检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on cyber science and Technology Congress, DASC/PiCom/CBDCom/cyberSciTech 2023

作者： Lin, Jingsong Guo, Xiaodong Dong, Chen Lyu, Chenxi Xu, Li Chen, Zhenyi College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China University of South Florida Department of Computer Science and Engineering TampaFL33620 United States

ISBN: (纸本)9798350304602

In the era of big data, the number of internet users is increasing yearly, and each user receives a massive amount of information every day. The low-value density of massive text data makes it difficult to utilize its information. In the face of this problem, text summarization generation technology that can extract the paper's leading content and condense the paper's main idea is a powerful solution. In text summarization generation, abstractive summarization techniques with high readability, clear expression, and closer proximity to human language habits are often favored. However, mainstream technologies still have issues like summarizations' detachment from focus. In this paper, keyword information is employed to join the text summarization generation task, and a keyword information module is added to the Transformer to better perform the summarization generation task. Finally, the ROUGE evaluation standard is used to compare the performance of the above models on the large-scale Chinese short text summarization (LCSTS) dataset. The experimental results show that the model generates higher-quality summarizations using keyword information, and the related methods have considerable potential. Future work can explore better model structures, more suitable methods for utilizing keyword information, and more accurate keyword extraction techniques to improve the quality of abstractive summarizations. © 2023 IEEE.

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

Personalized Federated Learning for Heterogeneous Residential Load Forecasting

引用

Big Data Mining and Analytics 2023年第4期6卷 421-432页

作者： Xiaodong Qu Chengcheng Guan Gang Xie Zhiyi Tian Keshav Sood Chaoli Sun Lei Cui the Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and TechnologyTaiyuan 030024China the Faculty of Engineering and Information Technology University of Technology SydneyUltimo 2007Australia the Centre for Cyber Security Research and Innovation Deakin UniversityMelbourne 3125Australia the School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China

Accurate load forecasting is critical for electricity production,transmission,and *** learning(DL)model has replaced other classical models as the most popular prediction ***,the deep prediction model requires users to provide a large amount of private electricity consumption data,which has potential privacy *** nodes can federally train a global model through aggregation using federated learning(FL).As a novel distributed machine learning(ML)technique,it only exchanges model parameters without sharing raw ***,existing forecasting methods based on FL still face challenges from data heterogeneity and privacy ***,we propose a user-level load forecasting system based on personalized federated learning(PFL)to address these *** obtained personalized model outperforms the global model on local ***,we introduce a novel differential privacy(DP)algorithm in the proposed system to provide an additional privacy *** on the principle of generative adversarial network(GAN),the algorithm achieves the balance between privacy and prediction accuracy throughout the *** perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

关键词： load forecasting personalized federated learning differential privacy

来源：评论

学校读者我要写书评

暂无评论

Multi-residual generative adversarial networks for QR code Deblurring

Multi-residual generative adversarial networks for QR code D...

引用

2022 International Conference on Electronic Information Technology, EIT 2022

作者： Wang, Mingyue Chen, Kecheng Lin, Fanqiang College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510655096

Motion blur and ambient noise are the main reasons that affect quick response (QR) code recognition. In this paper, we propose a novel deep learning approach to deblur the QR codes and realize the effective recognition of deblurring QR codes by using generative adversarial networks (GANs). We estimate the blur kernel and ambient noise of the blur QR code in the dataset using GANs, so as to realize the transformation from the blur QR code image to the sharp image. We also propose an expansion method of QR codes dataset, and achieve better generalization performance of the model. The experimental results show that our approach can effectively estimate the blur kernel and ambient noise that can realize the deblurring of QR code. © 2022 SPIE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Neuromorphic-Driven Agentic AI for Autonomous Decision-Making Systems 4

Neuromorphic-Driven Agentic AI for Autonomous Decision-Makin...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Manjunath Kamath, K. Samata Mehta, S. Akshaya, H.L. Shilpashree, N. Jadhav, Girish Mitra, Abhijit Department of Cyber Security Karkala India Bengaluru India Sarada Vilas College Department of Computer Science Mysuru India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department Of Electrical Engineering Post Limda India Pune India

