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4th International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Mao, Yanghong Chen, Chuixin Guangzhou Institute of Science and Technology Software Engineering Department Guangzhou China Guangzhou Institute of Science and Technology Electronic Information Department Guangzhou China

ISBN: (纸本)9798350343113

At present, with the rapid development of economy and technology, the quality of the people's life has been generally improved, and the importance of the security system has become increasingly prominent. Among them, the image monitoring system has attracted more and more attention because of its intuitive and rich information content, making it an important part of the security system. Based on this, an image monitoring system is designed. The system uses the GEC6818 platform and is equipped with the Samsung Cortex-A53 series high-performance eight-core processor S5P6818. The system uses ubuntu, selects the camera to collect the surrounding environment image in real time, studies the data structure and principle of the image acquisition software, introduces the process and key steps of image acquisition, and completes the design of the image acquisition program using API functions, The collected image is displayed on the touch screen after being filtered and enhanced. After debugging, the system has reached the expected goal. The system can play a good reference role in the security of homes, communities and other places. © 2023 IEEE.

关键词： Image enhancement

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Trusted Polarimetric Feature Fusion for Polsar Image Classification

Trusted Polarimetric Feature Fusion for Polsar Image Classif...

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2023 SAR in Big Data Era, BIGSARDATA 2023

作者： Han, Fangzhou Zhang, Lamei Dong, Hongwei Harbin Institute of Technology Department of Information Engineering Harbin150001 China Institute of Software Chinese Academy of Sciences Science & Technology on Integrated Information System Laboratory Beijing China

ISBN: (纸本)9798350326239

Benefiting from the rapid advancements in Deep Learning (DL), data-driven features exhibit remarkable separability in PolSAR image classification. However, data-driven features are challenging to interpret and susceptible to resolution and noise. On the contrary, polarimetric features based on the target decomposition not only have high robustness, but also has physical interpretability. Consequently, researchers have begun to explore ways to integrate both types of features to achieve enhanced performance, which typically concat the data-driven features and polarimetric features directly. However, polarimetric features are not stable in separability, which is because the inherent defects of the target decomposition algorithm. To mitigate the adverse effects of low-quality features, this paper proposes a trusted polarimetric feature fusion (TPFF). The new paradigm calculates the confidence of features and employs a novel feature fusion method. The proposed method has been tested on the EMISAR Foulum dataset, with experimental results demonstrating improved classification performance and robustness. © 2023 IEEE.

关键词： Image classification

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An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

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Computers, Materials & Continua 2024年第6期79卷 5201-5223页

作者： Chen Zhang Liming Liu Yufei Yang Yu Sun Jiaxu Ning Yu Zhang Changsheng Zhang Ying Guo Software College Northeastern UniversityShenyang110169China School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co. Ltd.Beijing100035China College of Computer Science and Engineering Ningxia Institute of Science and TechnologyShizuishan753000China

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

关键词： Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms

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System Report for CCL24-Eval Task 1: Construction of CFSP Model Based on Non-Finetuning Large Language Model 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Huang, Fugeng Guo, Zhongbin Li, Wenting Cheng, Haibo School of Software and Microelectronics Peking University Beijing102600 China School of Computer and Technology Beijing Institute of Technology Beijing100081 China School of Information Engineering Beijing Institute of Graphic Communication 102600 China National Engineering Research Center for Software Engineering Peking University 100871 China

ISBN: (纸本)9780000000002

Chinese Frame Semantic Parsing (CFSP) is an important task in the field of Chinese Natural Language Processing(NLP). Its goal is to extract the frame semantic structure from the sentence and realize the deep understanding of the events or situations involved in the sentence. This paper mainly studies the application of Large Language Model (LLM) for reasoning through Prompt engineering without fine-tuning the model, and completes three subtasks of Chinese Framework Semantic Parsing tasks: frame identification, argument Identification and role identification. This paper proposes a Retrieval Augmented Generation (RAG) method for target words, and constructs more refined sample Few-Shot method. We achieved the second place on the B rankings in the open track in the "CCL2024-Eval The Second Chinese Frame Semantic Parsing" competition* © 2024 China National Conference on Computational Linguistics.

关键词： Semantics

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Chinese Speech Emotion Recognition Based on Pre-Trained Model 5

Chinese Speech Emotion Recognition Based on Pre-Trained Mode...

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5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Xu, Sukui Jiang, Nan Lian, Shun Pan, Jingmin Anhui Institute of Information Technology College of Computer and Software Engineering Wuhu China Fujian Polytechnic of Information Technology School of General Education Fuzhou China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798331506612

This paper proposes a method based on large-scale speech pre-trained models for the task of Chinese speech emotion recognition. Similar to the transfer learning approach in image classification task, speech pre-trained model is used as a fixed feature extractor to extract features from speech data, followed by a fully connected network for classification. Experimental results show that, on the seven-class speech emotion recognition task of the Chinese M3ED dataset, the proposed method achieves classification accuracies of 0.506 and 0.560 using base and large sized pre-trained models respectively, surpassing the baseline accuracy of 0.454 for the ECAPA-TDNN model. © 2024 IEEE.

关键词： Transfer learning

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Transaction transmission model for blockchain channels based on non-cooperative games

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Science China(information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Spatial transformation based on integrated artificial intelligence system: a case study of an ancient village in Jiangxi Province 2

Spatial transformation based on integrated artificial intell...

