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

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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Limb movement detection and analysis based on visual recognition of human posture

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Discover Artificial Intelligence 2025年第1期5卷 1-12页

作者： Xiao, Zhiguo Wang, Chunxiang Ding, Tianjiao Shen, Xiangfeng Li, Xinyuan Li, Dongni School of Computer Science & Technology Beijing Institute of Technology Beijing100811 China School of Computer Science Technology Changchun University Changchun130022 China

Current motion detection and evaluation technologies face challenges such as limited scalability, imprecise feedback, and lack of personalized guidance. To address these challenges, this research integrated efficient BlazePose technology with pioneering DW_KNN* algorithm, resulting in the remarkable accuracy of 98.2% in action recognition and showcasing outstanding scalability. Furthermore, the established ACLstm time series prediction model could comprehensively analyze historical sports data and associated factors of users. In Rehab dataset, MAE(Mean Absolute Error, MAE) loss was 1.383 for motion count and 0.508 for motion time. This innovative framework delivered precise feedback and tailored guidance for physical exercise and medical rehabilitation. © The Author(s) 2025.

关键词： Time series

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Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models

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World Journal of Gastroenterology 2024年第48期30卷 5111-5129页

作者： Zhi-Guo Xiao Xian-Qing Chen Dong Zhang Xin-Yuan Li Wen-Xin Dai Wen-Hui Liang School of Computer Science Technology Changchun UniversityChangchun 130022Jilin ProvinceChina School of Computer Science Technology Beijing Institute of TechnologyBeijing 100811China

BACKGROUND Wireless capsule endoscopy(WCE)has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging ***,the complexity of the digestive tract structure,and the diversity of lesion types,results in different sites and types of lesions distinctly appearing in the images,posing a challenge for the accurate identification of digestive tract *** To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions,thereby improving the diagnostic efficiency of doctors,and creating significant clinical application *** In this paper,we propose a neural network model,WCE_Detection,for the accurate detection and classification of 23 classes of digestive tract lesion ***,since multicategory lesion images exhibit various shapes and scales,a multidetection head strategy is adopted in the object detection network to increase the model's robustness for multiscale lesion ***,a bidirectional feature pyramid network(BiFPN)is introduced,which effectively fuses shallow semantic features by adding skip connections,significantly reducing the detection error *** the basis of the above,we utilize the Swin Transformer with its unique self-attention mechanism and hierarchical structure in conjunction with the BiFPN feature fusion technique to enhance the feature representation of multicategory lesion *** The model constructed in this study achieved an mAP50 of 91.5%for detecting 23 *** than eleven single-category lesions achieved an mAP50 of over 99.4%,and more than twenty lesions had an mAP50 value of over 80%.These results indicate that the model outperforms other state-of-the-art models in the end-to-end integrated detection of human digestive tract lesion *** The deep learning-based object detection network detects multiple digestive tract lesi

关键词： Human digestive tract Artificial intelligence Deep learning Wireless capsule endoscopy Object detection

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Improvement Algorithm Analysis and Implementation of Plant Disease and Pest Recognition Based on YOLO 5

Improvement Algorithm Analysis and Implementation of Plant D...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wei, Yongxiang Pan, Xin Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China Changchun Institute of Technology School of Computer Technology and Engineering Changchun China Changchun Institute of Technology School of Computer Technology and Engineering Jilin Changchun China

ISBN: (纸本)9798350373820

Agriculture is one of the main economic pillars in many countries and regions, contributing significantly to the stable development of the national economy. However, the growth rate of agriculture is currently declining rapidly, and plant diseases and pests are increasingly prevalent due to various factors such as climate and pollution. In response to the detection and recognition of plant diseases and pests, we propose introducing a multi-channel BiFormer module into the basic YOLOv8 target detection algorithm, which can effectively improve recognition accuracy. In experiments, we tested our method using a cashew dataset, and the results showed that our method outperforms traditional methods in terms of precision (P), recall rate (R), and mean average precision (mAP), indicating its high effectiveness. © 2024 IEEE.

关键词： Object detection

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High-quality ghost imaging based on undersampled natural-order Hadamard source

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Chinese Physics B 2024年第9期33卷 404-411页

作者： Kang Liu Cheng Zhou Jipeng Huang Hongwu Qin Xuan Liu Xinwei Li Lijun Song School of Electronic Information Engineering Changchun UniversityChangchun 130022China School of Physics Northeast Normal UniversityChangchun 130024China College of Electronic and Information Engineering Changchun University of Science and TechnologyChangchun 130022China Jilin Engineering Laboratory for Quantum Information Technology Jilin Engineering Normal UniversityChangchun 130052China Changchun Institute of Technology Changchun 130012China

Improving the speed of ghost imaging is one of the main ways to leverage its advantages in sensitivity and imperfect spectral regions for practical *** of the proportional relationship between image resolution and measurement time,when the image pixels are large,the measurement time increases,making it difficult to achieve real-time ***,a high-quality ghost imaging method based on undersampled natural-order Hadamard is *** method uses the characteristics of the Hadamard matrix under undersampling conditions where image information can be fully obtained but overlaps,as well as deep learning to extract aliasing information from the overlapping results to obtain the true original image *** conducted numerical simulations and experimental tests on binary and grayscale objects under undersampling conditions to demonstrate the effectiveness and scalability of this *** method can significantly reduce the number of measurements required to obtain high-quality image information and advance application promotion.

