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

10,253 篇 会议
6,031 篇 期刊文献
140 册 图书

馆藏范围

16,424 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

10,673 篇 工学
- 8,341 篇 计算机科学与技术...
- 6,834 篇 软件工程
- 2,207 篇 信息与通信工程
- 1,368 篇 控制科学与工程
- 1,127 篇 生物工程
- 877 篇 电气工程
- 736 篇 机械工程
- 659 篇 电子科学与技术（可...
- 606 篇 光学工程
- 606 篇 生物医学工程（可授...
- 391 篇 化学工程与技术
- 358 篇 仪器科学与技术
- 354 篇 安全科学与工程
- 322 篇 动力工程及工程热...
- 321 篇 交通运输工程
- 304 篇 网络空间安全
- 276 篇 建筑学
4,828 篇 理学
- 2,816 篇 数学
- 1,236 篇 生物学
- 956 篇 物理学
- 816 篇 统计学（可授理学、...
- 521 篇 系统科学
- 412 篇 化学
3,067 篇 管理学
- 1,857 篇 管理科学与工程(可...
- 1,332 篇 图书情报与档案管...
- 750 篇 工商管理
522 篇 医学
- 450 篇 临床医学
- 390 篇 基础医学(可授医学...
443 篇 法学
- 330 篇 社会学
276 篇 经济学
- 275 篇 应用经济学
236 篇 教育学
154 篇 农学
43 篇 军事学
42 篇 艺术学
39 篇 文学
9 篇 哲学
3 篇 历史学

主题

624 篇 computer science
481 篇 software enginee...
337 篇 semantics
318 篇 computational mo...
315 篇 feature extracti...
306 篇 deep learning
272 篇 training
224 篇 data mining
218 篇 optimization
214 篇 educational inst...
174 篇 predictive model...
172 篇 neural networks
170 篇 application soft...
167 篇 image segmentati...
157 篇 computer archite...
156 篇 machine learning
155 篇 internet of thin...
142 篇 authentication
137 篇 servers
136 篇 convolution

机构

428 篇 college of compu...
359 篇 school of comput...
267 篇 school of inform...
264 篇 school of comput...
221 篇 school of comput...
217 篇 school of comput...
187 篇 state key labora...
152 篇 school of softwa...
139 篇 school of electr...
134 篇 school of comput...
124 篇 national enginee...
109 篇 school of comput...
106 篇 school of softwa...
102 篇 school of softwa...
102 篇 state key labora...
99 篇 college of compu...
98 篇 college of compu...
96 篇 school of comput...
96 篇 school of softwa...
90 篇 school of softwa...

作者

69 篇 tao dacheng
52 篇 chen zhong
51 篇 liu yang
51 篇 zheng zibin
39 篇 wang ping
37 篇 hu ruimin
35 篇 huang di
34 篇 limin xiao
34 篇 wang yasha
34 篇 gao yang
32 篇 wang lei
32 篇 mian ajmal
31 篇 bo du
31 篇 miao huaikou
31 篇 ruimin hu
31 篇 qi lianyong
30 篇 jun zhang
28 篇 niyato dusit
28 篇 wang wei
27 篇 cui lizhen

语言

15,189 篇 英文
978 篇 其他
274 篇 中文
3 篇 日文
2 篇 朝鲜文
1 篇 德文
1 篇 斯洛文尼亚文

检索条件"机构=School of Computer Science& Software Engineering"

共 16424 条记录，以下是521-530 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：

Image Compression and Stable Reconstruction Based on Multi-Level Hybrid Feature Guidance 2

Image Compression and Stable Reconstruction Based on Multi-L...

引用

2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Zeng, Heng Gong, Nanxu College of Software Engineering Sichuan University Chengdu China School of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9798350328530

Image compression is a technique that reduces storage space and transmission bandwidth requirements by reducing the amount of image data, aiming to improve storage and transmission efficiency while maintaining acceptable image quality. In recent years, learning-based image compression methods have demonstrated superior rate-distortion performance compared to traditional image compression standards. Nevertheless, existing approaches often struggle to simultaneously achieve high compression rates and high-quality reconstruction. To tackle this issue, we present an image compression and stable reconstruction method based on multi-level hybrid feature guidance. Firstly, a hybrid attention module that combines spatial, channel, and shuffle attention is designed to improve the model's ability to capture local features of the image. Secondly, we utilize boundary prediction and frequency domain feature prediction auxiliary branches to guide the network for image compression and reconstruction. Moreover, we propose an image compression and reconstruction algorithm framework based on multi-level feature guidance. Based on experimental results, our method surpasses existing approaches in achieving superior rate-distortion performance. These research results are of great significance to the further development of image compression and reconstruction technology. © 2023 IEEE.

