检索结果-内蒙古大学图书馆

Intelligent Communication and Networking (ICN), International Conference on

作者： Fulin Huang Jie Ning Lingjie Liu Ye Tian Jianghong Wu College of Computer Science and Technology Harbin Engineering University Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China Environment and Food and Drug Crime Investigation Detachment Hangzhou Public Security Bureau Hangzhou China Hangzhou Institute of Technology Xidian University Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

In response to the sharp increase in cases of counterfeit cigarettes and counterfeit alcohol in recent years, based on the application of knowledge graphs in social entities and social relationships, a knowledge graph for risk identification of counterfeit cigarettes and counterfeit alcohol was constructed and applied. Based on the historical cases of counterfeit cigarettes and counterfeit wines and the relevant groups, we process the data through ontology construction, knowledge extraction, and knowledge fusion, and build a knowledge map of relevant personnel for testing counterfeit wines. We use the TransH model to complete the constructed knowledge graph to obtain a more complete knowledge graph for risk identification of counterfeit cigarettes and counterfeit alcohol, and then use PageRank and Louvain community discovery algorithms to screen out communities where people with major suspicions gather, and control the system. Gangs selling counterfeit cigarettes and alcohol provide risk warning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding

arXiv

引用

arXiv 2023年

作者： Xu, Lixiang Cui, Qingzhe Hong, Richang Xu, Wei Chen, Enhong Yuan, Xin Li, Chenglong Tang, Yuanyan The College of Artificial Intelligence and Big Data Hefei University Hefei230027 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia The School of Artificial Intelligence Anhui University Hefei230601 China The Zhuhai UM Science and Technology Research Institute FST University of Macau China

In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to their huge size of parameters. To address this problem, we introduce a compression method based on knowledge distillation for this field, which largely reduces the number of parameters while preserving model performance as much as possible. Specifically, to enhance the capabilities of smaller models, we design a high-performing large model called Group Multi-view Vision Transformer (GMViT). In GMViT, the view-level ViT first establishes relationships between view-level features. Additionally, to capture deeper features, we employ the grouping module to enhance view-level features into group-level features. Finally, the group-level ViT aggregates group-level features into complete, well-formed 3D shape descriptors. Notably, in both ViTs, we introduce spatial encoding of camera coordinates as innovative position embeddings. Furthermore, we propose two compressed versions based on GMViT, namely GMViT-simple and GMViT-mini. To enhance the training effectiveness of the small models, we introduce a knowledge distillation method throughout the GMViT process, where the key outputs of each GMViT component serve as distillation targets. Extensive experiments demonstrate the efficacy of the proposed method. The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB. The smaller models, GMViT-simple and GMViT-mini, reduce the parameter size by 8 and 17.6 times, respectively, and improve shape recognition speed by 1.5 times on average, while preserving at least 90% of the classification and retrieval performance. The code is available at https://***/bigdatagraph/*** Codes 68 Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

来源：评论

学校读者我要写书评

暂无评论

Ascl: Accelerating Semi-Supervised Learning Via Contrastive Learning

SSRN

引用

SSRN 2024年

作者： Liu, Haixiong Li, Zuoyong Wu, Jiawei Zeng, Kun Hu, Rong Zeng, Wei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science Minjiang University Fuzhou350121 China School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China

SSL(Semi-supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo-labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo-labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug-and-play method named ASCL(Accelerating Semi-Supervised Learning via Contrastive Learning). This method combines contrastive learning with uncertainty-based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo-labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi-supervised learning. Uncertainty-based selection provides a robust mechanism for acquiring pseudo-labels. The combination of the contrastive learning module and the uncertainty-based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state-of-the-art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR-10 dataset, the application of ASCL to Pseudo-label, UDA (Unsupervised data Augmentation for Consistency Training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%. © 2024, The Authors. All rights reserved.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

来源：评论

学校读者我要写书评

暂无评论

An Efficient Differential Privacy Method with Wavelet Transform for Edge Weights of Social Networks

