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International Joint Conference on Neural Networks (IJCNN)

作者： Wenyue Hu Zhenyu Yang Yan Huang Zhibo Zhang Baojie Xu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The objective of sequential recommendation is to predict user preferences for items based on historical interaction sequences. This process often leads to a phenomenon known as popularity bias, where popular items are excessively recommended. Conformity, the tendency of users to follow popular items, is a significant factor contributing to this issue. Previous methods have not adequately disentangled conformity and interest, failing to accurately model users’ true intent. To address this, we propose a novel Disentangled Interest and Conformity Sequential Recommendation method (DICSRec) to mitigate the popularity bias. Specifically, we first design an Intent Encoding Module (IEM), which includes two independent encoders for conformity and interest to model their representations. To better disentangle these two factors, we design a disentangling task with proxy-based self-supervised learning and orthogonal regularization. Furthermore, to provide the Intent Encoding Module with more global information, we design a Global Conformity-aware Module (GCM), which supplies item popularity information and aids in enhancing user conformity representation. Lastly, recognizing the varying significance of user conformity and interest, we propose an adaptive Fusion Prediction Module (FPM) that adaptively aggregates user conformity and interest representations for final prediction. Experiments on four real-world datasets consistently demonstrate the superiority of our method over advanced sequential recommendation models. Code implementation is available at: https://***/lyra0611/DICSRec.

关键词： Adaptation models Codes Fuses Aggregates Neural networks Self-supervised learning Multitasking

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A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xitong Ma Yuansen Kuang Lin Yuan Cheng Tian Yijie Zeng Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation; 2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily; 3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models.

关键词： knowledge engineering Visualization Terminology Semantics MIMICs Memory management Radiology Feature extraction data models Biomedical imaging

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A Privacy Protection Scheme for Consumer Electronics data Based on Blockchain and AI

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Luping Yan, Chen Wu, Hongjie Zhu, Fa Kumari, Saru Alenazi, Mohammed J.F State Key Laboratory of Blockchain and Data Security Suzhou University of Science and Technology Suzhou215009 China Zhejiang University Hangzhou310027 China Suzhou University of Science and Technology Suzhou215009 China Nanjing Forestry University College of Information Science College of Artificial Intelligence Nanjing210037 China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut250001 India Department of Computer Engineering Riyadh11451 Saudi Arabia

As information technology advances, a significant amount of data related to consumer electronics has emerged, the consumer data is commonly stored on cloud servers, resulting in frequent data leaks and privacy breaches. To tackle these challenges, we propose a novel privacy protection scheme that combines encryption and blockchain technologies. This approach integrates Searchable Encryption, Blockchain, and Artificial Intelligence (AI) to facilitate efficient access control and policy enforcement. By employing a decentralized network structure, our scheme minimizes reliance on a central authority, thereby enhancing system security and reliability. User information is independently stored in both blockchain and cloud systems, with digital signatures ensuring data accuracy. To further bolster user privacy, we implement attribute-based encryption for access management and utilize AI for real-time monitoring of potential security threats and suspicious access patterns. Our scheme also features an attribute revocation mechanism to protect sensitive information from unauthorized access. Additionally, customized smart contracts promote equitable interactions between users and cloud service providers. Lastly, the theoretical analyses and experimental results validate that our solution is both secure and efficient in safeguarding consumer data, highlighting its significant innovative contributions to data privacy protection. © 1975-2011 IEEE.

关键词： Sensitive data

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Tumor State-Space Network for High and Low Grade Glioma Classification

Tumor State-Space Network for High and Low Grade Glioma Clas...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Qijian Chen Lihui Wang Zeyu Deng Li Wang Chen Ye YueMin Zhu Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 University Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Accurately predicting the grade of gliomas is crucial for choosing right treatment plans. While current methods using radiomics and deep learning can predict glioma grades effectively using magnetic resonance imaging (MRI), they overlook dynamic changes in the tumor and its neighboring areas. To address this issue, we propose a tumor state-space network (TSSNet), which fully exploits changes in the tumor region and its surroundings through dynamical updating strategy. The experimental results demonstrated that TSSNet achieves 90.32% accuracy and 93.55% area under the curve (AUC) for the prediction of high-grade and low-grade gliomas, respectively, which are 2.4% and 2.5% higher than the best state-of-the-art deep learning models. Moreover, the proposed state-space module can effectively enhance prediction performance, which is advantageous for personalized glioma treatment.

关键词： Deep learning Accuracy Magnetic resonance imaging Signal processing Predictive models Tumors Radiomics

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Application and development of elastic fibers focusing on composition and structure 6

Application and development of elastic fibers focusing on co...

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2021 6th International Seminar on Advances in Materials Science and engineering, ISAMSE 2021

作者： Zhang, Jin Sun, Qinyue Wang, Yumin Dong, Xiongwei Xiong, Yi Zeng, Wei Department of Pharmacy Zhongnan Hospital Wuhan University Wuhan430071 China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Hubei Wuhan430073 China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Anhui Hefei230601 China School of Physics and Technology MOE Key Laboratory of Artificial Micro- and Nano-Structures Wuhan University Wuhan430072 China

Elastic fabric is comfortable to wear, and has been a consumer textile due to its good shape retention and wrinkle resistance after washing. This paper briefly introduces the types and performance characteristics of several common elastic fibers, and analyzes the relationship between the structure and elasticity of different elastic fibers. Finally, the development trend of elastic fibers is proposed. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Elasticity

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Preparation of Uniform SiO2 Insulating Layer on the Inner Wall of TSV by Thermal Oxidation

Preparation of Uniform SiO2 Insulating Layer on the Inner Wa...