关键词： Image compression

来源：评论

学校读者我要写书评

暂无评论

Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

引用

23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Improving Event Coreference Resolution Using Prompt Learning and U-shaped Module 4

Improving Event Coreference Resolution Using Prompt Learning...

引用

4th International Conference on computer science and Blockchain, CCSB 2024

作者： Zhang, Mengyao Long, Jun Huang, Wenti School of Software Xinjiang University Urumqi China Institute of Big Data Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China

ISBN: (纸本)9798350356502

Event Coreference Resolution (ECR) focuses on clustering event mentions that allude to identical actual events. Previous research primarily focuses on encoding event mentions without incorporating human interpretations, making it difficult to integrate human-summarized rules for guiding the ECR task. Furthermore, the semantic relations between event mentions have not been thoroughly explored, which impacts coreference judgment. To address these issues, we first utilize the powerful Longformer to acquire event representations at the document level. In addition, we introduce a prompt learning template to better emphasize event types and parameters, enhancing the interaction between event modeling and coreference judgment. Ultimately, we create matching vectors for event mentions using tensor matching and incorporate a U-shaped module to effectively identify the semantic relationships among these mentions, which enhances coreference discrimination. DPTU surpasses other ECR models on the KBP2017 dataset in cause of some experiments. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification 30

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Mu...

引用

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

作者： Feng, Junjia Lin, Mingqian Shang, Lin Gao, Xiaoying State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9782493814104

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

MR Image Quality Assessment via Enhanced Mamba: A Hybrid Spatial-Frequency Approach

MR Image Quality Assessment via Enhanced Mamba: A Hybrid Spa...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lou, Yiwei Xu, Dexuan Zhang, Rongchao Zhang, Jiayu Cao, Yongzhi Wang, Hanpin Huang, Yu Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education China Peking University School of Computer Science Beijing China Peking University School of Software and Microelectronics Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350386226

Magnetic resonance (MR) image quality assessment plays a crucial role in disease diagnosis and data analysis. Existing methods typically treat the data as images and process them with convolutional networks, thereby ignoring the sequential characteristics of MR data. In this paper, we propose a hybrid spatial-frequency network (HSFNet) for MR image quality assessment, which extracts MR image quality features in both spatial and frequency domains. Specifically, within each data domain, information from local images and global sequences is iteratively integrated by applying a Mamba-based cascading processing module for multiple times. Extensive experiments on both T1-weighting and T2-weighting MR datasets demonstrate the proposed method's effectiveness and generalization ability in comparison with state-of-the-art MR image quality assessment methods. © 2024 IEEE.

关键词： Image quality

来源：评论

学校读者我要写书评

暂无评论

ScDCMAC:adaptive feedback clustering of scRNA-seq data with deep gene correlation mining

ScDCMAC:adaptive feedback clustering of scRNA-seq data with ...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wang, Ruiqian Wu, Wenhao Li, Hengxiao Wu, Jiarui Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder's dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis. © 2024 IEEE.

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

MG-CR: Factor Memory Network and Graph Neural Network Based Personalized Course Recommendation 28th

MG-CR: Factor Memory Network and Graph Neural Network Based...

引用

28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Yun Yu, Minghe Sun, Jintong Zhang, Tiancheng Yu, Ge School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China

ISBN: (纸本)9783031306716

Course recommendations in universities select the most suitable courses for students according to their interests and academic requirements. However, existing works often focus on modeling course selection history, without paying sufficient attention to rich features contained in students’ personal information and courses’ teaching attributes, and various relationships between them. In order to realize the personalization and dynamics of course recommendation, we consider students and courses as two types of nodes to construct a heterogeneous information network (HIN), and propose a factor Memory network and Graph neural network based personalized Course Recommendation (MG-CR) on top of HIN. MG-CR captures multiple features of students and courses simultaneously in the input module, thereby maximizing the retention of personalized information. The feature transfer module optimizes course features by considering various types of relationships between courses in the HIN, and together with the input module, it ensures high recommendation accuracy in the cold-start scenario. The updating module explicitly stores the student’s interest level for each factor in the state matrix, which is then used to dynamically recommend courses for students in the predicting module. Experimental results show that MG-CR provides accurate, dynamic and personalized recommendations, and has superior performance in the cold-start scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Efficient and Lossless Integrity-preserving Training Scheme for High-dimensional Logistic Regression over Vertical Data

Efficient and Lossless Integrity-preserving Training Scheme ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xu, Wei Zhu, Hui Liu, Ruikang Zheng, Yandong Wang, Fengwei Feng, Dengguo Xidian University School of Cyber Engineering Xi'an710126 China Institute of Software State Key Laboratory of Computer Science Beijing100190 China

