检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

9,878 篇 会议
288 篇 期刊文献
93 册 图书

馆藏范围

10,259 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,731 篇 工学
- 6,195 篇 计算机科学与技术...
- 4,318 篇 软件工程
- 1,296 篇 信息与通信工程
- 860 篇 控制科学与工程
- 694 篇 生物工程
- 593 篇 电气工程
- 447 篇 生物医学工程（可授...
- 428 篇 机械工程
- 357 篇 电子科学与技术（可...
- 350 篇 光学工程
- 297 篇 化学工程与技术
- 246 篇 仪器科学与技术
- 232 篇 网络空间安全
- 223 篇 动力工程及工程热...
- 218 篇 安全科学与工程
2,826 篇 理学
- 1,451 篇 数学
- 773 篇 物理学
- 726 篇 生物学
- 457 篇 统计学（可授理学、...
- 302 篇 化学
- 230 篇 系统科学
2,594 篇 教育学
- 2,580 篇 教育学
2,069 篇 管理学
- 1,259 篇 管理科学与工程(可...
- 914 篇 图书情报与档案管...
- 782 篇 工商管理
488 篇 法学
- 441 篇 社会学
477 篇 医学
- 360 篇 临床医学
- 310 篇 基础医学(可授医学...
- 218 篇 公共卫生与预防医...
210 篇 经济学
- 200 篇 应用经济学
136 篇 文学
120 篇 农学
40 篇 艺术学
13 篇 军事学
2 篇 哲学

主题

514 篇 students
472 篇 education
322 篇 technological in...
286 篇 computer science
192 篇 computer science...
162 篇 teaching
150 篇 engineering educ...
131 篇 artificial intel...
108 篇 deep learning
101 篇 computational mo...
100 篇 software enginee...
100 篇 machine learning
99 篇 training
97 篇 internet of thin...
92 篇 computer aided i...
91 篇 big data
87 篇 curricula
86 篇 semantics
83 篇 accuracy
80 篇 data mining

机构

110 篇 yerevan physics ...
77 篇 department of ph...
77 篇 department of ph...
77 篇 faculty of scien...
77 篇 department of ph...
76 篇 department of ph...
76 篇 departamento de ...
76 篇 department for p...
76 篇 department of ph...
76 篇 kirchhoff-instit...
76 篇 fakultät für phy...
74 篇 faculté des scie...
74 篇 graduate school ...
70 篇 tomsk state univ...
69 篇 department of ph...
69 篇 department of ph...
69 篇 department of ph...
69 篇 institute of phy...
68 篇 department of ph...
68 篇 institut für ast...

作者

77 篇 c. alexa
77 篇 j. m. izen
77 篇 g. bella
77 篇 j. strandberg
77 篇 d. calvet
76 篇 c. amelung
70 篇 s. veneziano
70 篇 h. a. gordon
69 篇 g. spigo
68 篇 f. siegert
68 篇 a. c. könig
68 篇 m. klein
67 篇 n. orlando
67 篇 r. ströhmer
67 篇 h. sakamoto
67 篇 m. rijssenbeek
67 篇 s. jin
67 篇 g. gaudio
66 篇 s. nektarijevic
66 篇 f. marroquim

语言

9,925 篇 英文
296 篇 其他
73 篇 中文
5 篇 法文
2 篇 西班牙文
1 篇 德文
1 篇 葡萄牙文

检索条件"任意字段=15th Innovation and Technology in Computer Science Education Conference"

共 10259 条记录，以下是121-130 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

the Need for Accuracy and Smoothness in Numerical Simulations 15th

The Need for Accuracy and Smoothness in Numerical Simulat...

引用

15th International conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Kjelgaard Mikkelsen, Carl Christian López-Villellas, Lorién Department of Computing Science Umeå University Umeå90187 Sweden Universidad de Zaragoza Zaragoza Spain

ISBN: (纸本)9783031856969

We consider the problem of estimating the error when solving a system of differential algebraic equations. Richardson extrapolation is a classical technique that can be used to judge when computational errors are irrelevant and estimate the discretization error. We have simulated molecular dynamics with constraints using the GROMACS library and found that the output is not always amenable to Richardson extrapolation. We derive and illustrate Richardson extrapolation using a variety of numerical experiments. We identify two necessary conditions that are not always satisfied by the GROMACS library. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Extrapolation

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Review of Capsule Networks with a Case Study on Potato Leaf Disease Detection Using CapsNet and Attention Mechanism 8th

Comprehensive Review of Capsule Networks with a Case Stud...

