检索结果-内蒙古大学图书馆

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

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

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE 5

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

引用

5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Liang, Yiyuan Jia, Hong Wang, Licai Huang, Yangchen Yang, Zhe Big Data Research and Development Center North China Institute of Computing Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350385557

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions. © 2024 IEEE.

关键词： document-level event extraction relation augment Universal Information Extraction

来源：评论

学校读者我要写书评

暂无评论

CHA-Sens: An End-to-End Comprehensive Residual Convolution Framework for CSI-based Human Activity Sensing 26

CHA-Sens: An End-to-End Comprehensive Residual Convolution F...

引用

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhou, Fujia Zhang, Wei Zhu, Guangxu Li, Hang Shi, Qingjiang Tongji University School of Software Engineering Shanghai China Science and Technology on Electronic Information Control Laboratory Chengdu China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798350331684

Channel state information (CSI)-based human activity recognition (HAR) receives increasing research interests due to its broad applications such as human-computer interaction, health care, and security surveillance. Deep learning (DL) methods have been widely adopted on CSI-based HAR tasks to extract features automatically, overcoming the complexity and unstableness of manual feature extraction process. However, many DL approaches fail to customize the designed model structure with the input CSI tensor shape, applying DL models recklessly. In addition, some researchers utilize attention mechanism yet independently along temporal, spatial, or frequency dimension. To address these issues, we propose an end-to-end comprehensive residual convolution framework, namely CHA-Sens, for general CSI-based human activity sensing. CHA-Sens consists of several comprehensive residual convolution modules (CRCM) that feature adaptive kernel size and stride, regional parameter-free attention mechanism and shortcut of identity mapping. Extensive experiments are conducted on three public CSI datasets for recognizing both single human activity (SHA) and human-to-human interaction (HHI) to show the superiority of the proposed design over other state-of-the-art benchmarks. © 2023 IEEE.

关键词： Channel state information

来源：评论

学校读者我要写书评

暂无评论

PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming 41

PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Li...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Li, Bingheng Yang, Linxin Chen, Yupeng Wang, Senmiao Mao, Haitao Chen, Qian Ma, Yao Wang, Akang Ding, Tian Tang, Jiliang Sun, Ruoyu Michigan State University United States School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center For Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Rensselaer Polytechnic Institute United States

Solving large-scale linear programming (LP) problems is an important task in various areas such as communication networks, power systems, finance, and logistics. Recently, two distinct approaches have emerged to expedite LP solving: (i) First-order methods (FOMs);(ii) Learning to optimize (L2O). In this work, we propose a FOM-unrolled neural network (NN) called PDHG-Net, and propose a two-stage L2O method to solve large-scale LP problems. The new architecture PDHG-Net is designed by unrolling the recently emerged PDHG method into a neural network, combined with channel-expansion techniques borrowed from graph neural networks. We prove that the proposed PDHG-Net can recover PDHG algorithm, thus can approximate optimal solutions of LP instances with a polynomial number of neurons. We propose a two-stage inference approach: first use PDHG-Net to generate an approximate solution, and then apply the PDHG algorithm to further improve the solution. Experiments show that our approach can significantly accelerate LP solving, achieving up to a 3× speedup compared to FOMs for large-scale LP problems. Copyright 2024 by the author(s)

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Radio Map Estimation via Latent-Domain Plug-and-Play Denoisers

Radio Map Estimation via Latent-Domain Plug-and-Play Denoise...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xu, Le Cheng, Lei Chen, Junting Pu, Wenqiang Fu, Xiao School of Electrical Engineering and Computer Science Oregon State University Corvallis United States College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798350368741

Radio map estimation (RME) aims to construct a map of radio strength across multiple domains (e.g., space and frequency) from limited measurements. data-driven deep neural model-based RME showed promising performance, yet requiring excessive training resources. This work puts forth an RME approach that can effectively incorporate learned information without training over radio map data. Our idea is to employ the plug-and-play (PnP) denoising scheme from computational imaging. The PnP framework allows incorporating denoisers trained over natural images to handle other types of data, e.g., ocean sound fields and medical images, due to the similarity of their denoising processes. Conventional PnP methods mostly use the learned denoisers in the data domain. Instead, the proposed approach applies PnP in the latent domain through a tailored algorithm design and spatial-spectral factorization of radio maps. This way, the proposed RME method exhibits enhanced scalability and noise robustness. Simulations are used to illustrate the effectiveness of the proposed approach. © 2025 IEEE.

关键词： plug-and-play algorithm design radio map estimation Spectrum cartography

来源：评论

学校读者我要写书评

暂无评论

Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

引用

Journal of Computer science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

来源：评论

学校读者我要写书评

暂无评论

Distributed Bilevel Optimization with Communication Compression 41

Distributed Bilevel Optimization with Communication Compress...

