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

3,214 篇 会议
19 册 图书
10 篇 期刊文献

馆藏范围

3,243 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,942 篇 工学
- 1,740 篇 计算机科学与技术...
- 1,594 篇 软件工程
- 423 篇 信息与通信工程
- 329 篇 控制科学与工程
- 318 篇 生物工程
- 185 篇 生物医学工程（可授...
- 176 篇 电气工程
- 145 篇 机械工程
- 116 篇 电子科学与技术（可...
- 101 篇 光学工程
- 99 篇 安全科学与工程
- 93 篇 化学工程与技术
- 92 篇 动力工程及工程热...
- 79 篇 网络空间安全
893 篇 理学
- 499 篇 数学
- 336 篇 生物学
- 210 篇 统计学（可授理学、...
- 158 篇 物理学
- 154 篇 系统科学
- 84 篇 化学
505 篇 管理学
- 266 篇 管理科学与工程(可...
- 258 篇 图书情报与档案管...
- 160 篇 工商管理
185 篇 医学
- 158 篇 临床医学
- 156 篇 基础医学(可授医学...
- 110 篇 药学(可授医学、理...
- 83 篇 公共卫生与预防医...
134 篇 法学
- 91 篇 社会学
82 篇 经济学
- 82 篇 应用经济学
78 篇 农学
- 75 篇 作物学
66 篇 教育学
4 篇 文学
1 篇 军事学

主题

499 篇 machine learning
104 篇 deep learning
86 篇 software
62 篇 machine learning...
59 篇 support vector m...
52 篇 internet of thin...
52 篇 artificial intel...
49 篇 decision trees
47 篇 forecasting
44 篇 software enginee...
40 篇 training
39 篇 predictive model...
36 篇 feature extracti...
35 篇 neural networks
33 篇 software testing
31 篇 software algorit...
26 篇 reinforcement le...
26 篇 adversarial mach...
25 篇 covid-19
25 篇 object detection

机构

31 篇 college of compu...
31 篇 chitkara univers...
12 篇 karnataka bangal...
12 篇 school of inform...
11 篇 software enginee...
11 篇 department of so...
10 篇 department of so...
10 篇 school of comput...
9 篇 maharishi school...
8 篇 maharishi school...
7 篇 maharishi school...
7 篇 school of comput...
7 篇 daffodil interna...
7 篇 department of co...
7 篇 school of inform...
6 篇 school of comput...
6 篇 school of comput...
6 篇 software enginee...
6 篇 department of co...
6 篇 department of so...

作者

8 篇 patidar sanjay
7 篇 liu han
7 篇 han liu
6 篇 alfurhood badria...
6 篇 ishu sharma
6 篇 sharma ishu
6 篇 badria sulaiman ...
5 篇 bitto abu kowshi...
5 篇 dharshana kasthu...
5 篇 ali raja hashim
5 篇 yibeltal assefa
5 篇 assefa yibeltal
5 篇 ajmeera kiran
5 篇 raja hashim ali
5 篇 sethia divyashik...
4 篇 heyn hans-martin
4 篇 krishna aneesh
4 篇 william p.
4 篇 iqbal muhammad w...
4 篇 liu xin

语言

1,802 篇 英文
1,440 篇 其他
69 篇 中文
1 篇 德文

检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3243 条记录，以下是451-460 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：

CFG2VEC: Hierarchical Graph Neural Network for Cross-Architectural software Reverse engineering 23

CFG2VEC: Hierarchical Graph Neural Network for Cross-Archite...

引用

IEEE/ACM 45th international conference on software engineering - software engineering in Practice (ICSE-SEIP)

作者： Yu, Shih-Yuan Achamyeleh, Yonatan Gizachew Wang, Chonghan Kocheturov, Anton Eisen, Patrick Al Faruque, Mohammad Abdullah Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA Siemens Technol Princeton NJ USA

ISBN: (纸本)9798350300376

Mission-critical embedded software is critical to our society's infrastructure but can be subject to new security vulnerabilities as technology advances. When security issues arise, Reverse Engineers (REs) use software Reverse engineering (SRE) tools to analyze vulnerable binaries. However, existing tools have limited support, and REs undergo a time-consuming, costly, and error-prone process that requires experience and expertise to understand the behaviors of software and vulnerabilities. To improve these tools, we propose cfg2vec, a Hierarchical Graph Neural Network (GNN) based approach. To represent binary, we propose a novel Graph-of-Graph (GoG) representation, combining the information of control-flow and function-call graphs. Our cfg2vec learns how to represent each binary function compiled from various CPU architectures, utilizing hierarchical GNN and the siamese network-based supervised learning architecture. We evaluate cfg2vec's capability of predicting function names from stripped binaries. Our results show that cfg2vec outperforms the state-of-the-art by 24.54% in predicting function names and can even achieve 51.84% better given more training data. Additionally, cfg2vec consistently outperforms the state-of-the-art for all CPU architectures, while the baseline requires multiple training to achieve similar performance. More importantly, our results demonstrate that our cfg2vec could tackle binaries built from unseen CPU architectures, thus indicating that our approach can generalize the learned knowledge. Lastly, we demonstrate its practicability by implementing it as a Ghidra plugin used during resolving DARPA Assured MicroPatching (AMP) challenges.

