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

60,474 篇 会议
2,740 篇 期刊文献
990 册 图书
12 篇 学位论文
1 件 标准
1 篇 专利
1 篇 科技报告

馆藏范围

64,219 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

32,709 篇 工学
- 26,912 篇 计算机科学与技术...
- 15,209 篇 软件工程
- 11,129 篇 信息与通信工程
- 5,608 篇 电气工程
- 3,648 篇 控制科学与工程
- 2,797 篇 电子科学与技术（可...
- 1,190 篇 动力工程及工程热...
- 1,065 篇 机械工程
- 895 篇 仪器科学与技术
- 873 篇 生物工程
- 666 篇 生物医学工程（可授...
- 496 篇 安全科学与工程
- 470 篇 光学工程
- 466 篇 交通运输工程
- 459 篇 网络空间安全
- 423 篇 建筑学
- 328 篇 材料科学与工程（可...
- 322 篇 土木工程
8,457 篇 理学
- 5,961 篇 数学
- 1,472 篇 统计学（可授理学、...
- 1,183 篇 系统科学
- 1,123 篇 物理学
- 1,039 篇 生物学
5,400 篇 管理学
- 3,934 篇 管理科学与工程(可...
- 1,959 篇 工商管理
- 1,709 篇 图书情报与档案管...
779 篇 法学
- 495 篇 社会学
613 篇 医学
- 464 篇 临床医学
- 398 篇 基础医学(可授医学...
575 篇 经济学
- 572 篇 应用经济学
202 篇 文学
187 篇 农学
160 篇 教育学
107 篇 军事学
18 篇 艺术学
2 篇 哲学

主题

7,055 篇 computer network...
4,661 篇 computer archite...
4,373 篇 computer science
4,158 篇 distributed comp...
3,697 篇 wireless sensor ...
2,958 篇 application soft...
2,777 篇 protocols
2,574 篇 computational mo...
2,300 篇 hardware
2,267 篇 neural networks
2,191 篇 bandwidth
2,152 篇 delay
2,136 篇 distributed comp...
2,036 篇 routing
1,849 篇 costs
1,608 篇 intelligent netw...
1,601 篇 resource managem...
1,507 篇 network topology
1,422 篇 control systems
1,401 篇 ip networks

机构

94 篇 school of comput...
66 篇 ibm thomas j. wa...
60 篇 department of co...
59 篇 department of co...
49 篇 school of comput...
45 篇 school of electr...
36 篇 school of comput...
36 篇 department of sy...
32 篇 college of compu...
32 篇 department of el...
29 篇 inria
29 篇 school of comput...
29 篇 department of el...
28 篇 department of el...
28 篇 ibm thomas j. wa...
28 篇 department of co...
27 篇 department of el...
27 篇 department of sy...
27 篇 department of co...
27 篇 shandong provinc...

作者

50 篇 m. takizawa
26 篇 kowalski dariusz...
26 篇 li li
25 篇 santoro nicola
25 篇 s.k. das
24 篇 yu wang
23 篇 k.g. shin
23 篇 kuhn fabian
23 篇 peleg david
23 篇 p.k. srimani
23 篇 jun wang
23 篇 klaus wehrle
23 篇 duato j.
22 篇 dhabaleswar k. p...
22 篇 l.m. ni
21 篇 sens pierre
21 篇 xiaodong lu
19 篇 guohong cao
19 篇 y. shibata
18 篇 torsten hoefler

语言

63,144 篇 英文
626 篇 其他
411 篇 中文
51 篇 葡萄牙文
7 篇 法文
4 篇 西班牙文
3 篇 德文
3 篇 日文
3 篇 俄文

检索条件"任意字段=International Symposium on Computer Networks and Distributed Systems"