引用

8th International Symposium on Intelligent Informatics, ISI 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819721467

Accurate solutions are needed for a variety of computer vision applications, including medical imaging, object detection, and recognition. Such complicated challenges are beyond the capabilities of artificial intelligence and machine learning, which mainly rely on data and algorithms to learn. the path to Deep Learning (DL) is thus paved. Even though deep learning is incredibly effective at solving complex problems, it is invariant, meaning that it ignores the spatial relationship between the features and instead searches for features only. this can cause the model’s performance to suffer. Additionally, because the model is invariant, gathering a large volume of training data is a difficult undertaking. To address this, the Capsule Network (CapsNet), whose performance outperforms Deep Learning techniques, was introduced. Despite its success, applications of CapsNet and working remain a mystery. thus, the performance of CapsNet across many applications and obstacles is examined in this study together with the predecessors of CapsNet. Additionally, this paper explores the use of Capsule Networks with an attention mechanism (CapsNet-ATM) for predicting potato leaf diseases. the performance of CapsNet-ATM in this crucial agricultural application is thoroughly assessed and contrasted with the well-known models VGG-16 and VGG-19, providing insight into the system’s efficacy and promise as a reliable tool for the detection and prediction of potato leaf disease. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Fair and Communication-Efficient Personalized Federated Learning 18th

Fair and Communication-Efficient Personalized Federated Lea...

引用

18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zheng, Yifu Zheng, Xu Wang, Tingqi Mu, Chong Zhakiyev, Nurkhat School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9783031714665

Personalized Federated Learning (pFL) is among the most popular tasks in distributed deep learning, which compensates for mutual knowledge and enables device-specific model personalization. However, the effectiveness of pFL is severely impeded by challenges on fairness and significant communication overhead, where devices holding essential samples have to devote extensive resources for model training. To address these issues, we introduce the Fair and Communication-Efficient Personalized Federated Learning (FCE-PFL) framework, which harmonizes performance and device fairness while maintaining communication costs. Based on the Dempster-Shafer theory, FCE-PFL employs assistance and contribution metrics to quantify the auxiliary information a client receives and provides. then FCE-PFL balances and adjusts the involvement of devices in each training iteration, so as to facilitate fair training by constraining the maximum resource consumption per device and reduce the overall communication overhead. Our framework has been proven to be superior to existing methods in accuracy through rigorous experiments on the CIFAR10 and CIFAR100 datasets, demonstrating its potential as a fair and efficient pFL solution. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

swDarknet: A Heterogeneous Parallel Deep Learning Framework Suitable for SW26010 Pro Processor 20th

swDarknet: A Heterogeneous Parallel Deep Learning Framework ...

引用

20th IFIP WG 10.3 International conference on Network and Parallel Computing, NPC 2024

作者： Liu, Huazeng Tian, Min Wu, Xiaoming Yang, Meihong Ren, Zenghui Li, Yanlong Jinan China Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819628292

As the size of AI models grows dramatically to achieve higher accuracy, more and more models are trained on supercomputers with massive computing and memory. However, it is still challenging to accelerate deep learning applications on supercomputer systems with many-core architectures. In this paper, we propose swDarknet: a Darknet-based heterogeneous parallel deep learning framework on the SW26010 Pro processor. First, We designed three heterogeneous parallel DNN layers: adaptive block-splitting strategy for matrix-multiplication layer, optimized memory access for convolutional layer, and block-based parallel pooling layer. Second, we proposed a model averaging (MA) method to efficiently scale swDarknet across many processors. Finally, we evaluated the performance of swDarknet on a core group. As compared with darknet, the convolutional layers achieved an average speedup of 32x. When training AlexNet and VGG-16, swDarknet can achieve up to 36.79× and 181.35× speedup, respectively. © IFIP International Federation for Information Processing 2025.

关键词： Supercomputers

来源：评论

学校读者我要写书评

暂无评论

Data Poisoning Attack Against Reinforcement Learning from Human Feedback in Robot Control Tasks 24th

Data Poisoning Attack Against Reinforcement Learning from H...

引用

24th International conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhou, Zihui Gao, Yutong Qi, Minfeng Wuhan430074 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China City University of Macau 999078 China

ISBN: (纸本)9789819615247

Reinforcement Learning from Human Feedback (RLHF) technology provides a method for agents to learn human preferences and perform actions that satisfy human desires. this technology was originally used to complete robot control tasks in situations where it was difficult to design a reward function. However, in the process of collecting human feedback, malicious human annotators may launch attacks on RLHF, posing a significant challenge to the technology. Previous research has mostly focused on studying the harm caused by different attacks on RLHF in fine-tuning large language models (LLMs). However, our study is focused on the field of robot control. We designed two data poisoning attacks against human feedback datasets and implemented our attacks in three different offline reinforcement learning (RL) environments. the experimental results show that RLHF is vulnerable to data poisoning attacks in robot control tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Recurrent Neural Network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

引用

13th International conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. the linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models 31

FinDABench: Benchmarking Financial Data Analysis Ability of ...