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2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhou, Yanli Liu, Liping Jiangxi Institute of Fashion Technology Nanchang330201 China School of Software Engineering Jiangxi Vocational and Technical College of Information Application Nanchang330043 China

ISBN: (纸本)9781510689053

With the rapid development of technology and people's increasing attention to healthy living quality, deeply integrating intelligent technology into the health field of elderly housing has become an irreversible and important trend. This article proposes an innovative intelligent management system based on artificial intelligence (AI) to meet the urgent needs of health monitoring and care for the elderly. This system cleverly integrates cutting-edge technologies such as the Internet of Things (IoT), big data analysis, and AI, achieving comprehensive and all-weather intelligent monitoring and management of the living environment and health status of the elderly. The system collects real-time physiological data and lifestyle habits of elderly people through intelligent devices, and uses big data algorithms for in-depth analysis to provide personalized health management and nursing advice for each elderly person. At the same time, the intelligent management system can also timely detect and warn potential health risks, ensuring that the elderly receive timely and effective medical care. The practical results show that the application of this intelligent management system can significantly improve the quality of life and happiness of the elderly, and promote the intelligent upgrading of the elderly housing health field. © 2025 SPIE.

关键词： Electronic health record

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Multi-view Clustering via Joint Self-representation and Semi-nonnegative Matrix Factorizations 4

Multi-view Clustering via Joint Self-representation and Semi...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Fu, Xiaochun Zhang, Xiangdong Li, Qin Xidian University School of Communication Engineering Xi'an China Shenzhen Institute of Information Technology School of Software Engineering Shenzhen China

ISBN: (纸本)9798350324303

The traditional clustering methods, which rely on sample spacing to construct clusters, cannot be applied to high-dimensional data due to its intricate data structure and numerous irrelevant attributes. In practice, high-dimensional data often possess a latent low-dimensional subspace structure. Multi-view clustering achieves consistency and complementarity of multi-view data by uncovering the underlying *** recent years, researchers have proposed many subspace representation clustering methods, which have improved the performance of multi-view clustering to a certain extent. However, general clustering methods need to construct Adjacency matrix and calculate the Eigendecomposition of a matrix of its Laplacian matrix, which makes the algorithm very complex and restricts the use of clustering algorithms in large-scale problems, based on the above issues, we propose a new multi-view subspace clustering model. In our model, by learning a data-anchor connection bipartite graph to achieve a fast clustering process, the weights of the edges of the bipartite graph are used to represent the similarity between the corresponding data and anchors, and matrix decomposition is utilized to reduce the view dimension to a low-dimensional space. In addition, the non-negative matrix decomposition technology is used to reduce the view dimension to a low dimensional space, and the \ell{1,2}-norm is used for the similarity matrix between views to mitigate the impact of outliers in the original data on the Matrix decomposition,and the Schatten p-norm based on t-SVD is employed to amplify the high-order correlation between different views. A multitude of experimental results on various multi-view datasets demonstrate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Clustering algorithms

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AI-Powered Smart and Personalized Education Platform

AI-Powered Smart and Personalized Education Platform

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Dunusinghe, A.V. Ranasinghe, T.K.S.A. Gamage, J.G.A.C.H. Perera, K.G.D.T. Thelijjagoda, Samantha Gunatilake, Poojani Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Research and International Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350319200

An AI assistant addresses online education's shortcomings, focusing on personalized learning and career guidance. It provides tailored recommendations, assesses performance, suggests subjects, and crafts learning plans. Objectives include accurate grade prediction, subject stream advice, customized assessments, and personalized strategies. The study shows data analytics and machine learning forecast grades, while multi-tenancy architecture improves data handling. Personalized assessments consider both academics and soft skills. Machine learning promises accurate subject stream predictions, and engagement detection aids in optimal learning. This AI system holds potential in reshaping online education, but ongoing improvements are vital for enhanced learning. © 2023 IEEE.

关键词： career guidance learning strategies Machine learning multi-tenancy Online education personalized assessment personalized learning subject stream recommendation

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A Study of DM-TPACK Evaluation Methods for Middle School Mathematics Teachers 24

A Study of DM-TPACK Evaluation Methods for Middle School Mat...

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8th International Conference on Education and Multimedia technology, ICEMT 2024

作者： Zhang, En Chen, Ruxian Chen, Sailan Chen, Yuanhua Wang, Ying Yin, Ruiyao Institute of Computing Science and Technology Guangzhou University Guangzhou China Institute of Mathematics and Information Science Guangzhou University Guangzhou China Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (纸本)9798400717611

The theoretical framework of Technological Pedagogical Content Knowledge (TPACK) provides a foundation for the integration of information technology and disciplines. Dynamic mathematical software, as the most widely used mathematics discipline software by mathematics teachers, can effectively improve classroom efficiency and improve students' mathematical thinking. This is exemplified by China's first leading dynamic mathematics discipline teaching tool in the mobile Internet environment: NetPad. In light of the ongoing evolution of information technology, junior high school mathematics teachers must consistently enhance their capacity to leverage dynamic mathematics technology for instructional purposes, in order to align with the evolving demands of teacher development in the contemporary era. Therefore, this paper analyzes the research status of TPACK measurement methods, based on the relevant theoretical studies of TPACK, takes the research of core component TPACK as the entry point, integrates the dynamic mathematical technology, divides the dimensions of junior high school mathematics teachers' Dynamic Mathematics Technological Pedagogical Content Knowledge (DM-TPACK) level, refers to the junior high school mathematics teachers' TPACK scale by Yuanmei Duan, constructs a DM-TPACK evaluation scale and uses Think Aloud Method and Delphi Method to revise and improve, forming the final DM-TPACK questionnaire for junior high school mathematics teachers. Finally, the questionnaire was subjected to a reliability test, and the results showed that the evaluation instrument had good internal consistency and reliability. This study provides a basis for measuring the TPACK level of junior high school mathematics teachers' integration of dynamic mathematical techniques, and provides a reference for the development of a TPACK assessment tool for teachers. © 2024 Copyright held by the owner/author(s).

关键词： Teaching

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