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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Vesal Ahsani Ali Rahimi Mehdi Letafati Babak Hossein Khalaj Department of Electrical Engineering and Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

In this research paper, the authors present a comprehensive overview of three key elements of Metaverse-as-a-service (MaaS) platforms: privacy and security, edge computing, and blockchain technology. The study begins by examining security concerns for wireless access to the Metaverse. It then delves into privacy and security issues within the Metaverse from data-centric, learning-centric, and human-centric perspectives. The authors propose novel and less-explored methods to aid mobile network operators and Metaverse service providers in creating secure and private MaaS platforms across various layers of the Metaverse, from access to social interactions. The article also explores the potential of edge computing to enhance various aspects of the Metaverse and the challenges associated with its implementation. Furthermore, the study examines the 10 main challenges of MaaS platforms and how blockchain technology can address them. Finally, the authors present future visions and recommendations, such as content-centric security and zero-trust Metaverse, as well as unresolved challenges in blockchain technology to provide insights for network designers in the Metaverse era.

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Quantifying the Influence of User Behaviors on the Dissemination of Fake News on Twitter with Multivariate Hawkes Processes

arXiv

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arXiv 2023年

作者： Jiang, Yichen Porter, Michael D. The Department of Systems and Information Engineering University of Virginia Charlottesville22903 United States The Department of Systems and Information Egineering School of Data Science University of Virginia Charlottesville22903 United States

Fake news has emerged as a pervasive problem within Online Social Networks, leading to a surge of research interest in this area. Understanding the dissemination mechanisms of fake news is crucial in comprehending the propagation of disinformation/misinformation and its impact on users in Online Social Networks. This knowledge can facilitate the development of interventions to curtail the spread of false information and inform affected users to remain vigilant against fraudulent/malicious content. In this paper, we specifically target the Twitter platform and propose a Multivariate Hawkes Point Processes model that incorporates essential factors such as user networks, response tweet types, and user stances as model parameters. Our objective is to investigate and quantify their influence on the dissemination process of fake news. We derive parameter estimation expressions using an Expectation Maximization algorithm and validate them on a simulated dataset. Furthermore, we conduct a case study using a real dataset of fake news collected from Twitter to explore the impact of user stances and tweet types on dissemination patterns. This analysis provides valuable insights into how users are influenced by or influence the dissemination process of disinformation/misinformation, and demonstrates how our model can aid in intervening in this process. Copyright © 2023, The Authors. All rights reserved.

关键词： Social networking (online)

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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms 5

Successful Integration of IoT and Blockchain Technologies Us...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Calarany, C. Indumathy, M. Senthilraja, P. Suganya, D. Gopi Vardhan, V. Rahul, Yelisetty Shanmukh Tagore Government Arts and Science College Department of Computer Science Puducherry India Rajiv Gandhi College of Engineering & Technology Department of Information Technology Kirumampakkam India K.S.Rangasamy College of Technology Department of Computer Science Tamil Nadu India Rajiv Gandhi College of Engineering & Technology Department of Computer Science Kirumampakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction. © 2024 IEEE.

关键词： IoT MML RNN

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Deep coastal sea elements forecasting using UNet-based models

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knowledge-Based systems 2022年 252卷

作者： Fernández, Jesús García Abdellaoui, Ismail Alaoui Mehrkanoon, Siamak Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Due to the recent development of deep learning techniques applied to satellite imagery, weather forecasting that uses remote sensing data has also been the subject of major progress. The present paper investigates multiple hours ahead coastal sea elements forecasting in the Netherlands using UNet based architectures. The hourly satellite image data from the Copernicus observation program spanned over a period of two years has been used to train the models and make the forecasting, including seasonal forecasting. Here, we propose 3D dimension Reducer UNet (3DDR-UNet), a variation of the UNet architecture, and further extend this novel model using residual connections, parallel convolutions and asymmetric convolutions which result in introducing three additional architectures, i.e. Res-3DDR-UNet, InceptionRes-3DDR-UNet and AsymmInceptionRes-3DDR-UNet respectively. In particular, we show that the architecture equipped with parallel and asymmetric convolutions as well as skip connections outperforms the other three discussed models. © 2022 The Author(s)

