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2022 International Conference on Algorithms, Microchips and Network applications

作者： Hu, Xiaohong Miao, Xianghua Yuan, Meiyu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9781510653290

In the increasingly complex network environment, various attacks emerge one after another. Being able to accurately detect elephant flow and mouse flow plays an extremely important role in defending against large-scale network attacks. Aiming at some shortcomings of current methods for detecting elephant flow and mouse flow, this paper proposes a detection method based on information entropy and improved random forest. After preprocessing the data, first calculate the data feature score with information entropy, screen out the truly valuable features according to the score, then put them into the improved random forest classifier, and finally get the detection results. In this paper, the grid search algorithm is used to optimize the tree and depth of random forest tree, so that the detection results can be obtained quickly and accurately. Experiments show that due to the significant difference between the data characteristics of elephant flow and mouse flow, this method can effectively identify elephant flow and mouse flow. The accuracy rate is 97.93%, the precision rate is 99.99%, the recall rate is 97.91%, and the F1-score is 98.94%, which is improved compared with other algorithms. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Complex networks

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MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTURAL MOTIFS 11

MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTUR...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhang, Zaixi Min, Yaosen Zheng, Shuxin Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Institute of Interdisciplinary Information Sciences Tsinghua University China Microsoft Research United States

Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric deep learning have made great progress in drug design, existing works either sample in the 2D graph space or fail to generate valid molecules with realistic substructures. To tackle these problems, we propose a Fragment-based LigAnd Generation framework (FLAG), to generate 3D molecules with valid and realistic substructures fragment-by-fragment. In FLAG, a motif vocabulary is constructed by extracting common molecular fragments (i.e., motif) in the dataset. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information. Then, our model selects the focal motif, predicts the next motif type, and attaches the new motif. The bond lengths/angles can be quickly and accurately determined by cheminformatics tools. Finally, the molecular geometry is further adjusted according to the predicted rotation angle and the structure refinement. Our model not only achieves competitive performances on conventional metrics such as binding affinity, QED, and SA, but also outperforms baselines by a large margin in generating molecules with realistic substructures. Our code is publicly available at https://***/zaixizhang/FLAG. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Molecules

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Development of China's spaceborne SAR satellite,processing strategy,and application:take Gaofen-3 series as an example

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地球空间信息科学学报（英文版） 2024年第1期27卷 221-236页

作者： Guo Zhang Shunyao Wang Zhenwei Chen Yuzhi Zheng Ruishan Zhao Taoyang Wang Yu Zhu Xinzhe Yuan Wei Wu Weitao Chen State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Law Wuhan UniversityWuhanChina School of Geomatics Liaoning Technical UniversityFuxinChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Institute of Spacecraft System Engineering China Academy of Space TechnologyBeijingChina National Disaster Reduction Center of China Ministry of Emergency ManagementBeijingChina School of Computer Science China University of GeosciencesWuhanChina Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources of the People's Republic of ChinaBeijingChina

After mastering the key technologies of manufacturing spaceborne Synthetic Aperture Radar(SAR),China's SAR satellites have been successfully launched into *** the only civil microwave satellite listed in the"National High-resolution Earth Observation System Major Project,"the Gaofen-3(GF-3)01 satellite is the first C-band multi-polarization SAR satellite with a resolution of 1 ***-3 series satellites stand out among civil SAR satellites worldwide because of their high resolution,wide swath,high geometric radiation quality,multiple imaging modes,and long operation *** GF-3 series satellites as an example,this study introduces the development of China's civil SAR satellites,as well as their processing strategies and *** success of the GF-3 series satellites shows that China's SAR remote sensing technology has stepped into a new era of high-quality and high-precision Earth observations.

关键词： High-resolution Synthetic Aperture Radar(SAR) GaoFen-3(GF-3)satellites earth observation

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IIESC-Net: Incorporating Implicit and Explicit Structural Constraints for Hip Joint Landmark Detection in pelvic X-ray

IIESC-Net: Incorporating Implicit and Explicit Structural Co...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Jing Guo, Lijun Ni, Lixin He, Xiuchao Wang, Jianhua Zhang, Rong Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications. © 2023 IEEE.

