检索结果-内蒙古大学图书馆

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ren, Haopeng Zeng, Yushi Cai, Yi Ye, Zhenqi Yuan, Li Zhu, Pinli School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

ISBN: (纸本)9782493814104

Much of commonsense knowledge in real world is in the form of procudures or sequences of steps to achieve particular goals. In recent years, knowledge extraction on procedural documents has attracted considerable attention. However, they often focus on procedural text but ignore a common multimodal scenario in the real world. Images and text can complement each other semantically, alleviating the semantic ambiguity suffered in text-only modality. Motivated by these, in this paper, we explore a problem of grounded multimodal procedural entity recognition (GMPER), aiming to detect the procedural entity and the corresponding bounding box groundings in images (i.e., visual entities). A new dataset (Wiki-GMPER) is built and extensive experiments are conducted to evaluate the effectiveness of our proposed model. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Adaptive Local Neighborhood Window Laplacian Eigenmaps 24

Adaptive Local Neighborhood Window Laplacian Eigenmaps

引用

13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Xu, Jiayi Huang, Qirui Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400717482

Manifold learning method is a dimensionality reduction method that treats the data in non-Euclidean space as a Euclidean space in a local scope. However, most existing manifold learning methods cannot obtain the true neighborbood relationship of each sample point well. Therefore, we propose a manifold learning method based on adaptive local neighborhood. This method can adaptively select a neighborhood window. It can be approximated as a local Euclidean space based on the sample characteristics. To implement this method, a specific manifold learning method, named Laplacian Eigenmap of Adaptive Local Neighborhood Window (ALNWLE) is proposed. ALNWLE mainly contains two modules, namely the manifold structure learning module (MSLM) and the manifold structure pattern recognition module (MSPR). MSLM constructs an adaptive local neighborhood adjacency matrix to constrain the learning of the manifold structure. MSPR uses the interpolation method to recognize new sample patterns. The adaptive local neighborhood method proposed in this paper is a general method which is applicable to any manifold learning method. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Organoids revealed:morphological analysis of the profound next generation in-vitro model with artificial intelligence

引用

Bio-Design and Manufacturing 2023年第3期6卷 319-339页

作者： Xuan Du Zaozao Chen Qiwei Li Sheng Yang Lincao Jiang Yi Yang Yanhui Li Zhongze Gu State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210008China Key Laboratory of Environmental Medicine Engineering Ministry of EducationSchool of Public HealthSoutheast UniversityNanjing 210009China

In modern terminology,“organoids”refer to cells that grow in a specific three-dimensional(3D)environment in vitro,sharing similar structures with their source organs or *** themorphology or growth characteristics of organoids through a microscope is a commonly used method of organoid ***,it is difficult,time-consuming,and inaccurate to screen and analyze organoids only manually,a problem which cannot be easily solved with traditional *** intelligence(AI)technology has proven to be effective in many biological and medical research fields,especially in the analysis of single-cell or hematoxylin/eosin stained tissue *** used to analyze organoids,AI should also provide more efficient,quantitative,accurate,and fast *** this review,we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis ***,we will summarize the development from machine learning to deep learning and the advantages of the latter,and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and ***,we will discuss the limitations of current AI used in organoid research,as well as opportunities and future research directions.

关键词： Artificial intelligence Organoids Morphology Growth characteristics Deep learning Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

DIG: Improved DINO for Graffiti Detection

引用

IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

来源：评论

学校读者我要写书评

暂无评论

CateEA: Enhancing Entity Alignment via Implicit Category Supervision 31

CateEA: Enhancing Entity Alignment via Implicit Category Sup...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Feng, Guandong Ren, Tao Hu, Jun Wang, Dandan State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761964

Entity Alignment (EA) is essential for integrating Knowledge Graphs (KGs) by matching equivalent entities across diverse KGs. With the rise of multi-modal KGs, which emerged to better depict real-world KGs by integrating visual, textual and structured data, Multi-Modal Entity Alignment (MMEA) has become crucial in enhancing EA. However, existing MMEA methods often neglect the inherent semantic category information of entities, limiting alignment precision and robustness. To address this, we propose Category-enhanced Entity Alignment (CateEA), which combines implicit entity category information into multi-modal representations. By generating pseudo-category labels from entity embeddings and integrating them into a multi-task learning framework, CateEA captures latent category semantics, enhancing entity representations. CateEA allows for adaptive adjustments of similarity measures, leading to improved alignment precision and robustness in multi-modal contexts. Experiments on benchmark datasets, such as FB15K-DB15K/YAGO15K, demonstrate that CateEA outperforms state-of-the-art methods in various settings. © 2025 Association for Computational Linguistics.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Load Balancing Algorithm for SDN Multi-Controller with Elite Genetic Algorithm 6

Load Balancing Algorithm for SDN Multi-Controller with Elite...

