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检索条件"任意字段=7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023"

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GRU-Based Winner Subcarrier Detection in Frequency Domain Contention 24

GRU-Based Winner Subcarrier Detection in Frequency Domain Co...

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7th international conference on information science and Systems, ICISS 2024

作者： Yao, Shumin Zhao, Qinglin Xu, Zezhou Feng, Li Li, Guanghui Tian, Liwei Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology China Department of Broadband Communication Pengcheng Laboratory China School of Artificial Intelligence and Computer Science Jiangnan University China Department of Computer Guangdong University of Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798400717567

Single-carrier frequency domain contention (S-FDC) is an efficient wireless contention mechanism based on orthogonal frequency-division multiplexing (OFDM). In each round of S-FDC, each node randomly selects and signals on one of the OFDM subcarriers while listening to all subcarriers simultaneously. Each node independently detects the activated subcarrier with the smallest index and regards it as the winner subcarrier. Accurately detecting the winner subcarrier is crucial to ensuring the functionality of the network. However, it is extremely difficult in practice due to power leakage problems that arise from frequency asynchronization among nodes. In this paper, we propose a gated-recurrent-unit (GRU)-based model that incorporates S-FDC’s unique features as domain knowledge to enable accurate winner subcarrier detection. Extensive simulation experiments verify that our DL model is effective and outperforms state of the art. this research highlights the potential of DL in improving wireless communication systems. © 2024 Copyright held by the owner/author(s).

关键词： Frequency domain analysis

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th international conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

the rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. this paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. they also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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Analysis of Spatial-Temporal Evolution of Water Surface Area in DaSuGan Lake Based on Multi-source Satellite Images and computer Vision technology 7th

Analysis of Spatial-Temporal Evolution of Water Surface Area...

引用

7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Shang, Liping Du, Yu′e Liu, Baokang Gansu Natural Energy Research Institute Lanzhou China Tianshui Normal University Tianshui China

ISBN: (纸本)9789819624089

Lakes are sensitive indicators of climate change, and obtaining the information of lake water quickly and accurately is of great significance for regional climate change research, the protection and governance of ecological environment. this article is based on multi-source satellite images such as Landsat and GF-6, and uses the method of empirical threshold and single band threshold and water index and other methods to extract the water area of DaSuGan lake. Based on the accuracy evaluation results, the optimal water identification model is selected, and the spatial-temporal evolution law of the water area of DaSuGan lake is analyzed from 1991 to 2020. the results show that: © the Author(s) 2025.

关键词： Lakes

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Graph Neural Network Knowledge Graph Recommendation Model Integrating Deep Domain information and Important Domain information 7th

Graph Neural Network Knowledge Graph Recommendation Model In...

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7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Zhang, Xuelian Computer Science Department Sichuan University Jinjiang College Meishan China

ISBN: (纸本)9789819624089

the importance between source and target nodes is often overlooked in higher-order information, which can introduce excessive noise in complex network scenarios, thereby affecting recommendation performance. To address this issue, we propose a graph neural network-based knowledge graph recommendation model that integrates deep domain information and important domain information, aiming to enhance recommendation accuracy and diversity. Initially, we pre-train the knowledge graph using graph embedding techniques to obtain structural information. Subsequently, we employ a graph convolutional network to delve deeper into the semantic information of the knowledge graph, capturing rich structural and semantic details from both depth and importance perspectives. Finally, we compute the interaction probability between users and items using the inner product of the enhanced vectors and user vectors, facilitating recommendations. Experiments on the Last-FM, Book-Crossing, and MovieLens-20M datasets yield AUC and F1 scores of 83.2% and 75.4%, 74.9% and 66.8%, 97.9% and 93.1%, respectively. Additionally, Recall@50 scores are 34.5%, 11.2%, and 35.0%. Our model outperforms RippleNet, KGCN, LKGR, and other models, indicating that integrating knowledge graph recommendation models with meta-graph neighborhoods effectively improves recommendation performance. © the Author(s) 2025.

关键词： Knowledge graph

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Development of EEG-Based Optimal Biomarkers for Decoding Fatigue during Motor Imagery Tasks 13

Development of EEG-Based Optimal Biomarkers for Decoding Fat...