ISBN: (纸本)9798350351255

Logistic regression is a widely used and efficient machine learning algorithm for data analysis. However, data is often distributed among different participants, and building high-quality models often involves data interaction, which might easily lead to sensitive information leakage. While some schemes have been proposed to address the data security issue through privacy-preserving technologies such as multi-party computation, homomorphic encryption, and trusted execution environment (TEE), they still suffer from some limitations in terms of security, efficiency, and model integrity and are not suitable for the secure outsourcing training. Therefore, in this paper, we propose an efficient and lossless integrity-preserving training for high-dimensional logistic regression over vertically partitioned data. First, we utilize a block strategy to encrypt the data in batches and outsource them to a cloud with TEE to achieve the non-interactive training. Considering TEE's limited memory, we employ random sampling technology to achieve mini-batch training within TEE, which ensures the data security. We find that the cloud might launch the active attacks to drop the model accuracy, such as lazy attack and replacement attack. To prevent the attacks, we introduce an integrity protection mechanism to verify data integrity and reduce verification frequency using a mixed-probability checking method, including fixed and random probability checking. Detailed analysis has confirmed that our scheme is secure and integrity-preserving. Extensive experiments have demonstrated that our scheme can achieve lossless, non-interactive, and efficient model training. © 2024 IEEE.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

GMA: Graph Multi-agent Microservice Autoscaling Algorithm in Edge-Cloud Environment

GMA: Graph Multi-agent Microservice Autoscaling Algorithm in...

引用

2023 IEEE International Conference on Web Services, ICWS 2023

作者： Tong, Ganghao Meng, Chunyang Song, Shijie Pan, Maolin Yu, Yang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Software Engineering Zhuhai China

ISBN: (纸本)9798350304855

The emerging edge-cloud computing paradigm, comprising cloud centers and multiple distributed edge servers, extends the computing capability from the cloud center to a range of servers. Although the microservice autoscaling problem has been intensively studied in the context of cloud computing, existing algorithms in most cases cannot be effectively migrated to the edge-cloud environment because servers are geographically distributed and heterogeneous, and information is not synchronized between servers. Existing works, however, mainly focus on centralized strategies with time-consuming synchronization methods, i.e. strategies shared by all servers, without comprehensively considering the heterogeneity and distribution of the environment. Soft information synchronization, autonomy and collaboration is proposed to tackle the aforementioned issues, and refer to it as SAC paradigm. According to the SAC paradigm, each server with inferred information of other servers can collaborate with others by a dedicated autoscaling strategy, that is, server collaboration. The microservice autoscaling problem is then transformed into the Graph-based Jointly Microservice Autoscaling (GJMA) problem based on spectral graph theory. GJMA problem aims to minimize average waiting time of microservice-based application while reducing service-level agreement(SLA) violation rate and fluctuations in the autoscaling process, taking into account resource heterogeneity. Graph-based Multi-agent Algorithm(GMA), an implementation of SAC paradigm based on graph convolutional networks and multi-agent reinforcement learning, is implemented to solve GJMA problem. Experimental results show that the proposed algorithm for the edge-cloud environment is always efficient to find a better autoscaling strategy compared to the implemented comparison algorithms. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

CARE-EMRs: Efficient and Privacy-Preserving Data Sharing Framework for Electronic Medical Records

CARE-EMRs: Efficient and Privacy-Preserving Data Sharing Fra...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tao, Yu Zhou, Lu Zhu, Yi Li, Yuhang Ge, Chunpeng Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Jiangsu University College of Computer Science and Communication Engineering Zhenjiang China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China and Software School Jinan China

ISBN: (纸本)9798350386226

Cross-institutional Electronic Medical Records (EMRs) sharing significantly improves healthcare quality and reduces costs. Current EMRs are often stored on cloud servers and protected by Attribute-based Encryption (ABE) for access control. Cross-institutional sharing allows doctors to request EMRs access from target Healthcare Institutions (HIs) through attribute-based collaboration, enabling flexible and secure access. However, existing collaboration in practical EMRs sharing scenarios suffers from inefficiency, hindering timely access and treatment. This paper proposes an efficient and privacy-preserving data sharing framework for EMRs, called CARE-EMRs. CARE-EMRs enables efficient EMRs data access for doctors across different HIs through an outsourcing mechanism. Furthermore, we designed an improved cryptographic primitive PPD-CP-ABE to serve as the security foundation for CARE-EMRs framework, ensuring privacy of the outsourced process. We prove the security of CARE-EMRs. Performance analysis indicates that it significantly reduces the computation and communication overhead. © 2024 IEEE.

关键词： Electronic health record

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 << < 49 50 51 52 53 54 55 56 57 58 > >>

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：