检索结果-内蒙古大学图书馆

International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Malak Aljabri Dorieh M. Alomari Menna Aboulnour Department of Computer Science College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Computer Engineering SAUDI ARAMCO Cybersecurity Chair College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Computer Science SAUDI ARAMCO Cybersecurity Chair College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (纸本)9781665487726

Nowadays, with the widespread use of technology, fake news and rumors are spreading too. People and society are greatly impacted by fake news, which also can be used as phishing attempts and a way of stealing their information. In many areas of our lives, Artificial Intelligence (AI) and Machine Learning (ML) have demonstrated their effectiveness. Furthermore, Natural Language Processing (NLP) has shown promising results in text classification applications. In this study, we proposed an experimental study for detecting fake news using ML models. The proposed model analyzes the main text of the news using NLP techniques and then classifies the news into fake or real news. We used a new dataset that combined multiple fake news datasets. Moreover, we studied the impact of features extraction methods on the performance of the developed models. Eight experiments were performed using Random Forest (RF) and Support Vector Machines (SVM) models, each with a different features extraction technique. The SVM model resulted in the best performance with an accuracy level of 98%. This result proves the model ability to be deployed and used in real-world with high reliability, to detect fake news.

关键词： Support vector machines Radio frequency Machine learning algorithms Phishing Text categorization Feature extraction Natural language processing

来源：评论

学校读者我要写书评

暂无评论

Quantum Resistance Group Key Management for IoTs

Quantum Resistance Group Key Management for IoTs

引用

Emerging Trends in Electrical, Control, and Telecommunication engineering (ETECTE), International Conference on

作者： Fouzia Samiullah Ming-Lee Gan Sedat Akleylek Yichiet Aun Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Perak Malaysia Centre and Department of Computer Engineering Cyber Security and Information Technologies Research and Development Ondokuz Mayis University Samsun Türkiye Chair of Security and Theoretical Computer Science University of Tartu Estonia

Group communication plays a vital role in our interconnected society, facilitating efficient and secure data exchange among multiple endpoints. The issue pertains to the intricate nature that emerges within the limited settings of the Internet of Things and sensor networks. GKM plays a crucial role in maintaining security in group communication schemes. Existing GKM schemes commonly rely on public-key cryptography, which may be susceptible to potential threats posed by quantum computers. This study investigated the usage of post-quantum GKM schemes in the realm of IoTs. To enhance communication security and efficiency in the era of quantum computing, we made significant progress in implementing a post-quantum GKM scheme. Specifically, we have chosen the SABER Key Encapsulation Mechanism, which is one of the four finalists in the National Institute for Standards and technology (NIST) process. We also present preliminary simulation results for our scheme.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Trend Analysis of Significant Topics Over Time in Machine Learning Research

引用

SN computer Science 2021年第6期2卷 1-13页

作者： Sharma, Deepak Kumar, Bijendra Chand, Satish Shah, Rajiv Ratn Department of Computer Engineering Netaji Subash Institute of Technology University of Delhi Sector-3 Dwarka New Delhi 110078 India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi 110067 India Multimodal Digital Media Analysis Lab Indraprastha Institute of Information Technology New Delhi 110020 India

A vast number of research papers on numerous topics publish every year in different conferences and journals. Thus, it is difficult for new researchers to identify research problems and topics manually, which research community is currently focusing on. Since such research problems and topics help researchers to be updated with new topics in research, it is essential to know trends in research based on topic significance over time. Therefore, in this paper, we propose a method to identify the trends in machine learning research based on significant topics over time automatically. Specifically, we apply a topic coherence model with latent Dirichlet allocation (LDA) to evaluate the optimal number of topics and significant topics for a dataset. The LDA model results in topic proportion over documents where each topic has its probability (i.e., topic weight) related to each document. Subsequently, the topic weights are processed to compute average topic weights per year, trend analysis using rolling mean, topic prevalence per year, and topic proportion per journal title. To evaluate our method, we prepare a new dataset comprising of 21,906 scientific research articles from top six journals in the area of machine learning published from 1988 to 2017. Extensive experimental results on the dataset demonstrate that our technique is efficient, and can help upcoming researchers to explore the research trends and topics in different research areas, say machine learning. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： information retrieval Latent Dirichlet allocation Machine learning Research trend analysis

来源：评论

学校读者我要写书评

暂无评论

An information Retrieval Approach to Building Datasets for Hate Speech Detection 35

An Information Retrieval Approach to Building Datasets for H...

