越庞大和多样化,如何安全可信地共享异构数据资源成为了亟待解决的问题.为实现大数据的可信互联,提出基于Hyperledger Fabric的数据可信共享平台.首先,针对数据异源异构的问题,定义了数据架构的转换规则;然后,以数据提供方和数据需求方之间的数据共享全过程为导向,提出了数据可信追溯机制,保证了数据共享的真实性和完整性;此外,文中设计了一种数据处理即服务的数据共享框架,在确保数据可信的前提下,支撑数据调用、数据训练和数据匹配操作.通过对执行效率和智能合约性能进行验证分析,证明了本平台的有效性和实用性.
Keyword ：
Hyperledger Fabric Hyperledger Fabric 区块链 区块链 可信凭证 可信凭证 数据共享 数据共享 智能合约 智能合约
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| GB/T 7714 | 林哲旭 , 陈汉林 , 刘漳辉 et al. 基于Hyperledger Fabric的数据可信共享平台 [J]. | 小型微型计算机系统 , 2025 , 46 (1) : 189-199 . |
| MLA | 林哲旭 et al. "基于Hyperledger Fabric的数据可信共享平台" . | 小型微型计算机系统 46 . 1 (2025) : 189-199 . |
| APA | 林哲旭 , 陈汉林 , 刘漳辉 , 陈星 , 莫毓昌 . 基于Hyperledger Fabric的数据可信共享平台 . | 小型微型计算机系统 , 2025 , 46 (1) , 189-199 . |
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Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Keyword ：
Contrastive Learning Contrastive Learning Knowledge graph Knowledge graph Prediction models Prediction models Semantic Segmentation Semantic Segmentation
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| GB/T 7714 | Liu, Zhanghui , Chen, Shijie , Chen, Yuzhong et al. A knowledge-enhanced interest segment division attention network for click-through rate prediction [J]. | Neural Computing and Applications , 2024 , 36 (34) : 21817-21837 . |
| MLA | Liu, Zhanghui et al. "A knowledge-enhanced interest segment division attention network for click-through rate prediction" . | Neural Computing and Applications 36 . 34 (2024) : 21817-21837 . |
| APA | Liu, Zhanghui , Chen, Shijie , Chen, Yuzhong , Su, Jieyang , Zhong, Jiayuan , Dong, Chen . A knowledge-enhanced interest segment division attention network for click-through rate prediction . | Neural Computing and Applications , 2024 , 36 (34) , 21817-21837 . |
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Community detection is widely used in network analysis, which seeks to divide network nodes into distinct communities based on the topology structure and attribute information of the network. Due to its interpretability, nonnegative matrix factorization becomes an essential method for community detection. However, it decomposes the adjacency matrix and attribute matrix separately, which do not tightly incorporate topology and attributes. And in the problem of division inconsistency based on topology and attributes caused by the mismatch between the topology similarity and attribute similarity of paired nodes, it ignores the difference in the matching degree of each attribute and each node. In this paper, we propose a nonnegative matrix factorization algorithm for community detection (MTACD) based on the matching degree between topology and attribute. First, we employ an attribute embedding mechanism to enhance the node-attribute relationship. Second, we design an attribute matching degree and a node topology-and-attribute matching degree in order to resolve the mismatch between topology and attribute similarity. Experiments on both real-world and synthetic networks demonstrate the effectiveness of our algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Matrix algebra Matrix algebra Matrix factorization Matrix factorization Population dynamics Population dynamics Topology Topology
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| GB/T 7714 | Zeng, Ruolan , Liu, Zhanghui , Guo, Kun . Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection [C] . 2024 : 137-151 . |
| MLA | Zeng, Ruolan et al. "Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection" . (2024) : 137-151 . |
| APA | Zeng, Ruolan , Liu, Zhanghui , Guo, Kun . Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection . (2024) : 137-151 . |
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Digital microfluidic biochips (DMFBs) have a significant stride in the applications of medicine and the biochemistry in recent years. DMFBs based on micro -electrode -dot -array (MEDA) architecture, as the nextgeneration DMFBs, aim to overcome drawbacks of conventional DMFBs, such as droplet size restriction, low accuracy, and poor sensing ability. Since the potential market value of MEDA biochips is vast, it is of paramount importance to explore approaches to protect the intellectual property (IP) of MEDA biochips during the development process. In this paper, an IP authentication strategy based on the multi-PUF applied to MEDA biochips is presented, called bioMPUF, consisting of Delay PUF, Split PUF and Countermeasure. The bioMPUF strategy is designed to enhance the non -linearity between challenges and responses of PUFs, making the challenge-response pairs (CRPs) on the MEDA biochips are difficult to be anticipated, thus thwarting IP piracy attacks. Moreover, based on the easy degradation of MEDA biochip electrodes, a countermeasure is proposed to destroy the availability of piracy chips. Experimental results demonstrate the feasibility of the proposed bioMPUF strategy against the brute force attack and modeling attack.