引用

International Journal of Network Security 2022年第2期24卷 181-192页

作者： Yan, Jun Liu, Hai Wu, Zhenqiang School of Computer Science Shaanxi Normal University Xi’an710119 China School of Mathematics and Computer Applications Shangluo College Shangluo72600 China Guizhou Provincial Key laboratory of Public Big Data Guizhou University Guiyang550025 China

With more and more attention paid to privacy preservation in social networks, many effective methods based on differential privacy have been presented. To preserve the sensitive information of edge weights in a weighted social network, we combine the differential privacy with wavelet transform and devise a DPEW (Differential Privacy based Edge weight with Wavelet Transform) method. The proposed method satisfies differential privacy and provides better data utility. In this method, WTDP (Wavelet Transform based on Differential Privacy) algorithm can achieve differential privacy preserving while less noise is added on the edge weights. In addition, the properties of the original graph are maintained by EDU (Enhancing data Utility) algorithm. The experimental results show that the DPEW method achieves ϵ-differential privacy and reduces the information loss of the edge weight than other methods © 2022. International Journal of Network Security. All Rights Reserved.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Ecological Security Evaluation Algorithm for Resource-Exhausted Cities Based on the PSR Model

引用

computers, Materials & Continua 2021年第10期69卷 985-1001页

作者： Xiaozhou Li Zhenggang Xu Yuanyuan Fu Qi Jin Yunlin Zhao Neal NXiong Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security for Hunan Province Central South University of Forestry and TechnologyChangsha410004China Hunan Urban and Rural Ecological Planning and Restoration Engineering Research Center Hunan City UniversityYiyang413000China College of Information and Electronic Engineering Hunan City UniversityYiyang413000China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA

Today,resource depletion threatens a number of resource-based cities in *** ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resource-exhausted *** 23 indicators,this study evaluated the ecological security status and development trends of 21 resource-exhausted cities in China from 2011 to *** results showed that from 2011 to 2015,the overall ecological security of this type of city was low,with over 60%of the cities at an unsafe ***,ecological security improved rapidly after 2016,and by 2017,all of the cities had reached the critical safety *** top 10 indicators of ecological security included industrial sulfur dioxide emissions,water supply,agricultural fertilizer application,and forest *** 10 indicators’cumulative contribution to ecological security was 48.3%;among them,reducing industrial sulfur dioxide emissions contributed the most at 5.7%.These findings can help governments better understand the ecological security status of resource-exhausted cities,and it can provide a reference for the allocation of funds and other resources to improve the ecological safety of these cities.

关键词： Ecological security PSR model evaluation resource-exhausted cities

来源：评论

学校读者我要写书评

暂无评论

An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

引用

International Conference on Advanced Cloud and big data (CBD)

作者： Yifan Wang Wei Zeng Guanyu Li Chenglong Xiong Zicheng Wang Yingchi Mao College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate.

关键词： Energy consumption Simulation Markov decision processes Termination of employment Inspection Autonomous aerial vehicles Critical infrastructure Vehicle dynamics Long short term memory Optimization

来源：评论

学校读者我要写书评

暂无评论

TPDNet: Triple phenotype deepen networks for monocular 3D object detection of melons and fruits in fields

引用

Plant Phenomics 2025年第2期7卷

作者： Wang, Yazhou Zhang, Tianhan Wu, Xingcai Li, Qinglei Li, Yuquan Wang, Qi School of Information Guizhou University of Finance and Economics Guiyang 550025 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang 550025 China

