检索结果-内蒙古大学图书馆

Many-objective emergency aided decision making based on knowledge graph

APPLIED INTELLIGENCE 2024年第17-18期54卷 7733-7749页

作者： Li, Xiaoxuan Zhao, Tianhao Wen, Jie Cai, Xingjuan Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However, information about emergencies is usually recorded and stored in textual form, and it is difficult for decision-makers to obtain effective information from large amounts of textual data and make decisions that balance various factors. To address these issues, this paper presents an emergency assisted decision-making model based on a knowledge graph and many-objective optimization. First, we preprocess the information by extracting entities to construct a knowledge graph of emergencies to make the event information more structured and easier to use. A knowledge graph is also used to narrow the range of matching historical events. Second, we construct a many-objective model with four objectives: similarity, diversity, processing time, and resource cost. Finally, combining the model characteristics, we design genetic operations for duplicate location matching and substitution removal strategies to obtain the nonrepetitive VaEA algorithm. This algorithm is used to optimize the model and generate a list of reference cases in the reduced matching range to provide rescue strategy suggestions for the current situation. The experimental results show that the algorithm outperforms the comparison algorithms under all four evaluation metrics. This indicates that the method in this paper can match the higher-quality historical emergency solutions applicable to the current decision-making situation in the case base and provide support for decision-makers to respond reasonably in emergencies.

关键词： Emergency-assisted decision-making Knowledge graph Optimization Many-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

A Generative Model-Based Network Framework for Ecological Data Reconstruction

引用

Computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

来源：评论

学校读者我要写书评

暂无评论

NeOR: neural exploration with feature-based visual odometry and tracking-failure-reduction policy

引用

Optoelectronics Letters 2025年第5期21卷 290-297页

作者： ZHU Ziheng LIU Jialing CHEN Kaiqi TONG Qiyi LIU Ruyu College of Computer Science and Technology College of Software Zhejiang University of Technology School of Information Science and Technology Hangzhou Normal University

Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.

关键词： A

来源：评论

学校读者我要写书评

暂无评论

parallel Adjoint Taping Using MPI 23rd

Parallel Adjoint Taping Using MPI

引用

23rd International Conference on computational science, ICCS 2023

作者： Towara, Markus Kremer, Jannick Naumann, Uwe Software and Tools for Computational Engineering RWTH Aachen University Aachen Germany

ISBN: (纸本)9783031359941

The adjoint reversal of long evolutionary calculations (e.g. loops), where each iteration depends on the output of the previous iteration, is a common occurrence in computational engineering (e.g. computational fluid dynamics (CFD) simulation), physics (e.g. molecular dynamics) and computational finance (e.g. long Monte Carlo paths). For the edge case of a scalar state, the execution, as well as adjoint control flow reversal, are inherently serial operations, as there is no spatial dimension to parallelize. Our proposed method exploits the run time difference between passive function evaluation and augmented forward evaluation, which is inherent to most adjoint AD techniques. For high dimensional states, additional parallelization of the primal computation can and should be exploited at the spatial level. Still, for problem sizes where the parallelization of the primal has reached the barrier of scalability, the proposed method can be used to better utilize available computing resources and improve the efficiency of adjoint reversal. We expect this method to be especially useful for operator-overloading AD tools. However, the concepts are also applicable to source-to-source transformation and handwritten adjoints, or a hybrid of all approaches. For illustration, C++ reference implementations of a low dimensional evolution (lorenz attractor) and a high dimensional evolution (computational fluid dynamics problem in OpenFOAM) are discussed. Both theoretical bounds on the speedup and run time measurements are presented. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Molecular dynamics

来源：评论

学校读者我要写书评

暂无评论

Improving the Performance of Lattice Boltzmann Method with Pipelined Algorithm on A Heterogeneous Multi-zone Processor 23rd

Improving the Performance of Lattice Boltzmann Method with...

