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ICYRIME 2024 - Proceedings of the 9th international conference of Yearly Reports on Informatics, Mathematics, and Engineering

ICYRIME 2024 - Proceedings of the 9th International Conferen...

引用

9th international conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2024

the proceedings contain 11 papers. the topics discussed include: the impact of digital analysis and large language models in digital humanity;solar trees: harnessing renewable energy for portable charging of low-capacity devices;speech and language impairment detection by means of AI-driven audio-based techniques;simulation and analysis of dual unbalanced rotor effects on natural frequency in a digital twin shaft model;an analysis of the state of art of the metaverse and its disruptive impact on services;efficient methods for time synchronization in distributed radar systems;a real-time machine learning based solution for privacy enforcement in video recordings and live streaming;high-performance computing for the optimization of double-pipe heat exchanger operations;and understanding parental characteristics of child adoption candidates using MMPI-2 and evolutionary clustering.

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Software Defined Wide Area Networks: Current Challenges and Future Perspectives 9

Software Defined Wide Area Networks: Current Challenges and ...

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9th IEEE international conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Navarro, Annalisa Canonico, Roberto Botta, Alessio Univ Napoli Federico II DIETI Dept Naples Italy

ISBN: (纸本)9798350399806

Software Defined Wide Area Network (SD-WAN) is rapidly becoming an attractive solution for enterprise networks as it offers several benefits such as cost efficiency, increased bandwidth, and improved application performance. However, SD-WAN also brings new challenges that must be addressed for effective deployment (i.e. openness, interoperability, network automation, monitoring, QoS guarantees, scalability and security). In this paper, we highlight the criticalities of this technology and analyze the solutions proposed by the state of the art. We then present a scalable framework based on distributed Reinforcement Learning agents for guaranteeing availability and QoS to business applications. We believe that our work provides valuable insights into the opportunities and challenges of SD-WAN technology and offers new perspectives for future research in this area.

关键词： SD-WAN SDN Intent Based networking Scalability QoS

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9th international conference on Engineering and Emerging Technology, ICEET 2023

9th International Conference on Engineering and Emerging Tec...

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9th international conference on Engineering and Emerging Technology, ICEET 2023

ISBN: (纸本)9798350316926

the proceedings contain 92 papers. the topics discussed include: optimizing solar-powered electric vehicle management with distributed decision making under shading conditions;slot antenna array with compact feed based on a substrate integrated Fabry-Perot cavity;wideband Chebyshev bandpass filter via RFT for RF2 (Ka and V) bands and communication applications;optimizing atomic layer deposition using a hybrid of machine learning methods;mitigating rate-aware jamming attacks: a deceptive anti-jamming approach with enhanced utility;advanced deep learning approach for multiclass breast cancer detection from mammogram images;studying the influential factors for intention to use social networking sites;review of IPv6 mitigation techniques for enterprise local area networks;and service assurance in the transport of goods, to encourage the optimization of processes related to transport logistics and the reduction of waste.

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DICer: distributed Coordination for In-Network Computations 9

DICer: Distributed Coordination for In-Network Computations

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22nd international conference on Information Centric networking

作者： Ambalavanan, Uthra Grewe, Dennis Nayak, Naresh Liu, Liming Mohan, Nitinder Ott, Joerg Robert Bosch GmbH Bosch Res Stuttgart Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9781450392570

Application domains such as automotive and the Internet of things may benefit from in-network computing to reduce the distance data travels through the network and the response time. Information Centric networking (ICN) based compute frameworks such as Named Function networking (NFN) are promising options due to their location independence and loosely-coupled communication model. However, unlike current operations, such solutions may benefit from orchestration across the compute nodes to use the available resources in the network better. In this paper, we adopt the State Vector Synchronization (SVS), an application dataset synchronization protocol in ICN, to enhance the neighborhood knowledge of in-network compute nodes in a distributed fashion. As such, we design distributed coordination for in-network computation (DICer) that assists the service deployments by improving the resolution of compute requests. We evaluate the performance of DICer against NFN and observe an increase in the resource utilization at the edge and a reduction in the request completion time.

关键词： distributed Coordination Named Function networking In-Network Compute Synchronization Protocols

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Comparative Study on Classification of Images in Compressed and Uncompressed Domains 9

Comparative Study on Classification of Images in Compressed ...

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9th international conference on Wireless Communications, Signal Processing and networking, WiSPNET 2024

作者： Ashwini, K. Neha, Perla Sree Jaiadityan, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350350845

Image Classification is the basis of Computer vision. Classification with image data finds a variety of applications in various fields. A comparative study of Classifying the images in the compressed and uncompressed domain is exploited in this research. A garbage classification dataset with 6 different categories is employed for this purpose. Naitve Convolutional neural network is utilized as the classifier. Compressed classification is achieved by compressing the images using discrete wavelet transform of three levels and by feeding the compressed data to the model for classification. the accuracy achieved by classification in the uncompressed domain is about 82.1% with a loss of 0.892. the accuracy achieved in the compressed domain by the three levels of the discrete wavelet transform is 81.7%,81.2%, and 74.6% with losses of 0.793, 0.891, and 1.071 respectively. there is minimal difference in accuracy achieved between uncompressed and compressed domains. Image compression by discrete wavelet transform overperforms the uncompressed domain by several factors like size, elapsed time and memory space. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Factors Affecting the Short-Form Video Consumption on Social Media Platforms 9

Factors Affecting the Short-Form Video Consumption on Social...

