检索结果-内蒙古大学图书馆

2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781957171395

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Optical fibers

来源：评论

学校读者我要写书评

暂无评论

TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Xinquan Xie, Jinheng Li, Xuechen Li, Xuguang Shen, Linlin Deng, Yongqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China National University of Singapore Show Lab Singapore Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University General Hospital Department of Stomatology Shenzhen China

ISBN: (纸本)9798350337488

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods. © 2023 IEEE.

关键词： Dental Implant Multi-module Detection Multi-slice Learning Text Guided Detection Transformer

来源：评论

学校读者我要写书评

暂无评论

Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

引用

CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Dispersion compensation

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey on Communication-Efficient Federated Learning in Mobile Edge Environments

引用

IEEE Communications Surveys and Tutorials 2025年

作者： Jia, Ninghui Qu, Zhihao Ye, Baoliu Wang, Yanyan Hu, Shihong Guo, Song Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Nanjing211100 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Kowloon Hong Kong

In traditional centralized machine learning, transmitting raw data to a cloud center incurs high communication costs and raises privacy concerns. This is particularly challenging in mobile edge environments, where devices are dynamic and resource-constrained. Federated Learning (FL) addresses these issues by allowing devices to train models locally and upload parameters to a central server without sharing local data. However, limited wireless channel resources and dynamic transmission performance make communication overhead a major bottleneck of FL in mobile edge environments. Concerning this challenge, this survey provides a comprehensive summary of methods to improve communication efficiency in FL, focusing on: 1) minimizing communication complexity to reduce total transmission volume, 2) scheduling resources appropriately to improve training efficiency, 3) utilizing over-the-air computation (OTA) to integrate computation into communication for accommodating the computation/communication characteristics of FL in mobile edge environments. Thus, this work analyzes research from the perspective of convergence and data heterogeneity to reduce communication rounds by optimizing algorithm performance. We hope that this survey could offer insights into communication-efficient FL for future research. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Preprocessing technology of consuming big data based on user interest with internet of location mining

引用

International Journal of computers and Applications 2021年第2期43卷 147-152页

作者： Dagao, Duan Yang, Gao Department of Computer Science and Information Engineering Beijing Technology and Business University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China

The current group discovery research mainly uses the communication relationship to mine user groups and fails to make full use of the user social relationships implied in the network, so that the mining groups can not truly reflect the user’s social relations in the social life. In this paper, the consumer big data preprocessing technology based on user interest location mining is proposed. The sequence pattern mining algorithm is used to mine user’s positional regularity sequence, and the position sequence correlation metrics are established. The positional regularity correlation is used to reveal the social relationship of the user’s social activities. Second, this paper combines the local similarity measure method to calculate the user’s communication distance index, which reflects the degree of understanding between users. Finally, using the communication distance index to weight the position correlation to calculate the user group correlation and then use the split clustering algorithm to mine user groups with communication relationship and social relationship. The experimental results showed that this method can effectively mine the communication relevance and position correlation in the user social activities and reflect the user’s group relations in real social activities. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings

arXiv

引用

arXiv 2024年

作者： Lin, Yan Zhou, Zeyu Liu, Yicheng Lv, Haochen Wen, Haomin Li, Tianyi Li, Yushuai Jensen, Christian S. Guo, Shengnan Lin, Youfang Wan, Huaiyu Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China Beijing101318 China Department of Computer Science Aalborg University Aalborg9220 Denmark

Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses. Thus, the qualities of embeddings are very important. Methods for pre-training embeddings, which leverage unlabeled trajectories for training universal embeddings, have shown promising applicability across different tasks, thus attracting considerable interest. However, research progress on this topic faces two key challenges: a lack of a comprehensive overview of existing methods, resulting in several related methods not being well-recognized, and the absence of a unified pipeline, complicating the development of new methods and the analysis of methods. We present UniTE, a survey and a unified pipeline for this domain. In doing so, we present a comprehensive list of existing methods for pre-training trajectory embeddings, which includes methods that either explicitly or implicitly employ pre-training techniques. Further, we present a unified and modular pipeline with publicly available underlying code, simplifying the process of constructing and evaluating methods for pre-training trajectory embeddings. Additionally, we contribute a selection of experimental results using the proposed pipeline on real-world datasets. Implementation of the pipeline is publicly available at https://***/Logan-Lin/UniTE. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Dts-Adapstnet: An Adaptive Spatiotemporal Neural Networks for Traffic Prediction with Mulit-Graph Fusion

SSRN

引用

SSRN 2023年

作者： Zhang, Jing Shi, Wenlong Ye, Xiucai Tsai, Peiwei School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science University of Tsukuba Tsukuba Japan Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn Australia

Traffic prediction is of vital importance in intelligent transportation systems. It can realize efficient route planning, avoid traffic congestion and reduce travel time, etc. However, it is difficult to make accurate road traffic prediction, due to the complex spatiotemporal dependencies of the traffic network. The construction and learning of spatial dependencies play a pivotal role in road traffic prediction, laying the foundation for accurate traffic prediction. Regrettably, most of the existing methods for capturing spatial dependencies only consider a single spatial relationship, ignoring other potential temporal and spatial correlations in the traffic network. Moreover, the end-to-end training methods are difficult to control the training direction during graph learning. Additionally, the result of traffic forecasting lacks of fusing multiple traffic data information. All of these have adverse effects on road traffic prediction. In other to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-adapSTNet), is proposed. First of all, in order to better extract the hidden spatial dependencies, a method of fusing multiple factors is designed, which includs the distance relationship, transfer relationship and same-road segment relationship of traffic data, etc. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, based on the experiments by two large real-world datasets, the DTS-adapSTNe

关键词： Travel time

来源：评论

学校读者我要写书评

暂无评论

LearnSC: An Efficient and Unified Learning-Based Framework for Subgraph Counting Problem

LearnSC: An Efficient and Unified Learning-Based Framework f...