ISBN: (纸本)9798350352931

Agentic AI represents a paradigm shift in the development of intelligent systems capable of adaptive and proactive interactions in dynamic and complex environments. By integrating reinforcement learning (RL) with cognitive frameworks, Agentic AI goes beyond traditional rule-based and reactive models, enabling autonomous systems to make informed decisions, anticipate future states, and learn from experience. This paper explores the theoretical foundations and practical applications of Agentic AI, highlighting its potential to transform a variety of fields, including robotics, autonomous driving, finance, and healthcare. Through a detailed review of state-of-the-art research, we illustrate how cognitive architectures such as ACT-R and Soar, combined with advanced RL techniques like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), contribute to the development of AI agents with human-like reasoning and decision-making capabilities. Experimental results demonstrate that Agentic AI significantly outperforms conventional AI approaches in terms of adaptability, learning efficiency, and decision accuracy. The findings suggest that Agentic AI offers a robust framework for creating intelligent systems capable of complex problem-solving, long-term planning, and proactive behavior, paving the way for the next generation of AI-driven applications. © 2024 IEEE.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Rapid Prototype Development Approach for Genetic Programming

引用

Journal of computer and Communications 2024年第2期12卷 67-79页

作者： Pei He Lei Zhang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Cyberspace Security Guangzhou University Guangzhou China

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

关键词： Genetic Programming Grammatical Evolution Gene Expression Programming Regression Analysis Mathematical Modeling Rapid Prototype Development

来源：评论

学校读者我要写书评

暂无评论

Improving Brain Tumor Diagnosis with Deep Learning Algorithms

Improving Brain Tumor Diagnosis with Deep Learning Algorithm...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： V Varun Rahul Palanivel Sumathi D Karthika Natarjanv VIT-AP University Guntur India School of Engineering (Cyber Security) School of Computer Science and Engineering (SCOPE)

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This paper proposes an innovative approach for brain tumour assessment employing deep learning procedures, specifically integrating the Segment Anything Module (SAM) with the VGG (Visual Geometry Group) architecture. SAM is utilized to segment various components within brain tumour images, enhancing the localization and differentiation of tumour regions from surrounding tissues. By leveraging SAM's capability to segment diverse structures, including tumours and adjacent anatomical features, the proposed framework aims to enhance the accuracy of tumour analysis. The VGG architecture is employed to get high-level features, while SAM refines the segmentation masks, facilitating more precise feature representation. Experimental evaluations on standard brain tumour data exhibit the effectiveness of the proposed one, showcasing improved segmentation accuracy compared to conventional approaches. This research contributes to advancing automated brain tumour analysis, potentially enhancing diagnostic accuracy and treatment planning in clinical settings.

关键词： Deep learning Location awareness Geometry Image segmentation Visualization Accuracy Planning Standards Collaborative intelligence Tumors

来源：评论

学校读者我要写书评

暂无评论

An enhanced LSTM-CRF based approach for Knowledge Integration based Sentiment Analysis

An enhanced LSTM-CRF based approach for Knowledge Integratio...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Allam Balaram M. SilpaRaj K. Likhitha Sree V. Pramodh Kumar D. Vinay Kumar K. Venu Kumar Reddy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering (Cyber Security) CVR College of Engineering Hyderabad India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Sentiment analysis is a key element for expert reviews but is at a loss in terms of detection of sarcasm and irony, which distort the actual sentiments. This paper proposes an approach that will fuse syntactic and contextual knowledge to respond to these requirements. Steps for preprocessing include tokenization, stop word removal, lemmatization, followed by feature extraction using BERT embedding. In addressing high-dimensional output, the Relief algorithm is then used to extract the relevant features. The classification model utilizes a combination of Deep Learning for sequence LSTM networks and Maximum entropy methods, i.e., Conditional Random Fields (CRF), and enables sequential dependency learning to give better accuracy. This application of data pertaining to time and context can effectively address subtle nuances such as sarcasm and irony and classify the text as positive, negative, or neutral. It brings several advancements with respect to traditional models, thus improving the understanding of emotion in text.

