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

7,746 篇 会议
98 篇 期刊文献
55 册 图书

馆藏范围

7,899 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,793 篇 工学
- 2,016 篇 计算机科学与技术...
- 1,391 篇 软件工程
- 653 篇 控制科学与工程
- 502 篇 信息与通信工程
- 497 篇 电气工程
- 252 篇 机械工程
- 206 篇 仪器科学与技术
- 186 篇 电子科学与技术（可...
- 136 篇 生物医学工程（可授...
- 122 篇 生物工程
- 91 篇 光学工程
- 79 篇 网络空间安全
- 78 篇 动力工程及工程热...
- 67 篇 化学工程与技术
- 64 篇 力学（可授工学、理...
- 53 篇 材料科学与工程（可...
- 51 篇 交通运输工程
1,052 篇 理学
- 680 篇 数学
- 288 篇 统计学（可授理学、...
- 220 篇 物理学
- 178 篇 系统科学
- 148 篇 生物学
- 79 篇 化学
671 篇 管理学
- 549 篇 管理科学与工程(可...
- 199 篇 工商管理
- 161 篇 图书情报与档案管...
128 篇 医学
- 99 篇 临床医学
- 80 篇 基础医学(可授医学...
68 篇 法学
- 67 篇 社会学
49 篇 经济学
- 48 篇 应用经济学
33 篇 教育学
20 篇 农学
19 篇 军事学
10 篇 文学
2 篇 艺术学

主题

1,884 篇 computational mo...
1,020 篇 measurement
439 篇 computer archite...
423 篇 neural networks
409 篇 predictive model...
348 篇 computer science
293 篇 accuracy
285 篇 biological syste...
258 篇 analytical model...
239 篇 noise measuremen...
224 篇 computer simulat...
221 篇 real-time system...
217 篇 training
216 篇 brain modeling
215 篇 mathematical mod...
211 篇 feature extracti...
187 篇 power system mod...
181 篇 neurons
178 篇 data models
175 篇 control systems

机构

20 篇 school of comput...
17 篇 department of el...
15 篇 school of electr...
14 篇 budapest univ te...
13 篇 chitkara univers...
13 篇 school of comput...
12 篇 department of co...
11 篇 department of co...
10 篇 college of compu...
10 篇 college of compu...
9 篇 tsinghua univers...
8 篇 department of el...
8 篇 department of co...
7 篇 school of comput...
7 篇 graphic era deem...
7 篇 school of comput...
7 篇 school of comput...
7 篇 peng cheng labor...
7 篇 school of contro...
6 篇 college of compu...

作者

11 篇 towsley don
10 篇 varró dániel
9 篇 wei wang
8 篇 liu alex x.
8 篇 anon
7 篇 liu yang
7 篇 wei li
7 篇 wang jia
6 篇 li zhang
6 篇 byung-ju yi
6 篇 reiner r. dumke
6 篇 yang liu
5 篇 deepak banerjee
5 篇 chunyan miao
5 篇 li li
5 篇 t.m. khoshgoftaa...
5 篇 rizzi antonello
5 篇 j.c. principe
5 篇 sakorn mekruksav...
5 篇 bergmann gábor

语言

7,791 篇 英文
83 篇 其他
26 篇 中文
5 篇 俄文

检索条件"任意字段=Joint International Conference on Measurement and Modeling of Computer Systems"

共 7899 条记录，以下是931-940 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：

Self-Supervised Monocular Depth Estimation with Effective Feature Fusion and Self Distillation

Self-Supervised Monocular Depth Estimation with Effective Fe...

引用

IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Zhenfei Liu Chengqun Song Jun Cheng Jiefu Luo Xiaoyang Wang Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Monocular depth estimation obtaining scene depth information from a single image is an important task in the field of computer vision. Constrained by the limitations of convolutional networks in conducting long-distance modeling and the underutilization of datasets, the generalization of existing models is not satisfactory. In this paper, we propose an adaptive backbone named Internal Fusion Transformer to improve generalization ability compared to convolutional backbone, like HRNet, and a Bilateral Attention module which pays more attention to low-level semantic features compared to previous fuse methods. Meanwhile, we introduce three data augmentation methods, namely cropping-resizing (cr), cropping-shuffling (cs), and mirroring (mi), for self distillation, as well as discuss their contributions to model performance improvement. Our model is trained on the KITTI dataset, and without fine-tuning, tested on NYUv2 and Make3D datasets to confirm the generalization. The experimental results illustrate the effectiveness of our design. Our model also demonstrates better performance compared to other models on the KITTI dataset.

