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SensFL:Privacy-Preserving Vertical Federated Learning with Sensitive Regularization

Computer Modeling in Engineering & Sciences 2025年第1期142卷 385-404页

作者： Chongzhen Zhang Zhichen Liu Xiangrui Xu Fuqiang Hu Jiao Dai Baigen Cai Wei Wang School of Automation and Intelligence Beijing Jiaotong UniversityBeijing100044China Shuohuang Railway Development Co. Ltd.National Energy GroupCangzhou062350China Beijing key laboratory of Security and Privacy in intelligent Transportation School of Computer Science and TechnologyBeijing Jiaotong UniversityBeijing100044China

In the realm of intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data *** Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training ***,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the *** vulnerability poses a significant threat to VFL-based intelligent railway transportation *** this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in ***,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared *** reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the *** experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of *** results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning *** results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.

关键词： Vertical federated learning privacy defenses

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A mechanical reinforced and antifreezing polyacrylate hydrogel electrolyte for high-performance zinc-ion batteries

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Nano Research 2025年第1期18卷 341-350页

作者： Zili Zhang Ruolin Wang Hongfei Lu Di Zhang Yu Zhao Jing Xu Bin Sun Zhi-Min Dang Yang Jin School of Electrical and information Engineering Zheng Zhou UniversityZhengzhou 450001China State key Lab of New Ceramics and Fine processing School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China

The operation of aqueous zinc-ion batteries in flexible energy storage field is plagued by the uncontrollable growth of Zn-dendrite and inevitable freeze of water below 0 ℃.Therefore,it is necessary to design a hydrogel electrolyte with good mechanical property and freezing resistance to uniform the Zn-deposition and resist flexibility loss at low *** find that the mechanical property(strength and toughness)of hydrogel electrolyte has a significant impact on the suppression of dendrite growth and the uniform deposition of zinc ***,a polyacrylate hydrogel is prepared in one step by ultraviolet(UV)curing method with Zn(CF_(3)SO_(3))_(2)salt and polyvinyl alcohol(PVA)addition to increase the antifreezing ability and mechanical *** adsorption of water molecules by 2-hydroxyethyl acrylate(HEA)and PVA reduces the freezing point of the hydrogel,which is beneficial for enhancing the electrochemical stability at low *** this basis,the Zn-symmetrical battery with hydrogel electrolyte has a long lifespan of 4710 h at 0.5 mA·cm^(-2)and 0.5 mAh·cm^(-2)at room ***,the hydrogel electrolyte exhibits an outstanding stability at low temperature of^(-2)0 ℃,the lifespan of symmetrical battery reaches to 4000 h at 0.5 mA·cm^(-2)and 0.5 mAh·cm^(-2).The assembled full cell with NaV3O8·1.5H_(2)O(NVO)cathode and hydrogel electrolyte possesses a high capacity retention ratio of 77%after 10,000 cycles at^(-2)0 ℃.The flexible cell can power light-emitting diode(LED)lamp under bending,warping and cutting without liquid leakage and an electronic watch at the operating temperature of^(-2)0 ℃.

关键词： zinc ion battery hydrogel electrolyte mechanical enhancement freezing resistance

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Lightweight Object Detection Networks for UAV Aerial Images Based on YOLO

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Chinese Journal of Electronics 2024年第4期33卷 997-1009页

作者： Yanshan LI Jiarong WANG Kunhua ZHANG Jiawei YI Miaomiao WEI Lirong ZHENG Weixin XIE ATR national key laboratory of Defense Technology Shenzhen University Guangdong key laboratory of intelligent information processing Shenzhen University

