检索结果-内蒙古大学图书馆

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Viswanathan.M.P Noor Kaylan Hamid V Rajyalakshmi Neelam Sharma Sharmeen Izzat Hassan S Muthuperumal Hindusthan College of Engineering and Technology Coimbatore India Department of Computer Education Al Kitab University Kirkuk Iraq Department of CSE (Cyber Security) Vignana Bharathi Institute of Technology India School of Physical Education Lovely Professional University Punjab India Department of Computer Engineering College of Engineering Knowledge University Erbil Iraq Department of Mathematics K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The key factor in promoting accessibility and improving the educational experience is accurate classification and labelling of educational materials. Research paper presents a novel approach to educational material classification that makes use of BERT-CNN, a hybrid model that combines and CNNs. The limitations of conventional approaches are addressed by research methodology, which seamlessly integrates a contextual understanding of BERT with CNN's extraction capabilities to address the prior difficulty. It has frequently been difficult for prior techniques to properly capture category elements and comprehend contextual complexities in educational text classification. Here, research leverage the pre-trained contextual embeddings of BERT to enable research model to capture complicated semantics in educational texts, and the CNN architecture successfully extracts categorical features from these embeddings. By combining these two potent models, research is able to capture the logical and structural characteristics of educational resources and create a thorough knowledge of them. Furthermore, research implementation in Python makes use of well-known deep learning packages like TensorFlow and PyTorch, guaranteeing repeatability and accessibility for practitioners and researchers. Research suggested model performs very well, achieving 99% classification accuracy through extensive manipulation and analysis of datasets on various educational resources. The findings demonstrate the efficiency and resilience of research approach in correctly categorizing and labelling instructional materials, underscoring its significance in the management of educational content and strategic enhancement.

关键词： Accuracy Scalability Text categorization Semantics Feature extraction Robustness Labeling Ensemble learning Random forests Context modeling

来源：评论

学校读者我要写书评

暂无评论

Price Prediction of Digital Currencies using Machine Learning 10

Price Prediction of Digital Currencies using Machine Learnin...

引用

10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

作者： Dwivedi, Ashutosh Dhar Dutta, Subhayu Adhikary, Subhrangshu Pedersen, Jens Myrup Aalborg University Cyber Security Group Department of Electronic Systems Copenhagen Denmark Dr. B.C. Roy Engineering College Department of Computer Science & Engineering West Bengal Durgapur713206 India Spiraldevs Automation Industries Pvt. Ltd. Department of Research & Development West Bengal Raiganj733123 India

ISBN: (纸本)9798350345032

Cryptocurrencies have gained immense significance and popularity in recent times. With thousands of digital currencies available, selecting the right one can be challenging for users. In the financial sector, accurately predicting future prices is crucial for profitable investments in digital currencies. However, price prediction in this realm poses unique challenges, as it lacks physical goods or services as the basis, unlike stock prices. Machine learning emerges as a pivotal tool for addressing this challenge and plays a vital role in price prediction. This research analyzes five prominent currencies - Monero, Bitcoin, Ethereum, IOTA, and Zcash - employing five models: SVR, LRG, Huber, RANSAC, MLP, and AdaBoost. The experimental results demonstrate promising outcomes, showcasing the ability to predict digital currency prices with an impressive R2 score of 1.0 for specific machine learning algorithms. This advancement opens new avenues for informed decision-making and profitable ventures in the dynamic world of digital currencies. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

Joint Automatic Speech Recognition And Structure Learning Fo...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hu, Jiliang Li, Zuchao Shen, Mengjia Ai, Haojun Li, Sheng Zhang, Jun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

ISBN: (纸本)9798350368741

Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not suitable for simultaneous speech recognition and understanding. In this paper, we propose a joint speech recognition and structure learning framework (JSRSL), an end-to-end SLU model based on span, which can accurately transcribe speech and extract structured content simultaneously. We conduct experiments on name entity recognition and intent classification using the Chinese dataset AISHELL-NER and the English dataset SLURP. The results show that our proposed method not only outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities but also achieves state-of-the-art performance on the two datasets. © 2025 IEEE.

关键词： Information Extraction Speech Recognition Spoken Language Understanding

来源：评论

学校读者我要写书评

暂无评论

Research on Distributed Sensor Network Faulty Recovery Method based on Attention Mechanism 3

Research on Distributed Sensor Network Faulty Recovery Metho...

