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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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An Artificial Intelligence-Based Segmentation Auxiliary Diagnostic System for Focal Liver Lesions 2

An Artificial Intelligence-Based Segmentation Auxiliary Diag...

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2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Gu, Jingjun Yu, Zhi Zhang, Wei Hu, Haoyu Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Hangzhou China Zhejiang University School of Software Technology Hangzhou China

ISBN: (纸本)9798350386639

In this paper, we introduce an artificial intelligence-based system designed for the segmentation and auxiliary diagnosis of focal liver lesions. This system can effectively segment lesions in both singlephase non-contrast CT images and multiphase contrast-enhanced CT images. Additionally, it has the capability to synchronize CT data from Picture Archiving and Communication Systems (PACS) and to visualize the segmentation results. Our AI system aims to meet the demand for rapid screening of liver lesions in primary hospitals and physical examination institutions, while also fulfilling the more precise segmentation needs of provincial and municipal hospitals. © 2024 IEEE.

关键词： Picture archiving and communication systems

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Block Transmission Scheduling Strategy Based on Deadline Priority

Block Transmission Scheduling Strategy Based on Deadline Pri...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Huang, Lei Shi, Huiling Zhang, Wei Yu, Kun Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China College of Computer Jingchu University of Technology Jingmen China

ISBN: (纸本)9798350327380

Emerging network applications, such as virtual reality technology, autonomous driving, and large-scale Internet of Things, usually require a significant amount of bandwidth and have high requirements for data delivery time. However, the existing Quick UDP Internet Connection (QUIC), TCP, and other protocols cannot fully meet these needs. A scheduling algorithm based on a time-sensitive protocol can meet the above requirements at the transport layer and reduce the development difficulty of the application layer. Therefore, this paper is based on Deadline-aware Transport Protocol (DTP) and implemented in a Python 3.0 environment. It focuses on the scenario where video data blocks are sent to the transport layer. In this paper, we study the decision-making method and congestion control method for data block priority and deadline, and optimize the delay-sensitive transmission protocol. We propose a scheduling algorithm to meet the service's delay demands and enhance the overall service quality. Additionally, we introduce an improved scheduling scheme for network scenarios involving multiple senders or receivers. Our research aims to effectively address the service delay requirements under different network conditions. © 2023 IEEE.

关键词： Scheduling algorithms

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Time series classification based on Unet with 2D transformation 12

Time series classification based on Unet with 2D transformat...

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12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Du, Mingsen Wang, Yunxia Wei, Yanxuan Wu, Xiaona Ji, Cun Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9798331510633

Time series classification has gained significant attention in the data mining field in recent years. Despite the proposal of numerous methods over the past decades, most of these approaches focus on feature extraction in one-dimensional space, which often results in lower classification accuracy. To address this issue, this paper proposes a Time Series Classification method based on U-Net with 2D transformation (TSC-Unet). This method first transforms univariate time series into 2D space and augments the data, effectively enhancing the diversity of the dataset. Subsequently, features are extracted from the 2D space using a fully convolutional network, fully exploiting multi-scale feature information. U-Netwith skip connections, effectively retains and transmits high-resolution features, significantly reducing information loss and improving segmentation performance. Additionally, the ability to learn multi-scale features enables the model to capture details at different levels, thereby enhancing its understanding of complex data. Experimental results demonstrate that TSC-Unet achieves high classification accuracy on the UCR datasets, validating the effectiveness and superiority of the proposed method. © 2024 IEEE.

关键词： Time series

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Non-symmetrical Sibling-Stream Network with Adaptive Positional Encoding for Automatic Medical Report Generation 5

Non-symmetrical Sibling-Stream Network with Adaptive Positio...

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5th International Conference on Intelligent Medicine and Image Processing, IMIP 2023

作者： Si, Jiayu Zhao, Haifeng Huang, Lili Tu, Zhengzheng Anhui University School of Computer Science and Technology Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei Anhui China

ISBN: (纸本)9798350337396

Automatic medical report generation technology aims to alleviate the workload of radiologists in writing reports and avoid possible misdiagnosis and missed diagnosis. Recent solutions in medical report generation are mainly based on two mainstream architectures: convolutional neural networks (CNNs) and transformer. However, these two architectures are insufficient for the medical report generation task due to the following three reasons. First, CNN lacks the global context information due to the constrained receptive field of convolution. Second, the complete image is cut into small patches before being fed to the transformer, which leads to the damage to the local details. Third, position encoding is critical to compensate the lost position information caused by serialized input, but the conventional position encoding (e.g., absolute positional encoding) inadequately encodes position information. Therefore, we propose a novel Non-Symmetrical Sibling-Stream Network with Adaptive Positional Encoding which includes three modules: sibling-stream encoder (SSE) module, feature interaction (FI) module, and memory-driven decoder (MDD) module. In detail, in order to cope with respective drawbacks of both architectures, we first propose sibling-stream encoder (SSE) module which combines the global context by transformer and local details (e.g., texture and edge) by CNN. Concurrently, we design a new encoding method named adaptive positional encoding (APE), which can effectively encode the absolute position information within the patch and the relative position information between patches, so as to improve the accuracy of medical report. Then, the feature interaction (FI) module is designed to enhance the image feature representation by interacting the information of sibling-stream. Finally, we introduce a memory-driven decoder to implicitly model and memorize similar patterns in different medical reports during the generation process. Extensive experiments on two public data

关键词： Textures

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BEW-YOLO: An Improved Method for PCB Defect Detection Based on YOLOv7 29

BEW-YOLO: An Improved Method for PCB Defect Detection Based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Zhiyao Li, Aimin Zhang, Yuechen Kong, Xiaotong Li, Wenqiang 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350330717

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models. © 2023 IEEE.

