检索结果-内蒙古大学图书馆

Chinese Physics B 2022年第5期31卷 780-790页

作者： Jinming Du Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Institute of Industrial and Systems Engineering College of Information Science and EngineeringNortheastern UniversityShenyang 110819China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China

Voter model is an important basic model in statistical *** recent years,it has been more and more used to describe the process of opinion formation in *** real complex systems,the interactive network of individuals is dynamically adjusted,and the evolving network topology and individual behaviors affect each ***,we propose a linking dynamics to describe the coevolution of network topology and individual behaviors in this paper,and study the voter model on the adaptive *** theoretically analyze the properties of the voter model,including consensus probability and *** evolution of opinions on dynamic networks is further analyzed from the perspective of evolutionary ***,a case study of real data is shown to verify the effectiveness of the theory.

关键词： statistical physics opinion dynamics evolutionary game theory complex systems

来源：评论

学校读者我要写书评

暂无评论

Learning Inter-Entity Interaction for Few-Shot knowledge Graph Completion

Learning Inter-Entity Interaction for Few-Shot Knowledge Gra...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Li, Yuling Yu, Kui Huang, Xiaoling Zhang, Yuhong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Enginerring Hefei University of Technology China

Few-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs. Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. Such practice, however, ignores the inter-entity interaction, resulting in low-discrimination representations for entity pairs, especially when these entity pairs are associated with 1-to-N, N-to-1, and N-to-N relations. To address this issue, this paper proposes a novel FKGC model, named Cross-Interaction Attention Network (CIAN) to investigate the inter-entity interaction between head and tail entities. Specifically, we first explore the interactions within entities by computing the attention between the task relation and each entity neighbor, and then model the interactions between head and tail entities by letting an entity to attend to the neighborhood of its paired entity. In this way, CIAN can figure out the relevant semantics between head and tail entities, thereby generating more discriminative representations for entity pairs. Extensive experiments on two public datasets show that CIAN outperforms several state-of-the-art methods. The source code is available at https://***/cjlyl/FKGC-CIAN. © 2022 Association for Computational Linguistics.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Multi-modal Self-perception Enhanced Large Language Model for 3D Region-of-Interest Captioning with Limited data

引用

IEEE Transactions on Multimedia 2025年 27卷 2935-2948页

作者： Shi, Lu Kan, Shichao Jin, Yi Zhang, Linna Cen, Yigang Bejing Jiaotong University and the Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing100044 China Central South University School of Computer Science and Engineering Hunan Changsha0410083 China Beijing Jiaotong University Key Laboratory of Big Data and Artificial Intelligence in Transportation Ministry of Education and the School of Computer and lnformation Technology Beijing100044 China Guizhou University School of Mechanical Engineering Guiyang550025 China

3D Region-of-Interest (RoI) Captioning involves translating a model's understanding of specific objects within a complex 3D scene into descriptive captions. Recent advancements in Large Language Models (LLMs) have shown great potential in this area. Existing methods capture the visual information from RoIs as input tokens for LLMs. However, this approach may not provide enough detailed information for LLMs to generate accurate region-specific captions. In this paper, we introduce Self-RoI, a Large Language Model with multi-modal self-perception capabilities for 3D RoI captioning. To ensure LLMs receive more precise and sufficient information, Self-RoI incorporates Implicit Textual Info. Perception to construct a multi-modal vision-language information. This module utilizes a simple mapping network to generate textual information about basic properties of RoI from vision-following response of LLMs. This textual information is then integrated with the RoI's visual representation to form a comprehensive multi-modal instruction for LLMs. Given the limited availability of 3D RoI-captioning data, we propose a two-stage training strategy to optimize Self-RoI efficiently. In the first stage, we align 3D RoI vision and caption representations. In the second stage, we focus on 3D RoI vision-caption interaction, using a disparate contrastive embedding module to improve the reliability of the implicit textual information and employing language modeling loss to ensure accurate caption generation. Our experiments demonstrate that Self-RoI significantly outperforms previous 3D RoI captioning models. Moreover, the Implicit Textual Info. Perception can be integrated into other multi-modal LLMs for performance enhancement. We will make our code available for further research. © 1999-2012 IEEE.