关键词： software Reverse engineering Binary Analysis Cross-Architecture machine learning Graph Neural Network

来源：评论

学校读者我要写书评

暂无评论

Multi-Label Classification of CS Papers Using Natural Language Processing Models 22

Multi-Label Classification of CS Papers Using Natural Langua...

引用

22nd IEEE international conference on machine learning and Applications, ICMLA 2023

作者： Zhang, Zheng Das, Amit Rahgouy, Mostafa Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

Computer science (CS) is a rapidly evolving field of knowledge with a significant volume and significance of scientific papers. Consequently, meticulous management and categorization of CS papers are crucial to ensure efficient access and retrieval of relevant information. This study aims to evaluate the effectiveness of eight traditional machine learning models, as well as Recurrent Neural Network (RNN) models, such as LSTM and BiLSTM, and state-of-the-art pre-trained self-attention-based natural language processing (NLP) models, including BERT, XLNet, RoBERTa, and DistilBERT, for the task of multi-label classification of arXiv CS paper classification. The results demonstrate that pre-trained self-attention-based models consistently outperform the other models regarding classification performance. Moreover, self-attention-based models exhibited superior performance and achieved a new state-of-the-art result. To the best of our knowledge, our paper represents the first endeavor to evaluate machine learning models, specifically on computer science-related documents, as part of a multi-label classification task. © 2023 IEEE.

关键词： BERT Deep learning LSTM machine learning multi-label classification

来源：评论

学校读者我要写书评

暂无评论

Research On machine Analogy Translation Technology Based On machine learning 2

Research On Machine Analogy Translation Technology Based On ...

引用

2nd international conference on Digital Society and Intelligent Systems, DSInS 2022

作者： Zhuang, Siyu Cai, Yingyi South China Normal University No. 55 West of Yat-sen Avenue Guangdong Province China

ISBN: (纸本)9781510663244

With the acceleration of economic globalization, the importance of translation services has gradually manifested. The machine translation technology has a long and glorious history from statistical machine translation to neural machine translation, At present, machine translation software is emerging, mostly following the rules of natural language processing. In order to achieve quality and efficiency in translation, the various ambiguities in the language should be processed accurately and the speed of translation should be increased, while the anthropomorphism of machine translation should be enhanced and the machine's knowledge of human language should be constantly improved. Therefore, it is important to strengthen the research of machine learning technology, and to insist on the research on machine analogy translation technology based on machine learning is an important research topic. © 2023 SPIE.

关键词： Computer aided language translation

来源：评论

学校读者我要写书评

暂无评论

BuggIn: Automatic Intrinsic Bugs Classification Model using NLP and ML 19

BuggIn: Automatic Intrinsic Bugs Classification Model using ...

引用

19th international conference on Predictive Models and Data Analytics in software engineering (PROMISE)

作者： Bhandari, Pragya Rodriguez-Perez, Gema Univ British Columbia Kelowna BC Canada

ISBN: (纸本)9798400703751

Recent studies have shown that bugs can be categorized into intrinsic and extrinsic types. Intrinsic bugs can be backtracked to specific changes in the version control system (VCS), while extrinsic bugs originate from external changes to the VCS and lack a direct bug-inducing change. Using only intrinsic bugs to train bug prediction models has been reported as beneficial to improve the performance of such models. However, there is currently no automated approach to identify intrinsic bugs. To bridge this gap, our study employs Natural Language Processing (NLP) techniques to automatically identify intrinsic bugs. Specifically, we utilize two embedding techniques, seBERT and TF-IDF, applied to the title and description text of bug reports. The resulting embeddings are fed into well-established machine learning algorithms such as Support Vector machine, Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors. The primary objective of this paper is to assess the performance of various NLP and machine learning techniques in identifying intrinsic bugs using the textual information extracted from bug reports. The results demonstrate that both seBERT and TF-IDF can be effectively utilized for intrinsic bug identification. The highest performance scores were achieved by combining TF-IDF with the Decision Tree algorithm and utilizing the bug titles (yielding an F1 score of 78%). This was closely followed by seBERT, Support Vector machine, and bug titles (with an F1 score of 77%). In summary, this paper introduces an innovative approach that automates the identification of intrinsic bugs using textual information derived from bug reports.