Existing high-precision object detection algorithms for UAV(unmanned aerial vehicle) aerial images often have a large number of parameters and heavy weight, which makes it difficult to be applied to mobile *** propose three YOLO-based lightweight object detection networks for UAVs, named YOLO-L, YOLO-S, and YOLO-M, respectively. In YOLO-L, we adopt a deconvolution approach to explore suitable upsampling rules during training to improve the detection accuracy. The convolution-batch normalization-Si LU activation function(CBS)structure is replaced with Ghost CBS to reduce the number of parameters and weight, meanwhile Maxpool maximum pooling operation is proposed to replace the CBS structure to avoid generating parameters and weight. YOLO-S greatly reduces the weight of the network by directly introducing CSPGhost Neck residual structures, so that the parameters and weight are respectively decreased by about 15% at the expense of 2.4% mAP. And YOLO-M adopts the CSPGhost Neck residual structure and deconvolution to reduce parameters by 5.6% and weight by 5.7%, while m AP only by 1.8%. The results show that the three lightweight detection networks proposed in this paper have good performance in UAV aerial image object detection task.

关键词： Training YOLO Deconvolution Accuracy Object detection Autonomous aerial vehicles Feature extraction

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Action Status Based Novel Relative Feature Representations for Interaction Recognition

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Chinese Journal of Electronics 2022年第1期31卷 168-180页

作者： LI Yanshan GUO Tianyu LIU Xing LUO Wenhan XIE Weixin ATR national key laboratory of Defense Technology Shenzhen University Guangdong key laboratory of intelligent information processing Shenzhen University Tencent

Skeleton-based action recognition has always been an important research topic in computer vision. Most of the researchers in this field currently pay more attention to actions performed by a single person while there is very little work dedicated to the identification of interactions between two people. However, the practical application of interaction recognition is actually more critical in our society considering that actions are often performed by multiple people. How to design an effective scheme to learn discriminative spatial and temporal representations for skeleton-based interaction recognition is still a challenging problem. Focusing on the characteristics of skeleton data for interactions, we first define the moving distance to distinguish the action status of the participants. Then some view-invariant relative features are proposed to fully represent the spatial and temporal relationship of the skeleton sequence. Further, a new coding method is proposed to obtain the novel relative feature representations. Finally, we design a three-stream CNN model to learn deep features for interaction recognition. We evaluate our method on SBU dataset, NTU RGB+D 60 dataset and NTU RGB+D 120 dataset. The experimental results also verify that our method is effective and exhibits great robustness compared with current state-of-the-art methods.

关键词： image representation relative feature representations skeleton-based interaction recognition skeleton sequence skeleton data image coding deep learning (artificial intelligence) image recognition temporal representations temporal relationship view-invariant relative features spatial relationship skeleton-based action recognition action status computer vision convolutional neural nets discriminative spatial representations image motion analysis

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Single-cell omics: experimental workflow, data analyses and applications

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Science China Life Sciences 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical laboratory the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City) Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang University key laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji University Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Research Institute of intelligent Computing Zhejiang Lab Shanghai Research Institute for intelligent Autonomous Systems Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of Medicine national key laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang University Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Beijing Institutes of Life Science Chinese Academy of Sciences Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology (CQB)Peking University Zhejiang key laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women's HospitalZhejiang University School of Medicine key laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal University

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome,proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial sc RNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Secure data rate maximization for full-duplex UAV-enabled base station

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Digital Communications and Networks 2024年第5期10卷 1387-1393页

作者： Chunlong He Xinjie Li Yin Huang Jianzhen Lin Gongbin Qian Xingquan Li Guangdong key laboratory of intelligent information processing Shenzhen University national Mobile Communications Research laboratory Southeast University Shenzhen Institute of information Technology

Unmanned Aerial Vehicle(UAV) is an air base station featuring flexible deployment and *** can significantly improve the communication quality of the system due to its line-of-sight channel connection with ground ***,due to the openness of UAV-to-Ground channels,the communication between ground users' devices and UAV is easily *** this paper,we aim to improve the security of communication system by using full-duplex UAV as a mobile air base *** UAV sends interference signals to eavesdroppers and receives signals from ground *** jointly optimize the scheduling between the UAV and ground devices,the transmission power of the UAV and ground devices,as well as the trajectory of the UAV to maximize the minimum average security communication data *** optimization problem is mixed with integers and nonconvex ***,this problem is not a standard convex optimization problem,which can not be solved with standard *** this in mind,we propose an effective algorithm which solves this problem iteratively by applying Successive Convex Approximation(SCA),variable relaxation and ***,numerical results demonstrate the effectiveness of the proposed algorithm.