Keyword ：
Hardware security Hardware security IP protection IP protection MEDA biochips MEDA biochips Modeling attack Modeling attack Multi-PUF Multi-PUF
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| GB/T 7714 | Dong, Chen , Guo, Xiaodong , Lian, Sihuang et al. Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (3) . |
| MLA | Dong, Chen et al. "Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 36 . 3 (2024) . |
| APA | Dong, Chen , Guo, Xiaodong , Lian, Sihuang , Yao, Yinan , Chen, Zhenyi , Yang, Yang et al. Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (3) . |
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Load prediction is an essential technique to improve edge system performance by proactively configuring and allocating system resources. Traditional load prediction methods obtain high prediction when handling loads exhibiting cyclical trend behavior, but they are unable to capturing highly-variable loads in edge computing environments. Existing studies fit prediction models via independent time series and output single-point real-value predictions. However, in practical edge scenarios, it is more valuable to obtain application value by utilizing the probability distribution of future loads rather than directly predicting specific values. To solve these problems, we propose an Edge Load Prediction method empowered by Deep Auto-regressive Recurrent networks (ELP-DAR). The ELP-DAR uses the time-series data of edge loads to train deep auto-regressive recurrent networks, which integrate Long Short-Term Memory (LSTM) into the S2S framework to calculate the parameters of the probability distribution at the next time-point. Therefore, the ELP-DAR can efficiently extract the essential representations of edge loads and learn their complex patterns, and the probability distribution for highly-variable edge loads can be accurately predicted. Extensive simulation experiments are conducted to validate the effectiveness of the proposed ELP-DAR method based on real-world edge load datasets. The results show that the ELP-DAR achieves higher prediction accuracy than other benchmark methods with different prediction lengths.
Keyword ：
deep auto-regression deep auto-regression Edge computing Edge computing load prediction load prediction probability distribution probability distribution recurrent neural networks recurrent neural networks
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| GB/T 7714 | Liu, Zhanghui , Chen, Lixian , Chen, Zheyi et al. Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks [J]. | ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS , 2023 : 809-814 . |
| MLA | Liu, Zhanghui et al. "Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks" . | ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (2023) : 809-814 . |
| APA | Liu, Zhanghui , Chen, Lixian , Chen, Zheyi , Huang, Yifan , Liang, Jie , Yu, Zhengxin et al. Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks . | ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS , 2023 , 809-814 . |
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Marine litter can cause significant damage to marine biodiversity, threatening the marine food chain and spreading harmful substances, posing a significant impact on the ocean ecosystem. Autonomous Underwater Vehicles (AUVs) can automatically remove marine litter using sensors and trained models. this paper evaluates the YOLOv7 series of models that utilize deep learning to detect targets in a real underwater environment. In order to improve the performance of the model, we introduced two attention mechanisms, and the experimental results showed a 2.5% increase in Mean Average Precision(mAP) values. We used a large publicly available dataset of re-annotated open-water debris to train convolutional neural networks for target detection, and we evaluated the trained models on a subset of the dataset, to provide insights into the ability of deep learning to detect marine litter. In addition, to prevent attacks by malicious actors during AUVs cloud platform access, we introduced data encryption protection to ensure that the model's predicted results can be correctly received by AUVs. © 2023 ACM.