关键词： Global Features Hip joint Hip Morphology-Aware Loss Landmark Detection Local Features

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Semi-Supervised Gland Segmentation via Feature-Enhanced Contrastive Learning and Dual-Consistency Strategy

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4314-4324页

作者： Yu, Jiejiang Li, Bingbing Pan, Xipeng Shi, Zhenwei Wang, Huadeng Lan, Rushi Luo, Xiaonan Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Ganzhou Municipal Hospital Department of Pathology Ganzhou341000 China Southern Medical University Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China

In the field of gland segmentation in histopathology, deep-learning methods have made significant progress. However, most existing methods not only require a large amount of high-quality annotated data but also tend to confuse the internal of the gland with the background. To address this challenge, we propose a new semi-supervised method named DCCL-Seg for gland segmentation, which follows the teacher-student framework. Our approach can be divided into follows steps. First, we design a contrastive learning module to improve the ability of the student model's feature extractor to distinguish between gland and background features. Then, we introduce a Signed Distance Field (SDF) prediction task and employ dual-consistency strategy (across tasks and models) to better reinforce the learning of gland internal. Next, we proposed a pseudo label filtering and reweighting mechanism, which filters and reweights the pseudo labels generated by the teacher model based on confidence. However, even after reweighting, the pseudo labels may still be influenced by unreliable pixels. Finally, we further designed an assistant predictor to learn the reweighted pseudo labels, which do not interfere with the student model's predictor and ensure the reliability of the student model's predictions. Experimental results on the publicly available GlaS and CRAG datasets demonstrate that our method outperforms other semi-supervised medical image segmentation methods. © 2013 IEEE.

关键词： Contrastive Learning

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A Dual-View Fusion Network for Automatic Spinal keypoint Detection in Biplane X-ray Images

A Dual-View Fusion Network for Automatic Spinal Keypoint Det...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhou, Dandan Guo, Lijun Zhang, Rong He, Xiuchao Wang, Qiang Wang, Jianhua Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

Accurate keypoint detection in medical images of the spine is critical for the assessment, diagnosis, treatment planning, and clinical investigation of spinal deformities. However, due to severe occlusions of spinal structures in lateral X-ray images, accurate keypoint detection can be hardly achieved in lateral X-ray images based on single-view information. Thus, methods based on both the anterior-posterior (AP) and lateral (LAT) X-ray image views have been proposed to alleviate occlusion problems and achieve better keypoint detection performance. Although some progress has been made with these dual-view methods, they do not effectively exploit a priori knowledge of the spine and hence cannot adequately account for the structural correlation of the vertebrae across views. In this paper, a new dual-view fusion network (DVFNet) framework is proposed for keypoint detection in spinal X-ray images. This framework obtains structural correlations between AP and LAT views of the spine based on a priori spine knowledge represented by high-level semantic features. Meanwhile, the proposed framework combines local and global features extracted respectively by a local subnetwork and a global subnetwork. On the one hand, the local subnetwork is constructed as an enhanced codec structure based on both the AP and LAT views. This subnetwork is trained to output local features that contain both joint semantic features of the two views and independent fine-grained features of each individual view. This scheme leads to accurate keypoint estimation locally. On the other hand, the global subnetwork utilizes a self-attention mechanism to extract view-specific global features based on either the AP view or the LAT view in order to eliminate ambiguity, and reduce confusion on keypoint locations. Further, we propose a weighted feature fusion (WFF) module for adaptive fusion of the local and global features. We evaluated the DVFNet model on a private dataset and found that our proposed metho

关键词： biplane X-ray images dual-view feature fusion spinal keypoint detection

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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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A New Adaptive Image Sharpening Scheme 13

A New Adaptive Image Sharpening Scheme

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Chen, Tzong-Jer Lu, Rong-Hui School of Mathematics & Computer Science Wuyi University Fujian Wuyishan354300 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry China Wuyi University Information Technology and Laboratory Management Center Fujian Wuyishan354300 China