关键词： ghost imaging natural-order Hadamard deep learning

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Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis

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computers, Materials & Continua 2023年第9期76卷 2649-2665页

作者： Jingyao Liu Qinghe Feng Jiashi Zhao Yu Miao Wei He Weili Shi Zhengang Jiang School of Computer and Information Engineering Chuzhou UniversityChuzhou239000China School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China Zhongshan Institute of Changchun University of Science and Technology ZhongshanChina School of Intelligent Engineering Henan Institute of TechnologyXinxiang453003China

The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care *** diagnosis and treatment have become increasingly important;however,the distribution and size of lesions vary widely among individuals,making it challenging to accurately diagnose the *** study proposed a deep-learning disease diagnosismodel based onweakly supervised learning and clustering visualization(W_CVNet)that fused classification with ***,the data were *** optimizable weakly supervised segmentation preprocessing method(O-WSSPM)was used to remove redundant data and solve the category imbalance ***,a deep-learning fusion method was used for feature extraction and classification recognition.A dual asymmetric complementary bilinear feature extraction method(D-CBM)was used to fully extract complementary features,which solved the problem of insufficient feature extraction by a single deep learning ***,an unsupervised learning method based on Fuzzy C-Means(FCM)clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease *** this study,5-fold cross-validation methods were used,and the results showed that the network had an average classification accuracy of 85.8%,outperforming six recent advanced classification models.W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and,in the case of COVID-19 patients,to further predict the area of the lesion.

关键词： Classification COVID-19 deep learning segmentation unsupervised learning weakly supervised

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B+-Tree-Quantization-Based Shared Secret Key Extraction Scheme From Atmospheric Optical Wireless Channel

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IEEE Transactions on Communications 2025年第5期73卷 3394-3408页

作者： Pan, Gang Chen, Chunyi Changchun University of Science and Technology School of Computer Science and Technology Changchun130022 China Changchun University of Science and Technology School of Computer Science and Technology Key Laboratory of Photoelectric Measurement & Control and Optical Information Transfer Technology Changchun130022 China Chongqing Research Institute Changchun University of Science and Technology Chongqing401135 China

To reduce key disagreement rate and increase key generation rate, this paper proposes a lightweight and robust shared secret key extraction scheme from atmospheric optical wireless channel. A conception of grouping samples by using B+ tree and quantizing groups is introduced to improve the balance of distribution of channel measurement values in quantization intervals. Based on the conception, a basic B+ tree quantization algorithm (B-BPTQ) and a compensation-technology-based B+ tree quantization algorithm (C-BPTQ) are designed. A LDPC-based multi-scale information reconciliation algorithm (LDPC-MSIR) is put forward to simplify information reconciliation and reduce its computation complexity. An enhanced randomness post-processing algorithm is formulated to increase statistical randomness of initial key sequence. Finally, the performance of the proposed scheme is validated by numerical simulations and field experiments. The experimental results show that it has better performance in terms of key disagreement rate, key generation rate and key randomness compared with other schemes. © 1972-2012 IEEE.

关键词： Optical communication

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Amino-functionalized UiO-66-doped mixed matrix membranes with high permeation performance and fouling resistance

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Chinese Journal of Chemical engineering 2024年第3期67卷 68-77页

作者： Yi Zhang Di Liu Zhaoli Wang Junjian Yu Yanyin Cheng Wenjing Li Zhe Wang Hongzhe Ni Yuchao Wang School of Chemical Engineering Changchun University of TechnologyChangchun 130012China Advanced Institute of Materials Science Changchun University of TechnologyChangchun 130012China Key Laboratory of Advanced Functional Polymer Membrane Materials of Jilin Province Changchun 130012China School of Chemistry and Life Science Changchun University of TechnologyChangchun 130012China

