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Adaptive network combination for single-image reflection removal:a domain generalization perspective

Frontiers of computer science 2025年第1期19卷 171-173页

作者： Ming LIU Jianan PAN Zifei YAN Wangmeng ZUO Lei ZHANG School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China

1 Introduction Recently,multiple synthetic and real-world datasets have been built to facilitate the training of deep single-image reflection removal(SIRR)***,diverse testing sets are also provided with different types of reflections and ***,the non-negligible domain gaps between training and testing sets make it difficult to learn deep models generalizing well to testing *** diversity of reflections and scenes further makes it a mission impossible to learn a single model being effective for all testing sets and real-world *** this paper,we tackle these issues by learning SIRR models from a domain generalization perspective.

关键词： testing generalization while

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Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis

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Journal of Bionic Engineering 2024年第6期21卷 3179-3200页

作者： Chaosheng Tang Wenle Xu Junding Sun Shuihua Wang Yudong Zhang Juan Manuel Górriz School of Computer Science and Technology Henan Polytechnic UniversityHenanJiaozuo454003People’s Republic of China Department of Biological Sciences School of ScienceXi’an Jiaotong Liverpool UniversitySuzhou215123JiangsuChina School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University21589JeddahSaudi Arabia Department of Signal Theory Networking and CommunicationsUniversity of Granada52005GranadaSpain

Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between *** address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global ***,these approaches typically neglect the topological structure information of the graph during the global feature extraction *** paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image *** approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual *** that different nodes contribute differently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global ***,we introduce a G-LFF module to fuse the local and global features effectively.

关键词： Convolutional neural networks Graph convolutional networks Graph pooling COVID-19

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From Perception to Action:Brain-to-Brain Information Transmission of Pigeons

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Journal of Bionic Engineering 2024年第6期21卷 2913-2923页

作者： Lifang Yang Long Yang Haofeng Wang Mengmeng Li Zhigang Shang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001China

Along with the flourishing of brain-computer interface technology,the brain-to-brain information transmission between different organisms has received high attention in recent ***,specific information transmission mode and implementation technology need to be further *** this paper,we constructed a brain-to-brain information transmission system between pigeons based on the neural information decoding and electrical stimulation encoding *** system consists of three parts:(1)the“perception pigeon”learns to distinguish different visual stimuli with two discrepant frequencies,(2)the computer decodes the stimuli based on the neural signals recorded from the“perception pigeon”through a frequency identification algorithm(neural information decoding)and encodes them into different kinds of electrical pulses,(3)the“action pigeon”receives the Intracortical Microstimulation(ICMS)and executes corresponding key-pecking actions through discriminative learning(electrical stimulation encoding).The experimental results show that our brain-to-brain system achieves information transmission from perception to action between two pigeons with the average accuracy of about 72%.Our study verifies the feasibility of information transmission between inter-brain based on neural information decoding and ICMS encoding,providing important technical methods and experimental program references for the development of brain-to-brain communication technology.

关键词： Pigeon Neural Information Decoding Electrical Stimulation Encoding Intracortical Microstimulation Brain-to-brain Information Transmission

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Solid solution strategy modulated defects engineering of(Cr_(1-x)V_(x))_(2)AlC MAX phase toward superior electromagnetic wave absorption

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Rare Metals 2024年第7期43卷 3205-3219页

作者： Ji-Lin Gou Yu-Kai Chang Shu Liu Peng-Hui Li Ping-Hao Cui Qian-Ku Hu Li-Bo Wang Xin Zhang Jun-Kai Wang Qi-Xun Xia Ai-Guo Zhou School of Materials Science and Engineering Henan Polytechnic UniversityJiaozuo 454003China Key Laboratory for Microstructural Material Physics of Hebei Province School of ScienceYanshan UniversityQinhuangdao 066004China State Key Laboratory of Metastable Materials Science and Technology Yanshan UniversityQinhuangdao 066004China

