检索结果-内蒙古大学图书馆

International Conference on data engineering

作者： Yu Zhang Feng Zhang Hourun Li Shuhao Zhang Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Information Systems Technology and Design Pillar Singapore University of Technology and Design

Stream processing prevails and SQL query on streams has become one of the most popular application scenarios. For example, in 2021, the global number of active IoT endpoints reaches 12.3 billion. Unfortunately, the increasing scale of data and strict user requests place much pressure on existing stream processing systems, requiring high processing throughput with low latency. To further improve the performance of current stream processing systems, we propose a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, without decompression. Particularly, CompressStreamDB involves eight compression methods targeting various data types in streams, and it also provides a cost model for dynamically selecting the appropriate compression methods. By exploring data redundancy among streams, CompressStreamDB not only saves space in data transmission between client and server, but also achieves high throughput with low latency in SQL query on stream processing. Our experimental results show that compared to the state-of-the-art stream processing system on uncompressed streams, CompressStreamDB achieves 3.24× throughput improvement and 66.0% lower latency on average. Besides, CompressStreamDB saves 66.8% space.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Volatility and risk contagion of international stock market in the context of COVID-19

引用

Procedia Computer Science 2023年 221卷 370-377页

作者： Wen Long Yuhan Zeng Man Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 P.R. China

In order to reveal the impact of the outbreak of COVID-19 epidemic on risk contagion in the international stock market, this paper establishes an ARMA-GARCH-Copula model based on the R-Vine structure to empirically analyze the stock index data of 19 important countries(regions) around the world, and uses complex network to display the risk contagion path. This research shows that the risk contagion correlation of the global stock market has significantly improved due to the impact of COVID-19; The correlation of risk transmission among European countries is significantly higher than that of other regions, reflecting a closer integration connection between EU countries; Finally, unlike previous literature, this article finds that stock market of Hong Kong in China has become an important node in the international stock market, playing an important role in the risk network before and after the epidemic.

关键词： COVID-19 R-Vine-Copula risk contagion complex network

来源：评论

学校读者我要写书评

暂无评论

Autonomous Human-Robot Collaborative Assembly Method Driven by the Fusion of Large Language Model and Digital Twin

Autonomous Human-Robot Collaborative Assembly Method Driven ...

引用

2024 International Conference on Intelligent Systems and Robotics, CISR 2024

作者： Chen, Jianpeng Luo, Haiwei Huang, Sihan Zhang, Meidi Wang, Guoxin Yan, Yan Jing, Shikai School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Capital Aerospace Machinery Corporation Limited Beijing100076 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China China North Artificial Intelligence & Innovation Research Institute Beijing100071 China

Human-robot collaboration (HRC) plays an important role in human-centric manufacturing, which requires cooperative robots to have the ability of collaborate with human autonomously. It is very complex to understand the intention of human during the assembly process, therefore, we proposed a method of autonomous HRC assembly driven by the fusion of large language model (LLM) and digital twin in this paper. The assembly state is recognized from two perspectives, including the perception of key parts based on transfer learning and YOLO, and perceive operator actions based on LSTM and attention mechanism. In order to improve the autonomy of HRC, a collaborative task decision method driven by fine-tuning LLM based on assembly domain knowledge is proposed. A case study of reducer assembly is presented to verify the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative robots

来源：评论

学校读者我要写书评

暂无评论

Temporal Gated Face Alignment Network for Camera-Based Physiological Sensing

SSRN

引用

SSRN 2024年

作者： Li, Qi Guo, Dan Qian, Wei Zhou, Yuanen Zhao, Haifeng Sun, Xiao Wang, Meng School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

The remote photoplethysmography (rPPG) technique enables the estimation of vital signs such as heart rate by analyzing pulse-induced subtle skin color variation from facial videos. Robustly deriving cardiac pulse information with head motion (\eg, rotation and swings) interference, remains a critical challenge, as it inevitably introduces complex noise disturbances such as facial geometric deformation and head motions. Most existing approaches primarily focus on how to extract the weak pulse signals, overlooking the detrimental effects induced by such noises. In response to this, we propose a Temporal Gated Face Alignment Network (TGFAN) to adaptively counteract motion noise in long-term video sequences. As key modules in TGFAN, the bidirectional temporal face alignment block first captures inter-frame motion discrepancies and achieves temporal alignment, and then extracts motion-robust pulse features by spatiotemporal convolution. Furthermore, we also propose a temporal gating mechanism, which can dynamically guide motion-disturbed segments for face alignment to alleviate local temporal interference. Experimental evaluations on four public datasets demonstrate the superior performance of our proposed model under both intra-dataset and cross-dataset tests. Our codes will be available at \url{https://***/Eadith/TGFAN}. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

