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Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

computer Systems Science & Engineering 2022年第12期43卷 1069-1084页

作者： Minghu Tang Wei Yu Xiaoming Li Xue Chen Wenjun Wang Zhen Liu key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Qinghai Minzu UniversityXining810007China School of Computer Science Qinghai Minzu UniversityXining810007China College of Intelligence and Computing Tianjin UniversityTianjin300350China School of International Business Zhejiang Yuexiu UniversityShaoxing312069China Law School Tianjin UniversityTianjin300072China Graduate School of Engineering Nagasaki Institute of Applied ScienceNagasaki851-0193Japan

Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future *** the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five rea

关键词： Link prediction cold-start nonnegative matrix factorization graph regularization

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SPECT bone imaging thyroid lesion segmentation

SPECT bone imaging thyroid lesion segmentation

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2021 International Conference on Big Data and Intelligent Algorithms, BDIA 2021

作者： Mingyang-Luo Qiang-Lin Tongtong-Li Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing and Application Northwest Minzu University Lanzhou730124 China

Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83. © Published under licence by IOP Publishing Ltd.

关键词： Nuclear medicine

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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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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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

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.

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Transcriptome Analysis of Female and Male Henosepilachna vigintioctopunctata(Coleoptera:Coccinellidae)Identifies Sex-related Genes

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Horticultural Plant Journal 2021年第2期7卷 142-148页

作者： Wei Guo Zhuoqi Liu Jiaqi Yu Jing Lü Xiaoguo Jiao Lan Kong Chunxiao Yang Huipeng Pan Key Laboratory of Bio-Pesticide Innovation and Application of Guangdong Province Engineering Technology Research Center of Agricultural Pest Biocontrol of Guangdong ProvinceSouth China Agricultural UniversityGuangzhou 510642China State Key Laboratory of Biocatalysis and Enzyme Engineering Center for Behavioral Ecology&EvolutionSchool of Life SciencesHubei UniversityWuhan 430062China Department of Computer Science Eastern Kentucky UniversityRichmond KY 40475USA State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources South China Agricultural UniversityGuangzhou 510642China

Henosepilachna vigintioctopunctata is an emerging horticultural pest of Solanaceae plants in ***,we employed transcriptome sequencing of three female and three male *** libraries to identify sex-related *** recorded 281599294 bp of clean reads,and de novo assembly generated 85206 unigenes,with N_(50) of 1311 bp and average length of 763 *** found 5002 genes highly expressed in males and 2179 genes highly expressed in *** profiles of six unigenes specific to females,six unigenes specific to males,and ten unigenes common to both sexes were verified using semi-quantitive RT-PCR;results showed expression levels of these 22 unigenes were relatively consistent with the transcriptome *** all unigenes as references,we identified 6657 putative simple sequence *** transcriptome sequences and gene expression profiles of female and male *** help explore genetic mechanisms of sexual dimorphism.

关键词： Henosepilachna vigintioctopunctata sex RNA-seq transcriptome annotation differential expression

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X-ray Classification of Tuberculosis Based on Convolutional Networks

X-ray Classification of Tuberculosis Based on Convolutional ...

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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Cao, Kai Zhang, Jingyi Huang, Mengge Deng, Tao Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Key Laboratory of Streaming Data Computing Technologies and Application School of Mathematics and Computer Science Northwest Minzu University Gansu Lanzhou730000 China

ISBN: (纸本)9780738110837

Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis, which can invade many organs, and pulmonary tuberculosis is the most common infection. It is the key to treat tuberculosis to detect and diagnose the disease in the early stage. The existing computer-aided detection system has made preliminary progress in the diagnosis of pulmonary tuberculosis based on chest X-ray, but there is still a lack of further research on the classification of image signs of tuberculosis. In recent years, with the in-depth research and development in the field of deep learning, convolutional networks have emerged. Convolutional networks have achieved the best current results in image recognition, image classification, image segmentation, and other fields. Therefore, this paper applies the convolutional network to tuberculosis CT images and uses different convolutional network models to study the classification of tuberculosis CT images. Experiments show that the DenseNet121 model has higher performance than VGGNet16, VGGNet19, and ResNet152 models. As a result of classification, the accuracy rate is over 90%. © 2021 IEEE.

关键词： Image classification

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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video Datasets

Temporal Transformation Network Based On Scale Sequences for...

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International Conference on computer and Communications (ICCC)

作者： Xiaoke Zhu Bo Zhao Zhiwei Dong Lingyun Dong Danyang Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline.

