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

4,243 篇 会议
103 册 图书
50 篇 期刊文献

馆藏范围

4,396 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,558 篇 工学
- 2,413 篇 计算机科学与技术...
- 1,799 篇 软件工程
- 347 篇 信息与通信工程
- 274 篇 控制科学与工程
- 257 篇 电气工程
- 173 篇 生物工程
- 93 篇 化学工程与技术
- 78 篇 生物医学工程（可授...
- 72 篇 机械工程
- 66 篇 网络空间安全
- 59 篇 电子科学与技术（可...
- 49 篇 仪器科学与技术
- 36 篇 光学工程
- 31 篇 材料科学与工程（可...
- 30 篇 动力工程及工程热...
- 29 篇 安全科学与工程
792 篇 管理学
- 543 篇 图书情报与档案管...
- 300 篇 管理科学与工程(可...
- 104 篇 工商管理
675 篇 理学
- 302 篇 数学
- 178 篇 生物学
- 156 篇 物理学
- 118 篇 统计学（可授理学、...
- 100 篇 化学
- 68 篇 系统科学
91 篇 医学
- 72 篇 临床医学
- 64 篇 基础医学(可授医学...
- 48 篇 公共卫生与预防医...
75 篇 法学
- 68 篇 社会学
74 篇 教育学
- 73 篇 教育学
35 篇 文学
28 篇 农学
25 篇 经济学
4 篇 艺术学
2 篇 军事学

主题

1,052 篇 natural language...
318 篇 semantics
314 篇 natural language...
299 篇 natural language...
231 篇 knowledge engine...
226 篇 data mining
210 篇 machine learning
168 篇 information retr...
150 篇 training
148 篇 artificial intel...
144 篇 deep learning
140 篇 accuracy
133 篇 computational mo...
120 篇 knowledge based ...
119 篇 sentiment analys...
118 篇 ontologies
111 篇 computer science
110 篇 databases
109 篇 feature extracti...
108 篇 knowledge graph

机构

27 篇 faculty of engin...
26 篇 maharishi school...
22 篇 school of inform...
15 篇 centre of interd...
14 篇 lovely professio...
14 篇 institute of inf...
13 篇 school of comput...
13 篇 department of co...
13 篇 school of cyber ...
12 篇 beijing universi...
12 篇 school of comput...
11 篇 school of comput...
10 篇 national laborat...
10 篇 knowledge engine...
10 篇 institute of com...
9 篇 school of comput...
9 篇 beijing univ pos...
9 篇 university of ch...
9 篇 school of electr...
9 篇 moe-ms key labor...

作者

67 篇 fuji ren
37 篇 ren fuji
16 篇 bo xu
14 篇 shingo kuroiwa
14 篇 yixin zhong
14 篇 xiaojie wang
11 篇 tiejun zhao
10 篇 dongfeng cai
10 篇 lei li
10 篇 f. ren
10 篇 jingbo zhu
9 篇 s. kuroiwa
8 篇 wenju liu
8 篇 tianrui li
8 篇 zhu jingbo
8 篇 degen huang
7 篇 tanioka tetsuya
7 篇 tingting he
7 篇 guiping zhang
7 篇 vogelsang andrea...

语言

4,216 篇 英文
162 篇 其他
25 篇 中文

检索条件"任意字段=2003 International Conference on Natural Language Processing and Knowledge Engineering"

共 4396 条记录，以下是661-670 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：

Honey, I Chunked the Passwords: Generating Semantic Honeywords Resistant to Targeted Attacks Using Pre-trained language Models 1

引用

20th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA)

