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arXiv 2024年

作者： Zhao, Changyuan Du, Hongyang Liu, Guangyuan Niyato, Dusit College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way # 08-01 Create Tower 138602 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong The Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore

Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combining score-based generative models with the gradient boosting algorithm, a multi-step supervised learning algorithm, to solve supervised learning tasks. However, existing generative model algorithms are often limited by the stochastic nature of the models and the long inference time, impacting prediction performances. Therefore, we propose a Supervised Score-based Model (SSM), which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Via the ablation experiment in selected examples, we demonstrate the outstanding performances of the proposed techniques. Additionally, we compare our model with other probabilistic models, including Natural Gradient Boosting (NGboost), Classification and Regression Diffusion Models (CARD), Diffusion Boosted Trees (DBT), and non-probabilistic GBM models. The experimental results show that our model outperforms existing models in both accuracy and inference time. © 2024, CC BY.

关键词： Self-supervised learning

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Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density

Classification Based on Texture Analysis for Synthetic Hazy ...

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International Conference on Electrical, Electronics and Information Engineering (ICEEIE)

作者： Fresy Nugroho Puspa Miladin Safitri A. Basid Yunifa Miftachul Arif Norizan Mat Diah Firma Sahrul Bahtiar Hani Nurhayati Fachrul Kurniawan Nurizal Dwi Priandani Dodik Arwin Dermawan Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi MARA Shah Alam Malaysia Faculty of Science and Technology Library and Information Science Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia Manajemen Informatika Program Vokasi Universitas Negeri Surabaya Surabaya Indonesia

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%.

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Assistive technology with device for self-inflating bag in neonatology

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Research on Biomedical Engineering 2025年第2期41卷

作者： Marcelino, Ana Carolina Rodovanski, Giovana Pascoali Bonetti, Karoline Panes, Caio Rabinovich de Almeida Martins, Fernando Moran, Cristiane Aparecida Araranguá Brazil Graduate Program in Neurosciences and Behavior Department of Experimental Psychology of the Institute of Psychology-University of São Paulo Sao Paulo Brazil Araranguá Brazil Computer Engineering Instituto Mauá de Tecnologia São Caetano do Sul Brazil Electronic Engineering and Computing with Emphasis on Electronic Devices and Systems at Instituto Tecnológico de Aeronáutica–ITA São Caetano do Sul Brazil Department of Physiotheraphy and at the Postgraduate Program in Rehabilitation Science of the Federal University of Santa Catarina-UFCS Ararangua Brazil

Purpose: The objectives of this study were to create a device with technological development of the self-inflating bag (SIB) equipment to measure the variables tidal volume, peak inspiratory pressure, and inspiratory flow and to evaluate the knowledge of undergraduate students in handling the SIB. To reduce the risks of mortality, length of hospital stay, and infections, SIB is a resource used to ventilate the patient through the inflation of the device, but it is not capable of measuring the ventilatory parameters. Excess ventilation and incorrect handling of the bag can cause lung injury in newborns. Therefore, measuring these parameters can prevent lung injury resulting from inadequate ventilation. Method: This is a cross-sectional and experimental study, with the development of a device SIB that allows the evaluation of variables of neonatal respiratory mechanics that were tested with an artificial lung. It was approved by the Research Ethics Committee and the research was developed in three stages: the first represents the search for scientific articles for the consensus of neonatal ventilatory variables for the construction of the SIB parameters;the second stage depicts the technological development of a device capable of monitoring such variables through the SIB. In the third stage, the content validation of the device with the handling of the SIB was carried out by students of medicine and physiotherapy courses of Brazil. Results: In this work, a device that analyzes the values of the main ventilatory parameters in neonatology was developed and to generate a safety range for the use of the device, curves with a tolerance of 10% up and down were created. All electronic components were coupled to the equipment, with a display that transmits the parameter values. The web page can be used on a cell phone, tablet, computer, or SmartTv, as long as it is connected to the "RespiratorIoT_AP" network. Among the 29 participants in the study, 8 said they were familiar w

关键词： Students

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Social Network Analysis of Biomedical Research Growth at a Primarily Undergraduate Institution

Social Network Analysis of Biomedical Research Growth at a P...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Armina Ferdous Diane B. Smith Tracy Yarnell Julia Thom Oxford Computing Program Center of Biomedical Research Excellence Boise State University Boise Idaho USA Center of Biomedical Research Excellence Boise State University Boise Idaho USA

The National Institutes of Health Institutional Development Award programs support the establishment and growth of biomedical research infrastructure in states that receive a low level of federal funding for biomedical research. The purpose of this investigation was to analyze the growth in research productivity over time. This program fostered an environment in which a biomedical research program could be developed and allowed to grow at Boise State University, a primarily undergraduate institution. The growth of the biomedical research community can be visualized through social network analysis.