Emergence of plasmid-mediated tigecycline resistance tet(X4)gene in Enterobacterales isolated from wild animals in captivity

Science in One Health

引用

Science in One Health 2024年第1期3卷 134-139页

作者： Lei Lei Panfeng Xiong Zelin Yan Yanyan Zhang Yuchen Wu Gongxiang Chen Houhui Song Rong Zhang Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Provincial Engineering Laboratory for Animal Health Inspection&Internet TechnologyZhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health ManagementChina-Australia Joint Laboratory for Animal Health Big Data AnalyticsCollege of Animal Science and Technology&College of Veterinary MedicineZhejiang Agriculture and Forestry UniversityHangzhouZhejiang 311300China Department of Clinical Laboratory The Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouZhejiang 310009China

Background:Over the past few decades,antimicrobial resistance(AMR)has emerged as a global health challenge in human and veterinary *** on AMR genes in captive wild animals has ***,the presence and molecular characteristics of tet(X)-carrying bacteria in these animals remain ***:Eighty-four samples were collected from captive wild ***(X)variants were detected using polymerase chain reaction and the isolates were identified using matrix-assisted laser desorption/ionization time-of-flight mass *** isolated strains were subjected to antimicrobial susceptibility testing and wholegenome *** virulence of an Escherichia coli strain carrying enterotoxin genes was assessed using a Galleria mellonella larval ***:We isolated two tet(X4)-positive *** strains and one tet(X4)-positive Raoultella ornithinolytica *** susceptibility tests revealed that all three tet(X4)-carrying bacteria were sensitive to the 13 tested antimicrobial agents,but exhibited resistance to ***,one tet(X4)-carrying *** strain producing an enterotoxin had a toxic effect on *** ***-genome sequencing analysis showed that the two tet(X4)-carrying *** strains had more than 95%similarity to tet(X4)-containing *** strains isolated from pigs and humans in ***:The genetic environment of tet(X4)closely resembled that of the plasmid described in previous *** study identified tet(X4)-positive strains in wildlife and provided valuable epidemiological data for monitoring drug *** identification of enterotoxin-producing *** strains also highlights the potential risks posed by virulence genes.

关键词： Wild animal tet(X4) Enterobacterales Enterotoxin Tigecycline resistance Epidemiology

来源：评论

学校读者我要写书评

暂无评论

A Multi-Label Classification Algorithm Based on Self-paced Learning and LDA

A Multi-Label Classification Algorithm Based on Self-paced L...

引用

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Qiangqiang Wang Shenming Gu School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan China

To address the problem that existing multi-label learning algorithms treat all samples equally during model training and ignore inter-sample variability, this paper proposes a multi-label classification algorithm based on self-paced learning and LDA (SPLDA for short). SPLDA first constructs an adaptive self-paced function based on the label prediction loss values in multi-label data, and introduces a self-paced learning framework. Secondly, the linear discriminant analysis regularization term in the low-dimensional space of the samples is constructed by combining the principles of minimizing the within-class distance and maximizing the between-class distance in this framework. Next, a multi-label classification model is trained using alternating optimization methods. Finally, the experimental results on 5 real datasets and 3 evaluation metrics show that SPLDA outperforms the existing multi-label classification algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Innovative remote sensing methodologies and applications in coastal and marine environments

引用

地球空间信息科学学报（英文版） 2024年第3期27卷 836-853页

作者： Qing Zhao Antonio Pepe Virginia Zamparelli Pietro Mastro Francesco Falabella Saygin Abdikan Caglar Bayik Fusun Balik Sanli Mustafa Ustuner Nevin Betul Avşar Jingjing Wang Peng Chen Zhengjie Li Adam T.Devlin Fabiana Calò Key Laboratory of Geographical Information Science Ministry of EducationEast China Normal UniversityShanghaiChina School of Geographic Sciences East China Normal UniversityShanghaiChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural ResourcesShanghaiChina Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly School of Engineering University of BasilicataPotenzaItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute of Methodologies for Environmental Analysis(IMAA) Italian National Research CouncilTito ScaloItaly Department of Geomatics Engineering Hacettepe UniversityAnkaraTurkey Department of Geomatics Engineering Zonguldak Bulent Ecevit UniversityZonguldakTurkey Department of Geomatic Engineering Yildiz Technical UniversityIstanbulTurkey Department of Geomatic Engineering Artvin Çoruh UniversityArtvinTurkey Department of Geomatics Engineering Izmir Katip Celebi UniversityİzmirTurkey School of Ocean and Earth Science and Technology University of Hawaii at MānoaHonoluluHIUSA

