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Develop a Lightweight MEC Platform Simulator

Develop a Lightweight MEC Platform Simulator

引用

2023 International Russian Automation Conference, RusAutoCon 2023

作者： Filianin, Ivan Bogatyrev, Vladimir Lesonen, Matvey Itmo University Faculty of Software Engineering and Computer Engineering St. Petersburg Russia Saint-Petersburg State University of Aerospace Instrumentation Department of Information Systems Security St. Petersburg Russia Itmo University Faculty of Infocommunication Technologies St. Petersburg Russia

ISBN: (纸本)9798350345551

This paper reviews the use of Minikube in multi-node mode to work with the ETSI MEC Sandbox as a lightweight multi-access edge computing platform simulator (LWMECPS). The work included a comparative analysis of MEC platforms in terms of functionality, cost of deployment, and complexity of adoption. An experiment was also conducted to test LWMECPS as a MEC platform using a Python3 test application. The experiment confirmed the feasibility of using LWMECPS as a MEC platform by deploying a Kubernetes deployment based on the number of users on the simulated 4g-macro network service zones from the ETSI MEC Sandbox. Particular attention was paid to the analysis of existing scientific literature on the use of multi-access edge computing platforms in various use cases. © 2023 IEEE.

关键词： Edge computing

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IOT-Based Intelligent Assistant Mirror For Smart Life & Daily Routine Using Raspberry PI 21

IOT-Based Intelligent Assistant Mirror For Smart Life & Dail...

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21st International Conference on Advances in ICT for Emerging Regions, ICter 2021

作者： Sirinayake, Spss Dasanayake, Dkak Rodrigo, Tmlu Perera, Wuy Hansika Mahaadikara, Mdjt Kahandawala, Surath Sri Lanka Institute of Information Technology Faculty of Computing Dept. Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665466837

Humans start their day by looking in the mirror at least once before leaving their homes every morning. In addition, they waste some considerable time of their busy workload in front of the mirror. To make this time more productive and useful, there ought to be a system that can be readily conducted, user-friendly, and smart according to the constant progress on the Internet of Things. The intelligent mirror is a new addition to the smart device family, which is a straightforward concept. There will be a screen placed behind a two-way mirror, and this Intelligent Mirror turns our room or bathroom mirror into a personal assistant with artificial intelligence. The purpose is to develop a smart mirror that can automate working humans' busy daily routines and manage their tasks when they spend their time in front of a mirror. To make the most of this moment, users can securely access all the relevant details of the day by looking in the mirror simultaneously. The intelligent mirror, which a single voice command can activate, will significantly help disabled persons and the general. Raspberry Pi has been used to build the proposed intelligent mirror, linked to the digital world via the Internet. The mirror can communicate with the user through voice commands and reply appropriately. The monitoring of emotions and health measuring function will provide a distinctive experience to the users. The mirror will reflect important elements such as weather, date & time, covid-19 situation reports, local news, To-do list, water reminder, home workouts, and meal plans. The mirror can also handle specialized functions such as automating and controlling home IoT devices. © 2021 IEEE.

关键词： Internet of things

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Hierarchical games with restrictions on the amount of information transmitted 14

Hierarchical games with restrictions on the amount of inform...

引用

14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Ereshko, Felix Gorelov, Mikhail Federal Research Center 'Computer Science and Control' Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (纸本)9781665412308

The paper examines a new class of models, the common feature of which is the presence of a limited volume of information exchanges between players. It is shown that taking such restrictions into account allows one to significantly expand the class of situations that can be adequately described using the games in normal form. Possible ways of formalizing the concept of 'amount of information' are discussed, returning to the constructions by A.N. Kolmogorov. A new concept of the maximal guaranteed result is discussed. The application of the outlined ideas in the information theory of hierarchical systems is considered. © 2021 IEEE.