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Liu, Shu Zhao, Shangqing Jia, Chenghao Zhuang, Xinlin Long, ZhaoGuang Zhou, Jie Zhou, Aimin Lan, Man Chong, Yang Lab of Artificial Intelligence for Education East China Normal University China Shanghai Institute of Artificial Intelligence for Education East China Normal University China School of Computer Science and Technology East China Normal University China Bytedance

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks. However, their proficiency and reliability in the specialized domain of financial data analysis, particularly focusing on data-driven thinking, remain uncertain. To bridge this gap, we introduce FinDABench, a comprehensive benchmark designed to evaluate the financial data analysis capabilities of LLMs within this context. the benchmark comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts. FinDABench assesses LLMs across three dimensions: 1) Core Ability, evaluating the models' ability to perform financial indicator calculation and corporate sentiment risk assessment;2) Analytical Ability, determining the models' ability to quickly comprehend textual information and analyze abnormal financial reports;and 3) Technical Ability, examining the models' use of technical knowledge to address real-world data analysis challenges involving analysis generation and charts visualization from multiple perspectives. We will release FinDABench, and the evaluation scripts at https://***/cubenlp/FinDABench. FinDABench aims to provide a measure for in-depth analysis of LLM abilities and foster the advancement of LLMs in the field of financial data analysis. © 2025 Association for Computational Linguistics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Integrated Word2Vec-based Speech Annotations for Enhanced EEG Decoding of Speech Intentions 13

Integrated Word2Vec-based Speech Annotations for Enhanced EE...

引用

13th International Winter conference on Brain-computer Interface, BCI 2025

作者： Jang, Se-Na Park, Hyeong-Yeong Kim, Seong-Wook Yu, Seong-Hyun Lee, Minji Jeong, Ji-Hoon Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interface (BCI) technology has promising applications as an intuitive communication tool and in fields such as language rehabilitation. this study aims to decode human speech intentions by analyzing EEG signals recorded during actual and imagined speech. EEG data were collected using a 64 channels system, with preprocessing to remove artifacts. Automatic speech recognition (ASR) was used to extract precise speech onset times, generating time-specific speech annotations corresponding with EEG data. Pretrained Word2Vec embeddings were integrated to provide semantic context, combining neural signals with high-level linguistic features. Support vector machine (SVM), linear discriminant analysis (LDA) were employed for decoding. the results demonstrate that integrating speech annotations improves decoding accuracy, even for imagined speech, highlighting the potential of BCI technology for advanced applications in communication and rehabilitation. © 2025 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Cross-Modal Mask and Detail Alignment for Text-Based Person Retrieval 24th

Cross-Modal Mask and Detail Alignment for Text-Based Perso...

引用

24th International conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Guo, Ao Liu, Xuan Liu, Xianggan Hu, Bingmeng Yuan, Jie Chu, Chiawei Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan Lingshui Li572499 China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China City University of Macau China

ISBN: (纸本)9789819615278

Text-based person retrieval primarily aims to retrieve the images of target persons represented by a given text query. In this task, how to effectively align images and text globally and locally is an important challenge. At the same time, since multiple modalities are involved, reducing the differences between different modalities is also an important challenge. Existing work has focused more on reconstructing an image rather than the semantic consistency between the image and text modalities. therefore, we introduce the Cross-Modal Mask and Detail Alignment (CMDA) framework to address these challenges. Under this framework, our proposed Cross-Modal Mask Alignment module (CMA) semantically aligns features generated by supplementing randomly masked image/text with another modality text/image. Additionally, to narrow the gap between the image and text modalities, we designed the Cross-Modal Detail Alignment module (CDA), which establishes connections between images and texts and facilitates interactions between these two different modalities. Experimental results show that our model exhibits outstanding performance across multiple public datasets, i.e., CUHK-PEDES, ICFG-PEDES, and RSTPReID. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Graph Structure-Feature Learning Network for Diagnosing Alzheimer’s Disease Based on Multi-modal Brain Biometric Feature 18th

A Graph Structure-Feature Learning Network for Diagnosing A...

引用

18th Chinese conference on Biometric Recognition, CCBR 2024

作者： Shang, Dongxu Wang, Huabin Zhang, Mengxin Peng, Yuhang Ye, Xingjian Wang, Zilin Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui Hefei China Stony Brook Institute at Anhui University Anhui Hefei China

ISBN: (纸本)9789819610709

Alzheimer’s Disease (AD) is a neurodegenerative disease, typically identified and diagnosed through brain biometric feature. However, certain common brain biometric feature exist among patients in different stages of AD, which result in incorrect edges within the graph structure. To address above issues, this paper proposes a Graph Structure-Feature Learning Network (GSFLN) model. the model simultaneously learns a graph structure that appropriately represents the relationships among patients and selects the significant pathogenic brain biometric feature of patients. Firstly, a Graph Convolutional Clustering (GCC) module is designed to capture the relationships between non-image data of patients. Secondly, a Pathogenic Feature Selection (PFS) module is designed to capture the relationships between image data of patients and select the pathogenic brain biometric feature. thirdly, an Unsupervised Predictive Clustering (UPC) module is designed to control the above two modules. Our proposed model has surpassed the current state-of-the-art level in diagnostic performance on both the NACC and Tadpole datasets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alzheimer’s Disease brain biometric feature Feature Selection Graph Convolutional Network Graph Structure Learning

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 9 10 11 12 13 14 15 16 17 18 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：