引用

3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Xing, Zheng Liu, Boya Guan, Haochen Zhang, Yiran Zhang, Ying Chengdu University of Technology The College of Nuclear Technology and Automation Engineering Sichuan Chengdu China Chengdu University of Technology College of Environment and Civil Engineering Sichuan Chengdu China Zhengzhou University of Light Industry International Education College Henan Zhengzhou China Hebei University School of Cyber Security and Computer Hebei Baoding China Northeast Petroleum University School of Electrical & Information Engineering Heilongjiang Daqing China

ISBN: (纸本)9781510663473

Faulty recognition and recovery is extremely essential component for existing sensor networks. However, current investigations concentrates on the recognition process and develop abundant advanced identification methods and existing recovery method is almost based on the multiple layers of neural network to achieve the recovery of sensor network, which would result in vast computation costs. Attention mechanism is proposed for neural network to focus some important neural nodes and perform high accuracy for training process with insignificant computation cost. In this article, we propose a novel neural network to recover the fault sensor network with attention mechanism. Thus, the developed mechanism can achieve rapid recovery of fault sensors network with reasonable computation costs and acceptable performance. From extensive experimental result, we can conclude that our proposed method can strictly process the fault sensor network and compared with existing fault sensor network recovery methods to illustrate the effective performance on recovery accuracy with reasonable costs of our proposed method. © 2023 SPIE.

关键词： Sensor networks

来源：评论

学校读者我要写书评

暂无评论

A Position-Aware Transformer for Image Captioning

引用

computers, Materials & Continua 2022年第1期70卷 2065-2081页

作者： Zelin Deng Bo Zhou Pei He Jianfeng Huang Osama Alfarraj Amr Tolba School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China Advanced Forming Research Centre University of StrathclydeRenfrewshirePA49LJGlasgowUnited Kingdom Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia UniversityEgypt

Image captioning aims to generate a corresponding description of an *** recent years,neural encoder-decodermodels have been the dominant approaches,in which the Convolutional Neural Network(CNN)and Long Short TermMemory(LSTM)are used to translate an image into a natural language *** these approaches,the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of ***,most conventional visual attention mechanisms are based on high-level image features,ignoring the effects of other image features,and giving insufficient consideration to the relative positions between image *** this work,we propose a Position-Aware Transformer model with image-feature attention and position-aware attention mechanisms for the above *** image-feature attention firstly extracts multi-level features by using Feature Pyramid Network(FPN),then utilizes the scaled-dot-product to fuse these features,which enables our model to detect objects of different scales in the image more effectivelywithout increasing *** the position-aware attentionmechanism,the relative positions between image features are obtained at first,afterwards the relative positions are incorporated into the original image features to generate captions more *** are carried out on the MSCOCO dataset and our approach achieves competitive BLEU-4,METEOR,ROUGE-L,CIDEr scores compared with some state-of-the-art approaches,demonstrating the effectiveness of our approach.

关键词： Deep learning image captioning transformer attention position-aware

来源：评论

学校读者我要写书评

暂无评论

cybersecurity Challenges In Grid-Tied Power Converters

Cybersecurity Challenges In Grid-Tied Power Converters

引用

International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vineet Kumar Adlin Jebakumari S Mansingh Meena Department of Computer Science and Engineering (Cyber Security) Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Computer Science Engineering Department Jain (Deemed to be University) Bangalore India Department of Electrical Engineering Vivekananda Global University Jaipur India

The integration of power electronic converters into electrical grids is essential for the interconnection of microgrids, electric cars, energy storage systems, renewable energy sources, and high-voltage DC cables for transmission. The increasing use of these inverters in modern grids has increased interest in tracking and managing their functioning to improve grid support from a practical and research standpoint. Current industry standards currently require grid-tied converters to have a defined set of control parameters, which may be changed remotely over a communication network. Although the ability to manage them remotely adds new features, it also leaves these converters vulnerable to possible cyberattacks. The purpose of this essay is to shed light on the vulnerabilities of power convert control systems to cyberattacks. It offers a summary of common cyberattacks while taking into account the various uses for grid-tied converters. The paper also looks at how different cyberattacks affect these converters' grid support capabilities. Finally, a summary and suggestions for more study are provided.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack Against Split Learning

A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiaoyang Xu Mengda Yang Wenzhe Yi Ziang Li Juan Wang Hongxin Hu Yong Zhuang Yaxin Liu Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Department of Computer Science and Engineering University at Buffalo SUNY

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However, these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces a new semi-honest Data Reconstruction Attack on SL, named Feature-Oriented Reconstruction Attack (FORA). In contrast to prior works, FORA relies on limited prior knowledge, specifically that the server utilizes auxiliary samples from the public without knowing any client's private information. This allows FORA to conduct the attack stealthily and achieve robust performance. The key vulnerability exploited by FORA is the revelation of the model representation preference in the smashed data output by victim client. FORA constructs a substitute client through feature-level transfer learning, aiming to closely mimic the victim client's representation preference. Leveraging this substitute client, the server trains the attack model to effectively reconstruct private data. Extensive experiments showcase FORA's superior performance compared to state-of-the-art methods. Furthermore, the paper systematically evaluates the proposed method's applicability across diverse settings and advanced defense strategies.