The growth of the global population has increased the demand for fruits and vegetables, while high harvesting labor costs severely constrain industry development. Currently, relevant personnel primarily utilize 2D object detection technology to facilitate automated harvesting, aiming to reduce labor costs. However, 2D detection technology is limited to providing planar information and cannot meet the requirements of scenarios that need 3D spatial data, whereas 3D object detection technology can effectively address these needs, including point cloud-based methods and monocular-based methods. Since point cloud-based object detection methods require expensive equipment, they are not suitable for low-cost agricultural harvesting scenarios. In contrast, monocular 3D object detection methods have the advantage of only requiring a camera and being easy to deploy. However, there is a lack of specialized monocular 3D object detection datasets and algorithms suited for natural scenes in the agricultural field, which limits the application and development of this technology in agricultural automation. To address this, we construct a 3D object detection dataset for wax gourds and propose a network called TPDNet, which aims to capture the 3D information of objects from a single RGB image for fruits and vegetables in fields. Specifically, we construct a depth estimation and enhance module that introduces depth information into the model with the help of depth auxiliary labels, and improves the representation of depth information by utilizing weight information across spatial and channel dimensions. Meanwhile, since depth features and image features are heterogeneous, we design the phenotype aggregation and phenotype intensify module to capture the correspondence between image and depth features, promoting the effective fusion of image and depth information. The experimental results show that our method significantly outperforms others in terms of mAP3D and mAPBEV<

关键词： 3D object detection Melons and fruits in fields Monocular

来源：评论

学校读者我要写书评

暂无评论

Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

SSRN

引用

SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

The relationship between physical activity in early pregnancy and hypertensive disorders of pregnancy: a cohort study in Chinese women

引用

World Journal of Emergency Medicine 2023年第3期14卷 204-208页

作者： Qian Lu Shi-jiao Yan Huan-jun Chen Xiong-fei Pan Yi-xiang Ye Xing-yue Song Ri-xing Wang Chuan-zhu Lyu International School of Public Health and One Health Hainan Medical UniversityHaikou 570100China Research Unit of Island Emergency Medicine Chinese Academy of Medical SciencesHainan Medical UniversityHaikou 570100China Section of Epidemiology and Population Health Ministry of Education Key Laboratory of Birth Defects and Related Diseases of Women and ChildrenWest China Second University Hospital&West China Biomedical Big Data CenterWest China HospitalSichuan UniversityShuangliu Institute of Women's and Children's HealthShuangliu Maternal and Child Health HospitalChengdu 610041China Department of Epidemiology and Biostatistics School of Public HealthTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430000China Department of Emergency Hainan Clinical Research Center for Acute and Critical Diseasesthe Second Affi liated Hospital of Hainan Medical UniversityHaikou 570100China Emergency Medicine Center Sichuan Provincial People's HospitalUniversity of Electronic Science and Technology of ChinaChengdu 610072China Ministry of Education Key Laboratory of Emergency and Trauma Hainan Medical UniversityHaikou 570100China

BACKGROUND:We aimed to examine prospective associations between diff erent intensities and diff erent types of physical activity(PA)in early pregnancy and hypertensive disorders of pregnancy(HDP)among Chinese ***:A total of 6,820 pregnant women from the Tongji-Shuangliu Birth Cohort were included in this *** pregnancy physical activity questionnaire(PPAQ)was used to assess PA,including household/caregiving,occupational,sports/exercise,and transportation activities in the first trimester of *** diagnosis of HDP was collected,including gestational hypertension(GH)and preeclampsia(PE).data were analyzed by unconditional multivariate logistic regression,and the odds ratio(OR)and 95%confi dence interval(CI)were ***:A total of 178(2.6%)of the 6,820 women were diagnosed with HDP,of which 126(1.8%)were GH and 52(0.8%)were ***,we found no association between PA in early pregnancy and PE.A trend toward lower risk was found only among women with GH and among those with higher levels of moderate-to-vigorous intensity physical activity(MVPA)(adjusted OR 0.54,95%CI 0.31–0.96).No association was observed between PA and HDP in early pregnancy,regardless of diff erent intensities or types of ***:MVPA in the first trimester is an influencing factor of *** pregnant women to engage in MVPA in the fi rst trimester may help to prevent GH.

关键词： Physical activity Gestational hypertension Preeclampsia Prospective cohort study

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：