关键词： software bugs classification intrinsic bugs extrinsic bugs natural language processing

来源：评论

学校读者我要写书评

暂无评论

A Detailed Study on Preventing the Malicious URLs from Cyber Attacks 3

A Detailed Study on Preventing the Malicious URLs from Cyber...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Adnaan, Shaik Swathi, K. Tejasri, Gorli Praveen Krishna, Anne Venkata Sai Reddy, Mulagolla Gowtham Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (纸本)9781665462167

Today, there are more malicious URLs and cyber-attacks than ever. Cybercrimes are developing along with the software system's progress. If we are able to stop one attack, then another one will follow in a different direction. These URLs were given via an unidentified website or email. The URLs were supplied in order to collect your personal information or to leak passwords and data content. You and your system are substantially impacted when you click on an unfamiliar URL. The difficulty is that browsers can only block well-known spam websites. At the moment, software incorporates several types of security measures, such as spam messages and call notifications, and our web browser implements these by employing blacklisting to protect from unknown URLs. As a result, not all cyberattacks could be alerted to. Our study is on identifying malicious URLs and phishing assaults from various websites, and we may apply cyber security by utilizing machine learning and deep learning. Many publishers have published on various attacks. These strategies allow us to stop URLs from spammers or hackers as well as cyberattacks like phishing. When we send an email or URL that appears to have come from a reliable source, the attacker can obtain our personal information if we click on it or access it. Therefore, we can protect our software utilizing machine learning and deep learning approaches from this kind of URLs. In order to prohibit or report assaults and cybercrimes, machine learning and deep learning algorithms are employed in cyber security to identify malicious and phishing URLs submitted by unidentified individuals. © 2023 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

machine learning-Based Topology Optimization in 3D Printing

Machine Learning-Based Topology Optimization in 3D Printing

引用

2023 international conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Askhatova, Almira Gabdulla, Yerlik Bekbolat, Amanali Shehab, Essam Ali, Md. Hazrat Nazarbayev University School of Engineering and Digital Sciences Department of Mechanical and Aerospace Engineering Astana010000 Kazakhstan

ISBN: (纸本)9798350302523

The extremely lengthy computational times, coupled with the impracticality of the Finite Element Method (FEM)-based simulations, created a strong demand to accelerate the procedures of topology optimization involved in product development. This paper proposes a solution in the form of a deep Convolutional Neural Network (CNN) designed for 3D structural topology optimization, which predicts a structurein a relatively short amount of time without employing any iterative strategy. The methodology was created to empower users to incorporate a machine-learning approach to topology optimization in their designs while enabling the non-iterative strategy and increasing the efficiency of the process. The proposed approach is anticipated to significantly reduce computational time while improving the printed component'sstructural performance and design quality. This study offers a new perspective on topology optimization in 3D printing andhighlights the potential of machine learning in advancing theefficiency and precision of this process. The outcomes of this study illustrate that the proposed model possesses the capacity to conduct topology optimization of the cantilever beam structureat a significantly accelerated pace, as compared to specialized software showing a reduction in computation time, amounting to approximately 99%. These results hold promising implications for the time-critical engineering and optimization processes. © 2023 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Reinforcement learning in Dynamic Industrial Context 47

Multi-Agent Reinforcement Learning in Dynamic Industrial Con...

引用

47th IEEE-Computer-Society Annual international conference on Computers, software, and Applications (COMPSAC)

作者： Zhang, Hongyi Li, Jingya Qi, Zhiqiang Aronsson, Anders Bosch, Jan Olsson, Helena Holmstrom Chalmers Univ Technol Gothenburg Sweden Ericsson Ericsson Res Stockholm Sweden Malmo Univ Malmo Sweden

ISBN: (纸本)9798350326970

Deep reinforcement learning has advanced significantly in recent years, and it is now used in embedded systems in addition to simulators and games. Reinforcement learning (RL) algorithms are currently being used to enhance device operation so that they can learn on their own and offer clients better services. It has recently been studied in a variety of industrial applications. However, reinforcement learning, especially when controlling a large number of agents in an industrial environment, has been demonstrated to be unstable and unable to adapt to realistic situations when used in a real-world setting. To address this problem, the goal of this study is to enable multiple reinforcement learning agents to independently learn control policies on their own in dynamic industrial contexts. In order to solve the problem, we propose a dynamic multiagent reinforcement learning (dynamic multi-RL) method along with adaptive exploration (AE) and vector-based action selection (VAS) techniques for accelerating model convergence and adapting to a complex industrial environment. The proposed algorithm is tested for validation in emergency situations within the telecommunications industry. In such circumstances, three unmanned aerial vehicles (UAV-BSs) are used to provide temporary coverage to mission-critical (MC) customers in disaster zones when the original serving base station (BS) is destroyed by natural disasters. The algorithm directs the participating agents automatically to enhance service quality. Our findings demonstrate that the proposed dynamic multi-RL algorithm can proficiently manage the learning of multiple agents and adjust to dynamic industrial environments. Additionally, it enhances learning speed and improves the quality of service.