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Joint 9th international conference on Digital Arts, Media and Technology with 7th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： May, Shin thant Siddoo, Veeraporn Prince of Songkla University College of Computing Phuket Campus Phuket Thailand

ISBN: (纸本)9798350318241

this paper aims to examine the various factors that influence the consumption of short-form video on social media platforms. the examination includes both internal and external factors affecting user engagement. the study integrated the Technology Acceptance Model (TAM) and the Uses and Gratifications theory (UGT). the research approach employed was a systematic literature review, wherein data were obtained from several databases spanning the years 2018 to 2023. there were 13 articles that matched the search criteria. there are five internal factors that explain the customers' motivation and twenty-eight external factors that explain the utilization of technology. this paper propose that future investigations should encompass a broader range of business domains and use other theoretical frameworks to better understand short-form video consumption's behavior. © 2024 IEEE.

关键词： Social networking (online)

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Reputation based Adaptive Federated Learning in Medical Scenarios 9

Reputation based Adaptive Federated Learning in Medical Scen...

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9th IEEE international conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Lianhai Liu, Tianrui Wang, Yingxiaochun Wang, Xinlei Li, Qi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

Federated Learning (FL) is a distributed machine learning paradigm designed to address data silos and protect data privacy. However, in medical scenarios, the heterogeneity in data quality and the non-independent and identically distributed (Non-IID) nature of medical data from different hospitals can lead to degraded model performance in FL. To tackle this challenge, this paper proposes an adaptive federated learning framework based on reputation. By using a reputation evaluation model, the framework adaptively adjusts the aggregation weights of the models to enhance the performance of the global model. Additionally, our reputation evaluation model, integrated with blockchain technology, can rapidly identify malicious clients and effectively prevent poisoning attacks. Experimental results on two medical datasets demonstrate that this framework achieves higher model accuracy and faster convergence. © 2024 IEEE.

关键词： Federated learning

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Modification Method for Internal Network Flow Anomaly Detection Based on Multi-timescale Analysis 9

Modification Method for Internal Network Flow Anomaly Detect...

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9th IEEE international conference on Data Science in Cyberspace, DSC 2024

作者： Zhang, Xinyu Ding, Junmei Lu, Yueming Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350391367

With the continuous development of information technology, security issues from internal networks are becoming more and more important. Many anomaly detection algorithms are designed to identify anomalies, but these algorithms usually do not care about temporal information, or only care about fine-grained temporal information. On the other hand, deep-learning-based algorithms must rely on complex neural networks to capture temporal information, which is difficult to deploy and maintain in actual internal network environments. Addressing the aforementioned problems, this paper proposes a multi-timescale analyzer using wavelet packet transform and semi-supervised learning, and a method that uses the analyzer to modify the original anomaly scores called Multi-timescale Modification (MTM) method. the method is validated using the network flow dataset CIC-IDS 2017 and a real-world dataset. Experimental evaluations show that using the proposed method can obtain better performance than the original algorithms. © 2024 IEEE.

关键词： Self-supervised learning

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Handwritten Word Recognition of Various Languages: A Review 9

Handwritten Word Recognition of Various Languages: A Review

引用

9th international conference on Smart computing and Communications, ICSCC 2023

作者： Jayanthi, V. thenmalar, S. School of Computing Srm Institute of Science and Technology Department of Networking and Communications TamilNadu Kattankulathur India

ISBN: (纸本)9798350314090

Handwriting recognition (HWR) is the ability of a computer to interpret and understand handwritten text. HWR involves gathering, storing, and processing digital pen data. HWR is used for many tasks, including writing automation, digital document archiving and retrieval, text entry automation, and document data extraction. Handwriting recognition software is used to help automate these tasks or to make them easier for the user. HWR is also used to improve accessibility for people with disabilities who cannot generate characters traditionally accurately. Recognizing the handwritten word is a complicated process because of the nature of the characters, such as similar shapes and sizes, spaces between the characters, discontinued characters, overlapped characters, complex stroke patterns, and writing styles in various languages. A deep learning approach has been mainly used in handwritten recognition recently. Deep learning can be used to process both labeled and unlabeled data. the advantage of the deep learning approach is to perform feature extraction independently. the survey focuses on the issues and challenges previous research has encountered. the study lists the types of methodologies and datasets utilized in different languages are described. © 2023 IEEE.

关键词： Deep learning

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE international conference on Data Science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. the emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. this research investigates the application of (DFL) in the medical domain, focusing on its limitations. this study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. the key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. this research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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