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Lee, Jun-Seok Lee, Seung-Won Kim, Sang-Rok Hwang, Han-Jeong Dept. Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of

ISBN: (纸本)9798331521929

this study aimed to identify electroencephalography (EEG)-based neurophysiological biomarkers for monitoring mental fatigue during motor imagery (MI) tasks. We used EEG data recorded from 29 healthy adults during MI tasks. For the preprocessing of the EEG data, we sequentially performed down-sampling into 200 Hz, independent component analysis (ICA), band-pass filtering between 0.5 and 50 Hz, and common average reference (CAR). the preprocessed EEG data were analyzed using a fast Fourier transform (FFT) to calculate spectral power in the δ (0.5–3 Hz), θ (4–7 Hz), α (8–13 Hz), and β (13–30 Hz) frequency bands. Subsequently, seventeen fatigue-related metrics were calculated using individual spectral powers and their combinations. these fatigue-related metrics were averaged for the frontal lobe (F3 and F4), central lobe (FCC3h and FCC4h), parietal lobe (P3 and P4), and occipital lobe (POO1 and POO2). the statistical significance of the average fatigue metrics was evaluated through Pearson correlation analysis with the sequence of trials. As a result, we observed strong positive correlations (r > 0.9, p © 2025 ieee.

关键词： Fast Fourier transforms

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GCFuzz: An Intelligent Method for Generating IoT Protocols Test Cases Using GAN with CVAE 7th

GCFuzz: An Intelligent Method for Generating IoT Protocols ...

引用

7th international conference on Attacks and Defenses for Internet-of-things, ADIoT 2024

作者： Zhong, Ming Zeng, Zisheng Guo, Yijia Zhao, Dandan Zhang, Bo Li, Shenghong Peng, Hao Ding, Zhiguo School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua China Key Laboratory of Intelligent Educational Technology and Application in Zhejiang Normal University Jinhua321004 China School of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031855924

the importance of Internet of things (IoT) systems security cannot be ignored, particularly in the realm of communication. IoT protocols serve as standards for communication and interaction among devices in IoT environments. this paper enhances IoT security by identifying and exposing protocol vulnerabilities through fuzzing, a crucial method for discovering security flaws. Traditional generation-based fuzzers require reverse engineering to understand protocol grammar and generate test cases. In this paper, we propose an intelligent method called GCFuzz, which generates high-quality test cases without prior knowledge of the protocol. To understand different classes of protocol grammar, GCFuzz applies Generative Adversarial Networks (GAN) with Conditional Variational Autoencoders (CVAE) to train a generative model on various classes of protocol messages. After training, the model generates fake but credible messages. Additionally, to accurately cluster real protocol messages, we devise a novel clustering method based on a keyword. Results show that, compared to GANFuzz, Peach, BooFuzz, and Sulley, the test cases generated by GCFuzz have stronger targeting and less redundancy, while GCFuzz has higher accurate testing efficiency. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Legacy Compatible and Sybil Resistant Decentralized Identity Management for IoTs 7th

Legacy Compatible and Sybil Resistant Decentralized Identit...

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7th international conference on Blockchain, ICBC 2024, Held as Part of the Services conference Federation, SCF 2024

作者： He, Songlin Lyu, Xukang Chu, Dongliang Southwest Jiaotong University Sichuan Chengdu610031 China Manufacturing Industry Chain Collaboration and Information Support Technology Key Laboratory of Sichuan Province SWJTU Sichuan Chengdu610031 China Zhejiang New Rise Digital Technology Co. Ltd. Hangzhou311899 China

ISBN: (纸本)9783031770944

With the exorbitant growth of Internet of things (IoTs) scale and the interconnections among such a vast realm of heterogeneous objects, it is vital to uniquely identify the IoT devices and build a robust identity management (IdM) system, thus enabling many desired security mechanisms including authentication, authorization and secure exchange. An appealing technology to overcome the shortcomings of conventional online identity models and empower the construction of a robust IoT IdM system lies in decentralized identity, also known as self-sovereign identity. the existing literature on IoT IdM systems omits several critical functionalities like legacy compatibility and consideration of the IoT device lifecycle. In this study, we tackle these issues and present DIdM-IoT, a system for realizing secure and robust decentralized identity management in the IoT setting. We elaborate on the system details and give security sketch. Experimental results demonstrate its effectiveness and efficiency. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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Improving Raft Consensus Algorithm with Relay and Lease Mechanism 7th

Improving Raft Consensus Algorithm with Relay and Lease Me...

