检索结果-内蒙古大学图书馆

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Huiying Zou Jia Duan Xi Liu Wei Ren Tao Li Xianghan Zheng Kim-Kwang Raymond Choo School of Computer Science China University of Geosciences Wuhan China The Third Surveying and Mapping Institute of Hunan Province Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China College of Computer and Big Data Fuzhou University School of Information and Intelligent Engineering Sanya College China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach’s security.

关键词： Privacy Systematics Costs Scalability Blockchains Security

来源：评论

学校读者我要写书评

暂无评论

Analysis and Detection of Fake News Using Machine Learning

Analysis and Detection of Fake News Using Machine Learning

引用

Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P Sathish Kumar P. Suthanthiradevi C Arul Stephen B Ebenezer Abishek S. Sivakumar M Mathiyarasu Computer and Communication Engineering Rajalakshmi institute of technology Chennai Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Electronics and Communication Engineering BIHER Chennai

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Tokenization Reliability Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Logging Multi-Component Supply Chain Production in Blockchain 2021

Logging Multi-Component Supply Chain Production in Blockchai...

引用

4th International Conference on computers in Management and Business, ICCMB 2021

作者： Madhwal, Yash Chistiakov, Ivan Yanovich, Yury Faculty of Computer Science National Research University Higher School of Economics Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Inst. of Sci. and Technol. and Lab. of Data Mining and Predictive Modelling Inst. for Info. Transmiss. Prob. Russia

ISBN: (纸本)9781450388610

The supply chain is a thriving industry where numerous parties have different interests. Subsequently, the immense volume of data produced is difficult to audit. Some information can be lost or intentionally distorted in the process. Blockchain as an open, public, borderless, neutral, and censorship-resistant architecture can significantly complement supply chains. A new supply chain architecture is proposed in this work, where the tokenized directed acyclic hypergraph (DAG) represents real-world production processes. An anti-aerosol respirator manufacturing is used as an illustration example. By tokenizing all parts of multi-component products, supply chain data is automatically timestamped and secured. Moreover, the DAG design allows one to trace-back all the elements of the final product to their origin. Blockchain can formally audit the entire supply chain without the need to go from place to place. A single incorruptible operations log creates an enabling environment for an unbiased reputation system to emerge. © 2021 ACM.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Characterizing Information Seeking Events in Health-Related Social Discourse

arXiv

引用

arXiv 2023年

作者： Sharif, Omar Basak, Madhusudan Parvin, Tanzia Scharfstein, Ava Bradham, Alphonso Borodovsky, Jacob T. Lord, Sarah E. Preum, Sarah M. Department of Computer Science Dartmouth College United States Department of Computer Science and Engineering CUET Bangladesh Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth College United States Department of Psychiatry Dartmouth Health United States

Social media sites have become a popular platform for individuals to seek and share health information. Despite the progress in natural language processing for social media mining, a gap remains in analyzing health-related texts on social discourse in the context of events. Event-driven analysis can offer insights into different facets of healthcare at an individual and collective level, including treatment options, misconceptions, knowledge gaps, etc. This paper presents a paradigm to characterize health-related information-seeking in social discourse through the lens of events. Events here are board categories defined with domain experts that capture the trajectory of the treatment/medication. To illustrate the value of this approach, we analyze Reddit posts regarding medications for OPIOID USE DISORDER (OUD), a critical global health concern. To the best of our knowledge, this is the first attempt to define event categories for characterizing information-seeking in OUD social discourse. Guided by domain experts, we develop TREAT-ISE, a novel multilabel treatment information-seeking event dataset to analyze online discourse on an event-based framework. This dataset contains Reddit posts on information-seeking events related to recovery from OUD, where each post is annotated based on the type of events. We also establish a strong performance benchmark (77.4% F1 score) for the task by employing several machine learning and deep learning classifiers. Finally, we thoroughly investigate the performance and errors of ChatGPT on this task, providing valuable insights into the LLM’s capabilities and ongoing characterization efforts. © 2023, CC BY-NC-SA.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG

Evaluation of Deep Training and Adapting Algorithms in Medic...

引用

Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Ali Hadi Abdulwahid Girija Rani Karetla KVB. Ganesh A Suresh Kumar K. Radha K. Shivakumar Control and Automation Engineering Department Engineering Technical College Southern Technical University Basra Iraq School of Computer Data and Mathematical Sciences Western Sydney University Australia KL Business School Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of CSE Graphic Era Deemed to be University Dept. of IT St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean Square (LMS) technique, the Connected Neural Network, and also the Recursive Least - square (RLS) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMSE and signal- to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

引用

7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Offloading Policies in Multi-Access Edge Computing Systems with Task Handover

arXiv

引用

arXiv 2023年

作者： Hou, Ling Li, Shi Shen, Zhishu Fu, Jing Wu, Jingjin Jin, Jiong School of Engineering STEM College RMIT University Melbourne Australia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Guangdong Key Laboratory of IRADS Department of Statistics and Data Science BNU-HKBU United International College China

