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Aligning enhanced feature representation for generalized zero-shot learning

Science China(Information Sciences) 2025年第2期68卷 74-88页

作者： Zhiyu FANG Xiaobin ZHU Chun YANG Hongyang ZHOU Jingyan QIN Xu-Cheng YIN School of Computer & Communication Engineering University of Science and Technology Beijing

Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders（VAEs） is a popular strategy for generalized zero-shot learning（GZSL）. However, due to the lack of fine-grained instance-wise annotations, existing VAE methods can easily suffer from the posterior collapse problem. In this paper, we propose an innovative asymmetric VAE network by aligning enhanced feature representation（AEFR） for GZSL. Distinguished from general VAE structures, we designed two asymmetric encoders for visual and semantic observations and one decoder for visual reconstruction. Specifically, we propose a simple yet effective gated attention mechanism（GAM） in the visual encoder for enhancing the information interaction between observations and latent variables, alleviating the possible posterior collapse problem effectively. In addition, we propose a novel distributional decoupling-based contrastive learning（D2-CL） to guide learning classification-relevant information while aligning the representations at the taxonomy level in the latent representation space. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. The source code is available at https://***/seeyourmind/AEFR.

关键词： generalized zero-shot learning gated attention mechanism contrastive learning multi-modal alignment

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PriChain: Efficient Privacy-Preserving Fine-Grained Redactable Blockchains in Decentralized Settings

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Chinese Journal of Electronics 2025年第1期34卷 82-97页

作者： Hongchen Guo Weilin Gan Mingyang Zhao Chuan Zhang Tong Wu Liehuang Zhu Jingfeng Xue School of Computer Science and Technology Beijing Institute of Technology School of Cyberspace Science and Technology Beijing Institute of Technology School of Computer and Communication Engineering University of Science and Technology Beijing

Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.

关键词： Resistance Access control Data privacy Dictionaries Blockchains Encryption Complexity theory Usability Protection

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Analysis of radiation diffusion of COVID-19 driven by social attributes

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Chinese Physics B 2024年第1期33卷 773-779页

作者：年福忠杨晓晨师亚勇 Lanzhou University of Technology School of Computer&CommunicationLanzhou 730050China University of Science and Technology Beijing School of Computer&Communication EngineeringBeijing 100083China

This paper first estimated the infectious capacity of COVID-19 based on the time series evolution data of confirmed cases in multiple countries. Then, a method to infer the cross-regional spread speed of COVID-19 was introduced in this paper, which took the gross domestic product(GDP) of each region as one of the factors that affect the spread speed of COVID-19 and studied the relationship between the GDP and the infection density of each region(China's Mainland, the United States, and EU countries). In addition, the geographic distance between regions was also considered in this method and the effect of geographic distance on the spread speed of COVID-19 was studied. Studies have shown that the probability of mutual infection of these two regions decreases with increasing geographic distance. Therefore, this paper proposed an epidemic disease spread index based on GDP and geographic distance to quantify the spread speed of COVID-19 in a region. The analysis results showed a strong correlation between the epidemic disease spread index in a region and the number of confirmed cases. This finding provides reasonable suggestions for the control of epidemics. Strengthening the control measures in regions with higher epidemic disease spread index can effectively control the spread of epidemics.

关键词： COVID-19 basic reproduction number gross domestic product(GDP) geographic distance cross-regional spread

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A Weibo local network growth model constructed from the perspective of following-followed

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Chinese Physics B 2024年第12期33卷 497-504页

作者： Fu-Zhong Nian Ran-Qing Yao School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

In order to explore the evolution process of the Weibo local network,this study first defines four factors influencing the evolution of the Weibo *** this basis,the BA scale-free network model was enhanced by incorporating these four factors and accounting for directionality,resulting in a Weibo local network evolution model based on user attributes and behavioral *** model's validity was validated by comparing simulation results with real *** findings indicate that the Weibo local network exhibits both small-world characteristics and distinctive *** results show that the Weibo local network exhibits both small-world characteristics and distinctive *** in-degree distribution follows a mixed pattern of exponential and power-law distributions,the degree-degree shows isomatching,and both the in-degree centrality and eigenvector centrality values are relatively *** research contributes to our understanding of user behaviour in the Weibo network,and provides a structural basis for exploring the impact of Weibo network structure on information dissemination.