For the reduction of bovine serum proteins from wastewater,a novel mixed matrix membrane was prepared by functionalizing the substrate material polyaryletherketone(PAEK),followed by carboxyl groups(C-SPAEKS),and then adding amino-functionalized UiO-66-NH_(2)(Am-UiO-66-NH_(2)).Aminofunctionalization of UiO-66 was accomplished by melamine,followed by an amidation reaction to immobilize Am-UiO-66-NH_(2),which was immobilized on the surface of the membrane as well as in the pore channels,which enhanced the hydrophilicity of the membrane surface while increasing the negative potential of the membrane *** nanoparticle-loaded ultrafiltration membrane has good permeation performance,with a pure water flux of up to 482.3 L·m^(-2)·h^(-1) for C-SPAEKS/AmUiO-66-NH_(2) and a retention rate of up to 98.7%for bovine serum albumin(BSA)-contaminated ***,after several hydrophilic modifications,the flux recovery of BSA contaminants by this series of membranes increased from 56.2%to 80.55%of pure *** results of ultra-filtration flux time tests performed at room temperature showed that the series of ultrafiltration membranes remained relatively stable over a test time of 300 ***,the newly developed mixed matrix membrane showed potential for high efficiency and stability in wastewater treatment containing bovine serum proteins.

关键词： Ultrafiltration Mixed matrix membranes Amino functionalization Hydrophilic modification Negatively charged

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High-rate metal-free MXene microsupercapacitors on paper substrates

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Carbon Energy 2024年第5期6卷 94-104页

作者： Han Xue Po‐Han Huang Lee‐Lun Lai Yingchun Su Axel Strömberg Gaolong Cao Yuzhu Fan Sergiy Khartsev Mats Göthelid Yan‐Ting Sun Jonas Weissenrieder Kristinn BGylfason Frank Niklaus Jiantong Li School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden School of Engineering Sciences KTH Royal Institute of TechnologyStockholmSweden

MXene is a promising energy storage material for miniaturized microbatteries and microsupercapacitors(MSCs).Despite its superior electrochemical performance,only a few studies have reported MXene-based ultrahigh-rate(>1000 mV s^(−1))on-paper MSCs,mainly due to the reduced electrical conductance of MXene films deposited on ***,ultrahigh-rate metal-free on-paper MSCs based on heterogeneous MXene/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)(PEDOT:PSS)-stack electrodes are fabricated through the combination of direct ink writing and femtosecond laser *** a footprint area of only 20 mm^(2),the on-paper MSCs exhibit excellent high-rate capacitive behavior with an areal capacitance of 5.7 mF cm^(−2)and long cycle life(>95%capacitance retention after 10,000 cycles)at a high scan rate of 1000 mV s^(−1),outperforming most of the present on-paper ***,the heterogeneous MXene/PEDOT:PSS electrodes can interconnect individual MSCs into metal-free on-paper MSC arrays,which can also be simultaneously charged/discharged at 1000 mV s^(−1),showing scalable capacitive *** heterogeneous MXene/PEDOT:PSS stacks are a promising electrode structure for on-paper MSCs to serve as ultrafast miniaturized energy storage components for emerging paper electronics.

关键词： direct ink writing femtosecond laser scribing MXene on-paper microsupercapacitors PEDOT:PSS ultrahigh rate capability

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GMS:A Novel Method for Detecting Reentrancy Vulnerabilities in Smart Contracts

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computers, Materials & Continua 2025年第5期83卷 2207-2220页

作者： Dawei Xu Fan Huang Jiaxin Zhang Yunfang Liang Baokun Zheng Jian Zhao School of Computer Science Changchun UniversityChangchun130012China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China School of InformationManagement for Law China University of Political Science and LawBeijing102249China

With the rapid proliferation of Internet ofThings(IoT)devices,ensuring their communication security has become increasingly *** and smart contract technologies,with their decentralized nature,provide strong security guarantees for ***,at the same time,smart contracts themselves face numerous security challenges,among which reentrancy vulnerabilities are particularly *** detection tools for reentrancy vulnerabilities often suffer from high false positive and false negative rates due to their reliance on identifying patterns related to specific transfer *** address these limitations,this paper proposes a novel detection method that combines pattern matching with deep ***,we carefully identify and define three common patterns of reentrancy vulnerabilities in smart ***,we extract key vulnerability features based on these ***,we employ a Graph Attention Neural Network to extract graph embedding features from the contract graph,capturing the complex relationships between different components of the ***,we use an attention mechanism to fuse these two sets of feature information,enhancing the weights of effective information and suppressing irrelevant information,thereby significantly improving the accuracy and robustness of vulnerability *** results demonstrate that our proposed method outperforms existing state-ofthe-art techniques,achieving a 3.88%improvement in accuracy compared to the latest vulnerability detection model AME(Attentive Multi-Encoder Network).This indicates that our method effectively reduces false positives and false negatives,significantly enhancing the security and reliability of smart contracts in the evolving IoT ecosystem.

关键词： Smart contract Internet of Things reentrancy vulnerabilities graph neural network

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