关键词： computer vision computer aided instruction Distance learning Transfer learning Training data Privacy breach Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Distributed Secure Storage Scheme Based on Sharding Blockchain

引用

computers, Materials & Continua 2022年第3期70卷 4485-4502页

作者： Jin Wang Chenchen Han Xiaofeng Yu Yongjun Ren R.Simon Sherratt School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Business Nanjing UniversityNanjing210093China School of Computer School of SoftwareSchool of Cyberspace SecurityEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Department of Biomedical Engineering University of ReadingRG66AYUK

Distributed storage can store data in multiple devices or servers to improve data ***,in today’s explosive growth of network data,traditional distributed storage scheme is faced with some severe challenges such as insufficient performance,data tampering,and data lose.A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed *** this scheme,the following improvements have been made in this *** paper first analyzes the problems faced by distributed *** proposed to build a new distributed storage blockchain scheme with sharding *** proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology,which can improve the efficiency of data verification between *** addition,this paper uses polynomial commitment to construct a new verifiable secret share scheme called *** new scheme is one of the foundations for building our improved distributed storage blockchain *** with the previous scheme,our new scheme does not require a trusted third party and has some new features such as homomorphic and batch *** security of VSS can be further *** comparisons show that the proposed scheme significantly reduces storage and communication costs.

关键词： Blockchain distributed storage verifiable secret share polynomial commitment

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Authentication Protocol for IoT-AmI Environment

An Enhanced Authentication Protocol for IoT-AmI Environment

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Shree Chand Khichar Awaneesh Kumar Yadav Manoj Misra Brij B. Gupta Dept. of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand India International Center for AI and Cyber Security Research and Innovations Asia University Taichung

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The rapid proliferation of the Internet of Things (IoT) in recent years has fostered the swift development of various applications, enabling seamless connections between consumers and everyday utilities. One sector that has seen significant transformation is healthcare, thanks to the adoption of IoT and Ambient Intelligence (AmI). However, this growing utilization of IoT-AmI systems has raised substantial concerns about privacy and security, particularly in user authentication. Numerous authentication techniques have been established for IoT-AmI systems. Nevertheless, the current state-of-the-art reveals that these methods are vulnerable to various severe attacks, such as impersonation, Man-in-the-middle (MITM), traceability, replay, privacy and violation of perfect forward secrecy. Considering the above, this paper aims to propose a secure authentication protocol for IoT-AmI systems. The security of the proposed protocol for IoT-AmI is verified using the formal analysis tool Scyther. Furthermore, the performance of the designed protocol is assessed to show that it has lower computational and communication costs compared to existing protocols.

关键词： Wireless communication Privacy Protocols Costs Authentication Medical services Ambient intelligence

来源：评论

学校读者我要写书评

暂无评论

Evaluating The Mixing Performance in A Planar Passive Micromixer With T-shape and SAR Mixing Chambers: Comparative Study 2

Evaluating The Mixing Performance in A Planar Passive Microm...

引用

2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Ababneh, Jafar Deif, Mohanad A. Solyman, Ahmad Zekry, Rahma S. Hafez, Mohamed Sharfo, Samer M. Cyber Security Zarqa University The Faculty of Information Technology Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Artificial Intelligence 12566 Egypt School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow United Kingdom University Department of Bioelectronics Cairo Egypt INTI-IU-University Faculty of Engineering Feqs Nilai Malaysia

ISBN: (纸本)9798350373363

In Microfluidic devices have gained significant interest in various fields, including biomedical diagnostics, environmental preservation, animal epidemic avoidance, and food safety regulation. Micromixing phenomena are crucial for these devices' functionality, as they accurately and efficiently manipulate fluids within microchannels. The process aims to blend samples accurately and swiftly within these scaled-down devices, governed by the promotion of dispersion among distinct fluid species or particles. Advancements in passive and active micromixers have led to innovative designs incorporating diverse processes to enhance mixing efficiency. Examples include two-dimensional impediments, controlled imbalanced collisions, and complex configurations like spiral and convergence-divergence structures. Active micromixers use external cues to initiate and regulate mixing processes, including thermal, magnetic, sound, pressure, and electrical fields. The trajectory of micromixing technologies is significantly influenced by current developments in microfluidics. One notable advancement is the incorporation of micromixers into 3D printing methodologies, facilitating the development of adaptable microfluidic systems. Additionally, the incorporation of microfluidic principles into paper-based channels creates opportunities for the development of cost-effective and portable diagnostic devices. The process of micro-mixing is critical in boosting the functionalities of these devices. © 2023 IEEE.

关键词： Microfluidics

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：