ISBN: (纸本)9798350319156

The sharpening is a scheme applied to highlight the intensity transitions in an image. This enhancement increases edge acutance and significantly improves the overall sharpness of processed images. Image sharpening is also of enormous importance in medicine for diagnosis. Noise overshoot and over-sharpening effects occur in traditional sharpening algorithms. The adaptive approach skips the noise producing a better sharpening result. Abrupt pixel value changes, if adequately emphasized, can be utilized effectively in the sharpening process. Since a residual image contains noise with edges, the bigger the filter size the greater the content of edges left in the residual image. Two residual images are produced first with different filter sizes and assumed a relationship between two residuals. A simple linear regression is measured using these residuals. This linear equation is an expectation and the deviation is the difference between two residuals. The window may contain edges if the residual value measured by larger size filter to some degree higher than that by smaller size filter. One standard deviation (±σ) is proposed as the threshold, in this work. A sensitive sharpening filter is adaptively then applied to those locations in image. It has been demonstrated that the proposed approach yields better results than the global sharpening filter on the measurements of Pratt's P indices. A sharpening scheme without latent image noise amplification is useful for pattern recognition and machine-learning. In the future this scheme will be used to preprocess image segmentation and medical image. © 2023 IEEE.

关键词： Image segmentation

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TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction 5th

TGNN: A GNN-Based Method with Multi-entity Node for Person...

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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Mo, Zhikang Shao, Qixiang Wu, Likang Yu, Runlong Liu, Qi Xu, Jiexin Song, Hongmei Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Computer Science and Techonology University of Science and Technology of China Hefei China Department of Information Technology China Merchant Bank Shenzhen China

ISBN: (纸本)9789811989148

The offline business of banks is deeply involved in bank clients’ financial lives and directly affects the satisfaction of clients. For offline banking scenarios, accurate prediction of clients’ banking time is essential for the service system of the banks, which can be used for multiple downstream tasks, such as queuing time prediction, real-time queuing planning, etc. However, the problem of banking time prediction remains open due to many technical and domain challenges, such as Anonymous Client Problem (ACP) and Sparse Interaction Problem (SIP). The existing works mainly utilize the traditional machine learning method and are relatively plain in modeling this practical scenario thus leading to an unpromising performance. Different from existing works, we predict the personal banking time by modeling the clients and banking services in the banking process through mining a large number of banking records. In this paper, we propose a Tuple-node-based Graph Neural Network (TGNN) framework to learn representations of clients and services of banks and estimate the personal banking time based on the representations. Empirical studies on two real-world datasets collected from a fintech bank demonstrate the effectiveness of TGNN. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Residual and Attention Mechanism-Based Network for Aluminum-Silicon Alloy Microstructure Image Classification 11

Residual and Attention Mechanism-Based Network for Aluminum-...

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11th International Conference on Information Systems and Computing technology, ISCTech 2023

作者： Yang, Xiao Shen, Tao Yin, Jiancheng Liu, Yingli Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Materials Science and Engineering Kunming650093 China

ISBN: (纸本)9798350342406

To enhance the accuracy of aluminum-silicon alloy microstructure image classification, we propose an aluminum-silicon alloy microstructure image classification model based on an improved residual network, incorporating attention mechanisms and multi-scale thinking. The model is built upon the ResNet18 architecture, utilizing enhanced basic residual blocks to select informative features. Additionally, a multi-scale attention module is incorporated into the network to fuse multi-scale features and further extract effective features. Experimental results on an aluminum-silicon alloy microstructure image classification dataset demonstrate that compared to the best-performing model currently available, the proposed model achieves an increase in classification accuracy, precision, recall, and F1 score by 3.17%, 0.74%, 5.53%, and 3.26%, respectively, reaching 94.97%, 96.39%, 93.97%, and 95.17%. These results indicate that the proposed model exhibits strong capabilities in classifying aluminum-silicon alloy microstructure images and can facilitate further research on the properties of aluminum-silicon alloys. © 2023 IEEE.

关键词： Image classification

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