关键词： Hierarchical systems

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Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling

Scalable and Consistent Graph Neural Networks for Distribute...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Barwey, Shivam Balin, Riccardo Lusch, Bethany Patel, Saumil Balakrishnan, Ramesh Pal, Pinaki Maulik, Romit Vishwanath, Venkatram Transportation and Power Systems Argonne National Laboratory United States Leadership Computing Facility Argonne National Laboratory United States Argonne National Laboratory Computational Science Division United States Pennsylvania State University Information Sciences and Technology United States Mathematics and Computer Science Argonne National Laboratory United States

ISBN: (纸本)9798350355543

This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is arithmetically equivalent to evaluations on multiple ranks (a partitioned graph). This concept is demonstrated by interfacing GNNs with NekRS, a GPU-capable exascale CFD solver developed at Argonne National Laboratory. It is shown how the NekRS mesh partitioning can be linked to the distributed GNN training and inference routines, resulting in a scalable mesh-based data-driven modeling workflow. We study the impact of consistency on the scalability of mesh-based GNNs, demonstrating efficient scaling in consistent GNNs for up to O(1B) graph nodes on the Frontier exascale supercomputer. © 2024 IEEE.

关键词： graph neural networks mesh-based modeling

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On the Development of a Novel Chatbot Generator Architecture: Design and Assessment Issues 8

On the Development of a Novel Chatbot Generator Architecture...

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8th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2023

作者： Bouras, Christos Diasakos, Damianos Katsigiannis, Chrysostomos Kokkinos, Vasileios Gkamas, Apostolos Karacapilidis, Nikos Charalabidis, Yannis Lachana, Zoi Alexopoulos, Charalampos Papadopoulos, Theodoros Karamanolis, Georgios Psalidas, Michail University of Patras Computer Engineering & Informatics Department Patras Greece University Ecclesiastical Academy of Athens Athens Greece University of Patras Department of Mechanical Engineering Patras Greece University of Aegean Department of Information & Communication Systems Engineering Samos Greece Crowd Policy Athens Greece

ISBN: (纸本)9798350341652

Chatbots are witnessing remarkable growth in various fields. In modern society, chatbots have evolved into innovative digital entities that have revolutionized the way people interact with technology. These conversational AI agents have gone beyond their initial uses to become an integral part of various industries. One of the most important roles of chatbots is customer service, providing 24/7 support, quick problem resolution, and personalized interactions. Chatbots free up human agents by handling routine queries and tasks, allowing them to focus on complex and specific issues, and optimizing overall efficiency and customer satisfaction. To this end, this paper aims to present the architecture of a novel chatbot generator with improved functionality in terms of quality of communication with end users and level of provided services. In addition, the paper presents the results of two pilot chatbot assessments by users with experience in artificial intelligence and chatbot technology. © 2023 IEEE.

关键词： Machine learning

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Meaning of the Clusters on Dimensionality Reduction by Word Clustering 12

Meaning of the Clusters on Dimensionality Reduction by Word ...

引用

12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Deguchi, Toshinori Seo, Sinsuke Ishii, Naohiro Dept. of Electrical and Computer Engineering Gifu College Gifu Japan Interdisciplinary Technology Development Gifu College Gifu Japan Information Systems Architecture Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

In text mining, Latent Semantic Analysis (LSA) is the popular method to reduce the dimension of document vectors. Since LSA produces a set of topics by statistical information, the meaning of each topic is not *** proposed a method to reduce the dimension by clustering the words in the documents. This method produces a set of clusters of words instead of topics. Using Word2vec to vectorize the words, the mean vector of the cluster is calculated, which shows the meaning of the *** this paper, we show the dimensionality reduction and the meaning of the generated clusters by word cloud, on document classification problem with a subset of BBC Dataset. © 2022 IEEE.

关键词： Classification (of information)

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Edge-Intelligence based Federated Learning in the Internet of Medical Things

Edge-Intelligence based Federated Learning in the Internet o...

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International Multidisciplinary Conference on computer and Energy Science (SpliTech)

作者： Suresh Chavhan Beerelly Srinitha Sikander Kathat Ashit Kumar Dutta Joel J. P. C. Rodrigues Department of Computer Science and Engineering Indian Institute of Information and Technology Raichur Karnataka India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Kingdom of Saudi Arabia Amazonas State University Manaus - AM Brazil

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

In the evolving healthcare landscape, the Internet of Medical Things (IoMT) enables real-time data collection through connected devices. Concerns about data privacy in electronic health records are driving changes in health assessment. Blended learning is emerging as a solution, allowing simulation training without sharing critical information centrally. The proposed techniques emphasize privacy and efficiency and use federated averaging to analyze edge computation. Smart healthcare addresses the benefits and challenges of integrating AI and edge tech. In this revolutionary approach, federated learning uses server-side federated averaging, combining local client model parameters while reducing edge computation latency and energy consumption The integration of AI and edge technologies not only increases efficiency but also provides forward-looking approaches for personalized and responsive healthcare. Experimental validation with the Covid-pneumonia dataset highlights the effectiveness of the integrated learning approach, confirming the important contribution to privacy protection and efficient machine learning applications in computing in healthcare of the policies established in countries.