关键词： Training Adaptation models Accuracy Depth measurement Computational modeling Semantics Data augmentation Transformers Data models Robustness

来源：评论

学校读者我要写书评

暂无评论

joint Embeddings for Graph Instruction Tuning

Joint Embeddings for Graph Instruction Tuning

引用

international conference on Intelligent Computing in Data Sciences (ICDS)

作者： Aaron Haag Vlad Argatu Oliver Lohse Siemens Technology Department Munich Germany

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

Large Language Models (LLMs) have achieved impressive performance in text understanding and have become an essential tool for building smart assistants. Originally focusing on text, they have been enhanced with multimodal capabilities in recent works that successfully built visual instruction following assistants. As far as the graph modality goes, however, no such assistants have yet been developed. Graph structures are complex in that they represent relation between different features and are permutation invariant. Moreover, representing them in purely textual form does not always lead to good LLM performance even for finetuned models. As a result, there is a need to develop a new method to integrate graphs in LLMs for general graph understanding. This work explores the integration of the graph modality in LLM for general graph instruction following tasks. It aims at producing a deep learning model that enhances an underlying LLM with graph embeddings and trains it to understand them and to produce, given an instruction, an answer grounded in the graph representation. The approach performs significantly better than a graph to text approach and remains consistent even for larger graphs.

关键词： Training measurement Visualization Computational modeling Virtual assistants Large language models Graphics processing units Focusing computer architecture Tuning

来源：评论

学校读者我要写书评

暂无评论

Real-time Evaluation System for Top Taekwondo Athletes: Project Overview 14

Real-time Evaluation System for Top Taekwondo Athletes: Proj...

引用

14th international joint conference on Biomedical Engineering systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired systems and Signal Processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Cunha, Pedro Barbosa, Paulo Ferreira, Fabio Fitas, Carlos Carvalho, Vitor Soares, Filomena IPCA Sch Technol 2Ai Barcelos Portugal Univ Minho Algoritmi Res Ctr Guimaraes Portugal

ISBN: (纸本)9789897584909

Assessing athletes' performance is a constant challenge for coaches, whatever the sport is. In some sports there are no technological solutions to assist coaches in this task. This is the case of Taekwondo, where currently the methods used are mainly manual. Following this trend, this article presents the work developed in a PhD project whose main objective is the development of a friendly and low-cost system for assessing the performance of Taekwondo athletes in real time. Thus, the system uses a 3D camera with depth sensor (Orbbec Astra), a computer and software developed for data collection and processing. The system also provides the inclusion of Inertial measurement Units (IMUs). The system allows an accurate feedback for the correction or improvement of the athletes techniques, enabling an increase in the athletes performance in a shorter period of time. In all, the project contributes to the evolution of the techniques used during Taekwondo training, as well as to the technological development in the practice of Taekwondo.

关键词： Deep Learning Motion Analysis Neural Networks Wearable computer Vision Taekwondo

来源：评论

学校读者我要写书评

暂无评论

Multimodal Emotion Cause Pair Extraction in Conversations Using Knowledge Distillation and Large Language Models

Multimodal Emotion Cause Pair Extraction in Conversations Us...

引用

Computational Intelligence and Network systems (CINS), international conference on

作者： Aruna Gladys A Vetriselvi V Rajasekar S K Department of Computer Science and Engineering College of Engineering Guindy Anna University Chennai Tamil Nadu India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

This paper presents a novel approach to Multimodal Emotion Cause Pair Extraction in Conversations (ECPEC), by addressing the complexities of emotion recognition by integrating features from different data sources. The method consists of a two-stage process. In the first context modeling stage, context-rich summaries of conversations filled with emotion cues are generated using a teacher-student knowledge distillation approach, lever-aging a Large Language Model (LLM) as the teacher and fine-tuning smaller LLM to mimic its summarizing capability. The generated summaries serve as input for the second stage, where the linguistic, acoustic, and visual features are fused to extract both emotions and emotion-cause pairs. To enhance the learning process, Direct Preference Optimization (DPO) is employed to fine-tune the compact LLM model by creating a preference dataset using a Ground Truth Augmented Reasoning technique. The responses from the fine-tuned LLM are converted into structured outputs using Mistral 7B model facilitating objective evaluation through the traditional performance metrics used in classification tasks. The findings demonstrate the critical role of multimodal data in capturing emotions, by enhancing the role of each modality through the reasoning capabilities of the LLM models. This work establishes a robust framework for ECPEC, demonstrating the effectiveness of integrating multimodal cues and advanced LLM fine-tuning strategies in affect computing tasks.