关键词： Reinforcement learning Multi-Agent machine learning software engineering Emergency Communication Network

来源：评论

学校读者我要写书评

暂无评论

Proceedings of JCSSE 2023 - 20th international Joint conference on Computer Science and software engineering

Proceedings of JCSSE 2023 - 20th International Joint Confere...

引用

20th international Joint conference on Computer Science and software engineering, JCSSE 2023

ISBN: (纸本)9798350300505

The proceedings contain 93 papers. The topics discussed include: topic modelling using latent Dirichlet allocation (LDA) and analysis of students sentiments;two-stage Thai misspelling correction based on pre-trained language models;text classification using improving term frequency with gaussian and multilayer perceptron techniques;weighting histogram of oriented gradients for spondylolisthesis classification from x-ray images;comparison of CNN models for urban garbage image classification in Thailand;design and stability guarantee of variable center-frequency bandpass filters;sentiment analysis of consumer reviews using machine learning approach;a comparison of machine learning and neural network algorithms for an automated Thai essay scoring;effects of genetic operators on neural architecture search using multi-objective genetic algorithm;and a weakly-supervised glass bottle defect detection system based on multi-view analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing IoT Network Defense: A Comparative Study of machine learning Algorithms for Attack Classification 33rd

Enhancing IoT Network Defense: A Comparative Study of Machin...

引用

33rd international conference on software and Data engineering

作者： McNair, Alkendria Precious-Esue, Divine Newson, Soundra Rahimi, Nick Univ Southern Mississippi Sch Comp Sci & Comp Engn Hattiesburg MS 39401 USA

ISBN: (纸本)9783031752001;9783031752018

As the Internet of Things (IoT) continues to expand rapidly, securing these interconnected devices and networks from cyber threats has become a critical challenge. This research investigates the application of machine learning techniques for accurately classifying IoT network traffic data to discriminate between benign activities and various types of cyber-attacks targeting IoT systems. We propose a program that employs multiple machine learning algorithms, including Decision Tree, Logistic Regression, Naive Bayes, and Random Forest, trained on a comprehensive IoT network traffic dataset the CICIoTDataset2023. Through extensive experiments, we evaluate the performance of these classification models in detecting different IoT attack categories such as web-based attacks, spoofing, denial-of-service, Mirai, reconnaissance, distributed denial-of-service, and brute force attacks. Our results demonstrate the efficacy of machine learning approaches, with the Random Forest algorithm emerging as the top performer, achieving an overall accuracy of 98.41%. We also address challenges like class imbalance through hybrid sampling techniques and implement strategies like regularization and hyperparameter tuning to mitigate overfitting and enhance model generalization. Additionally, we conduct a performance analysis of the classification models on different IoT attack categories to gain insights into their specific strengths and weaknesses. By leveraging machine learning for accurate IoT attack classification, this research contributes to developing robust security solutions that can proactively identify and mitigate cyber threats, enabling a more secure IoT ecosystem. The findings pave the way for safeguarding interconnected devices, protecting user privacy, and fostering confidence in the widespread adoption of IoT technologies.

关键词： Internet of Things (IoT) machine learning Data Classification Decision Tree Logistic Regression Naive Bayes Overfitting Random Forest

来源：评论

学校读者我要写书评

暂无评论

Development of Classification Model based on Arabic Textual Analysis to Detect Fake News: Case Studies 1

Development of Classification Model based on Arabic Textual ...

引用

1st international conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Himdi, Hanen Tarik Assiri, Fatmah Yousef University of Jeddah College of Computer Science and Engineering Computer Science and Artificial Intelligence Department Jeddah Saudi Arabia University of Jeddah College of Computer Science and Engineering Software Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The ease of communication that has been made possible by chat messaging platforms, such as WhatsApp and Line, and their increased use and ubiquity in society, have motivated purveyors of fake news to create and present their news as legitimate. Though different organisations continued their efforts to resolve this problem, many of the developed solutions rely on verifying the associated metadata, which are not always available. Several studies have attempted to identify fake news by analysing textual content;however, there is a lack of studies on Arabic language sources. Our work filled this gap;we proposed a machine learning model that classifies Arabic fake news based on textual analysis. This paper shows the feasibility of our approach to classify Arabic text in social media applications. © 2023 IEEE.

关键词： machine 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325页 << < 42 43 44 45 46 47 48 49 50 51 > >>

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：