引用

23rd International Conference on parallel and Distributed Computing, Applications, and Technologies, PDCAT 2022

作者： Zhang, Qingyang Xu, Lei Chen, Rongliang Chen, Lin Chen, Xinhai Wang, Qinglin Liu, Jie Yang, Bo Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410000 China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410000 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9783031299261

Lattice Boltzmann method (LBM) has become a powerful method in computational fluid dynamics and has drawn more and more attention in high-performance computing due to its particulate nature and local dynamics, especially on recent multi-core or many-core platforms. This paper develops a parallel software framework for 3D LBM simulation on a heterogeneous multi-zone processor, MT-3000. An improved pipelined algorithm named Pencil-H is proposed, which can not only fully exploit the advantages of each component of MT-3000 but also overlap the time of calculation and communication. Moreover, an architecture-aware multi-level parallelization algorithm is developed to fully utilize the computational performance of MT-3000. A benchmark test is performed to verify the reliability and test the performance of the LBM code. Experimental results show that the optimized code achieves a 32.02 × speedup compared with using 16 CPU cores and achieves a performance of 286.03MLUPS which reaches 72.3% of the theoretical peak performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computational fluid dynamics

来源：评论

学校读者我要写书评

暂无评论

Assessing the Performance of Chinese Open Source Large Language Models in Information Extraction Tasks

arXiv

引用

arXiv 2024年

作者： Cai, Yida Sun, Hao Huang, Hsiu-Yuan Wu, Yunfang MOE Key Laboratory of Computational Linguistics Peking University School of Software and Microelectronics School of Computer Science China

Information Extraction (IE) plays a crucial role in Natural Language Processing (NLP) by extracting structured information from unstructured text, thereby facilitating seamless integration with various real-world applications that rely on structured data. Despite its significance, recent experiments (Zhang et al., 2023;Wang et al., 2023) focusing on English IE tasks have shed light on the challenges faced by Large Language Models (LLMs) in achieving optimal performance, particularly in sub-tasks like Named Entity Recognition (NER). In this paper, we delve into a comprehensive investigation of the performance of mainstream Chinese open-source LLMs in tackling IE tasks, specifically under zero-shot conditions where the models are not fine-tuned for specific tasks. Additionally, we present the outcomes of several few-shot experiments to further gauge the capability of these models. Moreover, our study includes a comparative analysis between these open-source LLMs and ChatGPT1, a widely recognized language model, on IE performance. Through meticulous experimentation and analysis, we aim to provide insights into the strengths, limitations, and potential enhancements of existing Chinese open-source LLMs in the domain of Information Extraction within the context of NLP. Copyright © 2024, The Authors. All rights reserved.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

EFFICIENT PARTITION-OF-UNITY RADIAL-BASIS-FUNCTION INTERPOLATION FOR COUPLED PROBLEMS

引用

SIAM JOURNAL ON SCIENTIFIC COMPUTING 2025年第2期47卷 B558-B582页

作者： Schneider, David Uekermann, Benjamin Univ Stuttgart Inst Parallel & Distributed Syst D-70569 Stuttgart Germany

Mapping of data between nonmatching meshes is a key ingredient of multiphysics simulations. Black-box data mapping, which only operates on clouds of mesh vertices without connectivity, enables modular software environments. In this paper, we develop such a black-box approach that is capable of handling very large data sets on parallel systems. More precisely, we implement partition-of-unity radial-basis-function interpolation into the coupling library preCICE. The method tackles the data mapping problem by decomposing it into smaller, independent subproblems, which makes it well-suited for parallel computing. To this end, we develop a tailor-made clustering algorithm and study numerical details to ensure robustness and accuracy. We, moreover, deduce user-friendly mapping parameters for which we determine robust default values. Tests on real-world geometries show that the method is scalable and orders of magnitude more efficient than previous data mapping in preCICE. Consequently, the implementation greatly extends the applicability of preCICE, benefiting the library's large user community.

关键词： . multiphysics coupling data mapping partition-of-unity radial-basis functions high-performance computing math software

来源：评论

学校读者我要写书评

暂无评论

Research on Prediction of ChiNext and Blue-Chip Stocks in the Chinese Stock Market Based on Random Forest Algorithm 14

Research on Prediction of ChiNext and Blue-Chip Stocks in th...