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NFT-Based Blockchain-Oriented Security Framework for Metaverse Applications

arXiv

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arXiv 2023年

作者： Manzoor, Khadija Noor, Umara Rashid, Zahid Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University 1 Gwanak-Ro Gwanak-Gu Seoul08826 Korea Republic of

The Metaverse is rapidly evolving, bringing us closer to its imminent reality. However, the widespread adoption of this new automated technology poses significant research challenges in terms of authenticity, integrity, interoperability, and efficiency. These challenges originate from the core technologies underlying the Metaverse and are exacerbated by its complex nature. As a solution to these challenges, this paper presents a novel framework based on Non-Fungible Tokens (NFTs). The framework employs the Proof-of-Stake consensus algorithm, a blockchain-based technology, for data transaction, validation, and resource management. PoS efficiently consume energy and provide a streamlined validation approach instead of resource-intensive mining. This ability makes PoS an ideal candidate for Metaverse applications. By combining NFTs for user authentication and PoS for data integrity, enhanced transaction throughput, and improved scalability, the proposed blockchain mechanism demonstrates noteworthy advantages. Through security analysis, experimental and simulation results, it is established that the NFT-based approach coupled with the PoS algorithm is secure and efficient for Metaverse applications. © 2023, CC BY.

关键词： Blockchain

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Multilevel Monte Carlo Methods for the Grad-Shafranov Free Boundary Problem

SSRN

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SSRN 2023年

作者： Elman, Howard C. Liang, Jiaxing Sánchez-Vizuet, Tonatiuh Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States Applied Mathematics & Statistics and Scientific Computing Program University of Maryland College Park United States Department of Mathematics The University of Arizona United States

The equilibrium configuration of a plasma in an axially symmetric reactor is described mathematically by a free boundary problem associated with the celebrated Grad-Shafranov equation. The presence of uncertainty in the model parameters introduces the need to quantify the variability in the predictions. This is often done by computing a large number of model solutions on a computational grid for an ensemble of parameter values and then obtaining estimates for the statistical properties of solutions. In this study, we explore the savings that can be obtained using multilevel Monte Carlo methods, which reduce costsby performing the bulk of the computations on a sequence of spatial grids that are coarser than the one that would typically be used for a simple Monte Carlo simulation. We examine this approach using both a set of uniformly refined grids and a set of adaptively refined grids guided by a discrete error estimator. Numerical experiments show that multilevel methods dramatically reduce the cost of simulation, with cost reductionstypically on the order of 60 or more and possibly as large as 200. Adaptive gridding results in more accurate computation of geometric quantities such as x-points associated with the model. © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Impacts of Faculty Development on Interdisciplinary Undergraduate Teaching and Learning in the Food-Energy-Water Nexus

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Journal of College Science Teaching 2022年第6期51卷 66-74页

作者： Sommers, Amie S. White, Holly Dauer, Jenny M. Forbes, Cory STEM education specialist in the Engineering and Computing Education Core College of Engineering University of Nebraska-Lincoln United States Ecology and Environmental Sciences program at the University of Maine United States School of Natural Resources at the University of Nebraska-Lincoln United States Department of Curriculum and Instruction Science Education STEM Education Research Collaboratorium and Resource Center College of Education University of Texas Arlington United States

To support undergraduate instruction and learning outcomes (i.e., systems thinking and decision-making in interdisciplinary contexts) grounded in the Food-Energy-Water Nexus (FEWNexus), we implemented a multiyear faculty development program around a new minor. In this article, we build on a previous study to investigate teaching in FEW Nexus minor courses, as well as student outcomes before and after participation in the faculty development program. We analyzed video recordings of courses to identify teaching style and scored pre- and postcourse student assessments of decision-making and systems thinking quantitatively. Post-program teaching was more student centered (50%) than pre-program (37.5%;p = 1.9e^-6). Faculty who taught courses with longer class periods incorporated more active learning than in short class periods (pre: p = 2.2e^-16;post: p = 2.2e^-16). Despite changes in instruction, we did not observe changes in decision-making and systems thinking outcomes. However, results indicate that providing faculty with resources and time can facilitate implementation of best practices and that further support and research are needed to connect these practices with positive outcomes. © 2022 National Science Teaching Association.