关键词： measurement Emotion recognition Visualization Large language models Computational modeling Soft sensors Feature extraction Cognition Optimization Network systems

来源：评论

学校读者我要写书评

暂无评论

Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201

Knee Osteoarthritis detection and classification using a cus...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： N. Shirisha K. Shilpa CH. Meghana Mohammed Abdul Rahman Dept. of Computer Science and Engineering MLR Institute of Technology Hyderabad India Dept. of Computer Science and Engineering MLR Institute of Technology Nalgonda India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Knee osteoarthritis (KOA) is a prevalent joint disorder diagnosed using imaging modalities like MRI, CT scans, and X-rays, with X-rays being the most cost-effective. Early detection is crucial for effective management. This study presents an automated deep learning approach to detect and classify KOA severity based on the Kellgren-Lawrence (KL) grading system using single posteroanterior standing knee X-ray images. Utilizing the Osteoarthritis Initiative dataset, we employed transfer learning to fine-tune DenseNet-201, enhancing model performance. Additionally, knowledge distillation was applied to reduce computational complexity while maintaining accuracy. Our model achieved over 95% accuracy on both testing and cross-validation datasets, outperforming existing methods. This approach offers a reliable tool for early KOA diagnosis and grading, potentially aiding clinical decision-making

关键词： Training Deep learning Accuracy Computational modeling Magnetic resonance imaging Computed tomography X-rays Feature extraction Osteoarthritis Testing

来源：评论

学校读者我要写书评

暂无评论

Overcoming Dataset Imbalances and Computational Challenges in IoT Intrusion Detection: A SMOTE-Enhanced Transformer-Based Model

Overcoming Dataset Imbalances and Computational Challenges i...

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： S. Venkatasubramanian S. K. B. Pradeep Kumar Ch Bairysetti Prasad Babu Computer Science and Business Systems Saranathan College of Engineering Trichy India Electirical and Electronics Engineering Ramachandra College of Engineering Eluru India Computer Science and Engineering Ramachandra College of Engineering Eluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Due to security holes in IoT-enabled networks, the massive adoption of IoT has unleashed a new wave of threats into computer networks. Due to their limited resources, these devices are ill-equipped to handle security measures, making them the most vulnerable part of our computer networks and putting our data and systems at risk. Some have suggested Intrusion Detection systems (IDS) as a means to lessen the impact of intrusions connected to the IoT in an effort to solve this problem. Although IDS are essential for detecting threats, they are not widely used because of their high computing costs and reliance on labelled data. Finding a happy medium between fast detection and excellent accuracy is the objective of this model development effort. In order to accomplish this, to present a feature selection method based on the black-winged kite algorithm (BKA), a groundbreaking swarm intelligence technique that combines the Leader tactic and the Cauchy variation procedure to find the expansive appropriate convergence solution. BKA is based on the migratory and predatory instincts of black-winged kites. The study used few-shot learning techniques by leveraging transfer learning models and specifically using pre-trained architectures for categorisation. It also fine-tuned four pre-trained transformers. To take into account the imbalance in these datasets as a significant component and use the Synthetic Minority Oversampling Technique (SMOTE) to address it. The research team used BoT-IoT, TON-IoT, and CIC-DDoS2019 as benchmark datasets to assess the impact of the chosen features on the suggested model's performance. Metrics for evaluation include F1-score, recall, precision, and accuracy of detection. Evidence from experiments shows that the suggested ensemble model outperforms state-of-the-art AIDS topologies for IoT networks in terms of detection accuracy and efficiency.

关键词： measurement Accuracy Computational modeling Intrusion detection Feature extraction Transformers Prediction algorithms Internet of Things Security Few shot learning

来源：评论

学校读者我要写书评

暂无评论

GNSS Interference Classification Using Federated Reservoir Computing

GNSS Interference Classification Using Federated Reservoir C...

引用

international conference on Communication Technology (ICCT)

作者： Ziqiang Ye Yulan Gao Xinyue Liu Yue Xiao Ming Xiao Saviour Zammit National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu China Division of Information Science and Engineering KTH Royal Institute of Technology Stockholm Sweden SWJTU-Leeds Joint School Southwest Jiaotong University Chengdu China Department of Communications and Computer Engineering University of Malta Malta

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The expanding use of Unmanned Aerial Vehicles (UAVs) in vital areas like traffic management, surveillance, and environmental monitoring highlights the need for robust communication and navigation systems. Particularly vulnerable are Global Navigation Satellite systems (GNSS), which face a spectrum of interference and jamming threats that can significantly undermine their performance. While traditional deep learning approaches are adept at mitigating these issues, they often fall short for UAV applications due to significant computational demands and the complexities of managing large, centralized datasets. In response, this paper introduces Federated Reservoir Computing (FedRC) as a potent and efficient solution tailored to enhance interference classification in GNSS systems used by UAVs. Our experimental results demonstrate that FedRC not only achieves faster convergence but also sustains lower loss levels than traditional models, highlighting its exceptional adaptability and operational efficiency.