Keyword ：
Autonomous underwater vehicles Autonomous underwater vehicles Biodiversity Biodiversity Convolutional neural networks Convolutional neural networks Cryptography Cryptography Deep learning Deep learning Large datasets Large datasets Learning systems Learning systems
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| GB/T 7714 | Wu, Qiaowen , Ke, Yaojie , Liu, Zhanghui et al. Marine litter detection based on YOLOv7 algorithm and data encryption protection [C] . 2023 : 82-87 . |
| MLA | Wu, Qiaowen et al. "Marine litter detection based on YOLOv7 algorithm and data encryption protection" . (2023) : 82-87 . |
| APA | Wu, Qiaowen , Ke, Yaojie , Liu, Zhanghui , Zhang, Yuanyuan , Wu, Qiyan . Marine litter detection based on YOLOv7 algorithm and data encryption protection . (2023) : 82-87 . |
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虽然图像语义分割因其有助于更好地分析和理解图像而被广泛应用于多个领域,但是基于全卷积神经网络的模型在语义分割方面依然存在分辨率重构及如何利用上下文信息的问题.因此,文中提出基于语义传播与前/背景感知的图像语义分割网络.首先,提出联合语义传播上采样模块,提取高层特征的全局语义信息与局部语义信息,用于得到语义权重,将高层特征语义传播到低层特征,缩小两者之间的语义差距,再通过逐层上采样实现分辨率重构.此外,还提出金字塔前/背景感知模块,通过两个并行分支提取不同尺度前景特征与背景特征,建立前景与背景间的依赖关系,捕获多尺度的前/背景感知特征,增强前景特征的上下文表示.语义分割基准数据集上的实验表明,文...
Keyword ：
上下文信息 上下文信息 全卷积神经网络 全卷积神经网络 分辨率重构 分辨率重构 语义分割 语义分割
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| GB/T 7714 | 刘漳辉 , 占小路 , 陈羽中 . 基于语义传播与前/背景感知的图像语义分割网络 [J]. | 模式识别与人工智能 , 2022 , 35 (01) : 71-81 . |
| MLA | 刘漳辉 et al. "基于语义传播与前/背景感知的图像语义分割网络" . | 模式识别与人工智能 35 . 01 (2022) : 71-81 . |
| APA | 刘漳辉 , 占小路 , 陈羽中 . 基于语义传播与前/背景感知的图像语义分割网络 . | 模式识别与人工智能 , 2022 , 35 (01) , 71-81 . |
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随着大数据和人工智能的发展,多轮对话算法受到了越来越多的关注.多轮对话回答选择是多轮对话算法中的关键问题之一,其目标是选择与输入消息和对话内容最相关的回答作为应答.近年来,深度神经网络模型在多轮对话回答选择问题上取得了较大进展.然而,如何提取对话上下文和回答中的相关语义信息并从中提取丰富的多粒度语义匹配特征仍然是多轮对话回答选择问题面临的巨大挑战.针对上述问题,本文提出了一种结合词注意力机制的多粒度循环神经网络模型MRNA(MultiGranularity Recurrent Neural Netw ork w ith Word Attention).首先,M RNA使用双通道网络,融合字符级...
Keyword ：
回答选择 回答选择 多轮对话 多轮对话 层级粒度 层级粒度 词注意力机制 词注意力机制
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| GB/T 7714 | 谢琪 , 陈羽中 , 刘漳辉 . 一种基于多粒度循环神经网络与词注意力的多轮对话回答选择方法 [J]. | 小型微型计算机系统 , 2021 , 42 (12) : 2553-2560 . |
| MLA | 谢琪 et al. "一种基于多粒度循环神经网络与词注意力的多轮对话回答选择方法" . | 小型微型计算机系统 42 . 12 (2021) : 2553-2560 . |
| APA | 谢琪 , 陈羽中 , 刘漳辉 . 一种基于多粒度循环神经网络与词注意力的多轮对话回答选择方法 . | 小型微型计算机系统 , 2021 , 42 (12) , 2553-2560 . |
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