引用

41st International Conference on Machine Learning, ICML 2024

作者： He, Yutong Hu, Jie Huang, Xinmeng Lu, Songtao Wang, Bin Yuan, Kun Peking University China University of Pennsylvania United States IBM Research United States Zhejiang University China National Engineering Labratory for Big Data Analytics and Applications China AI for Science Institute Beijing China

Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conventional distributed bilevel methods, each worker must transmit full-dimensional stochastic gradients to the server every iteration, leading to significant communication overhead and thus hindering efficiency and scalability. To resolve this issue, we introduce the first family of distributed bilevel algorithms with communication compression. The primary challenge in algorithmic development is mitigating bias in hypergradient estimation caused by the nested structure. We first propose C-SOBA, a simple yet effective approach with unbiased compression and provable linear speedup convergence. However, it relies on strong assumptions on bounded gradients. To address this limitation, we explore the use of moving average, error feedback, and multi-step compression in bilevel optimization, resulting in a series of advanced algorithms with relaxed assumptions and improved convergence properties. Numerical experiments show that our compressed bilevel algorithms can achieve 10× reduction in communication overhead without severe performance degradation. Copyright 2024 by the author(s)

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Networked ISAC Beamforming Design with Capacity-Limited Fronthaul Links

Networked ISAC Beamforming Design with Capacity-Limited Fron...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Kexin Xu, Yanqing Chang, Tsung-Hui School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Science & Engineering The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

ISBN: (纸本)9798350368741

This study investigates a networked integrated sensing and communication (ISAC) system, where a central processor coordinates multiple ISAC transmitters and a sensing receiver through capacity-limited fronthaul links. The system aims to serve multiple mobile users and simultaneously sense a point target. The primary objective is to minimize the total transmit power while meeting the minimum signal-to-interference-plus-noise ratio (SINR) requirements for both communication and sensing, as well as adhering to the fronthaul capacity constraints. This leads to a complicated joint fronthaul compression and beamforming design (J-FCBD) problem. Although the J-FCBD problem is challenging to solve, we demonstrate that the optimal fronthaul compression variables can be determined in closed form alongside the beamformers. This novel finding has not been previously reported in the literature. Leveraging this insight, we prove that the remaining problem can be globally solved using the semidefinite relaxation (SDR) technique. Simulation results verify the effectiveness of the proposed design. © 2025 IEEE.

关键词： beamforming design fronthaul compression Networked integrated sensing and communication

来源：评论

学校读者我要写书评

暂无评论

A Novel Price-Oriented Supply-Demand Interaction Scheduling Model Optimizing Virtual Power Plant Operations 16

A Novel Price-Oriented Supply-Demand Interaction Scheduling ...

引用

16th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2024

作者： Wang, Yingxiao Ou, Pengfei Wu, Jianghua Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Shenzhen China School of Mathematics Nanjing University Nanjing China School of Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798350386127

Virtual Power Plants (VPPs) provide a promising direction for managing the complexities and uncertainties in operating Distributed Energy Resources (DERs). However, existing approaches rely on a 'source-following-load' static strategy, falling short in dynamically leveraging market opportunities and transferable demands. To pursue optimal economic performance, this paper introduces a novel price-oriented supply-demand interaction scheduling (SDIS) model for VPP operation. SDIS integrates transferable demands and price signals from the market, renewable generators, and storage systems, enabling VPPs to dynamically balance supply and demand while optimizing both operational efficiency and economic returns. Case studies sampled from the Shanghai energy market with uncertain climatic conditions demonstrate that our model improves the operational efficiency of VPP, reduces economic costs, and enhances the grid stability. Our model paves a way to deal with the complex VPP operation, e.g. grid scenarios involving solar generators, urban metro systems, and central air conditioning clusters. © 2024 IEEE.

关键词： Virtual Power Plants

来源：评论

学校读者我要写书评

暂无评论

Increase in global per capita cropland imbalance across countries from 1985 to 2022:A threat to achieving Sustainable Development Goals

引用

Geography and Sustainability 2025年第2期6卷 253-264页

作者： Tingting Zhao Xiao Zhang Wendi Liu Jinqing Wang Zhehua Li Liangyun Liu School of Geography and Ocean Science Nanjing UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China

Sustainable Development Goal 2(SDG 2,zero hunger)highlights that global hunger and food insecurity have worsened since 2015,driven in part by growing *** the challenge of achieving SDG 2 in the face of rapid global population growth requires sustained attention to global and national cropland *** quantifying the correlation between population and cropland area(i.e.,SDG 2.4.1 per capita cropland)and analyzing the trends of global cropland imbalance are essential for a comprehensive understanding of SDG *** this study,we utilized a new global 30 m land-cover dynamic dataset(GLC_FCS30D)to analyze cropland dynamics,quantify per capita cropland and its changes across various countries and levels of *** results indicate that the global cropland area expanded by 0.944 million km^(2)from 1985 to 2022,with an average expansion rate of 2.42×10^(4)km^(2)/***,the global per capita cropland area decreased from 0.347 ha in 1985 to 0.217 ha in 2022,mainly due to a higher population increase of nearly 65%in the same *** the context of globalization,cropland expansion and per capita cropland exhibited spatial imbalances globally,particularly in developing *** countries saw an increase in total cropland area by 7.09%but a significant decrease in per capita cropland area by 37.38%.From a temporal perspective,the global imbalance has been steadily increasing with the Gini index rising from 0.895 in 1985 to 0.909 in ***,this study reveals an increasing imbalance of global per capita cropland across various countries,which threatens the attainment of the targets of SDG 2.

关键词： Sustainable Development Goals GLC_FCS30D Cropland changes Population Imbalance

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：