作者： Yu, Fangyi Martin, Miguel Vargas Ontario Tech Univ Oshawa ON L1G 0C5 Canada

ISBN: (数字)9783031355042

ISBN: (纸本)9783031355035;9783031355042

Honeywords are fictitious passwords inserted into databases in order to identify password breaches. The major challenge is producing honeywords that are difficult to distinguish from real passwords. Although the generation of honeywords has been widely investigated in the past, the majority of existing research assumes attackers have no knowledge of the users. These honeyword generating techniques (HGTs) may utterly fail if attackers exploit users' personal identifiable information (PII) and the real passwords include users' PII. The literature has demonstrated that password guessing is more effective when focusing on each of the chunks that compose a password (e.g., "P@ssword123" contains two chunks: "P@ssword" and "123") and it has been suggested that, when available, PII should be used to generate honeywords. We thus leverage these findings to base our HGT method on any possible PII contained within passwords, and introduce a new, and more robust than its literature counterparts, method to generate honeywords, which consists of generating honeywords with GPT-3 using the semantic chunks of their corresponding real passwords. Furthermore, we propose a new metric, HWSimilarity, to evaluate the capability of HGTs. HWSimilarity is a pre-trained language model-based similarity metric that considers the semantic meaning of passwords when measuring the indistinguishability of honeywords and their counterparts. Comparing our chunk-level GPT-3 HGT to two state-of-the-art HGTs and using GPT-3 alone, we show that our HGT can generate honeywords that are more indistinguishable than its counterparts.

关键词： authentication chunking honeywords natural language processing language models

来源：评论

学校读者我要写书评

暂无评论

Seri: Sketching-Reasoning-Integrating Progressive Workflow for Empathetic Response Generation 48

Seri: Sketching-Reasoning-Integrating Progressive Workflow f...

引用

48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Bi, Guanqun Cao, Yanan Li, Piji Xie, Yuqiang Fang, Fang Lin, Zheng Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9781728163277

Empathy is a key ability for a human-like dialogue system. Inspired by social psychology, empathy includes both affective and cognitive aspects. Previous works on this topic have merely focused on recognizing emotions or modeling cognition with commonsense knowledge. Nevertheless, the generated results of these works still have a big gap with human-like empathetic responses. In this paper, we propose Seri, a SkEtching-Reasoning-Integrating framework for empathetic response generation. In particular, we define an empathy planner to capture and reason about multi-source information that considers cognition and affection. Further, we introduce a dynamic integrator module that allows the model dynamically select the appropriate information to generate empathetic responses. Experimental results on EmpatheticDialogue show that our method outperforms competitive baselines and generates responses with higher diversity and cognitive empathy levels. © 2023 IEEE.

关键词： Dialogue System Empathetic Response Generation Empathy natural language Generation

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Potential of Deep Learning for Enhancing Data Science Practices 3

Analyzing the Potential of Deep Learning for Enhancing Data ...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mercilin Raajini, X. Deshmukh, Gopal B. Prabha, Savita Singh, Jagtej Agarwal, Ankita Manu, S.E. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India

ISBN: (纸本)9798350319125

Deep learning is a progressive new field of artificial intelligence that has drastically changed the landscape of technology over the last few years. It has enabled the creation of clever packages, including self-using motors, facial recognition, natural language processing, and others. It has also affected information science, with deep getting to know presenting a new set of tools to system and examine information. This paper discusses the capacity of deep gaining knowledge for enhancing records technological know-how practices, consisting of predictive analytics, visible insights, and self-studying structures. For each subject, the paper provides a short evaluation accompanied by an evaluation and discussion. Subsequently, it provides an outline of cutting-edge developments and guidelines for how records scientists can OK use their abilities and revel to capitalize on the capacity of deep learning. © 2023 IEEE.

关键词： capitalize discussion learning recommendations scientists

来源：评论

学校读者我要写书评

暂无评论

Classification of Online Sentiment Data tweets using Various Approaches in Machine Learning

Classification of Online Sentiment Data tweets using Various...

引用

2023 international conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Rawat, Vandana Bisht, Paritosh Suryakant Kothiyal, Shruti Singh, Neelam Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India Department of Computer Science and Engineering France

ISBN: (纸本)9798350323917

Online data is the data that is present on an online platform such as social media. Social media contains various information through which it can analyze users' attitudes & behavior. Sentimental analysis enables the automatic analysis of expression whether it is positive or negative. It can be about places, people, events, organizations, brands, products, etc. By using tools provided by machine learning & natural language processing with different approaches sentiment analysis can be made possible. The ubiquitous use of the Online World has given people a new method for expressing their emotions. It also functions as a platform with a shedload of knowledge where viewers may examine other users' thoughts, which are categorized into so many sentiment classifications and have become more and more essential in judgment. The representation of the number of comments that contain complex multidimensional sentiments that are either good or bad or anything in between the two can really be analyzed by means of this paper's approach to sentiment classification for consumer review classification. In this research, sentiment contribution in the field of online data tweets are collected, and using various machine learning algorithms results are identified as collective actions using different accuracy. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Text Mining for Sentiment Analysis in Bond Portfolio Construction 24

Text Mining for Sentiment Analysis in Bond Portfolio Constru...