The rapid growth of mobile devices and the increasing complexity of tasks have made energy efficiency a critical challenge in Multi-Access Edge Computing (MEC) systems. This paper explores energy-efficient offloading strategies in large-scale MEC systems with heterogeneous mobile users, diverse network components, and frequent task handovers to capture user mobility. The problem is inherently complex due to the system’s scale, task and resource diversity, and the need to maintain real-time performance. Traditional optimization approaches are often computationally infeasible for such scenarios. To tackle these challenges, we model the offloading problem using the restless multi-armed bandit (RMAB) framework and develop two scalable online policies that prioritize resources based on their marginal costs. The proposed policies dynamically adapt to the system’s heterogeneity and mobility while ensuring near-optimal energy efficiency. Through extensive numerical simulations, we demonstrate that the policies significantly outperform baseline methods in power conservation and show robust performance under non-exponentially distributed task lifespans. These results highlight the practical applicability and scalability of our approach in dynamic MEC *** Codes 93B70 (primary), 68U35 (secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness

arXiv

引用

arXiv 2023年

作者： Chen, Huajie Zhu, Tianqing Zhao, Yuan Liu, Bo Yu, Xin Zhou, Wanlei Centre for Cyber Security and Privacy The School of Computer Science University of Technology Sydney SydneyNSW2007 Australia School of Information Technology and Electrical Engineering University of Queensland BrisbaneQLD4067 Australia Institute of Data Science City University of Macau China

Image deep steganography (IDS) is a technique that utilizes deep learning to embed a secret image invisibly into a cover image to generate a container image. However, the container images generated by convolutional neural networks (CNNs) are vulnerable to attacks that distort their high-frequency components. To address this problem, we propose a novel method called Low-frequency Image Deep Steganography (LIDS) that allows frequency distribution manipulation in the embedding process. LIDS extracts a feature map from the secret image and adds it to the cover image to yield the container image. The container image is not directly output by the CNNs, and thus, it does not contain high-frequency artifacts. The extracted feature map is regulated by a frequency loss to ensure that its frequency distribution mainly concentrates on the low-frequency domain. To further enhance robustness, an attack layer is inserted to damage the container image. The retrieval network then retrieves a recovered secret image from a damaged container image. Our experiments demonstrate that LIDS outperforms state-of-the-art methods in terms of robustness, while maintaining high fidelity and specificity. By avoiding high-frequency artifacts and manipulating the frequency distribution of the embedded feature map, LIDS achieves improved robustness against attacks that distort the high-frequency components of container images. © 2023, CC BY-SA.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

SSRN

引用

SSRN 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Yao, Huaxiu Zhang, Yue Wang, Ren Xu, Kaidi Shi, Xiaoshuang Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence interaction systems, particularly in the healthcare engineering domain. However, a general method for quantifying the uncertainty of free-form answers has yet to be established in open-ended medical question-answering (QA) tasks, where irrelevant words and sequences with limited semantic information can be the primary source of uncertainty due to the presence of generative inequality. In this paper, we propose the Word-Sequence Entropy (WSE ), which calibrates the uncertainty proportion at both the word and sequence levels according to the semantic relevance, with greater emphasis placed on keywords and more relevant sequences when performing uncertainty quantification. We compare WSE with 6 baseline methods on 5 free-form medical QA datasets, utilizing 7 "off-the-shelf" large language models (LLMs), and show that WSE exhibits superior performance on accurate uncertainty measurement under two standard criteria for correctness evaluation. Additionally, in terms of the potential for real-world medical QA applications, we achieve a significant enhancement (e.g., +6.36% improvement of model accuracy on the COVID-QA dataset) in the performance of LLMs when employing sequences with lower uncertainty that are identified by WSE as final answers, without requiring additional task-specific fine-tuning or architectural modifications. © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

Luminescence Properties and Optical Sensing Behaviors of Sr2gdsbo6:Eu3+ Phosphors

SSRN

引用

SSRN 2023年

作者： Tai, Youfa Zhang, Jun Cui, Ruirui Wang, Chunkai Zhao, Tingyu Zhang, Beiqiao Deng, Chaoyong Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang550025 China College of Computer and Information Engineering Guizhou University of Commerce Guiyang550014 China School of Electronics and Information Engineering Guiyang University Guiyang550005 China

Novel orange-red Sr2GdSbO6:xEu3+ (x = 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6) phosphors were successfully prepared by the traditional high-temperature solid-state method. The phosphors were analyzed in terms of their phase purity, particle morphology, absorption spectra, photoluminescence spectra, and thermal stability. Under excitation at 393 nm, the Sr2GdSbO6:Eu3+ emission spectra showed a prominent orange-red emission band at 593 nm. The optimum doping concentration of Eu3+ ions was 0.2. The main concentration quenching mechanism of Eu3+ was attributed to dipole-dipole interaction. At 423 K, Sr2GdSbO6:0.2Eu3+ exhibited abnormal thermal quenching with 114% of the emission intensity at 298 K. Based on the abnormal thermal quenching under 527 nm excitation, an optical temperature sensing strategy was proposed. The results showed that Sr2GdSbO6:Eu3+ phosphors exhibited a promising relative sensitivity, which implies its potential application in optical temperature measurement. © 2023, The Authors. All rights reserved.

关键词： Photoluminescence

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：