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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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GET3DGS: Generate 3D Gaussians Based on Points Deformation Fields

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4437-4449页

作者： Yu, Haochen Gong, Weixi Chen, Jiansheng Ma, Huimin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

The 3D Gaussian Splatting method has recently shown significant advancements in rendering speed and scene composition quality, enhancing its industrial applications and boosting the demand for 3D Gaussian asset generation. However, existing mature 3D generation technologies predominantly rely on implicit representations, which often struggle to balance geometric quality with editability. The production of 3D Gaussian assets generally involves diffusion models that require a dual-stage process of reconstruction and generation, resulting in substantial training and inference costs. To overcome these challenges, we introduce GET3DGS, an innovative approach that combines 3D-aware GANs with 3D Gaussian Splatting representations. This method facilitates the manipulation of the physical attributes of 3D Gaussians, such as geometry and texture, via point deformation fields. Offering faster inference speeds and end-to-end training capabilities, our model outperforms existing diffusion model-based methods. By deriving high-quality Gaussian point cloud geometric representations from 2D images, our approach reduces material accumulation costs and produces data compatible with 3D Gaussian rendering engines. We have evaluated the generative performance of our model on ShapeNet and OmniObject3D and demonstrate competitive results in terms of image and geometric quality relative to previous methods. © 1991-2012 IEEE.

关键词： Rendering (computer graphics)

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Prediction Model of the End-point Carbon Content in BOF by Constructing the Graph of Heats

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ISIJ International 2025年第5期65卷 613-618页

作者： Ning, Wen Hongbing, Wang Li, Li Anjun, Xu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing100083 China

For the differences in composition and proportions of input materials, end-point quality requirements and slagging specifications, it is difficult to construct a generalized model to guide steelmaking production. There is a complex connection among different heats in BOF, and this combination of correlations can be thought of as a hidden working pattern. A method named "attribute level division" is developed to exploit the correlations among heats to construct the graph of heats. The model based on the combination of label propagation algorithm (LPA) and back propagation neural network (BPNN), LPA-BP, is proposed for end-point carbon content prediction in BOF. LPA is used to discover the different community in the graph of heats and BPNN is trained to construct different models for end-point carbon content prediction for the heats from different community. The results of comparative experiment show that the LPA-BP model is higher than the baseline 2.5% when prediction error is within ±0.012%. The LPA-BP model also outperforms in some metrics, such as RMSE, MAE. This model provides a novel idea to improve the endpoint hit rate by distinguishing different communities to uncover the hidden working patterns among heats and constructing different models. © 2025 The Iron and Steel Institute of Japan.

关键词： Steelmaking

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Preamble slice orderly queue access scheme in cell-free dense communication systems

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Digital communications and Networks 2025年第1期11卷 126-135页

作者： Jun Sun Mengzhu Guo Jian Liu School of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer and Communication Engineering University of Science and TechnologyBeijing 100083China

High reliability applications in dense access scenarios have become one of the main goals of 6G *** solve the access collision of dense Machine Type communication(MTC)devices in cell-free communication systems,an intelligent cooperative secure access scheme based on multi-agent reinforcement learning and federated learning is proposed,that is,the Preamble Slice Orderly Queue Access(PSOQA)*** this scheme,the preamble arrangement is combined with the access *** preamble arrangement is realized by preamble slices which is from the virtual preamble *** access devices learn to queue orderly by deep reinforcement *** orderly queue weakens the random and avoids collision.A preamble slice is assigned to an orderly access queue at each access *** orderly queue is determined by interaction information among multiple *** the federated reinforcement learning framework,the PSOQA scheme is implemented to guarantee the privacy and security of ***,the access performance of PSOQA is compared with other random contention schemes in different load *** results show that PSOQA can not only improve the access success rate but also guarantee low-latency tolerant performances.

关键词： Machine type communication Random access Reinforcement learning Access success probability Federated learning

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Photonic generation of multi-format and reconfigurable microwave signals based on cascaded external modulation

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Optical and Quantum Electronics 2025年第3期57卷 1-14页

作者： Chen, Mo Chen, Hongyao Zhang, HongYu Li, ZhuoYang Wang, Jianping Feng, LiFang Lu, HuiMin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing10083 China

This paper presents a photonic scheme for generating multi-format, multi-band, and reconfigurable microwave photonic signals through cascaded external modulation. The proposed system utilize dual-parallel Mach–Zehnder modulators (DP-MZM) and Mach–Zehnder modulators (MZM) to produce high-order optical sidebands, enabling flexible frequency multiplication factors switching and pulse waveform reconstruction. Additionally, using a polarization control structure allows for spurious suppression and encoded signal loading. The system can generate various signal formats, including rectangular optical frequency combs, frequency-multiplied signals, dual-frequency encoded signals, and multiple pulse waveforms. Simulation results demonstrate the successful generation of 16 GHz frequency-doubled signal and 32 GHz frequency-quadrupled signal, with spurious suppression ratios of 37.9 dB and 44.8 dB, respectively. It can also generate four-line rectangular optical frequency combs with frequency multiplication factors of 2 and 4, achieving flatness values of 0.24 dB and 0.16 dB. Furthermore, the system can generate symmetric triangular pulses and Sinc-Nyquist pulses with adjustable repetition frequencies, as well as encoded signals with good pulse compression performance, having PCR values close to the theoretical value of 13. This work demonstrates a versatile and reconfigurable approach to microwave photonic signal generation, offering promising applications in advanced radar systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Microwave photonics

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Multitarget Cooperative Motion Tracking Based on Quantum Belief Propagation

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IEEE Internet of Things Journal 2025年第10期12卷 14845-14855页

作者： Wan, Jiawang Xu, Cheng Shi, Yuchen Chen, Weizhao Ye, Fangwen Wang, Ran Zhang, Xiaotong University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China University of Science and Technology Beijing Shunde Innovation School Foshan528399 China

In this article, we introduce a novel cooperative target tracking algorithm, namely, the quantum-inspired belief propagation (BP), aimed at rectifying the limitations observed in existing localization algorithms employed in multitarget cooperative tracking scenarios. Leveraging the principles of quantum superposition, our algorithm seeks to alleviate the uncertainty inherent in message fusion within BP frameworks, thereby enhancing the accuracy and stability of multitarget cooperative localization. The utilization of the quantum Monte Carlo method facilitates the simulation of the message distribution process, with quantum particles embodying the superposition of multiple states concurrently. This approach effectively addresses the intractable integrations encountered in message updating on factor graphs (FGs), rendering the algorithm agnostic to the number of particles involved. Moreover, quantum unitary transformations and quantum closed box operations are deployed to encode FG function nodes for the propagation of quantum messages. This innovation surmounts the challenge posed by traditional FG function nodes’ inability to process quantum messages. Experimental findings corroborated the superiority of the proposed algorithm in terms of accuracy and robustness. © 2014 IEEE.

关键词： Belief propagation

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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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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