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Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Guo Fengjie Ran Jing Yang Wang Shuo Ma Kui Yang Fa Shun Guizhou Provincial Key Laboratory of Micro and nano Electronics and Software Technology College of Big Data and Information Engineering Guizhou University Guiyang City Guizhou Province China

ISBN: (纸本)9781665471343

SiO 2 insulating layer is an indispensable part of a TSV. In the current process, the SiO 2 insulating layer is commonly deposited on the inner wall of the TSV based on deep trench sputtering method. The thickness at different position (neck, middle, bottom) of the SiO 2 insulating layer, deposited by deep trench sputtering, is non-uniform. In this paper, the thickness uniformity of SiO 2 insulating layer prepared on the inner wall of TSV based on CVD&PVD process and thermal oxidation method is comparatively studied. The experimental results show that, based on the CVD&PVD process, the average thickness of the SiO 2 insulating layer at middle and bottom position of the TSV has changed by - 54.02% and - 58.30% compared with that at the top position, respectively. Based on the thermal oxidation method, the average thickness of the SiO 2 insulating layer at middle and bottom position of the TSV has changed by 1.17% and 0.26% compared with that at the top position, respectively. The thermal oxidation method can realize the SiO 2 insulating layer with uniform thickness on the inner wall of TSV.

关键词： Silicon compounds Semiconductor device manufacture Oxidation Neck Through-silicon vias Sputtering

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Bioinspired Pedot:Pss-Pvdf(Hfp) Flexible Sensor for Machine-Learning-Assisted Multimodal Recognition

SSRN

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SSRN 2024年

作者： Wu, Pingping Shao, Songtao Liu, Junchao Li, Lin Wang, Jingxia School of Chemistry and Chemical Engineering Xi′an University of Architecture and Technology Xi′an710055 China CAS Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing100190 China School of Mathematics Data Science Shaanxi University of Science and Technology Xi'An710021 China School of Sciences Xi’an University of Technology Xi’an710048 China

Multifunctional flexible sensor (E-Skin) plays an important role in artificial intelligence. Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) has been proven to be a promising material for developing flexible sensors due to its high conductivity, ease of processing, biocompatibility, and low cost-effectiveness. However, existing PEDOT:PSS sensors only perceive external stimuli, lacking the intelligent extraction of stimulus information and accurate recognition of multimodal applications. Here, inspired by human skin, a double-layer structure PEDOT:PSS-PVDF(HFP) film (P-P film) is fabricated based on the synchronous induction effect of dimethyl sulfoxide. The P-P film presents excellent conductivity, piezoelectricity, and humidity sensitivity owing to the conformational transition (benzene-quinone) of PEDOT and piezoelectricity enhancement (β-phase) of polyvinylidene fluoride-hexafluoropropylene copolymer (PVDF(HFP)). The bioinspired sensor assembled with P-P film shows multimodal applications, such as surface contact modal for movement monitoring (strain sensing), point contact modal for touch device (pressure sensing), and non-contact modal for self-protection (humidity sensing). The machine learning is further used to intelligently extract/analyze different sensing features, and the sensor achieves a high recognition rate for multimodal applications (97.46%). This work provides a distinctive approach in multifunctional applications of PEDOT:PSS flexible sensors and offers essential research value for the material structures, mechanical properties and application platforms of E-Skin. © 2024, The Authors. All rights reserved.

关键词： Piezoelectricity

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A Collaborative Heterogeneous Graph Neural Network for Personalized News Recommendation

A Collaborative Heterogeneous Graph Neural Network for Perso...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenglong Shi Haibin Geng Wenfeng Jiang Jinbao Li Chao Wu Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models.

关键词： Correlation Message passing Semantics Collaboration Logic gates Graph neural networks Topology History data mining Cybernetics

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lin Lu Qingzhi Zou Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Due to the exceptional performance of Transform-ers in 2 $D$ medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%.

关键词： Image segmentation Solid modeling Visualization Three-dimensional displays Current transformers Multilayer perceptrons Convolutional neural networks Kernel Synapses Biomedical imaging

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An Entity Relation Extraction Framework Based on Large Language Model and Multi-Tasks Iterative Prompt engineering

An Entity Relation Extraction Framework Based on Large Langu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haibin Geng Chenglong Shi Xuesong Jiang Zan Kong Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Document-level entity relation extraction is an important task in the field of natural language processing, which plays an important role in semantic understanding and knowledge graph construction. However, existing deep neural networks and graph neural networks models are limited by their performance and parameters number, which can not capture global semantics and have poor generalization ability. Furthermore, existing methods employing large language model for entity relation extraction do not establish good relationships among multi-tasks of entity relation extraction task, resulting in more information can not be effectively shared and transmitted between tasks. In addition, previous approaches can not effectively eliminate false entities and relationships. To solve these problems, we propose an entity relation extraction framework based on large language model and multi-tasks iterative prompt engineering. In our model, we design an iterative prompt engineering, which can better establish the relationship among multi-tasks, and ensure every task to obtain the optimal results. Moreover, we design semantic merging, group disambiguation and self-verification modules to eliminate the false entity relations and noise nodes. Additionally, we design summary prompts to provide sufficient global semantics for better text segmentation. Finally, we evaluated our model on wikiann, wikineural, ACE2005, CoNLL2003, CoNLL2004, and SciERC datasets and compared it with other baseline models.

关键词： Large language models Semantics Noise Merging knowledge graphs Multitasking Natural language processing Iterative methods Prompt engineering data mining

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