关键词： UAV relay Physical layer security Full-duplex UAV Power optimization Trajectory optimization

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TGSIFT: Robust SIFT Descriptor Based on Tensor Gradient for Hyperspectral Images

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Chinese Journal of Electronics 2020年第5期29卷 916-925页

作者： LI Yanshan FAN Leidong XIE Weixin ATR national key Lab.of Defense Technology Shenzhen University Guangdong key laboratory of intelligent information processing College of Information Engineering Shenzhen University

This paper proposes a robust sparse descriptor based on tensor theory by using the spatial and spectral information synthetically, namely the Tensor gradient SIFT(TGSIFT), for Hyperspectral image(HSI).TGSIFT integrates both spatial and spectral information and considers the natural vector feature of HSIs. Based on the HSI Gaussian scale space, a new tensor model for HSI is proposed which takes the vectorial nature of HSI into consideration and preserves all the necessary structural information distributed over all the *** TGSIFT descriptor is formed based on the model proposed. Experimental results of HSI matching show that the TGSIFT descriptor achieves better matching performance than other SIFT descriptors under different transformations, including illumination change, sensor noise, image rotation, viewpoint change, and scale change.

关键词： Hyperspectral image (HSI) Interest points Scale-invariant feature transform (SIFT) Sparse descriptor Tensor gradient SIFT (TGSIFT)

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Phrase-based hashtag recommendation for microblog posts

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Science China(information Sciences) 2017年第1期60卷 132-144页

作者： Yeyun GONG Qi ZHANG Xiaoying HAN Xuanjing HUANG Shanghai key laboratory of intelligent information processing School of Computer Science Fudan University

In microblogs, authors use hashtags to mark keywords or topics. These manually labeled tags can be used to benefit various live social media applications(e.g., microblog retrieval, classification). However,because only a small portion of microblogs contain hashtags, recommending hashtags for use in microblogs are a worthwhile exercise. In addition, human inference often relies on the intrinsic grouping of words into ***, existing work uses only unigrams to model corpora. In this work, we propose a novel phrase-based topical translation model to address this problem. We use the bag-of-phrases model to better capture the underlying topics of posted microblogs. We regard the phrases and hashtags in a microblog as two different languages that are talking about the same thing. Thus, the hashtag recommendation task can be viewed as a translation process from phrases to hashtags. To handle the topical information of microblogs, the proposed model regards translation probability as being topic specific. We test the methods on data collected from realworld microblogging services. The results demonstrate that the proposed method outperforms state-of-the-art methods that use the unigram model.

关键词： recommendation topic model translation phrase extraction hashtag

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Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 214-222页

作者： Shuping Liu Yantuan Xian Huafeng Li Zhengtao Yu School of information Engineering and Automation and key laboratory of intelligent information processing Kunming University of Science and Technology

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection *** order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

关键词： Dictionary learning Laplacian sparse regularization morphological component analysis(MCA) sparse representation text detection

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More relaxed condition for dynamics of discrete time delayed Hopfield neural networks

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Chinese Physics B 2008年第1期17卷 125-128页

作者：张强 Liaoning key laboratory of intelligent information processing Dalian University

The dynamics of discrete time delayed Hopfield neural networks is investigated. By using a difference inequality combining with the linear matrix inequality, a sufficient condition ensuring global exponential stability of the unique equilibrium point of the networks is found. The result obtained holds not only for constant delay but also for time-varying delays.

关键词： discrete time delayed Hopfield neural networks difference inequality

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