共 64220 条记录，以下是141-150 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：

3rd international Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

引用

3rd international Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616978

The proceedings contain 27 papers. The special focus in this conference is on Machine Learning, Cloud Computing and Intelligent Mining. The topics include: Decentralized Optimal Tracking Control of MRMs Under Health Indicator-Based Event-Triggered Mechanism;iterative Learning Control for Nonlinear Switched Continuous-Time systems with Time-Varying Delay;Model Migration and Joint Communication and Computing Resource Allocation in Native AI Wireless networks;FTB-RRP Control for 2-D Continuous-Discrete systems in Roesser Model;Optimal UAVs Placement for TDOA Localization Method Based On An Improved Grey Wolf Optimization Algorithm;A Bat-BP Optimization Algorithm Based System of Dry Ice Decontamination for UAV;Optimal Resource Allocation Algorithm for Multi-user AI Tasks in Native AI networks;study on Non-intrusive Load Monitoring Method Based on K-means Clustering and Dual Convolutional Neural networks;research and Application of computer Vision and Predictive Maintenance in Health Management of Conveying Equipment;attitude Stabilization Control for Unmanned Aerial Vehicle with Robotic Arm Based on Fixed-Time Observer Active Disturbance Rejection Control;state Estimation and Control of Switched Neural networks with Mode-Dependent Hybrid Dwell Time;distributed Consensus Control Research of Unmanned Aerial Vehicle (UAV) Swarms Based on Lennard-Jones Potential;lifetime Prediction of Fuel Cell Composite Operating Conditions Based on Genetic Algorithm Optimized Elman Neural Network;a Tiered Native Digital Twin for Telco networks;defender: The Possibility of Repairing Jailbreak Defects;research on Special Vision Recognition and Fault Alarm System for Vertical Roller;malicious Traffic Classification Algorithm Based on Multimodal Fusion;Enhanced Affine Formation Control in Multi-agent systems with Disturbance Rejection Using RISE Controller;model Predictive Control for Contour Control in Biaxial System.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tiered Mitigation Strategy 25

NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tie...

引用

26th international Conference on distributed Computing and Networking, ICDCN 2025

作者： Jha, Prianshu Singh, Gurpreet Kumar, Amit Agrawal, Dewesh Patel, Yashwant Singh Forough, Javad Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computing Science Umea University Umeå Sweden

ISBN: (纸本)9798400710629

Web servers are the backbone of modern Internet infrastructure, serving as the primary medium for online information distribution. Despite their critical role, web servers are susceptible to cyber-attacks. While current firewall mechanisms provide some level of protection against cyber threats, the evolving nature of these attacks and emerging vulnerabilities continue to pose significant risks. One of the most prevalent yet lethal attacks known today is DDoS (distributed Denial of Service) attacks. These growing risks emphasize the urgent need for dynamic and robust threat detection and mitigation systems. This paper presents a comparative analysis of ensemble learning models (e.g., Random Forest, XGBoost, and LightGBM) and neural network-based models (e.g., Graph Neural networks (GNN), Long Short-Term Memory networks (LSTM) with attention layers, and Gated Recurrent Units (GRU)) for DDoS attack detection and classification. Based on this analysis, we propose a real-time DDoS attack detection system integrated with a mitigation mechanism. The proposed system utilizes a two-tiered mitigation strategy assisted by UFW (Uncomplicated Firewall) and Apache server configuration files to block the incoming and outgoing traffic associated with suspicious IP addresses. The system's overall complexity, integrating both detection and response processes, ensures its efficiency in real-time environments while handling large volumes of traffic. Furthermore, the proposed approach achieves 15% improvement in detection accuracy and 20% reduction in false positives compared to traditional techniques, making it an effective and scalable solution for modern web server security. © 2025 Copyright held by the owner/author(s).

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

3rd international Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

引用

3rd international Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819624676

关键词：

来源：评论

学校读者我要写书评

暂无评论

Disinformation and Misinformation in the Age of Generative AI 25

Disinformation and Misinformation in the Age of Generative A...

引用

18th ACM international Conference on Web Search and Data Mining, WSDM 2025

作者： Rudra, Koustav Ganguly, Niloy Mifsud Bonnici, Jeanne Müller-Budack, Eric Manuvie, Ritumbra IIT Kharagpur Kharagpur India University of Groningen Groningen Netherlands TIB - Leibniz Information Centre for Science and Technology Hannover Germany

ISBN: (纸本)9798400713293

The rapid rise of generative AI (GenAI) technologies has revolutionized the way content is created and disseminated. As a result, highly convincing human-like malicious content including disinformation, misinformation, and propaganda can now be easily produced and distributed across the web. The diversity of generation models combined with various manipulation strategies applied to different modalities presents significant challenges for fact-checking systems and content moderation. To address this issue, we organize a workshop that focuses on harmful content that has been created intentionally (disinformation) and unintentionally (misinformation) in the era of generative AI. The workshop features specialized tracks on multimodal solutions, investigating narratives, trustworthy AI systems, and policy interventions. By bringing together experts from computer science and law, the workshop offers a comprehensive framework for combating fake content online. © 2025 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Non-Ergodic Theory vs. Fractal Geometry in Organizational Learning and Dynamic Skills at a Healthcare Food Catering Service 21st

Non-Ergodic Theory vs. Fractal Geometry in Organizational Le...