关键词： Convolution

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Predicting Partial Discharges of Transformers: Decision Support System for Factory Acceptance Test

Predicting Partial Discharges of Transformers: Decision Supp...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Gigerl, B. Zhao, Y. Raminger, J. Bakakeu, J. Kern, R. Thalmann, S. Siemens Energy Department of Process Digitalization Weiz Austria Siemens Energy Department of Manufacturing Technology Nürnberg Germany Siemens Energy Department of Test Field Weiz Austria Alteryx Department of Sales Engineering München Germany Institute of Interactive Systems and Data Science Technical University of Graz Graz Austria Institute of Operations and Information Systems University of Graz Graz Austria

ISBN: (纸本)9798350323153

Partial discharges, mainly caused by an insufficient drying processes or different types of contamination, reduce the lifetime of a transformer, and thus lead to expensive rework costs. Herein, a decision support system for partial discharges of transformers occurring in oven and filling processes is introduced. Based on machine learning (ML), partial discharge results are evaluated in dependency of various manufacturing parameters and an automated prediction tool to guide the production with preventive actions is developed. The required data, obtained from sensors and manufacturing sources, is used to train supervised learning algorithms that aim to predict and classify partial discharges. To achieve adequate accuracy and reliability, multiple ML and data mining techniques are applied, including feature engineering, clustering, and final evaluation of performance by cost factors. The evaluation results show that the introduced ML models effectively detect and classify early test failures in oven and filling processes, resulting in a successful identification of key factors and consequently to more efficient action derivation. Overall, the potential of decision support systems as a valuable tool in the field of transformers is emphasized. © 2023 IEEE.

关键词： Decision Support System Machine Learning Partial Discharges Predictive Quality Control

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound Images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Peng, Boyuan Liu, Yiyang Zhu, Xin Huang, Jie Wang, Wenwen Fang, Li Zhou, Qin Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350350548

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness. © 2024 IEEE.

关键词： Self-supervised learning

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Regression coefficient estimation from remote sensing maps

arXiv

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arXiv 2024年

作者： Lu, Kerri Kluger, Dan M. Bates, Stephen Wang, Sherrie Laboratory for Information and Decision Systems MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Mechanical Engineering MIT United States Institute for Data Systems and Society MIT United States

Remote sensing map products are used to estimate regression coefficients relating environmental variables, such as the effect of conservation zones on deforestation. However, the quality of map products varies, and — because maps are outputs of complex machine learning algorithms that take in a variety of remotely sensed variables as inputs — errors are difficult to characterize. Thus, population-level estimates from such maps may be biased. In this paper, we apply prediction-powered inference (PPI) to regression coefficient estimation. PPI generates unbiased estimates by using a small amount of randomly sampled ground truth data to correct for bias in large-scale remote sensing map products. Applying PPI across multiple remote sensing use cases in regression coefficient estimation, we find that it results in estimates that are (1) more reliable than using the map product as if it were 100% accurate and (2) have lower uncertainty than using only the ground truth and ignoring the map product. Empirically, we observe effective sample size increases of up to 17-fold using PPI compared to only using ground truth data. This is the first work to estimate remote sensing regression coefficients without assumptions on the structure of map product errors. data and code are available at https://***/Earth-Intelligence-Lab/ uncertainty-quantification. © 2024, CC BY.