引用

24th international conference on Digital Signal processing, DSP 2023

作者： Konstantinidis, Thanos Golan, Maya Xu, Yao Lei Constantinides, Tony G. Mandic, Danilo P. Department of Electrical and Electronic Engineering Imperial College London London United Kingdom

ISBN: (纸本)9798350339598

The Efficient Market Hypothesis stipulates that financial instruments within a market perfectly reflect all public information. As the predominant method by which information is disseminated, it is considered that information contained within news influences the value of financial instruments. Thus, if the Efficient Market Hypothesis holds, then understanding the implications of such information is vital in informing portfolio construction. Sentiment analysis is the attempt of a machine to quantify the opinion of natural language. Therefore, this paper visits the Efficient Market Hypothesis by using a novel approach of analysing the relationship between financial news sentiment and corporate bonds in the time frame between 2015 and 2021. Several methods of sentiment analysis are employed to compare the efficacy of traditional lexicon-based knowledge extraction methods and the novel Transformer model approach. Furthermore, the short- and long-term impact of news is evaluated by comparing lagging and decaying signals. The outputs yielded from the sentiment analysis are used as a parameter in portfolio construction ant their returns are examined in light of Sharpe's Ratio to find success in demonstrating the continued viability of the Efficient Market Hypothesis, and the strong relationship between this theory and modern methods of financial sentiment analysis. Ultimately, this paper yields literature leading results with a Sharpe Ratio of 2.1. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

AnxiBERT - Prompt engineering for Social Media Anxiety Detection 24

AnxiBERT - Prompt Engineering for Social Media Anxiety Detec...

引用

8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Senanayake, Ashala Lakmini Liyanage, Sidath Faculty of Computing and Technology University of Kelaniya Colombo Sri Lanka

ISBN: (纸本)9798400717383

Social media anxiety detection refers to the approach of recognizing various signs of anxiety in a person based on their social media engagements. A person who experiences anxiety may express negative emotional responses like frustration, apprehension, and nervousness. Often, an individual's emotional tone in social media posts resonates with their feelings, providing valuable insights into their mental condition. The increase in socioeconomic adversities has resulted in a rapid growth of emotional disturbances in society. Identifying these emotional disturbances has become vital necessity for obtaining timely support and fostering better overall well-being by addressing the mental health component of the definition of health. Adapting pretrained language models (PLMs) for downstream tasks like digital mental health has therefore become a current trend even though there is no specific model for anxiety detection. There are many approaches for enhancing PLMs to determine downstream performance. In this research, we introduce a novel model, AnxiBERT, consists of soft prompt engineering logic, to adapt a PLMs knowledge for anxiety detection. To our knowledge, this is the first attempt to adapt a PLM's knowledge for anxiety detection using prompt engineering. Multiple evaluations show that our model demonstrates state-of-the-art performance compared to existing baselines. © 2024 Copyright held by the owner/author(s).

关键词： Sociology

来源：评论

学校读者我要写书评

暂无评论

Investigation on Digital Forensic Using Graph Based Neural Network with Blockchain Technology 2

Investigation on Digital Forensic Using Graph Based Neural N...

引用

2nd IEEE international conference on Data Science and Network Security, ICDSNS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Bhujang, Raghavi K. Pareek, Piyush Kumar Manoj, H.M. Deepika, K.S. Information Technology University of the Cumberlands United States Ms Ramaiah Institute of Management Department of Entrepreneurship Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Computer Science and Engineering Bms Institute of Technology and Management Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Digital forensics has evolved to focus on leveraging social media evidence, offering substantial support for investigating various crimes. However, scrutinizing social media data for evidence presents significant challenges for authorities. This article proposes employing blockchain infrastructure and natural language processing (NLP) techniques as foundational elements in digital forensic investigations. Specifically, it advocates for a Graph Convolutional Neural Network (GCNN) based approach to multi-class classification. NLP is integral to various stages of this process, including data collection, feature selection, classifier assessment, and vectorization. Moreover, to enhance data security against hackers and network attacks, a blockchain-based system is utilized. Real-world dataset experimentation demonstrates the efficacy of the proposed system. The findings reveal significant improvements in GCNN classification performance when incorporating topology knowledge. Furthermore, optimizing the model necessitates considering various input factors, such as document length, training data representation, and network architecture. This structured approach to digital forensic investigation holds promise for bolstering law enforcement efforts. © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey of Machine Learning: Advancements, Applications, and Challenges 2

A Comprehensive Survey of Machine Learning: Advancements, Ap...