引用

21st international symposium on distributed Computing and Artificial Intelligence, DCAI 2024

作者： Lima, Rui Marreiros, Goreti Fdez-Riverola, Florentino Vicente, Henrique Neves, José CINBIO Universidade de Vigo Department of Computer Science ESEI – Escuela Superior de Ingeniería Informática Ourense32004 Spain SERGAS-UVIGO Vigo Spain Instituto Politécnico de Saúde do Norte CESPU Famalicão Paredes Portugal Departamento de Engenharia Informática Instituto Superior de Engenharia do Porto Porto Portugal Departamento de Química e Bioquímica Escola de Ciências e Tecnologia REQUIMTE/LAQV Universidade de Évora Évora Portugal Centro Algoritmi/LASI Universidade do Minho Braga Portugal

ISBN: (纸本)9783031809453

This paper examines the adoption of non-Ergodic Theory and Fractal Geometry to enhance organizational learning in healthcare food catering. It critiques conventional linear models, promoting an understanding of varied learning processes across organizational strata. Non-Ergodic Theory emphasizes the diversity of learning among individuals and groups, while Fractal Geometry examines learning as self-similar patterns at multiple scales. This work analyses how employee diversity, knowledge sharing, and cross-level collaboration contribute to a fractal learning environment that supports adaptability and innovation. Findings indicate that implementing these theoretical models can enhance service delivery and customer approval by promoting a culture of relentless education and creative ideas. For managers and practitioners, this study provides a blueprint for enhancing learning processes that can lead to greater efficiency and performance in healthcare food services, suggesting a strategic embrace of employee diversity and interconnected learning patterns for long-term organizational success. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fractals

来源：评论

学校读者我要写书评

暂无评论

Using LLMs to Enhance Research Output Tracking in Weakly Categorized Research Areas: A Case Study to Classify Publications in Islamic Economics, Finance, Banking and Business 5th

Using LLMs to Enhance Research Output Tracking in Weakly Cat...

引用

5th international symposium on Intelligent Computing systems

作者： Kasem, Asem Alvi, Imran Fares, Elie Higher Colleges Technol Sharjah U Arab Emirates Oxford Intellect Oxford OX1 3HJ England

ISBN: (纸本)9783031829307;9783031829314

This paper investigates the application of Machine Learning techniques and use of Large Language Models (LLMs) to categorize research output in weakly classified academic fields, specifically focusing on classifying publications in Islamic Economics, Finance, Banking, and Business (IEFBB). Using a dataset of about 3,958 Scopus-indexed publications, we employed text classification techniques as a scalable enabler to analyze research trends and collaborations within this niche area. Initial experiments using traditional machine learning methods, among which Neural Network model provided the highest cross-validated accuracy of 85%, and 75% test set accuracy. This provided a benchmark that we then used to explore LLM-based approaches, leveraging GPT-4o-mini model with prompt engineering and fine-tuning. Our results demonstrate that zero-shot prompt approach achieved 82% and 71% accuracy on validation and test sets, respectively. While iterative prompt engineering attempts after error analysis improved classification accuracy to 86% and 73% accuracy on validation and test sets;fine-tuning GPT-4o-mini with a tailored prompt significantly improved classification accuracy, achieving up to 86% on validation and 83% on the test sets. These findings underscore the potential of LLMs in automating the categorization of research output in specialized fields that lack established categorization systems.

关键词： Text Classification Large Language Models Neural networks Finetuning LLMs Prompt Engineering

来源：评论

学校读者我要写书评

暂无评论

Exploring Anomaly Detection for Marine Radar systems 19th

Exploring Anomaly Detection for Marine Radar Systems

引用

19th international Workshop on Data Privacy Management, DPM 2024, 8th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control systems and of Cyber-Physical systems, CyberICPS 2024 which were held in conjunction with the 29th European symposium on Research in computer Security, ESORICS 2024

作者： Saillard, Antoine Wolsing, Konrad Wehrle, Klaus Bauer, Jan Cyber Analysis and Defense Fraunhofer FKIE Wachtberg Germany Communication and Distributed Systems RWTH Aachen University Aachen Germany

ISBN: (纸本)9783031823480

Marine radar systems are a core technical instrument for collision avoidance in shipping and an indispensable decision-making aid for navigators on the ship’s bridge in limited visibility conditions at sea, in straits, and harbors. While electromagnetic attacks against radars can be carried out externally, primarily by military actors, research has recently shown that marine radar is also vulnerable to attacks from cyberspace. These can be carried out internally, less "loudly", and with significantly less effort and know-how, thus posing a general threat to the shipping industry, the global maritime transport system, and world trade. Based on cyberattacks discussed in the scientific community and a simulation environment for marine radar systems, we investigate in this work to which extent existing Intrusion Detection System (IDS) solutions can secure vessels’ radar systems, how effective their detection capability is, and where their limits lie. From this, we derive a research gap for radar-specific methods and present the first two approaches in that direction. Thus, we pave the way for necessary future developments of anomaly detection specific for marine navigation radars. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Marine radar