引用

International Conference on data engineering

作者： Wenzhe Hou Xiang Zhao Bo Tang Laboratory for Big Data and Decision National University of Defense Technology China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graphs are valuable data structures used to represent complex relationships between entities in a wide range of applications, such as social networks and chemical reactions. Subgraph counting problem is a well-known hard problem, as its core subroutine, the subgraph matching, is NP-complete. In this work, we propose an efficient and unified deep learning-based solution framework LearnSC, which solves the subgraph counting problem approximately. This framework offers two key advantages: (i) it is a generic solution that is orthogonal to the existing techniques of learning-based solutions; and (ii) it is equipped with a suite of optimizations to significantly improve the accuracy of the estimated results. Our experimental results on 7 datasets demonstrate that our proposal is highly accurate, robust, and scalable, making it an excellent solution for subgraph counting problem among all statistics-based and learning-based competitors.

关键词： Accuracy Social networking (online) Algorithms Estimation data structures data engineering Chemical reactions

来源：评论

学校读者我要写书评

暂无评论

Improved Ensemble-Based Approaches with Stacking for Imbalanced Medical data Classification

Improved Ensemble-Based Approaches with Stacking for Imbalan...

引用

Electrical, computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Mouna Lamari Assia Boukhamla Tamer Abderrahmane Lafia Nabiha Azizi Lynda Kouadria Nacer Eddine Hammami Computer science department LRI Laboratory Badji Mokhtar University Annaba Algeria Computer science department Labged Laboratory Badji Mokhtar University Annaba Algeria Data Science and AI Industrial engineering department Ecole NationalePolytechnique Algiers Algeria Computer science department Labged Laboratory Badji Mokhtar University Annaba Computer science department Badji Mokhtar University Annaba Algeria Computer Engineering department Mustaqbal University Buraidah Saudi Arabia

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Classimbalance is a prevalent challenge in medical databases, wherein minority pathology classes are under-represented, resulting in biased classification models that favor the majority class. In this study, we evaluate the efficacy of prominent ensemble-based methods (Balanced Bagging, Balanced Random Forest, RUSBoost, XGBoost, and EasyEnsemble) designed to address the challenge of imbalanced data without generating synthetic samples. Through experimentation on eight datasets with varying degrees of imbalance, no single method demonstrated consistent superiority across all cases. To address this, we propose a stacking meta-predictor that combines the outputs of these models, leveraging their strengths to create a more robust classification system. The stacked model demonstrates improved generalization and superior classification performance across diverse imbalanced medical datasets.

关键词： Pathology databases Heuristic algorithms Stacking Diversity reception data models Robustness Ensemble learning Random forests Bagging

来源：评论

学校读者我要写书评

暂无评论

Adaptive Methods for Kernel Initialization of Convolutional Neural Network Model Applied to Plant Disease Classification 11

Adaptive Methods for Kernel Initialization of Convolutional ...

引用

11th IEEE Open Conference of Electrical, Electronic and Information Sciences, eStream 2024

作者： Lagnaoui, Saloua Boumais, Khaoula Fallah, Saad El En-Naimani, Zakariae Haddouch, Khalid Matuzevicius, Dalius ENSA Sidi Mohamed Ben Abdellah University Laboratory Engineering Systems and Applications Fez Morocco Sidi Mohammed Ben Abdallah University ENSA Laboratory AI Data Science and Emerging Systems Fez Morocco Private University of Fez Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences Fez Morocco ENSET Laboratory Computer Science AI and Cyber Security University of Hassan II Casablanca Mohammedia Morocco Vilnius Gediminas Technical University Faculty of Electronics Department of Electronic Systems Vilnius Lithuania

ISBN: (纸本)9798350352412

Convolutional Neural Networks are instrumental in artificial intelligence, especially in image processing, where their ability to autonomously learn hierarchical features has led to significant breakthroughs. However, the success of these models is intricately tied to the judicious choice of hyperparameters, which include the configuration of convolutional layers, activation functions, and kernel initialization methods. This research explores kernel initialization methods in Convolutional Neural Network models, seven diverse initialization methods (Glorot Uniform, Ones initialization, Zero initialization, Constant initialization, Random initialization, HeNormal initialization, and Orthogonal initialization) are comprehensively compared. The primary objective is to showcase the sensitivity of Convolutional Neural Networks to these various initialization techniques. The study not only aims to reveal the nuanced impact of kernel initialization but also introduces an adaptive method to enhance model performance. By delving into the intricacies of initialization methods, this research contributes to the improvement of Convolutional Neural Networks effectiveness, especially in critical applications like Plant Disease classification. © 2024 IEEE.

关键词： Convolutional neural networks

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：