关键词： Deep learning Global navigation satellite system Adaptation models Computational modeling Reservoir computing Interference Traffic control Autonomous aerial vehicles Jamming Convergence

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence (AI)-Enhanced Security Monitoring and Threat Detection in Cloud Infrastructures

Artificial Intelligence (AI)-Enhanced Security Monitoring an...

引用

Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Ramesh Bishukarma Sumeet Mathur Sandeep Gupta University of Waikato NZ – Joint Institute at Zhejiang University Hangzhou China Department of Computer Science Engineering SATI Vidisha India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Threat identity and counteraction measures are critical to data security and system integrity in the rapidly evolving environment of cloud computing. The current paper proposes a method to improve security monitoring and threat detection in cloud environments with ML algorithms. The approach involves data pre-processing on a data set obtained from KDD Cup’99 comprising of data imputation for missing values, normalisation using min-max scaling respectively, and feature selection by employing RFE for the efficient execution of the model. The data is divided into training and testing datasets and the proportion used is 80% training and 20% test. Classification is done with Isolated Heuristic Neural Networks (IHNN), as well as with GRU and Random Forest methods. As seen from the result analysis, the IHNN model established a high performance with a 99.35% accuracy, 96.70% precision, 91.20% recall, and a 96.32% F1 score. The efficiency of the model is then measured with parameters such as accuracy and error rate, precision, recall rate and F-score and confusion matrix in order to study errors committed. IHNN outperforms other comparable models, such as GRU and RF, in the context of threat detection in cloud environments, thus providing assured security analysis.

关键词： Training Radio frequency Cloud computing Analytical models Accuracy Neural networks measurement uncertainty Threat assessment Monitoring Random forests

来源：评论

学校读者我要写书评

暂无评论

Enhancing Intrusion Detection systems with Deep Learning and Machine Learning Algorithms for Real-Time Threat Classification

Enhancing Intrusion Detection Systems with Deep Learning and...

引用

Multi-Agent systems for Collaborative Intelligence (ICMSCI), international conference on

作者： Farooque Kadri Priyanka Singla ODYSSEY CLOUD Pune India BPIT Delhi India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

computer network intrusion has surged dramatically over the past decade, fueled by a lucrative underground cybercrime economy and the proliferation of sophisticated tools for executing such attacks. For over forty years, researchers from both industry and academia have been developing methods and systems to detect and prevent these security breaches. Machine learning (ML) techniques have emerged as a powerful tool among the various methods used to classify normal and abnormal behavior. This study presents an advanced preprocessing strategy combined with a comprehensive evaluation of traditional machine learning models and a deep neural network (DNN) for real-time network intrusion detection. Preprocessing includes univariate analysis, feature selection, outlier handling, data normalization, and PCA-based dimensionality reduction. Further, the dataset was divided into train and test sets in the ratio 80:20. Traditional models GB, DT, and ET have been tested; however, ET performs better than all concerning accuracy, precision, recall, Fl score, and kappa. The proposed DNN model further enhanced detection capabilities, achieving the highest metrics across all evaluation parameters, including an AUC of 1.00, an accuracy of 99.50% and the lowest misclassification rate. The performance of both traditional and deep learning models was assessed using precision, recall, kappa, accuracy, Fl score, ROC curve analysis, and confusion matrices. The results indicate that the proposed approach is efficient for real-time network intrusion detection and threat analysis.

关键词： Deep learning measurement Industries Accuracy Machine learning algorithms Biological system modeling Network intrusion detection Artificial neural networks Real-time systems Security

来源：评论

学校读者我要写书评

暂无评论

Time Series Class-Incremental Learning with Residual Mamba Encoder

Time Series Class-Incremental Learning with Residual Mamba E...

引用

computers and Artificial Intelligence Technologies (CAIT), international conference on

作者： Bailing Zhang Kexuan Zhou School of Computer and Data Engineering NingboTech University Ningbo China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

Class-incremental learning aims to enable systems to adapt to new classes over time without forgetting existing knowledge, a critical challenge in dynamic, non-stationary environments such as time series classification. While significant progress has been made in class-incremental learning for images and text, time series data remains underexplored. In this paper, we investigate the effectiveness of the Mamba model, based on Structured State Space Models (SSMs), in time series class-incremental learning. We utilize a residual Mamba encoder and compare it with mainstream 1D-CNN and PathTST’s TST encoders across various class-incremental learning algorithms. To our knowledge, this is the first application of the Mamba model to time series class-incremental learning. Extensive experiments demonstrate that the residual Mamba encoder achieves comparable or superior performance and computational efficiency, particularly on time-series-specific algorithms.

关键词： measurement computers Adaptation models Incremental learning Computational modeling Time series analysis Learning (artificial intelligence) State-space methods Computational efficiency Classification algorithms

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 << < 90 91 92 93 94 95 96 97 98 99 > >>

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：