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检索条件"任意字段=3rd International Conference on Computing and Data Science, CONF-CDS 2021"

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Quantum spiking neural networks for image classification 3

Quantum spiking neural networks for image classification

引用

3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Liu, Shilong Gu, Yongjian Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

As the field of artificial intelligence experiences the emergence of various large-scale models, the computing power requirements are increasing. Simultaneously, Moore’s Law is approaching its limits, which intensifies the need to address how to perform high-dimensional complex data computations efficiently and with minimal energy consumption. Quantum computing and neuromorphic computing, with their unique features, offer potential solutions to this contemporary challenge. In this study, we aim to integrate quantum computing with neuromorphic computing, and develop a quantum spiking neural networks architecture. We proposed three measurement strategy to reducing operational costs, making it more friendly to near-term quantum hardware. We evaluated the performance of these three models on the standard MNIST dataset for classification tasks and found that they maintained a high level of accuracy despite a reduced number of parameters and a substantial decrease in the number of quantum circuit shots. © 2025 SPIE.

关键词： Quantum computers

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Reconstruction and Update of 3D Model of Mechanical Products Based on 3D Point Cloud data 3

Reconstruction and Update of 3D Model of Mechanical Products...

引用

3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Zhou, Fujing Anhui Vocational And Technical College Anhui Hefei China

ISBN: (纸本)9798350318609

With the help of high-resolution images, 3D model reconstruction can obtain and extract the original feature information of the object, thereby achieving accurate measurement of product geometric dimensions, materials and other parameters. This technology has broad application prospects in the field of mechanical processing. This article first analyzes the current research status of 3D micro-measurement technology and three-dimensional surface reconstruction methods at home and abroad. Subsequently, this article uses three-dimensional point cloud data for modeling, and performs real-time shooting and data processing of parts. Afterwards, this article uses cloud data processing technology to extract the mechanical products in the sample, and obtains the dimensional parameters of the mechanical products through image fusion. Finally, this article conducts a performance test on the reconstructed three-dimensional model of the mechanical product. The test results show that after optimization, the dynamic response time of the model is reduced from 8-12s to 3-5s. This shows that the model can quickly respond to machine data and achieve high-efficiency operation;after optimization, the durability basically reaches more than 79%. The results of this study can show that the reconstruction effect of this model has been significantly improved, but it still needs improvement. © 2024 IEEE.

关键词： Image fusion

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Research on federal learning privacy protection based on secure multi-party computing 24

Research on federal learning privacy protection based on sec...

引用

3rd international conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Liu, Bin Blancaflor, Eric B. Mapua Univ Sch Informat Technol Manila Philippines Xiamen Univ Technol Sch Software Engn Xiamen 361024 Fujian Peoples R China

ISBN: (纸本)9798400718212

Federated learning (FL) exchanges model changes without sharing raw data. As an innovative approach to solving data security and privacy protection issues, it has recently become popular. In federated learning, data remains in the hands of various institutions or data owners, and model parameters are exchanged and shared among parties through encryption technology, which enables collaborative training of the model while protecting data privacy and security. Therefore, the privacy protection of federated learning has become very important. However, the existing research schemes of federated learning are all based on the open sharing of data, whether it is the sharing of plain text data or encrypted data, which brings about issues of security and efficiency. Therefore, while maintaining sufficient model accuracy, improving computational efficiency and security is a major challenge in federated learning research. This paper proposes a privacy protection scheme based on the SPDZ protocol for secure multi-party computation, using Chi-square binning related technologies to achieve better privacy protection. Meanwhile, secure multi-party computation has more obvious advantages in complex application scenarios. Finally, through experiments, it is proved that the proposed scheme in this paper has significantly improved computational efficiency compared with existing schemes while ensuring security and basic accuracy.

关键词： Federated learning Privacy protection Secure multi-party computation Chi-square binning

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Research and Applications of Large Language Models for Converting Unstructured data into Structured data 3

Research and Applications of Large Language Models for Conve...