引用

6th IEEE International Conference on Electronics and Communication engineering, ICECE 2023

作者： Sun, Shimin Wang, Meiyu Zhou, Le Dong, Jinqi Tiangong University Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tianjin China

ISBN: (纸本)9798350328721

In software Defined Networking (SDN), the distributed deployment of multiple controllers improves the scalability and reliability of the network control plane. However, there is an issue of load imbalance in multi-controller deployments. By modeling the SDN controller load balancing problem, it is transformed into a switch migration strategy selection problem, and a multi-objective optimization model is constructed with the average transmission delay, load balancing degree and migration cost of controller cluster as optimization objectives. In this paper, an Elite Genetic Algorithm (EGAI) Improvement is proposed to solve the model. The convergence speed and stability of the algorithm are improved by introducing self-identification crossover operator and elite retention strategy. Simulation results show that this algorithm can optimize the mapping relationship between controller and switch, and optimize the performance of the network. © 2023 IEEE.

关键词： software defined networking

来源：评论

学校读者我要写书评

暂无评论

Dynamic Fair Federated Learning Based on Reinforcement Learning 5

Dynamic Fair Federated Learning Based on Reinforcement Learn...

引用

5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Chen, Weikang Du, Junping Shao, Yingxia Wang, Jia Zhou, Yangxi Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing China

ISBN: (纸本)9798350393521

Federated learning enables a collaborative training and optimization of global models among a group of devices without sharing local data samples. However, the heterogeneity of data in federated learning can lead to unfair representation of the global model across different devices. To address the fairness issue in federated learning, we propose a dynamic q fairness federated learning algorithm with reinforcement learning, called DQFFL. DQFFL aims to mitigate the discrepancies in device aggregation and enhance the fairness of treatment for all groups involved in federated learning. To quantify fairness, DQFFL leverages the performance of the global federated model on each device and incorporates α-fairness to transform the preservation of fairness during federated aggregation into the distribution of client weights in the aggregation process. Considering the sensitivity of parameters in measuring fairness, we propose to utilize reinforcement learning for dynamic parameters during aggregation. Experimental results demonstrate that our DQFFL outperforms the state-of-the-art methods in terms of overall performance, fairness and convergence speed. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Relation Extraction Model Based on Semantic Enhancement Mechanism 5

Relation Extraction Model Based on Semantic Enhancement Mech...

引用

5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Liu, PeiYu Du, JunPing Shao, YingXia Guan, Zeli Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia BeiJing China

ISBN: (纸本)9798350393521

Relational extraction is one of the basic tasks related to information extraction in natural language processing, and is an important link and core task in the fields of information extraction, natural language understanding, and information retrieval. None of the existing relation extraction methods can effectively solve the problem of triple overlap. The CasAug model proposed in this paper based on the CasRel framework combined with the semantic enhancement mechanism can solve this problem to a certain extent. The CasAug model enhances the semantics of the identified possible subjects by adding a semantic enhancement mechanism, First, based on the semantic coding of possible subjects, pre-classify the possible subjects, and then combine the subject lexicon to calculate the semantic similarity to obtain the similar vocabulary of possible subjects. According to the similar vocabulary obtained, each word in different relations is calculated through the attention mechanism. For the contribution of the possible subject, finally combine the relationship pre-classification results to weight the enhanced semantics of each relationship to find the enhanced semantics of the possible subject, and send the enhanced semantics combined with the possible subject to the object and relationship extraction module. Complete the final relation triplet extraction. The experimental results show that, compared with the baseline model, the CasAug model proposed in this paper has improved the effect of relation extraction, and CasAug's ability to deal with overlapping problems and extract multiple relations is also better than the baseline model, indicating that the semantic enhancement mechanism proposed in this paper It can further reduce the judgment of redundant relations and alleviate the problem of triple overlap. © 2023 IEEE.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Take-Over Performance Prediction and Its Application on intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on intelligent Vehicles 2024年 1-15页

作者： Liu, Weimin Li, Qingkun Wang, Wenjun Wang, Zhenyuan Zeng, Chao Cheng, Bo State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE

关键词： Automation

来源：评论

学校读者我要写书评

暂无评论

A Transparent Polymer-Composite Film for Window Energy Conservation

引用

Nano-Micro Letters 2025年第7期17卷 9-19页

作者： Xianhu Liu Haoyu Zhang Yamin Pan Jun Ma Chuntai Liu Changyu Shen College of Materials Science and Engineering State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou University Uni SA STEM and Future Industries Institute University of South Australia

As living standards improve, the energy consumption for regulating indoor temperature keeps increasing. Windows, in particular, enhance indoor brightness but also lead to increased energy loss, especially in sunny weather. Developing a product that can maintain indoor brightness while reducing energy consumption is a challenge. We developed a facile, spectrally selective transparent ultrahigh-molecular-weight polyethylene composite film to address this trade-off. It is based on a blend of antimony-doped tin oxide and then spin-coated hydrophobic fumed silica, achieving a high visible light transmittance(> 70%) and high shielding rates for ultraviolet(> 90%) and near-infrared(> 70%). When applied to the acrylic window of containers and placed outside, this film can cause a 10 ℃ temperature drop compared to a pure polymer film. Moreover, in building energy simulations, the annual energy savings could be between 14.1% ～ 31.9% per year. The development of energy-efficient and eco-friendly transparent films is crucial for reducing energy consumption and promoting sustainability in the window environment.

关键词： Composite Energy conservation Polymer Radiative cooling Transparent films

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：