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Predicted High-Temperature Superconductivity in Rare Earth Hydride ErH2 at Moderate Pressure

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Chinese Physics Letters 2022年第12期39卷 84-88页

作者： Yiding Liu Qiang Fan Jianhui Yang Lili Wang Weibin Zhang Gang Yao College of Mathematics and Physics Leshan Normal UniversityLeshan 614004China Institute of Atomic and Molecular Physics Sichuan UniversityChengdu 610065China School of New Energy Materials and Chemistry Leshan Normal UniversityLeshan 614004China Institute of Computer Application China Academy of Engineering PhysicsMianyang 621900China College of Physics and Electronics Information Yunnan Key Laboratory of Optoelectronic Information TechnologyYunnan Normal UniversityKunming 650500China School of Physical Science and Technology Southwest UniversityChongqing 400715China Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghai 200240China

Hydrides offer an opportunity to study high critical temperature(high-Tc)superconductivity at experimentally achievable ***,the pressure needed remains extremely *** density functional theory calculations,herein we demonstrate that a new rare earth hydride ErH2could be superconducting with Tc~80 K at 14.5 GPa,the lowest reported value for compressed hydrides to ***,due to Kondo destruction,superconductivity was prone to exist at 15 *** also reveal an energy gap at 20 GPa on the background of normal metallic *** 20 GPa,this compressed system could act as a host of superconductor judged from a sharp jump of spontaneous magnetic susceptibility with an evanescent spin density of state at Fermi ***,electron pairing glue for ErH2at these three typical pressures was attributed to the antiferromagnetic spin fluctuation.

关键词： Rare Kondo superconductor

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Oriented Object Detection in Aerial Images with Double Midpoint Vectors

Oriented Object Detection in Aerial Images with Double Midpo...

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International Conference on computer and Communications (ICCC)

作者： Xiaoke Zhu Qi Hu Xiaopan Chen Caihong Yuan Minghao Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

In recent years, oriented object detection technology has made significant progress. Most oriented object detectors represent oriented bounding boxes by adding additional angle information to the horizontal bounding box representation, which leads to serious angle periodicity issues. To address these issues, this paper proposes double midpoint vectors (DMvectors) to represent oriented objects. For any oriented bounding box, we align the center point with the origin in the Cartesian coordinate system, and evenly distribute the midpoints of the box edges across the four quadrants. We use the midpoint of the first and second quadrants, as well as the width and height of the box, to represent the oriented bounding box. DMvectors solve the periodic problem of angles by implicitly including angle information in vectors. To demonstrate the effectiveness of our proposed method, we conducted extensive experiments on two common remote sensing datasets (DOTA and HRSC2016), and our method achieved competitive results.

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FEMD: Feature Enhancement-aided Multimodal Feature Fusion Approach for Smart Contract Vulnerability Detection

FEMD: Feature Enhancement-aided Multimodal Feature Fusion Ap...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Juan Li Rui Li Youshui Lu Bowen Cai Yulin Cao Hang Lin Xingxing Li Chan Li School of Computer Science Qinghai Normal University Xining China School of Computer Science and Technology Xidian University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Qinghai Normal University Xian China School of the Instrument Science and Technology and School of Electrical Engineering Xi’an Jiaotong University Xian China School of Science and Engineering University of Minnesota - Twin City Minneapolis USA

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model’s feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness.

关键词： Economics Automation Accuracy Limiting Smart contracts Buildings Focusing Feature extraction Robustness Data mining

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TMFIF:Transformer-based multi-focus image fusion

TMFIF:Transformer-based multi-focus image fusion

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computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Rui Li Shengling Geng Dan Zhang Mingquan Zhou School of Computer Science Qinghai Normal University Xining China School of Computer and Software Nanyang Institute of Technology Nanyang China Academy of Plateau Science and Sustainability People’s Government of Qinghai Province & Beijing Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Multi-focus image fusion is a hot topic in the field of image processing, and it is a fundamental problem in the fields of image editing, image synthesis, and target retrieval. In previous fusion methods, although feature-rich datasets, models, and algorithms have been provided, there are still many problems with the effective fusion of distant and near views in complex backgrounds. To solve the challenging multi-focus image fusion problem more accurately, we introduce the Transformer Network based on Encoder Decoder (TMFIF), which can extract more generalized features based on the features of multi-focused images to achieve image fusion in complex backgrounds for better visual effects. In this work, we achieve fusion by inputting two images, near and far view. We compare the performance with other multifocal image fusion algorithms by conducting experiments on publicly available datasets and illustrated using existing evaluation methods and evaluation metrics; the results of the experiments show that our method visualizes the fusion effect better through the encoder and the decoder and the evaluation metrics are also relatively good.

关键词： Measurement Deep learning computer vision Image synthesis Feature extraction Visual effects Transformers

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