引用

35th Conference on Neural information Processing Systems - Track on Datasets and Benchmarks, NeurIPS Datasets and Benchmarks 2021

作者： Rahman, Md Mustafizur Balakrishnan, Dinesh Murthy, Dhiraj Kutlu, Mucahid Lease, Matthew School of Information The University of Texas Austin United States Department of Computer Science The University of Texas Austin United States School of Journalism and Media The University of Texas Austin United States Department of Computer Engineering TOBB University of Economics and Technology Turkey

Building a benchmark dataset for hate speech detection presents various challenges. Firstly, because hate speech is relatively rare, random sampling of tweets to annotate is very inefficient in finding hate speech. To address this, prior datasets often include only tweets matching known "hate words". However, restricting data to a predefined vocabulary may exclude portions of the real-world phenomenon we seek to model. A second challenge is that definitions of hate speech tend to be highly varying and subjective. Annotators having diverse prior notions of hate speech may not only disagree with one another but also struggle to conform to specified labeling guidelines. Our key insight is that the rarity and subjectivity of hate speech are akin to that of relevance in information retrieval (IR). This connection suggests that well-established methodologies for creating IR test collections can be usefully applied to create better benchmark datasets for hate speech. To intelligently and efficiently select which tweets to annotate, we apply standard IR techniques of pooling and active learning. To improve both consistency and value of annotations, we apply task decomposition and annotator rationale techniques. We share a new benchmark dataset for hate speech detection on Twitter that provides broader coverage of hate than prior datasets. We also show a dramatic drop in accuracy of existing detection models when tested on these broader forms of hate. Annotator rationales we collect not only justify labeling decisions but also enable future work opportunities for dual-supervision and/or explanation generation in modeling. Further details of our approach can be found in the supplementary materials [52]. © 2021 Neural information processing systems foundation. All rights reserved.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

Evaluating the Performance to Detecting Topic Shift of Large...

引用

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

来源：评论

学校读者我要写书评

暂无评论

Blockchain-based Security Framework for Metaverse: A Decentralized Approach 15

Blockchain-based Security Framework for Metaverse: A Decentr...

引用

15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Rajawat, Anand Singh Goyal, S.B. Solanki, RamKumar Raboaca, Maria Simona Mihaltan, Traian Candin Illes, Zoltan Verma, Chaman School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Icsi Energy Department National Valcea Romania Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca40033 Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary

ISBN: (纸本)9798350321388

People can act out fake social exchanges in the Metaverse, which is an online community. Security has become a problem in the Metaverse because there are so many online transactions and digital assets. In this thesis, a Blockchain-based security system for the Metaverse is proposed. This would be a decentralised way to keep the virtual world safe. Our system uses Blockchain's autonomous structure to make sure that all financial transactions in the Metaverse are safe, anonymous, and true. We suggest a new consensus process that combines Proof of Stake (PoS) and Proof of Authority (PoA) to make networks safer and allow them to grow. We also have a user-focused, decentralised identity management system that keeps their information safe. We also suggest a way to process Metaverse transactions safely that is based on smart contracts. Our method makes sure that transactional proof, recording, and validation are done in a safe and reliable way. We also show a reputation system that benefits people who follow the law and punishes people who break the law. Lastly, we test how well our suggested structure works with simulations and real-world tests. The results show that our way works to reduce possible security risks in the Metaverse and create a safe, scalable environment for online business. Overall, our Blockchain-based Security Framework for Metaverse: A Decentralised Approach is a good way to keep the security and privacy of digital activities in Metaverse. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Adapted Genetic Algorithm for Orienteering Problem

Adapted Genetic Algorithm for Orienteering Problem

引用

Machine Intelligence Theory and Applications (MiTA), International Conference on

作者： Nuo Xu Dan-Ting Duan Qiang Yang Xiao-Min Hu Chang-Jun Zhou Zhong-Long Zheng Jian-Min Zhao Sang-Woon Jeon School of Computer Science and Technology (School of Artificial Intelligence) Zhejiang Normal University Jinhua China Key Laboratory of Media Audio & Video Communication University of China Beijing China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Department of Electrical and Electronic Engineering Hanyang University Ansan South Korea