引用

3rd international conference on Cloud computing, Big data Application and Software Engineering, CBASE 2024

作者： Liu, Xiangfeng Sun, Jianting Lei, Aidi Zhu, Junzhi Co. Ltd Chengdu610073 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798331506582

The rapid growth of data has led to a significant increase in unstructured data, such as text, audio, and images, which dominate modern information processing. However, the complexity of unstructured data presents challenges for automated analysis and processing. Converting unstructured data into structured formats is crucial for tasks like data mining, information extraction, and knowledge graph construction. Traditional methods that rely on manual rules or statistical models struggle with complex, context-dependent data. Recently, large language models (LLMs), such as GPT-4 and BERT, have demonstrated great potential in unstructured data processing due to their powerful natural language understanding and generation capabilities. This study explores the application of large language models in transforming unstructured data into structured formats. It begins by reviewing the limitations of traditional approaches and then presents a framework for unstructured data processing using LLMs, covering data preprocessing, information extraction, and structured representation. The study's experiments demonstrate the superior performance of LLMs in handling various types of unstructured data, particularly in tasks like named entity recognition, relation extraction, and contextual understanding. The results show that LLMs achieve higher accuracy and generalization while reducing reliance on manual rules. The paper discusses the strengths and limitations of this method, proposing future improvements, such as combining domain-specific knowledge for model optimization and expanding applications to multimodal data processing. The research highlights the promising role of large language models in converting unstructured data into structured formats. © 2024 IEEE.

关键词： Structured Query Language

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Construction of an Innovation Information Mining Platform Integrating Knowledge Graph 3

Construction of an Innovation Information Mining Platform In...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Wan, Dawei Huang, Peisen Sichuan University of Arts and Science Sichuan Dazhou635000 China

ISBN: (纸本)9798331509675

With the continuous expansion of data mining scenarios, an accurate data analysis algorithm framework is urgently needed. In this study, the construction of an innovation information mining platform integrating knowledge graph is discussed. The designed platform combines multiple layers of knowledge fusion, matching and problem solving. This architecture can greatly improve the intelligent extraction and mining of innovative information. The designed platform mainly consists of four key parts: 1. At the data acquisition level, network logs are analyzed through Web data mining. At the same time, GoAccess tool is used to extract access logs and conduct preliminary analysis. 2. At the data mining level, this study uses a probabilistic graph model based on a word pair model to perform cluster analysis on large-scale text data. At the same time, the innovative information points are classified through calorific value calculation and information entropy theory to obtain the mining effect. 3. At the system improvement level, we integrate knowledge graphs and data mining algorithms, and use the Jena inference engine to reason and complete data relationships. 4. At the platform optimization level, the cloud platform architecture of this study further optimizes the allocation of computing resources and application deployment, while using Kubernetes and Docker container technologies to ensure the high performance of the system. Through data mining accuracy test, K-mean and FCM algorithms were used to verify the algorithm effect of this study. © 2025 IEEE.

关键词： Knowledge graph

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Secure Computation of Maximum and Minimum Values in data Aggregation Based on Cloud computing 3

Secure Computation of Maximum and Minimum Values in data Agg...

引用

3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Li, Sen Luo, Dan Liu, Xin Luo, Rong School Of Information Network Security Xinjiang University Of Political Science And Law Xinjiang Uygur Autonomous Region Tumshuk City China College Of Intelligence And Computing Tianjin Ren'ai University Tianjin China Changji University Xinjiang Changji China

ISBN: (纸本)9798350318609

data aggregation is the process of combining multiple data points or data sets into a larger data set. Through calculation, statistics or other means, it can provide higher level data information, which is widely used in different data types and fields such as database query, data analysis, data mining and data processing. In data processing scenarios, data aggregation functions play a key role. They are used to perform aggregate calculations on data, such as finding the maximum, minimum, and maximum difference of multiple data. However, it is important to pay special attention to protecting sensitive information and privacy when performing data aggregation. Aiming at this problem, based on the threshold NTRU encryption algorithm with additive homomorphism, combining the vector encoding method and the ciphertext re-randomization method, this paper proposes a secure calculation protocol for the maximum value, minimum value and maximum difference in multi-party data sets under the semi-honest model. To verify the security of the proposed protocol, we use the simulation paradigm approach for security proof and show that the protocol can resist the attack of the maximum number of colluders. Through theoretical analysis and simulation experiments, we show the high efficiency of the proposed scheme, and prove that the protocol has practical value. © 2024 IEEE.

关键词： data mining

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Reliability Optimization Analysis of Financial Management System Based on BP Neural Network Algorithm 3

Reliability Optimization Analysis of Financial Management Sy...