关键词： Weibo network user similarity attribute characteristics network evolution topology analysis

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Long-term urban traffic flow forecasting based on feature fusion and S-T transformer

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The Journal of China Universities of Posts and Telecommunications 2025年第1期32卷 61-73页

作者： Zhang Xijun Cui Yong Zhang Hong Xia Ziyao School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

As a fundamental component of intelligent transportation systems, existing urban traffic flow forecasting models tend to overlook the spatio-temporal and long-term time-dependent patterns that characterize transportation networks. Among these, the long sequence time-series forecasting(LSTF) model is susceptible to the issue of gradient disappearance, which can be attributed to the influence of a multitude of intricate factors. Accordingly, in this paper, the standpoint of multi-feature fusion was studied, and a traffic flow forecasting network model based on feature fusion and spatio-temporal transformer(S-T transformer)(STFFN) was proposed. The model combined predictive recurrent neural network(Pred RNN) and S-T transformer to dynamically capture the spatio-temporal dependence and long-term time-dependence of traffic flow, thereby achieving a certain degree of model interpretability. A novel gated residual network-2(GRN-2) was proposed to investigate the potential relationship between multivariate features and target values. Furthermore, a hybrid quantile loss function was devised to alleviate the gradient disappearance in LSTF problems effectively. In extensive real experiments, the rationality and effectiveness of each network of the model were demonstrated, and the superior forecasting performance was verified in comparison to existing benchmark models.

关键词： traffic flow forecasting multi-feature fusion transformer interpretability

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KNN spatio-temporal attention graph convolutional network for traffic flow repairing

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The Journal of China Universities of Posts and Telecommunications 2025年第1期32卷 48-60页

作者： Zhang Xijun Li Zhe School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

In the process of obtaining information from the actual traffic network, the incomplete data set caused by missing data reduces the validity of the data and the performance of the data-driven model. A traffic flow repair model based on a k-nearest neighbor(KNN) spatio-temporal attention(STA) graph convolutional network(KAGCN) was proposed in this paper. Firstly, the missing data is initially interpolated by the KNN algorithm, and then the complete index set(CIS) is constructed by combining the adjacency matrix of the network structure. Secondly, a STA mechanism is added to the CIS to capture the spatio-temporal correlation between the data. Then, the graph neural network(GNN) is used to reconstruct the data by spatio-temporal correlation, and the reconstructed data set is used to correct and optimize the initial interpolation data set to obtain the final repair result. Finally, the PEMSD4 data set is used to simulate the missing data in the actual road network, and experiments are carried out under the missing rate of 30%, 50%, and 70% respectively. The results show that the mean absolute error(MAE), root mean square error(RMSE), and mean absolute percentage error(MAPE) of the KAGCN model increased by at least 3.83%, 2.80%, and 5.33%, respectively, compared to the other baseline models at different deletion rates. It proves that the KAGCN model is effective in repairing the missing data of traffic flow.