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

The Orienteering Problem (OP) is a well-known route optimization problem with various real-world applications. It aims at finding the most valuable path by visiting a set of nodes within a given path length limit. It involves both the node selection and the path optimization and thus is difficult to solve. Genetic algorithm (GA) is known for its effectiveness in solving combinatorial optimization problems. Inspired from this, this paper adapts the genetic algorithm to solve OP mainly by adapting the crossover operation. To this end, this paper adapts seven popular crossover operations, namely position-based crossover based on order selection (OSPBX), position-based crossover based on greedy selection (GSPBX), order crossover based on order selection (OSOX), order crossover based on greedy selection (GSOX), partially mapped crossover (PMX), heuristic crossover (HX), and sequential constructed crossover (SCX). To further improve the quality of the obtained solution, this paper first leverages the 2-opt method to fine-tune the path and then further devises a vertex insertion method to additionally add more nodes in the fine-tuned path when its travelling length is within the given limit. Experiments have been conducted on various OP instances from small-scale to large-scale to investigate the effectiveness of the adapted GA with the seven crossover operators. The experimental results reveal that the adapted GA with seven crossover operators are very promising for solving OP.

关键词： Encoding Optimization Machine intelligence Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

A Review of Methods for the Image Automatic Annotation

A Review of Methods for the Image Automatic Annotation

引用

International Laser technology and Optics Symposium in Conjunction with Photonics Meeting 2020, ILATOSPM 2020

作者： Adnan, Myasar Mundher Rahim, Mohd Shafry Mohd Hasan Ali, Mohammed Al-Jawaheri, Karrar Neamah, Karrar School of Computing Faculty of Engineering University Technology of Malaysia Johar Bahru Malaysia Islamic University Najaf Iraq UTM-IRDA Digital Media Institute of Human-Centred Engineering University Technology of Malaysia Johar Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam ja'Afar Al-sadiq University Najaf Iraq

Nowadays, image annotation has attracted extensive attention due to the explosive growth of image data. Large amount of researches on AIA have been proposed, mainly including classification-based methods and probabilistic modeling methods. In this paper, a detailed study on state-of-the-art of image annotation was presented devoted to a detailed study of image annotation methods. Differences between manual, semi-automatic and automatic annotation were completely distinguished. The criteria for evaluating annotation systems are also presented in this study. In conclusion, a synthesis of methods of automatic image annotation were shown by presenting the pros and cons of each. This synthesis allowed us to examine our choice for automatic image annotation and the importance of integrating user feedback and a semantic. Finally, we participated in our perspective on the issues and challenges in AIA as well as research tendency in the future. © Published under licence by IOP Publishing Ltd.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-aided Parametric Sparse Channel Estimation for Terahertz Massive MIMO Systems

arXiv

引用

arXiv 2024年

作者： Kim, Jinhong Ahn, Yongjun Kim, Seungnyun Shim, Byonghyo Samsung Electronics Seoul06765 Korea Republic of The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Electrical and Computer Engineering The Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Terahertz (THz) communications is considered as one of key solutions to support extremely high data demand in 6G. One main difficulty of the THz communication is the severe signal attenuation caused by the foliage loss, oxygen/atmospheric absorption, body and hand losses. To compensate for the severe path loss, multiple-input-multiple-output (MIMO) antenna array-based beamforming has been widely used. Since the beams should be aligned with the signal propagation path to achieve the maximum beamforming gain, acquisition of accurate channel knowledge, i.e., channel estimation, is of great importance. An aim of this paper is to propose a new type of deep learning (DL)-based parametric channel estimation technique. In our work, DL figures out the mapping function between the received pilot signal and the sparse channel parameters characterizing the spherical domain channel. By exploiting the long short-term memory (LSTM), we can efficiently extract the temporally correlated features of sparse channel parameters and thus make an accurate estimation with relatively small pilot overhead. From the numerical experiments, we show that the proposed scheme is effective in estimating the near-field THz MIMO channel in THz downlink environments. © 2024, CC BY-NC-ND.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：