引用

2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Nithya, T. Nivas Kumar, V. Gayathri, S. Deepa, S. Varun, C.M. Siva Subramanian, R. Rajalakshmi Institute of Technology Dept of CSE India Jawahar Engineering College Dept of Ece India S.A Engineering College Dept of CSE India Rmd Engineering College Dept of Csbs India R.M.K. Engineering College Dept of Csbs India R.M.K. College of Engg and Tech Dept of CSE India

ISBN: (纸本)9798350325799

This article provides a comprehensive review of the subject of machine learning. In addition to covering the subject in depth, the essay also investigates the many applications, difficulties, and techniques that are used in this rapidly evolving field. This article makes an attempt to synthesize and synthesize the most recent research and knowledge on machine learning. It does so by offering insights into fundamental principles and methodologies, as well as fresh advancements and practical applications. This study analyzes how machine learning can be used in different fields, such as image recognition, natural language processing, and recommendation systems. Machine Learning (ML) techniques, such as supervised learning, unsupervised learning, and reinforcement learning are analyzed. This study also investigates the limitations and challenges presented by machine learning, including interpretability, data quality, and ethical considerations. This study provides a comprehensive overview on the topic, which will pave way for more research and advancements in the field of machine learning. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Prompt Learning for Subjective and Objective Recognition of Text 6

Prompt Learning for Subjective and Objective Recognition of ...

引用

6th IEEE international conference on Computer and Communication engineering Technology, CCET 2023

作者： Cheng, Baoping Lu, Shaoshuai Duan, Yiping Wang, Sheng Zhang, Qinlong Information Technology Co. Ltd Department of Electronic Engineering Beijing China Information Technology Co. Ltd. Dpartment of Converged Communication Systems Hangzhou China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350340686

Subjective expressions generally contain the user's emotional opinions and thus can better represent the sentiment information of the originating text reviews, while objective descriptions usually appear in declarative sentences that do not reflect the sentiment polarity of the comment text. Therefore, subjective and objective recognition techniques for text are necessary for natural language processing tasks (e.g. opinion mining, sentiment analysis). However, most existing deep learning-based methods need large human-annotated data, but labeling large amounts of high-quality emotional texts is labor-intensive. The lack of labeled data has become a bottleneck restricting the deep algorithm. To cope with the shortage of labeled data, we employ Prompt Learning strategy in Subjective and Objective recognition task of text, thus proposing a simple PLSO method. PLSO can effectively inherit the abundant prior knowledge from the pre-trained model and learn high-level semantic features of text by reducing the differences between upstream and downstream tasks. Meanwhile, we introduce triplet loss as a regular term in the training objective, which guides the model to capture the category distribution of data in joint training. Benefiting from the above advantages, the PLSO approach significantly outperforms the other baseline methods in the data sparsity scenario, demonstrating its superiority and effectiveness. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Cyberbullying Detection and Severity Classification Using Bi-LSTM 9

Cyberbullying Detection and Severity Classification Using Bi...

引用

9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Devi, K. Nirmala Rajasekar, Vani Jayanthi, P. Balasubramani, Kavin Kandasamy, Kaviya Gowrisankar, Keerthana Kongu Engineering College Dept. of Computer Science Erode India

ISBN: (纸本)9798350377972

Cyberbullying has grown to be a significant issue in the internet age, affecting individuals of all ages, particularly teens. It involves using internet channels to harass, threaten, or disparage people, often with lingering emotional and psychological repercussions. In social media platforms cyber bullying is becoming more and more common, which has raised the need for efficient tools to identify and remove harmful content. Based on the frequency of objectionable words, a system was created for this research to categorize tweets into four groups: neither, low, medium, or high. A Kaggle dataset was first cleaned and prepared by pre-processing with Keras and natural language Toolkit (NLTK). The offending severity level of the tweets was determined using a fuzzy logic with a triangular membership function. Several models were trained using that categorized datasets like Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Encoder Representations from Transformers (BERT-Base) and DistilBERT. Among those models Bi-LSTM showed a high degree of accuracy with a precision score of 95.52%, recall of 95.48%, accuracy score of95.48%, and F1 score of 95.49%. © 2024 IEEE.

关键词： Deep 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440页 << < 63 64 65 66 67 68 69 70 71 72 > >>

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：