关键词： attention mechanism defect detection PCB YOLOv7

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Single-shot phase retrieval for randomly fluctuated and obstructed vortex beams

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science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 72-80页

作者： Hao Zhang Jun’an Zhu Xingyuan Lu Zhiquan Hu Jianbo Gao Kun Liu Qiwen Zhan Yangjian Cai Chengliang Zhao School of Physical Science and Technology Soochow UniversitySuzhou 215006China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shandong Provincial Engineering and Technical Center of Light Manipulations Shandong Provincial Key Laboratory of Optics and Photonic DeviceSchool of Physics and ElectronicsShandong Normal UniversityJinan 250358China Joint Research Center of Light Manipulation Science and Photonic Integrated Chip of East China Normal University and Shandong Normal University East China Normal UniversityShanghai 200241China

Vortex beams with orbital angular momentum play a crucial role in increasing the information capacity in optical *** magnitude of orbital angular momentum determines the ability of information *** practice,a vortex beam can encounter random objects or turbulence during free-space propagation,resulting in information ***,accurately measuring the orbital angular momentum of a randomly fluctuated and obstructed vortex beam is a considerable ***,we propose a single-shot method for the phase retrieval of a randomly fluctuated and obstructed vortex beam by combining the phase-shift theorem and self-reference *** results reveal that the sign and magnitude of the initial orbital angular momentum can be simultaneously determined based on their quantitative relation with the number of coherence singularities on the observation plane,thus addressing the effects of random occlusion and atmospheric *** proposed method considerably improved the accurate decoding of orbital angular momentum information in nonideal freespace optical communications.

关键词： optical vortex phase retrieve orbital angular momentum measurement antiturbulence antidisturbance

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Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

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computers, Materials & Continua 2023年第7期76卷 175-194页

作者： Huaxin Zhou Ziying Fang Yilin Wang Mengjun Tong School of Mathematics and Computer Science Zhejiang A&F UniversityHangzhou311300China Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research Hangzhou311300China School of Humanities and Law Zhejiang A&F UniversityHangzhou311300China Department of Computer Science and Engineering Lehigh UniversityPA18015USA

Plant diseases have become a challenging threat in the agricultural *** learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases ***,deep learning entails extensive data for training,and it may be challenging to collect plant *** though plant datasets can be collected,they may be uneven in *** a result,the problem of classification model overfitting *** study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available ***,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image ***,a gradient penalty mechanism was added to the loss function to improve the training stability of the *** show that the proposed method can best improve the recognition accuracy of the classifier with the doubled *** AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the ***,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original ***,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this *** general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.

关键词： Deep learning ACGAN CA gradient penalty tomato diseases identification

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Enrich Code Search Query Semantics with Raw Descriptions 19th

Enrich Code Search Query Semantics with Raw Descriptions

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Liu, Xiangzheng Liu, Jianxun Hu, Haize Liu, Yi School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9783031545207

Code search can recommend relevant source code according to the development intention (query statement) of the demander, thereby improving the efficiency of software development. In the research of deep code search model, code description is used to replace query sentences for training. However, the heterogeneity existing between the query statement and the code description will seriously affect the accuracy of the code search model. In order to make up for the shortcomings of code search, this paper proposes a sentence-integrated query expansion method—SIQE. Unlike previous query expansion methods that focus on word-level expansion, SIQE uses the entire code description fragment as the source of query expansion. And by learning the mapping relationship between the query statement and the code description, the heterogeneity problem between them is compensated. In order to verify the effect of the proposed model in code search tasks, the article conducts code search experiments and analyzes on two languages: python and java. Experimental results show that, compared with the baseline model, SIQE has higher code search results. Therefore, the SIQE model can effectively improve the search effect of query statements, improve the accuracy of code search, and further improve the development of software engineering. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Deep learning

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3D-CA Net: A 3D-CNN with composite multidimensional attention for the assessment of lung aging 3

3D-CA Net: A 3D-CNN with composite multidimensional attentio...

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2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024

作者： Tian, Tangbin Pan, Jingshan Ge, Jing Li, Na Yu, Dexin Zuo, Liping Hu, Tao Liu, Shengyu 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 Department of Radiology Qilu Hospital of Shandong University Jinan China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

As the population ages, early diagnosis and treatment of lung diseases become increasingly important. Accurate assessment of aging-related changes in lung CT images is crucial for the prevention and treatment of related diseases. Traditional methods for lung aging assessment from CT images are time-consuming, subjective, and heavily reliant on the clinical experience of doctors. To address these issues, this paper proposes a lung aging assessment method with 3D-CA Net. The feature extraction part of the proposed network consists of four main 3D Convolutional and Composite Multidimensional Attention Modules. By introducing the Composite Multidimensional Attention Module, the advantages of spatial attention and self-attention are both utilized. Additionally, an improved E-cross-entropy loss function is employed to reduce overfitting and enhance generalization. Experimental results demonstrate that the 3D-CA Net significantly outperforms existing methods in terms of accuracy, macro-averaged precision, macro-averaged recall and macro-averaged F1 score. This work provides a comprehensive solution for lung CT image aging assessment and offers insights for future advancements in medical imaging analysis. © 2024 SPIE.

关键词： Lung cancer

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