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Multilevel Monte Carlo methods for the Grad-Shafranov free boundary problem

arXiv

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arXiv 2023年

The equilibrium configuration of a plasma in an axially symmetric reactor is described mathematically by a free boundary problem associated with the celebrated Grad-Shafranov equation. The presence of uncertainty in the model parameters introduces the need to quantify the variability in the predictions. This is often done by computing a large number of model solutions on a computational grid for an ensemble of parameter values and then obtaining estimates for the statistical properties of solutions. In this study, we explore the savings that can be obtained using multilevel Monte Carlo methods, which reduce costs by performing the bulk of the computations on a sequence of spatial grids that are coarser than the one that would typically be used for a simple Monte Carlo simulation. We examine this approach using both a set of uniformly refined grids and a set of adaptively refined grids guided by a discrete error estimator. Numerical experiments show that multilevel methods dramatically reduce the cost of simulation, with cost reductions typically on the order of 60 or more and possibly as large as 200. Adaptive gridding results in more accurate computation of geometric quantities such as x-points associated with the *** Codes 65Z05, 65C05, 62P35, 35R35, 35R60 © 2023, CC BY.

关键词： Monte Carlo methods

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Saasport model: Exploring protocol portability, resource elasticity and microservice architecture in the efficient execution of iot applications 20

Saasport model: Exploring protocol portability, resource ela...

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20th International Conferences on WWW/Internet 2021 and Applied computing 2021

作者： da Silveira, Maria Gisele Flores da Silva Silveira, Wagner da Rosa Righi, Rodrigo da Costa, Cristiano André Griebler, Dalvan Três de Maio - RS Brazil Applied Computing Graduate Program Unisinos University Av. Unisinos 950 RS São Leopoldo Brazil Av. Ipiranga 6681 RS Porto Alegre Brazil

ISBN: (纸本)9789898704344

Despite the high growth of the Internet of Things and the multitude of applications that use the information generated, it is estimated that 90 % of these data are not yet fully used. This is because IoT systems are based on heterogeneous networks and devices with resource restrictions, which cannot execute significant processing. So one of the alternatives that have been addressed is the use of cloud applications, where the data can be appropriately processed, stored, and analyzed. In the cloud, applications can then employ resource elasticity and be developed in microservice architectures. In state-of-the-art, the main efforts to provide portability and efficient treatment of IoT applications are described. However, a platform that provides protocol portability, resource elasticity using microservices architecture for the efficient execution of IoT applications has not yet been implemented. In this context, this work presents SaaSport, which among its contributions, will allow the efficient treatment of data generated by IoT devices that use the MQTT and CoAP protocols. The final result of this work demonstrates that the model provides an efficient treatment of the data generated in IoT environments. © 20th International Conferences on WWW/Internet 2021 and Applied computing 2021.

关键词： Elasticity

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Predicting User Preferences with Neural Network Learning to Rank Method 5

Predicting User Preferences with Neural Network Learning to ...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Qomariyah, Nunung Nurul Fajar, Ahmad Nurul Bina Nusantara University Faculty Of Computing And Media Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master Of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

User preference learning has been around for many years. This is a common problem arise in e-commerce system, where the companies need to understand their customers in order to sell the correct products to their target customers. The user preference is also needed in the movie domain area, as the film companies who produce the movie also need to understand how the customers value their movie products, whether the market like it or not. This can help them to predict the best components for the future movie. In order to produce the recommendation to the customer, we can show a list of item in a correct order, sorted from the most relevant/the most liked one to the most irrelevant item. In this paper, we utilize the Learning to Rank (LTR) approach from information retrieval domain area to learn the user preference in recommender system domain. We applied LTR with pairwise approach from the two most popular algorithms in this area, i.e. RankNet and LambdaRank, on 25, 345 user ratings from 5, 000 movie dataset and evaluate both algorithms. We used a Neural Network architecture as the learning mechanism. We also measuring the speed performance of the two algorithms. © 2021 IEEE.

关键词： Sales

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