关键词： Digital elevation model

来源：评论

学校读者我要写书评

暂无评论

A Summary of Research on China Basic Education Evaluation with the Help of Intelligent Technology 5th

A Summary of Research on China Basic Education Evaluation wi...

引用

5th International Conference on Computer Science and Educational Informatization, CSEI 2023

作者： Shi, Xiaobo Li, Ningning Zhao, Bingying Lian, Weiwei Li, Gongli Key Laboratory of Artificial Intelligence and Personalized Learning in Education Big Data Engineering Lab of Teaching Resources and Assessment of Education Quality College of Computer and Information Engineering Henan Normal University Xinxiang China

ISBN: (纸本)9789819994915

Under the background of China Education Modernization 2035 and Education 4.0, promoting the integration and innovation of intelligent technology and basic education evaluation is the necessary path for education evaluation reform in the new era. Using visualization tools, interdisciplinary research method and literature research method, this paper systematically analyses the current situation of research on basic education evaluation and systematically describes the various achievements of basic education evaluation based on intelligent technology in China from three stages of rapid, fluctuating and stable rise. It also reveals the problems existing in the practice of basic education evaluation. This paper proposes the development direction of the integration and innovation of intelligent technology and basic education evaluation, breaks through the two bottlenecks of "mechanism" and "technology", gets out of the dilemma of basic education evaluation reform, and provides a reference for exploring the optimization of intelligent technology and deepening basic education evaluation reform. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Modernization

来源：评论

学校读者我要写书评

暂无评论

Feature Enhancement for Multi-modal Entity Alignment Transformer

Feature Enhancement for Multi-modal Entity Alignment Transfo...

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Zewen Deng Zan Zhang Bingbing Dong Xiulin Zheng Xindong Wu Key Laboratory of Knowledge Engineering With Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Multi-modal entity alignment (MMEA) aims to identify equivalent entities across different multi-modal knowledge graphs. In these graphs, entities are enriched with information from various modalities, such as text, images, and numerical data, making the alignment task both challenging and crucial for improving knowledge graph quality. Current MMEA algorithms typically encode entity information separately for each modality using corresponding encoders, and then integrate these representations through various modal fusion strategies. However, these methods often fail to fully exploit the multi-modal information of entities. To address this issue, we propose a feature-enhanced multi-modal entity alignment transformer (FEMEAT). FEMEAT enhances entity attribute information by incorporating modal distribution data, which captures the inherent distribution of different modalities for each entity. This inclusion allows the model to have a richer understanding of entity characteristics across modalities. Additionally, FEMEAT utilizes an Optical Character Recognition (OCR) model to extract and incorporate textual information from images. By integrating this text extracted from images, the model can better utilize the visual modality, enhancing its ability to understand and process multi-modal information. Furthermore, FEMEAT employs a multi-head cross-modal attention (MHCA) mechanism for modal fusion to achieve comprehensive multi-modal entity representation. This mechanism enables the model to attend to different modalities simultaneously and learn a detailed representation of entities by considering the interactions between modalities. The multi-head cross-modal attention mechanism facilitates a nuanced understanding and integration of multi-modal data. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance across various training scenarios. The code and datasets used in this study can be accessed at https://***/zewenD/FEMEAT.

关键词： Training Visualization Attention mechanisms Optical character recognition Noise knowledge graphs Transformers Robustness Numerical models data mining

来源：评论

学校读者我要写书评

暂无评论

The Fault Location of Distribution Network Based on Narrowband Communication Technology 4th

The Fault Location of Distribution Network Based on Narrowba...