关键词： Sentiment analysis Accuracy Reviews Viterbi algorithm Syntactics Feature extraction Conditional random fields Tokenization Classification algorithms Long short term memory

来源：评论

学校读者我要写书评

暂无评论

A Novel Fast Video Fragment Matching Algorithm for Copyright Protection

A Novel Fast Video Fragment Matching Algorithm for Copyright...

引用

作者： Chen, Yan Yang, Zhongliao Dong, Chen Lin, Hui Chen, Zhenyi College of Computer and Data Science Fuzhou University Fuzhou350108 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China University of South Florida Department of Computer Science and Engineering TampaFL33620 United States

ISBN: (纸本)9798350304602

The rise of the short-form video industry marks a revolutionary change in the digital media landscape. This has opened up new opportunities for content distribution and creation but has also brought about several challenges, one of which is the difficulty of effectively protecting video copyrights. In the industrial sector, for example, the industrial digital design, manufacturing, and production process often generate a large amount of video data. If these videos are stolen, copied, or tampered with by unauthorized third parties, infringement issues can arise. To effectively monitor and protect the video copyrights of users or enterprises, this paper proposes a lightweight video clip fast matching (KMP-FVFM) algorithm. The algorithm utilizes the idea of Knuth-Morris-Pratt (KMP) algorithm, which takes into account the inter-frame fragment relationship of the query video to improve the video matching speed. At the same time, the duplicate segment locations are pinpointed in the original video. This paper also implements a plain method for video fragment matching, and the experimental results show that KMP-FVFM can effectively improve the efficiency of video fragment matching. © 2023 IEEE.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning-based Data Usability Enhancement Scheme for Differential Privacy 9

A Deep Learning-based Data Usability Enhancement Scheme for ...

引用

9th IEEE Smart World Congress, SWC 2023

作者： Yan, Haonan Li, Xiaoguang Zheng, Gewei Li, Hui Li, Fenghua Lin, Xiaodong Xidian University University of Guelph China Xidian University State Key Laboratory of Isn School of Cyber Engineering Xi'an China University of Cas State Key Laboratory of Information Security Institute of Information Engineering Cas School of Cyber Security Beijing China University of Guelph School of Computer Science Guelph Canada

ISBN: (纸本)9798350319804

While differential privacy (DP) is widely used to ensure privacy, it can also significantly reduce data accuracy. Current research attempts to improve accuracy by leveraging post-processing techniques, but these methods are sub-optimal and only applicable to specific data types. To address the issue, in this work, we propose a novel deep learning-based data usability enhancement method for differential privacy that is data-type independent. By using image denoising technology, the proposed scheme reduces the mean square error (MSE) of DP-perturbed data and theoretically justifies the relationship between data noise reduction and image noise reduction. The effectiveness of the proposed scheme is demonstrated through a comprehensive evaluation. © 2023 IEEE.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

Data Breaches: Financial and Reputational Impacts of Vulnerabilities on Organizations to Enhance cybersecurity Strategies

Data Breaches: Financial and Reputational Impacts of Vulnera...

引用

Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Aishwarya Gite Ashutosh Joshi Jigyasa Agrawal Tanisha Agarwal Namruth Reddy Mohana Minal Moharir Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India NVIDIA Bengaluru India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

In the current digital era, organizations and businesses face increasing vulnerability to cybersecurity threats, with data breaches presenting substantial risks to their operational and financial well-being. Proposed work seeks to examine and monitor vulnerabilities within organizations by investigating a data of companies that have suffered data breaches. This research employs Power BI to identify trends and patterns in these incidents, shedding light on the techniques employed by attackers and the types of organizations most susceptible to breaches. The findings indicate that hacking and unauthorized access are the primary methods of breaches across various industries. By visualizing these patterns, this research offers valuable insights that can assist organizations in enhancing their cybersecurity strategies and addressing potential vulnerabilities. The results emphasize the necessity of ongoing monitoring and proactive measures to protect sensitive information.

关键词： Industries Renewable energy sources Surveillance Data visualization Organizations Medical services Data breach Market research computer crime Faces

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：