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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zihe Jia Peng Xue Zhiqiang Dai Qian Gao Xiaomeng Zhang Key Laboratory of Computing Power Network and Information Security 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

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users’ long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data; secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier; finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved.

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Medical Image Segmentation Use Convolutional Attention Augmentation TransUNet with Skip Connection Enhancement

Medical Image Segmentation Use Convolutional Attention Augme...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qingzhi Zou Jing Zhao Ming Li Ling Chen Lin Yuan Ronghuan Zhang Yushuai Hu Key Laboratory of Computing Power Network and Information Security 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The primary objective of medical image segmentation is the isolation of pathological tissues and distinct organs from medical images, thereby assisting in medical diagnosis. The methods employed for medical image segmentation encompass convolutional neural networks and transformer-based methods. Although the self-attention mechanism in Transformers improves its capability to capture long-range dependencies, it have limitations in learning local (contextual) relationships between pixels. Some previous research has tried to solve this problem by incorporating convolutional layers into the encoder or decoder of transformers, but sometimes feature inconsistencies arise. To better extract local features from images using convolutional neural networks and to fuse low-resolution and high-resolution features from higher-level and lower features, we propose the Convolutional Attention Augmentation TransUNet (CAA-TransUNet) model. In our model, firstly, we propose a convolutional attention augmentation module that enhances both local and global features by suppressing irrelevant background information. Secondly, we have integrated attention gates into the skip connections to aggregate feature information from various stages of the encoder during the upsampling process. Finally, we employ the technique of aggregating the loss of multi-stage features to expedite convergence speed and enhance performance. The experimental results on three public datasets demonstrate that our proposed model significantly outperforms the baseline methods.

关键词： Image segmentation Pathology Computational modeling Aggregates Logic gates Feature extraction Transformers

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou Key Laboratory of Computing Power Network and Information Security 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity.

关键词： Accuracy Computational modeling Memory management Neural networks Feature extraction Transformers Sensors data mining Computational complexity Remote sensing

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ran Qiu Shengrong Zhao Hu Liang Key Laboratory of Computing Power Network and Information Security 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

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images.

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High-spatial-resolution light field 3D perception based on unidirectional object motion

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Optics letters 2025年第11期50卷 3672-3675页

作者： Dongyang Wang Zhijun Zhang Jianpang Zhai Yan Kang Qingyang Wu College of Applied Sciences Shenzhen University Shenzhen 518060 China Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen 518060 China College of Big Data and Internet Shenzhen Technology University Shenzhen 518060 China MSI (Shenzhen) Technology Co. Ltd. Anbo Technology Park 2 2 Baolong 4th Road Shenzhen 518060 China College of New Materials and New Energies Shenzhen Technology University Shenzhen 518060 China College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen 518060 China

Light field cameras can capture comprehensive light information within a scene. Their core architecture incorporates a micro-lens array (MLA) positioned in front of the imaging sensor, mimicking the compound eye structure of insects. By processing the acquired data, these cameras enable the reconstruction of 3D spatial information. Despite their numerous advantages, light field cameras suffer from a critical drawback: low spatial resolution. To address this limitation, this study proposes a high-spatial-resolution light field imaging approach based on unidirectional object motion (UOMLF). A non-focused light field camera equipped with a hexagonally arranged MLA was developed as the foundation for a unidirectional object motion platform. Additionally, a 3D reconstruction algorithm tailored to high-resolution light field imaging was designed to complement this system. Experimental evaluations were conducted using the USAF resolution test chart and detector chip leads. Results demonstrate that the proposed method significantly enhances xy-plane resolution (approximately four times improvement) while maintaining -axis resolution. This high-spatial-resolution light field technique based on unidirectional object motion offers broad potential for applications in both industrial and scientific fields.

关键词： Imaging systems Optical imaging Plenoptic imaging Spatial resolution Systems design Three dimensional reconstruction

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SCTAR: A Multi-Layer BiLSTM-Based Chinese Short Text Similarity Computation Model with Attention Mechanism

SCTAR: A Multi-Layer BiLSTM-Based Chinese Short Text Similar...

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International Conference on Computer Supported Cooperative Work in Design

作者： Shuo Zhao Yiyun Xing Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security 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 Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Text semantic similarity is a crucial research area in Natural Language Processing (NLP). However, traditional methods for calculating the similarity of short Chinese texts often fall short in accuracy and other crucial aspects. The Bidirectional Long Short-Term Memory Network (BiLSTM) has demonstrated remarkable performance in computing the similarity of short Chinese texts by effectively capturing long-range dependencies and semantic information. Additionally, the multi-head attention mechanism considers the interaction between different text locations and semantic information, enhancing the model’s representative capacity. Building upon this foundation, our paper proposes an enhanced model known as SCTbilstmAttRdrop (SCTAR). This model is constructed based on a multilayer BiLSTM architecture and incorporates an SE-gated convolutional module and a convolutional multi-head attention-aware model. We conducted extensive evaluations using two Chinese short text datasets, Chinese-SNLI and CCKS2018_Task3. The experimental results unequivocally demonstrate that our SCTAR model surpasses other common methods in terms of accuracy, precision, recall, and F1 scores when tasked with computing the similarity of Chinese short texts.