引用

4th International Conference on Energy Power and Automation engineering, ICEPAE 2023

作者： Deng, Fei Li, Dong Yu, Jing Wang, Yujiao Lu, Wenmin Huang, Yu College of Information Engineering Kunming University Kunming650214 China Key Laboratory of Data Governance and Intelligent Decision Universities of Yunnan Kunming650214 China

ISBN: (纸本)9789819988778

In this paper, we propose a fault location of distribution network based on narrowband communication technology, which belong to the field fault monitoring and location of distribution network. The whole system consists of master station server, light source module, EDFA optical amplifier module, optical wavelength division multiplexer, optical wave guide magnetic field sensor, photoelectric conversion module, electrical signal filtering and amplification module, analog digital conversion module, electrical signal processing module, narrowband communication module and solar cell module. The optical wave guide magnetic field sensor is installed on the cable, and the other parts are installed on the pole. The fault location system adopts narrowband wireless communication technology for data transmission, which reduces the system cost and power consumption. At the same time, the measured data and historical data are used to obtain the fault point, which improves the accuracy of fault location. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Magnetic fields

来源：评论

学校读者我要写书评

暂无评论

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

引用

IEEE International Conference on Cloud Computing and Big data Analysis (ICCCBDA)

作者： Tao Hu Zan Zhang Chenyang Bu Lei Li Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency.

关键词： Proteins Location awareness Social networking (online) Social groups Heuristic algorithms knowledge graphs Hardware

来源：评论

学校读者我要写书评

暂无评论

Bearing domain adversarial fault diagnosis method based on Wasserstein distance driven by digital twin 15

Bearing domain adversarial fault diagnosis method based on W...

引用

15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Xingwang, Jiang Xu, Yang Jinrui, Wang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China College of Mechanical and Electronic Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350354010

When solving the problems of data scarcity and cross-domain adaptability in bearing fault diagnosis, traditional methods often find it difficult to obtain a large amount of fault data, which limits the application and development of data-driven methods. Therefore, this paper proposes a novel domain adversarial fault diagnosis method driven by digital twins. First, by constructing a dynamic simulation model of bearing faults, the vibration signals of bearing faults are successfully simulated, thereby obtaining rich simulated fault data. Secondly, by using the SAE network and Wasserstein distance transfer strategy, the fault diagnosis knowledge is automatically learned from the simulated time domain signals, and this knowledge is successfully transfer to the diagnosis problems under different working conditions. Finally, by introducing the domain adversarial mechanism and gradient reversal layer (GRL), the transfer of target domain data features to source domain data features is realized. At the same time, the accurate classification of fault features and the adaptation of domain distribution are realized in the feature extraction process, which effectively solves the domain offset problem in cross-domain fault diagnosis. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

SLMP: A Scientific Literature Management Platform Based on Large Language Models 15

SLMP: A Scientific Literature Management Platform Based on L...

引用

15th IEEE International Conference on knowledge Graph, ICKG 2024

作者： Guo, Menghao Jiang, Jinling Wu, Fan Sun, Shanxin Zhang, Chen Li, Wenhui Sun, Zeyi Chen, Guangyong Wu, Xindong Research Center for Life Sciences Computing Zhejiang Lab Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Hefei China

ISBN: (纸本)9798331508821

This paper presents a Scientific Literature Management Platform (SLMP, demo link1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge. © 2024 IEEE.

关键词： Question answering

来源：评论

学校读者我要写书评

暂无评论

Steel surface Defect Detection Algorithm Based on Improved YOLOv5s 6

Steel surface Defect Detection Algorithm Based on Improved Y...

引用

6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Su, Yingying Deng, Yuanyuan Zhou, Nengyang Si, Hongyun Peng, Jie Chongqing University of Science and Technology College of Electrical Engineering Chongqing China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing China

ISBN: (纸本)9798350316520

To address challenges in steel surface defect detection, such as low accuracy and slow processing speed, an enhanced algorithm is proposed. The C3 module is replaced with GSConv (multi-channel shuffle convolution) to improve parameter efficiency and detection accuracy. The nearest neighbor up-sampling is substituted with CARAFE (a lightweight operator) to enhance the receptive field and utilize semantic information. GhostNetV2 replaces the CBS convolutional module for efficient feature extraction through stacking operations. Additionally, the Hard Swish activation function is incorporated to boost accuracy. Experimental results on the NEU-DET dataset demonstrate that the average accuracy of the improved YOLOv5s algorithm is 81.2%, a 1.7% increase compared to the original algorithm, the amount of computation is 17.2GFLOPs and the number of model parameters is 8.65M. The model effectively enhances detection accuracy. © 2024 IEEE.

关键词： Surface defects

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：