引用

14th International Conference on Electronics, Communications and Networks, CECNet 2024

作者： Xie, Guoli Yang, Lei Tian, Liwei Li, Sijin Lai, Jiacheng Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing China

ISBN: (纸本)9781643685632

This research focuses on the predictive analysis of ChiNext and blue-chip stocks in the Chinese stock market, employing the random forest model to conduct in-depth research on the stock trends of these two sectors. In the experimental process, we randomly selected six stocks from each sector as research samples, aiming to explore whether there are significant differences in prediction results between the two. Through rigorous experimental verification and data analysis, we found no significant difference in the predictive effect of random forest models between ChiNext and blue-chip stocks. © 2024 The Authors.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

FedEAE: Federated Learning Based Privacy-Preserving Event Argument Extraction 12th

FedEAE: Federated Learning Based Privacy-Preserving Event Ar...

引用

12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Hu, Fei Dong, Shenpo Chang, Tao Zhou, Jie Li, Haili Wang, Jingnan Chen, Rui Liu, Haijiao Wang, Xiaodong National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031446955

Benefiting from Pre-trained Language Model (PLM), Event Argument Extraction (EAE) methods have achieved SOTA performance in general scenarios of Event Extraction (EE). However, with increasing concerns and regulations on data privacy, aggregating distributed data among different institutions in some privacy-sensitive territories (e.g., medical record analysis, financial statement analysis, etc.) becomes very difficult, and it’s hard to train an accurate EAE model with limited local data. Federated Learning (FL) provides promising methods for a large number of clients to collaboratively learn a shared global model without the need to exchange privacy-sensitive data. Therefore, we propose a privacy-preserving EAE method named FedEAE based on FL to solve the current difficulties. To better adapt to federated scenarios, we design a dataset named FedACE generated from the ACE2005 dataset under IID and Non-IID for our experiments. Extensive experiments show that FedEAE achieves promising performance compared to existing baselines, thus validates the effectiveness of our method. To the best of our knowledge, FedEAE is the first to apply FL in the EAE task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

ON THE EFFECT OF BATCH SIZE IN BYZANTINE-ROBUST DISTRIBUTED LEARNING 12

ON THE EFFECT OF BATCH SIZE IN BYZANTINE-ROBUST DISTRIBUTED ...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Yang, Yi-Rui Shi, Chang-Wei Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Byzantine-robust distributed learning (BRDL), in which computing devices are likely to behave abnormally due to accidental failures or malicious attacks, has recently become a hot research topic. However, even in the independent and identically distributed (i.i.d.) case, existing BRDL methods will suffer a significant drop on model accuracy due to the large variance of stochastic gradients. Increasing batch size is a simple yet effective way to reduce the variance. However, when the total number of gradient computation is fixed, a too-large batch size will lead to a too-small iteration number (update number), which may also degrade the model accuracy. In view of this challenge, we mainly study the effect of batch size when the total number of gradient computation is fixed in this work. In particular, we show that when the total number of gradient computation is fixed, the optimal batch size corresponding to the tightest theoretical upper bound in BRDL increases with the fraction of Byzantine workers. Therefore, compared to the case without attacks, a larger batch size is preferred when under Byzantine attacks. Motivated by the theoretical finding, we propose a novel method called Byzantine-robust stochastic gradient descent with normalized momentum (ByzSGDnm) in order to further increase model accuracy in BRDL. We theoretically prove the convergence of ByzSGDnm for general non-convex cases under Byzantine attacks. Empirical results show that when under Byzantine attacks, using a relatively large batch size can significantly increase the model accuracy, which is consistent with our theoretical results. Moreover, ByzSGDnm can achieve higher model accuracy than existing BRDL methods when under deliberately crafted attacks. In addition, we empirically show that increasing batch size has the bonus of training acceleration. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Stochastic systems

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：