关键词： Accuracy Attention mechanisms Computational modeling Semantics Bidirectional control Feature extraction Nonhomogeneous media

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A Novel Text Matching Model Based on Multilayer Coding and Feature Enhancement

A Novel Text Matching Model Based on Multilayer Coding and F...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security 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 Shandong Branch of China Mobile Communication Group Design Institute Co Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the rapid development of Natural Language Processing (NLP), text matching has become the basis of many downstream tasks in NLP, and the study of text matching is of great research significance for solving tasks such as question and answer and information retrieval in NLP. Most of the current text matching methods tend to have problems such as mismatch of grammatical structures and insufficient interaction information in sentences. In order to solve the problems of insufficient interaction information and lack of features and ability to capture keyword and sequence information in text matching, this paper proposes a text matching method based on multi-layer coding and soft attention mechanism. The method first embeds the text and sends it to a gating module for processing, then sends the processed result to a module containing a combination of multilayer coding and soft attention mechanism for further operations such as multiple alignment, and finally sends it to a classifier containing a three-layer fully-connected network for predicting whether the input text pairs match or not. The two modules proposed in this paper are practically feasible, and comparison and ablation experiments have been conducted on the publicly available datasets LCQMC dataset and BQ dataset, and the experimental results show that the two modules improve the accuracy of text matching by 2.44% and 1.02%, respectively.

关键词： Attention mechanisms Accuracy Computational modeling Impedance matching Nonhomogeneous media Information retrieval Encoding

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Selenium nanoparticles alleviates cadmium induced hepatotoxicity by inhibiting ferroptosis and oxidative stress in vivo and in vitro

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Chemosphere 2024年 364卷 143004页

作者： Shao, Chunyan Luo, Tongwang Wang, Shujie Li, Zhuoyue Yu, Xiaoqiang Wu, Ya Jiang, Sheng Zhou, Bin Song, Quanjiang Song, Shengzhe Wang, Xiaodu Song, Houhui Shao, Chunyan Luo, Tongwang Wang, Shujie Li, Zhuoyue Yu, Xiaoqiang Wu, Ya Jiang, Sheng Zhou, Bin Song, Quanjiang Song, Shengzhe Wang, Xiaodu Song, Houhui Shao, Chunyan Luo, Tongwang Wang, Shujie Li, Zhuoyue Yu, Xiaoqiang Wu, Ya Jiang, Sheng Zhou, Bin Song, Quanjiang Song, Shengzhe Wang, Xiaodu Song, Houhui Shao, Chunyan Luo, Tongwang Wang, Shujie Li, Zhuoyue Yu, Xiaoqiang Wu, Ya Jiang, Sheng Zhou, Bin Song, Quanjiang Song, Shengzhe Wang, Xiaodu Song, Houhui Shao, Chunyan Luo, Tongwang Wang, Shujie Li, Zhuoyue Yu, Xiaoqiang Wu, Ya Jiang, Sheng Zhou, Bin Song, Quanjiang Song, Shengzhe Wang, Xiaodu Song, Houhui College of Veterinary Medicine Zhejiang A&F University Zhejiang Hangzhou311300 China Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Hangzhou311300 China Zhejiang Provincial Engineering Research Center for Animal Health Diagnostics & Advanced Technology Zhejiang Hangzhou311300 China Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management Zhejiang Hangzhou311300 China China-Australia Joint Laboratory for Animal Health Big Data Analytics Zhejiang Hangzhou311300 China

Cadmium (Cd) is an important environmental toxicant that could cause serious damage to various organs including severe hepatotoxicity in intoxicated animals. Selenium has been reported to possess the protective effects against Cd toxicity, but the specific mechanism is still unclear. The purpose of this study was to explore the effects and mechanism of chitosan coated selenium nanoparticles (CS-SeNPs) against Cd-induced hepatotoxicity in animal and cellular models. ICR mice and rat hepatocyte BRL-3A cells were exposed to cadmium chloride (CdCl2) to evaluate the therapeutic efficiency of CS-SeNPs. Analysis of histopathological images, mitochondrial membrane potential (MMP) and ultramicrostructure, serum liver enzyme activities, ferroptosis-related indicators contents, and further molecular biology experiments were performed to investigate the underlying mechanisms. In vivo experiment results showed that CdCl2 caused significant pathological damage involving significant increase of liver index, contents of tissue MDA and serum ALT and AST, and significant decrease of serum GSH-Px activity. Moreover, CdCl2 exposure upregulated ACSL4 and HO-1 protein levels, downregulated GPX4, TfR1, ferritin protein levels in the liver. Notably, CS-SeNPs increased the expression level of GPX4 and ameliorated CdCl2-induced changes in above-mentioned indicators. In vitro experimental results showed that treatment with CS-SeNPs significantly elevated GSH-Px activity and GPX4 protein level, reversed CdCl2-induced expression of several ferroptosis-related proteins TfR1, FTH1 and HO-1, and repressed ROS production and increased MMP of the cells exposed to CdCl2. Our research indicated that CdCl2 induced hepatocyte injury by inducing ferroptosis, while CS-SeNPs can inhibit ferroptosis and reduce the degree of hepatocyte injury. This study is of great significance for further revealing the mechanism of Cd hepatotoxicity and expanding the clinical application of SeNPs. © 2024