来源：评论

学校读者我要写书评

暂无评论

Effective Transparent Monitoring of Personal Data

Effective Transparent Monitoring of Personal Data

引用

PhD symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international Conference on Web Information systems Engineering, WISE 2024

作者： Abdullah Punjabi, Talha Alban, Ahmad Qadeib Barhamgi, Mahmoud Qatar University Doha Qatar Universit Ṕaris Dauphine-PSL Paris France

ISBN: (纸本)9789819614820

Traditional online file management systems, such as Google Drive and OneDrive, provide convenient platforms for collaborative editing and sharing, but they come with inherent limitations. These systems often offer limited storage space and operate on centralized architectures, which pose significant privacy risks as user data is controlled by service providers. Additionally, many fail to comply with regulations like the General Data Protection Regulation (GDPR) and the Personal Data Privacy Protection Law (PDPPL) by not offering clear transparency over who accessed data, when, and for what purpose. To address these challenges, we propose a decentralized peer-to-peer (P2P) file management framework that integrates IPFS and Hyperledger Fabric. This framework provides secure, personal storage spaces for individuals or organizations to store and share files. The P2P network enables distributed storage, while the Hyperledger Fabric network maintains a consistent blockchain ledger for tracking file-related transactions. Our system reduces storage and communication costs through its decentralized structure, allowing file owners to maintain full control over access permissions and safeguard privacy. This paper presents the system architecture, core functionalities, and security measures designed to mitigate common attacks. We also provide an evaluation framework to assess the system’s efficiency and performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Data Privacy distributed Mechanisms File Sharing Hyperledger Fabric Inter Planetary File System Transparent Data Monitoring

来源：评论

学校读者我要写书评

暂无评论

DURLLCON: Deep Reinforcement Learning for URLLC Optimization in Multi-edge networks

DURLLCON: Deep Reinforcement Learning for URLLC Optimizatio...

引用

作者： Dawoud, Heba Ansari, Shuja Mohamed, Amr Imran, Muhammad Popoola, Olaoluwa University of Glasgow GlasgowG12 8QQ United Kingdom Qatar University Doha Qatar

ISBN: (纸本)9789819614820

Ultra-Reliable and Low Latency Communications (URLLC) is crucial for enabling next-generation applications, particularly in areas that require stringent delay constraints, such as healthcare and vehicular networks. Multi-access Edge Computing (MEC) enhances URLLC applications by bringing computation closer to end users, maximizing resource utilization and minimizing latency. This paper introduces Deep Reinforcement Learning for URLLC Optimization in Multi-Edge networks (DURLLCON), a Deep Reinforcement Learning (DRL)-based framework for efficient task offloading in a hybrid MEC environment, specifically targeting both URLLC and non-URLLC applications. By leveraging DRL and Long Short Term Memory (LSTM) networks, the proposed method dynamically adjusts the offloading decisions based on user energy levels, task priority, and network congestion. The simulation results show that the proposed DURLLCON framework achieves up to 30% improvement in energy efficiency and 20% reduction in average delay compared to existing methods, significantly extending user device battery life while maintaining high Quality of Service (QoS). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Attack Detection in Smart Home IoT networks: A Survey on Challenges, Methods and Analysis 14th

Attack Detection in Smart Home IoT Networks: A Survey on Cha...

引用

14th EAI international Conference on Broadband Communications, networks, and systems, BROADNETS 2024

作者： Rreddy, M. Vinay Kuma Lathigara, Amit Reddy, Muthangi Kantha RK University Gujarat India Computer Engineering RK University Gujarat India Shri Vishnu Engineering College for Women Andhra Pradesh India

ISBN: (纸本)9783031811678

The ubiquity of Internet of Things (IoT) gadgets in smart homes has transformed our interactions with our living environments by providing never-before-seen levels of automation and convenience. However, because IoT devices are becoming possible targets for malicious attacks, this broad connectivity also poses serious security risks. Ensuring the privacy, safety, and integrity of smart home ecosystems requires prompt detection and mitigation of these threats. Data from IoT devices is gathered, pre-processed, feature engineered, labelled, and divided into training, validation, and testing sets as part of a machine learning method to threat detection in smart home IoT networks. The process of choosing and training appropriate machine learning models—which can include everything from classification techniques to anomaly detection algorithms—is crucial. Methods are surveyed to review different types of cyber-attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. To protect user information, data anonymization and encryption techniques are used with privacy considerations. Another strategy that has been put forth aims to improve the security of IoT networks in smart homes by providing a strong defence against new threats and equipping users with the information and resources they need to keep their connected world safe. To provide a full overview of the numerous advancements in this field, a list of all works published in the literature to date is incorporated. Lastly, the study also includes suggestions for future research directions. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Smart homes

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 << < 11 12 13 14 15 16 17 18 19 20 > >>

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：