引用

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

作者： Zhang, Zhihong Guangzhou Institute of Science and Technology Guangdong Guangzhou510540 China

ISBN: (纸本)9798350319125

The reliability optimization analysis of financial management systems based on BP neural network algorithm is an important topic in the financial field. The financial management system plays a crucial role in the financial management of an organization, and its reliability is crucial for the smooth operation of the organization. The BP neural network algorithm is a powerful tool that can be used to optimize the reliability of financial management systems. This algorithm can analyze a large amount of data and identify patterns that help improve the performance and reliability of financial management systems. By using this algorithm, organizations can identify potential problems in their financial management systems before they arise. This enables them to take proactive measures to prevent these issues from occurring, ultimately improving performance and profitability. In summary, reliability optimization analysis based on BP neural network algorithm is an important research field in the financial field. It provides organizations with a powerful tool to improve their financial management systems and ensure their long-term success. © 2023 IEEE.

关键词： BP neural algorithm Financial management system Financial sector Reliability analysis

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Improved Models Based on Deep Learning for Person Re-Identification: A Survey 3

Improved Models Based on Deep Learning for Person Re-Identif...

引用

3rd IEEE international conference on Social sciences and Intelligence Management, SSIM 2023

作者： Qiu, Ziqiao Yang, Lei Chen, Jing School of Computer Science Guangdong University of Science and Technology Dongguan China School of Computer Science Guangdong University of Science and Technology Dongguan Kunpeng Key Laboratory of Computing Dongguan China

ISBN: (纸本)9798350327786

Person re-identification technology continually tracks particular targets via video image data from cameras at various angles. This technology is used for monitoring in security fields. Incorporating deep learning into person reidentification has increased its importance in computer vision technology. Various common recognition methods in recent years were analyzed in this article focusing on three fundamental deep learning models for person re-identification. Suggestions for further research in person re-identification were made, too. © 2023 IEEE.

关键词： Deep learning

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The impact of the stratospheric sudden warming event in 2021 on local atmospheric environment 3

The impact of the stratospheric sudden warming event in 2021...

引用

3rd international conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Liu, Bainian Yao, Qiang Zhu, Di College of Meteorologhy and Oceanography National University of Defense Technology NUDT Changsha China

ISBN: (纸本)9781510689077

This study pioneers the utilization of the YHGSM dataset to conduct an in-depth analysis of the ramifications of the stratosphere's sudden warming (SSW) event in 2021 on local atmospheric circulation, temperature, and density. The research first validates the precision of the YHGSM forecast data;subsequently, by computing the meridional temperature gradient and potential height gradient within the polar vortex, it elucidates the spatiotemporal dynamics of the SSW event's intensity;ultimately, it scrutinizes the SSW's influence on the ambient atmospheric wind field, density, and temperature. The findings ascertain that the YHGSM forecast data is reliable in accuracy and can faithfully delineate the inception, evolution, and dissipation of SSW, precipitating alterations in atmospheric circulation patterns, pronounced temperature oscillations, and consequently impacting the flight safety of aerial vehicles. © 2025 SPIE.

关键词： Atmospheric density

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Analysis and Forecasting of COVID-19 Pandemic Using ARIMA Model 3

Analysis and Forecasting of COVID-19 Pandemic Using ARIMA Mo...

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3rd international conference on Advances in computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Singh, Soni Mittal, Sonam Singh, Sunaina Lovely Professional University School of Computer Science and Engineering Department of Computer Science and Engineering Punjab Phagwara144411 India Chitkara University Institute of Engineering Technology Chitkara University Punjab India Department of Electrical & Electronics School of Engineering Dehradun Bidholi India

ISBN: (纸本)9798350346169

The global community is now seriously threatened by the COVID-19 pandemic. The government of every nation must pay close attention to the analysis of this disease to take the required actions to lessen the impact of this worldwide epidemic. This research focused on the disease outbreak in the Indian region through July 21st, 2021, and evaluated the incidence and mortality. Machine learning techniques, such as the ARIMA model, are applied to perform the prediction analysis on collected data from the World Health Organization (WHO) official portal for India between January 20, 2020, and July 21, 2021. Mean Square Error (MSE), a measure of model performance, was used to assess performance, and it came in between 2170.636098 and 46.839689. In the four weeks of test data, the Expected instances are estimated to be between 192K and 230K, which is fairly similar to the actual figures. The government and physicians will be able to make future strategies with the aid of this study. © 2023 IEEE.

关键词： COVID-19

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