Defects engineering is an effective strategy for manipulating electromagnetic parameters and enhancing electromagnetic wave(EMW)absorption ***,the relationship between them is not clear,especially in solid solution *** this work,a series of(Cr_(1-x)V_(x))_(2)AlC MAX phase solid solutions with layered structure were prepared via tuning the ratio of Cr and V to explore their EMW absorption *** experimental results indicated that the doping of V atoms at the M-site could effectively regulate its impedance matching and EMW absorption properties by introducing appropriate numbers of defects in the crystal,such as twin boundaries,dislocations and lattice *** them,if Cr:V=3:1,Cr_(1.5)V_(0.5)AlC,as radar absorption materials,could reach a strong reflection loss of-51.8 dB at the frequency of 12.8 GHz under an ultra-thin thickness of 1.3 *** reflection loss value could attain-10 dB in a wide frequency range of 2.7-18 GHz and thickness range of 1-5 *** addition,after high temperature and acid-alkali immersion treatment,this sample still had good EMW absorption capability,and the effective absorption bandwidth was enhanced from 2.3 to 2.6 GHz after concentrated acid immersion or 3.1 GHz after concentrated alkali *** work has great reference significance for the research and development of high-performance MAX-based EMW absorption materials in harsh environments.

关键词： MAX phases Solid solution Defects engineering Electromagnetic wave absorption Harsh environments

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Mechanical modeling and decoupling analysis of the large deformation six-axis force sensor based on sensing units 16

Mechanical modeling and decoupling analysis of the large def...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Huang, Xiaoming Yin, Zhongjun School of Mechanical Engineering University of Science and Technology Beijing Beijing China School of Mechanical and Power Engineering Henan Polytechnic University Henan China

ISBN: (纸本)9781510689251

To improve the accuracy of the measurement and to minimize the coupling between the directions is the key to the research of large deformation six-axis force sensors with good compliance. Utilizing large-deformation six-axis force sensors for accurate monitoring of ground reaction forces (GRFs) during human locomotion is of great significance in the field of biomechanics and pathological gait diagnosis. The proposed flexible six-dimensional force sensor has a flexible optical waveguide-based sensing unit as the detection element to make it capable of detecting large deformations. Firstly, the design and mechanical modeling of the sensor were carried out, and the force information measurement of the corresponding channel was achieved by arranging the sensing unit with the centering position of the isotropic viewpoints, and at the same time simplifying the solution of the hyperstatic structure. Secondly, by adopting a split structure, a complete mechanical decoupling can be realized in theory. The designed measurement scales are Fx, Fy = 0-160 N, Fz = 0-800 N, Tx, Ty, Tz = 0-20 Nm, which satisfy the requirement of monitoring the ground reaction force information when human body is in motion for wearable devices. © 2025 SPIE.

关键词： Gait analysis

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Policy-Based Group Signature Scheme from Lattice

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computers, Materials & Continua 2022年第8期72卷 4069-4085页

作者： Yongli Tang Yuanhong Li Qing Ye Ying Li Xiaojun Wang School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China School of Electronic Engineering Dublin City UniversityDublin 9Ireland

Although the existing group signature schemes from lattice have been optimized for efficiency,the signing abilities of eachmember in the group are relatively *** may not be suitable for complex *** by the pioneering work of Bellare and Fuchsbauer,we present a primitive called policy-based group *** policy-based group signatures,group members can on behalf of the group to sign documents that meet their own policies,and the generated signatures will not leak the identity and policies of the ***,the group administrator is allowed to reveal the identity of signer when a controversy *** the analysis of application scenarios,we concluded that the policy-based group signature needs to meet two essential security properties:simulatability and *** we construct a scheme of policy-based group signature from lattice through techniques such as commitment,zero-knowledge proof,rejection *** security of our scheme is proved to be reduced to the module short integer solution(MSIS)and module learning with errors(MLWE)hard ***,we make a performance comparison between our scheme and three lattice-based group signature *** result shows that our scheme has more advantages in storage overhead and the sizes of key and signature are decreased roughly by 83.13%,46.01%,respectively,compared with other schemes.