关键词： missing data repair complete index set(CIS) interpolation-reconstruction k-nearest neighbor(KNN)algorithm spatio-temporal correlation

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Secure Medical Image Retrieval Based on Multi-Attention Mechanism and Triplet Deep Hashing

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computers, Materials & Continua 2025年第2期82卷 2137-2158页

作者： Shaozheng Zhang Qiuyu Zhang Jiahui Tang Ruihua Xu School of Computer and Communication Lanzhou University of TechnologyLanzhou730000China

Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a designed multi-attention mechanism focuses on important local features during the feature extraction stage. Moreover, a triplet loss function is utilized to learn discriminative hash codes to construct a compact and efficient triplet deep hashing. Finally, upsampling is used to restore the original resolution of the images during retrieval, thereby enabling more accurate matching. To ensure the security of medical image data, a lightweight image encryption method based on frequency domain encryption is designed to encrypt the chest X-ray images. The findings of the experiment indicate that, in comparison to various advanced image retrieval techniques, the suggested approach improves the precision of feature extraction and retrieval using the COVIDx dataset. Additionally, it offers enhanced protection for the confidentiality of medical images stored in cloud settings and demonstrates strong practicality.

关键词： Secure medical image retrieval multi-attention mechanism triplet deep hashing image enhancement lightweight image encryption

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Privacy-preserving explainable AI: a survey

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Science China(Information Sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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On-device diagnostic recommendation with heterogeneous federated BlockNets

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Science China(Information Sciences) 2025年第4期68卷 33-49页

作者： Minh Hieu NGUYEN Thanh Trung HUYNH Thanh Toan NGUYEN Phi Le NGUYEN Hien Thu PHAM Jun JO Thanh Tam NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Information Technology HUTECH University School of Information and Communication Technology Hanoi University of Science and Technology Commonwealth Scientific and Industrial Research Organization (CSIRO)

The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize centralized recommendations, relying on servers to store client data and dispatch advice to ***, these conventional approaches raise significant concerns regarding data privacy and often result in computational inefficiencies. E-health recommendation services, distinct from other recommendation domains, demand not only precise and swift analyses but also a stringent adherence to privacy safeguards, given the users' reluctance to disclose their identities or health information. In response to these challenges, we explore a new paradigm called on-device recommendation tailored to E-health diagnostics, where diagnostic support(such as biomedical image diagnostics), is computed at the client *** leverage the advances of federated learning to deploy deep learning models capable of delivering expert-level diagnostic suggestions on clients. However, existing federated learning frameworks often deploy a singular model across all edge devices, overlooking their heterogeneous computational capabilities. In this work, we propose an adaptive federated learning framework utilizing BlockNets, a modular design rooted in the layers of deep neural networks, for diagnostic recommendation across heterogeneous devices. Our framework offers the flexibility for users to adjust local model configurations according to their device's computational power. To further handle the capacity skewness of edge devices, we develop a data-free knowledge distillation mechanism to ensure synchronized parameters of local models with the global model, enhancing the overall accuracy. Through comprehensive experiments across five real-world datasets, against six baseline models, within six experimental setups, and various data distribution scenario

关键词： intelligent recommendation federated learning heterogeneous devices E-health diagnostics

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Bimodal Stock Price Prediction Based on Holt-Winters Exponential Smoothing and PCA Whitening Transformation

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第1期52卷 187-200页

作者： Zhu, Changsheng Lu, Pengyun Feng, Wenfang Wang, Yanbo School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China School of Economics and Management Lanzhou University of Technology Lanzhou730050 China School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China

Aiming at the nonlinear and high frequency characteristics of stock data, a hybrid stock price prediction model is proposed, which combines the Holt-Winters triple exponential smoothing method, PCA whitening transformation, PatchTST and BERT, called HW-PCAW-PB. The PCA whitening transformation is performed on the feature vector of investor stock reviews constructed by the BERT model, and the stock price features are processed using the Holt-Winters triple exponential smoothing algorithm and the PatchTST model. The cross-attention mechanism fuses the processed stock review feature information, stock price feature information, and multi-source features such as stock review reading volume and number of comments to predict stock prices. The prediction performance of the HW-PCAW-PB model is compared with benchmark models such as SVR, BiLSTM, TimesNet, KNN, ANN, LSTM, Autoformer and Informer using RMSE, MAE, MAPE and R2 evaluation metrics. The experimental results show that the HW-PCAW-PB model achieves better scores than the benchmark models in all evaluation metrics. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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