关键词： Regression analysis

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Non-stationary experimental design under linear trends 23

Non-stationary experimental design under linear trends

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Proceedings of the 37th International Conference on Neural information Processing systems

作者： David Simchi-Levi Chonghuan Wang Zeyu Zheng Institute for Data Systems and Society Department of Civil and Environmental Engineering Operations Research Center MIT and Laboratory for Information and Decision Systems MIT Laboratory for Information and Decision Systems MIT Department of Industrial Engineering and Operations Research University of California Berkeley

Experimentation has been critical and increasingly popular across various domains, such as clinical trials and online platforms, due to its widely recognized benefits. One of the primary objectives of classical experiments is to estimate the average treatment effect (ATE) to inform future decision-making. However, in healthcare and many other settings, treatment effects may be non-stationary, meaning that they can change over time, rendering the traditional experimental design inadequate and the classical static ATE uninformative. In this work, we address the problem of non-stationary experimental design under linear trends by considering two objectives: estimating the dynamic treatment effect and minimizing welfare loss within the experiment. We propose an efficient design that can be customized for optimal estimation error rate, optimal regret rate, or the Pareto optimal trade-off between the two objectives. We establish information-theoretical lower bounds that highlight the inherent challenge in estimating dynamic treatment effects and minimizing welfare loss, and also statistically reveal the fundamental trade-off between them.

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Prediction of mechanical properties of eco-friendly concrete using machine learning algorithms and partial dependence plot analysis

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Smart Construction and Sustainable Cities 2025年第1期3卷 1-21页

作者： Roy, Tonmoy Das, Pobithra Jagirdar, Ravi Shhabat, Mousa Abdullah, Md Shahriar Kashem, Abul Rahman, Raiyan Data Analytics & Information Systems Utah State University Logan USA Department of Civil Engineering Leading University Sylhet Bangladesh Traffic Engineer Fastech Consulting Engineers New Jersey USA Faculty of Engineering Department of Civil Engineering The University of Jordan Amman Jordan Department of Civil and Environmental Engineering Lamar University Beaumont USA Department of Information Science and Library Management University of Rajshahi Rajshahi Bangladesh

Rice husk ash concrete (RHAC) shows promise as a beneficial supplementary material in concrete. However, determining mechanical properties such as compressive strength (CS) and splitting tensile strength (STS) of RHAC through conventional lab-scale methods is laborious and time-consuming. In this research, seven important variables were selected as inputs to predict CS and STS using machine learning (ML) models, including Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) with grid search optimization. The result presented revealed that selected machine learning models provide well accuracy for CS and STS estimates. Among these, the DTR model demonstrated superior performance, with CS prediction R2, RMSE, MAE, and MAPE values of 0.964, 3.314, 2.225, and 5.068, at the testing stage respectively. For STS at the testing stage, DTR achieved R2 of 0.969, RMSE of 0.177, MAE of 0.1322, and MAPE of 3.413. GPR and RFR models also performed well, with R2 values of 0.9434 and 0.9530 for CS prediction. The partial dependence plot (PDP) analysis revealed the optimal mix design parameters for achieving the desired strength. These results offer valuable insights for sustainable construction, allowing engineers to efficiently predict and optimize material performance, reducing the reliance on time-consuming lab methods.

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Collaborative Hybrid Human AI Learning through Conceptual Exploration 3

Collaborative Hybrid Human AI Learning through Conceptual Ex...

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3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2024

作者： Ganter, Bernhard Hanika, Tom Hirth, Johannes Obiedkov, Sergei Ernst-Schröder-Zentrum Karolinenpl. 5 Darmstadt64289 Germany TU Dresden Dresden01062 Germany Intelligent Information Systems Universisty of Hildesheim Universitätsplatz 1 Hildesheim31141 Germany Knowledge & Data Engineering Group University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Faculty of Computer Science cfaed ScaDS.AI TU Dresden Dresden01062 Germany

Conceptual Exploration is a sophisticated method for the interactive and structured acquisition of knowledge from experts. It is therefore particularly suitable for the use in hybrid settings where both humans and AIs act as experts. This article provides a brief summary of how conceptual exploration can be used in the context of Hybrid Human AI systems, as discussed within a tutorial during the third HHAI conference in Malmö, Sweden. We will recapitulate two small experiments that were carried out with the participants of the tutorial and their results. Finally, we give some pointers on how this promising link can be further researched. © 2024 Copyright for this paper by its authors.

关键词： Contrastive Learning

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