关键词： Group signature policy-based signature lattice-based cryptography zero-knowledge proof

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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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LayerCFL:an efcient federated learning with layer-wised clustering

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Cybersecurity 2025年第1期8卷 72-85页

作者： Jie Yuan Rui Qian Tingting Yuan Mingliang Sun Jirui Li Xiaoyong Li School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijingChina Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijingChina Institute of Computer Science Faculty of Mathematics and Computer ScienceUniversity of GoettingenGöettingenGermany School of Information Technology Henan University of Chinese MedicineZhengzhouHenanChina

Federated Learning(FL)sufers from the Non-IID problem in practice,which poses a challenge for efcient and accurate model *** address this challenge,prior research has introduced clustered FL(CFL),which involves clustering clients and training them *** its potential benefts,CFL can be computationally and communicationally expensive when the data distribution is unknown *** is because CFL involves the entire neural networks of involved clients in computing the clusters during training,which can become increasingly timeconsuming with large-sized *** tackle this issue,this paper proposes an efcient CFL approach called LayerCFL that employs a Layer-wised clustering *** LayerCFL,clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental *** experimental results demonstrate the efectiveness of LayerCFL in mitigating the impact of Non-IID data,improving the accuracy of clustering,and enhancing computational efciency.

关键词： Federated learning Clustered federated learning Non-IID Layer-wised clustering

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Analysis of online marketing user participation preference attribute based on social network text mining

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International Journal of Business Intelligence and Data Mining 2025年第3-4期26卷 473-489页

作者： Zhang, Lu Chen, Wanqing Nie, Hengzhi Department of Economics and Trade Henan Polytechnic Institute Hennan Nanyang473001 China Department of Computer Science and Technology Huazhong University of Science and Technology Wuhan430073 China Zhengzhou Fengyang Foreign Language School Hennan Zhengzhou450000 China Fuzhou350003 China

In order to improve the accuracy of online marketing users’ participation preference feature attribute recognition, an analysis method of online marketing users’ participation preference feature attribute based on social network text mining is proposed. Firstly, the TF-IDF algorithm is used to calculate the weight value of keywords in the tag, and then the user portrait of the social network platform is constructed after sorting. Then, the collaborative filtering algorithm is used to determine the user’s preference characteristics for products containing keywords, and the K-L feature compressor is used to extract the user’s participation preference characteristics of online marketing. Finally, the online marketing user participation preference characteristic attributes are classified to realise the analysis of online marketing user participation preference characteristic attributes. The experimental results show that the accuracy of this method is always above 90% and the average time is 3.88s. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Tweets

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Log Anomaly Detection Method Based On Multi-Feature Graph Embedding 4

Log Anomaly Detection Method Based On Multi-Feature Graph Em...

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4th International Conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： Liu, JiZhao Li, Le ZhongYuan University of Technology School of Computer Science Henan Zhengzhou China

ISBN: (纸本)9798331534622

Modern software systems generate a large amount of logs during runtime, which reflect the system's operational status. The reliability of system services relies on automatic log anomaly detection. Researchers have proposed anomaly detection methods based on log features and sequence structures. However, existing methods, while utilizing various log features and structured information, still do not exploit the implicit feature information of logs and fail to fully capture the structural information of log sequences when fusing contextual event features of logs. Therefore, we propose an anomaly detection method based on multi-feature graph embedding, LogMFG, which fully extracts explicit, implicit, parameter, and semantic features of logs using a defined multi-feature extraction method for log messages. Log messages are represented as graph nodes with multiple feature attributes. Finally, GTN (Graph Transformer Network) is utilized to effectively fuse multi-feature information of log events with the global structure of log sequences for anomaly detection in log sequences. Experimental results on public datasets HDFS and BGL show that LogMFG outperforms eight log anomaly detection methods, with an anomaly log detection F1 score higher than 0.9992, and surpasses the state-of-the-art LogGT algorithm in offline log anomaly detection. © 2024 IEEE.

关键词： Graph embeddings

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