关键词： Training Data privacy Privacy Federated learning Internet of Medical Things Vectors Real-time systems

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Design and Development of Drone Seed Dispersal Mechanism using Novel Narcondam Hornbill Algorithm in Barren Lands

Design and Development of Drone Seed Dispersal Mechanism usi...

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International Multidisciplinary Conference on computer and Energy Science (SpliTech)

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

Seed dispersal plays a crucial role in the survival of plant species, but human activities are disrupting this process, leading to ecosystem degradation. There is a need to develop innovative seed dispersal algorithms to restore degraded ecosystems and prevent further biodiversity loss. Drones can be used as powerful tools to assess the state of ecosystems, identify areas requiring seed dispersal, and monitor restoration progress. By leveraging drones, more effective seed dispersal algorithms can be developed to promote diverse plant communities and ensure long-term ecosystem sustainability. One of the challenges in simulating seed dispersal is the difficulty in accounting for the complex interactions between different environmental factors and seed characteristics. The proposed algorithm aims to address existing models' limitations and improve seed dispersal efforts' effectiveness. This algorithm utilizes the endozoochory process of the Narcondam Hornbill to optimize seed dispersal efficiency, which has been successfully tested and implemented in simulated environments.

关键词： Degradation Biological system modeling Ecosystems Seeds (agriculture) Biodiversity Sustainable development Biomedical monitoring

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Probabilistic Prediction for Start-Up Success through Deep Learning based Stacked DAE Model 3

Probabilistic Prediction for Start-Up Success through Deep L...

引用

3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Syed, Shakir Sondinti, Lakshminarayana Reddy Kothapalli Sapra, Puneet Joshi, Amit Shobana, M. Deepan Chakravarthi, A.V. Shivam hills India Us Bank LB. Nagar India Rayat Bahara University Department of Computer Science Engineering Mohali India Isma University Department of Information Technology Rīga Latvia M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India Velammal College of Engineering and Technology Department of Civil Engineering Madurai India

ISBN: (纸本)9798331508456

Researchers and financial entities have long been fascinated by the ability to accurately predict the success of start-ups. Modern start-ups, which are creative, tech-focused, and quickly growing companies, face this challenge more than most. This study introduces a deep learning Stacked Denoising Autoencoder (SDAE) model that uses important technology domains to forecast the likelihood of a startup's success. The three primary steps of the proposed approach are as follows: preprocessing, feature extraction, and training the model. To ensure statistical independence of signal components in multivariate datasets, preprocessing steps include min-max normalisation for data standardisation and Independent Component Analysis (ICA) for feature extraction. The predictive power of the SDAE model is then enhanced by training. The proposed model outperforms traditional methods like Decision Trees (DT) and Denoising Autoencoders (DAE) with an average accuracy of 94.28%, according to the experimental data. This makes it a strong contender for predicting startup success in changing technical contexts. © 2025 IEEE.

关键词： Independent component analysis

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RoleScan: Enhancing Social Bot Detection Using Social Role Vector 19th

RoleScan: Enhancing Social Bot Detection Using Social Role V...

引用

19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Wen, Wen Gao, Min Gong, Qingyuan Wang, Xin Chen, Yang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China

ISBN: (纸本)9789819623723

The increase in social media users has led to the rise of social bots, which can disrupt online environments and threaten user privacy. This study presents RoleScan, a novel framework that integrates Social Role Vector (SRV) and Graph Attention Network v2 (GATv2) to detect social bots more accurately. We are the first to formulate the social bot detection problem by considering users’ properties and social influence. RoleScan extracts user features and text features from user profiles and tweets, and uses SRV to capture the social features. SRV is based on node centrality measurement, structural hole theory, and social capital theory, offering a comprehensive view of the influence of a user within the network. By leveraging an attention mechanism, RoleScan focuses on pivotal features and connections, enhancing its ability to differentiate between bots and humans. The experimental results show that RoleScan outperforms existing models, achieving an AUC of 0.887. An ablation study further highlights the critical role of incorporating social features into the detection process. Our study not only enhances the social bot detection performance, but also offers new insights into the users’ diverse roles within social networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bot (Internet)

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