关键词： Chlorine compounds

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Black Box Watermarking for DNN Model Integrity Detection Using Label Loss

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Journal of Computers (Taiwan) 2024年第4期35卷 277-290页

作者： Song, Yunfei Zhu, Yujia Yang, Liu Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang550000 China Guizhou Key Laboratory of Information and Computing Science Guizhou Normal University Guiyang550000 China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang550000 China

After significant investments of time and resources, the accuracy of deep neural network (DNN) models has reached commercially viable levels, leading to their increasing deployment on cloud platforms for commercial services. However, ongoing research indicates that the challenges facing deep neural network models are continually evolving, particularly with various attacks emerging to compromise their integrity. Deep neural networks are susceptible to poisoning attacks and backdoor attacks, both of which involve malicious fine-tuning of the deep models. Malicious fine-tuning can lead to unpredictable outputs from deep neural network models. Although at-tempts have been made to address this issue, these solutions often increase model complexity or diminish model performance. We propose a black-box watermarking technique based on trigger image sets, which can effectively detect malicious fine-tuning while also enabling copyright authentication. This watermarking technique builds upon black-box watermarking methods, leveraging trig-ger image sets and utilizing a two-stage alternating training approach to fine-tune the model. During training, a novel loss function is employed to optimize the trigger images, thereby embedding the watermark while preserving the model’s original classification capabilities. The proposed watermarking model is highly sensitive to malicious fine-tuning, resulting in unstable classification outcomes for trigger images. Ultimately, by inputting trigger image sets and analyzing the output of the watermarking model, the integrity of the deep neural network model can be verified. Experimental results demonstrate the effectiveness of this approach in detecting the integrity of DNN models. © 2024 Codon Publications. All rights reserved.

关键词： Neural network models

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Contamination, biofilm formation, virulence and molecular characterization of multi–drug resistant Staphylococcus aureus isolated from pork under low temperature in Zhejiang Province, China

SSRN

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SSRN 2025年

作者： Zhai, Ruidong Liang, Yilun Lei, Lei Xu, Xinlu Wu, Qin Han, Yue Deng, Simin Xu, Jiali Xia, Jing Yang, Yongchun Cocolin, Luca Simone Rantsiou, Kalliopi Cheng, Changyong Song, Houhui Key Laboratory of Applied Biotechnology on Animal Science & Veterinary Medicine of Zhejiang Province Zhejiang Engineering Research Center for Veterinary Diagnostics & Advanced Technology Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management Belt and Road International Joint Laboratory for One Health and Food Safety China-Australia Joint Laboratory for Animal Health Big Data Analytics College of Veterinary Medicine of Zhejiang Agricultural and Forestry University Zhejiang Province Hangzhou China Department of Agricultural Forest and Food Sciences University of Turin Torino Italy

Staphylococcus aureus is considered to be an increasingly food safety concern. Low temperature is usually used to limit bacterial reproduction on meat. The aim of this study was to investigate the contamination and characterization about S. aureus on pork under low temperature at cold storage warehouses in Zhejiang Province, China. A total of 344 samples were collected to detect S. aureus, and All 52 S. aureus were isolated from 11.6% samples with present in 18 batches from 13 cold storage warehouses, of note, 51.9% isolates was enterotoxigenic, and 71.2% isolates were multi-drug resistant strains with strong biofilm formation, notably, 13 MRSA. Moreover, all 52 isolates were classified into 10 clusters of RAPD with 13 SPA types, especially the new frequently Spa-t20663. And ST398, ST188, ST6 types were ranked among the top three accompanied by close genetic relationships with other food-borne S. aureus in China. In addition, high virulence isolates were observed on Galleria mellonella, and ST188 S. aureus with skin abscesses in rabbits. Findings from this study highlight the contamination, long-term survival, virulence and diverse molecular characteristics of multi-drug resistant S. aureus isolated from frozen pork. Therefore, integrated surveillance and control measures are urgent to mitigate the associated risk. © 2025, The Authors. All rights reserved.

关键词： Meats

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