Remote sensing(RS)technologies are extensively exploited by scientists and a vast audience of local authorities,urban managers,and city *** regions,geohazard-prone areas,and highly populated cities represent natural laboratories to apply RS technologies and test new *** the last decades,many efforts have been spent on improving Earth's surface monitoring,including intensifying Earth Observation(EO)operations by the major national space *** oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development oppor-tunities in the geoscience *** instance,within this framework,the European Space Agency(ESA)and the Ministry of Science and Technology of China(MOST)have sponsored,since the early 2000s,the DRAGON initiative jointly carried out by the European and Chinese RS scientific *** manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further *** main findings are related to ⅰ)the analysis of flood risk in China,ⅱ)the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar(SAR)methods,ⅲ)the analy-sis of the inundation risk in low-lying regions using coherent and incoherent SAR methods;andⅳ)the use of SAR-based technologies for marine applications.

关键词： Disaster risk management Remote Sensing(RS) Earth Observation(EO) Synthetic Aperture Radar(SAR) flooding subsidence coastal/marine environments

来源：评论

学校读者我要写书评

暂无评论

Unifying Large Language Models and knowledge Graphs: A Roadmap

arXiv

引用

arXiv 2023年

作者： Pan, Shirui Luo, Linhao Wang, Yufei Chen, Chen Wang, Jiapu Wu, Xindong Griffith University QLD Australia The Department of Data Science and AI Monash University Melbourne Australia The Nanyang Technological University Singapore The Faculty of Information Technology Beijing University of Technology Beijing China The Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei China The Research Center for Knowledge Engineering Zhejiang Lab Hangzhou China

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolve by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs;2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering;and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions. Copyright © 2023, The Authors. All rights reserved.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Sparse Learning for Neural Networks with A Generalized Sparse Regularization

引用

Procedia Computer Science 2022年 214卷 747-754页

作者： Anda Tang Tongsheng Yao Lingfeng Niu Yong Shi School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Deep neural networks (DNNs) is very important and have achieved remarkable accuracies in tasks such as image processing. However, the success of DNNs heavily relies on excessive computation and parameter storage costs. To cut down the overheads, a wide range of regularization terms are proposed on netwok compression, which have their own scope of application though. To further reduce computation, structural sparsity learning is a matter of great concern where group sparse regularizations are blossoming in radiant splendour. However, the group sparse regularizations used in network compression is relatively scarce. A majority of structural compression only stem from ℓ 2,1 regularization. In addition, sparse regularization in structural network compression lacks unified form and theoretical guidance. Therefore, we focus our attention on a generalized sparse regularization in sparse learning of deep learning. We put a series of sparse regularization into a framework where some group sparse regularizations with different properties are introduced. The transformation of regularizations can be completed through the selection of hyper-parameters. In this case, we can use the optimization strategy for unification and theoretical guidance, and transform different sparse regularizations to adapt to different tasks. To our knowledge, it is the first work applying the generalized sparse regularization with novel group sparsity in compression of DNNs. The proposed ℓ p , q , r regularization can achieve both neuron-level and connection-level sparsity. And we give the analytical solutions for some specific ( p,q, r ) thus the compression can be achieved throughout the standard optimization process. We perform extensive experiments to illustrate the advantages and characteristics of our new method for further applications.

关键词： Deep neural networks Model compression Group Sparse optimization Regularization Sparse Learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Federated Learning via Mean Field Approach 15

Adaptive Federated Learning via Mean Field Approach

引用

2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Tu, Kaifei Zheng, Shensheng Wang, Xuehe Hu, Xiping School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

In order to solve the problem of "data island"and preserve individual's privacy, federated learning, as a distributed machine learning technology, has emerged recently. In federated learning, the model training is distributed over edge clients and coordinated by a central server. Each client only needs to send the updated model parameter to the central server for aggregation without sharing its private data. However, due to the data divergence of heterogeneous clients, the convergence rate of the global model training may be very slow, especially for non-IID data case. To deal with this issue and achieve fast convergence, we propose an adaptive learning rate strategy for each client by considering the deviation of the local model parameter from the global model parameter at each global training iteration. To enable decentralized learning rate design for each client, a mean-field scheme is introduced to estimate the global model parameters over time, which does not even require many clients to communicate frequently. Finally, we run numerical experiments to validate our results. © 2022 IEEE.

关键词： Learning algorithms

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：