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7th international conference on Blockchain, ICBC 2024, Held as Part of the Services conference Federation, SCF 2024

作者： Sun, Yufang Guo, Bing Jia, Daiwei He, Songlin Chengdu Technological University Sichuan Chengdu611730 China Sichuan University Sichuan Chengdu610065 China Southwest Jiaotong University Sichuan Chengdu610031 China Manufacturing Industry Chain Collaboration and Information Support Technology Key Laboratory of Sichuan Province SWJTU Sichuan Chengdu610031 China

ISBN: (纸本)9783031770944

Consensus algorithms are critical in distributed systems, especially with the wide adoption of blockchain technology in various scenarios. For the sake of efficiency, consortium blockchains such as Hyperledger Fabric usually adopt crash fault tolerant (CFT) algorithms, i.e., Raft, to reach consensus amongst peers as entities are often authenticated and mutually trustful in a permissioned setting. Raft algorithm replicates the state among peers and therefore plays a fundamental role in ensuring strict consistency. However, with the ever-growing system scale, the leader becomes a bottleneck in Raft, considerably impacting its efficiency and scalability. In this study, we propose a consensus algorithm Praft where a relay node-based method and a lease mechanism are designed to mitigate the aforementioned issues. the details of the consensus algorithm are elaborated with its security sketch. Extensive experiments are also conducted to demonstrate its efficiency. Compared with the related solutions, e.g., etcd-raft, in the same cluster scale, Praft can decrease message load by 20%–90% and improve consensus throughput by 35%–400%. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blockchain Consensus Crash Fault Tolerance Raft

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Station-Wise Boarding Passenger Flow Prediction for Public Transport Using Various Machine-Learning Methods 7th

Station-Wise Boarding Passenger Flow Prediction for Public T...

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7th international conference of Transportation Research Group of India, CTRG 2023

作者： Patel, Madhuri Patel, Samir B. Swain, Debabrata Patel, Shubh Department of Computer Engineering Pandit Deendayal Energy University Gandhingar India Department of Information Technology L D College of Engineering Ahmedabad India Department of Computer Science & Engineering Nirma University Ahmedabad India

ISBN: (纸本)9789819610365

the migration behavior of regular passengers in city transport refers to the movement patterns and preferences of individuals who use public transportation on a regular basis. these passengers may have specific routes, travel times, and preferences for certain modes of transportation. Passenger flow prediction is crucial for understanding and managing this behavior. Accurate predictions enable transportation operators to optimize their services by adjusting vehicle frequency and capacity, deploying additional services during peak periods, providing real-time information, identifying high-demand areas, and expanding the network. this improves the efficiency and reliability of public transportation, catering to the needs of regular passengers. Data science and machine-learning methods allow us to extract correlations from historical data, improving the accuracy of passenger flow prediction. the passenger flow on a station is highly affected by various factors such as the day of the week, holiday, rain, available routes from that station, and some uncertain events like COVID-19. In this study, we have successfully reported passenger flow prediction at various stations using ensemble machine-learning algorithms. Comparative analysis of implemented work has been carried out with the help of statistical parameters and visual infographic details. For accurate prediction model, accurate data cleaning, pre-processing and feature selection based on correlation have been performed on real dataset of thane Municipal Transport (TMT) from April 2021 to May 2022. We have also compared the performance of prediction models based on month, day and individual station for moderately deviated data and highly deviated data resulted due to effect of COVID-19 pandemic and Taukte cyclone. An exhaustive comparative analysis between train set and test set have been reported with necessary parameters. Comparing to benchmark models, the XGB regressor and Random forest model can reach most accur

关键词： Decision trees

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Convolutional Autoencoders-Based Image Watermarking Techniques 7

Convolutional Autoencoders-Based Image Watermarking Techniqu...

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7th ieee international conference of computer science and information technology, icosnikom 2023

作者： Yadav, Shikha Bala, Jeevan Lovely Professional University Department of Computer Applications Punjab Phagwara India Lovely Professional University Department of Computer Punjab Phagwara India

ISBN: (纸本)9798350360752

Digital image watermarking plays an important role in securing multimedia content against unauthorized use and manipulation. the realm of image watermarking focuses on its vulnerability to various attacks and the integration of Convolutional Neural Networks (CNNs) to bolster its robustness. this proposed paper illustrates an overview of common attacks such as geometric transformations, compression, and signal processing attacks, which can compromise watermark robustness and integrity. these findings highlight the effectiveness and reliability of the encoder, embedder, decoder, and extractor modules in achieving secure and visually appealing watermarking. © 2023 ieee.

关键词： Image watermarking

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