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Leveraging Social Media data to Understand the Impact of COVID-19 on Residents' Dietary Behaviors: Observational Study

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Journal of Medical Internet Research 2025年 27卷 e51638页

作者： Li, Chuqin Jordan, Alexis Ge, Yaorong Park, Albert Department of Software and Information Systems College of Computing and Informatics University of North Carolina Charlotte NC United States School of Data Science College of Computing and Informatics University of North Carolina Charlotte NC United States

Background: The COVID-19 pandemic has inflicted global devastation, infecting over 750 million and causing 6 million deaths. In an effort to control the spread of the virus, governments around the world implemented a variety of measures, including stay-at-home orders, school closures, and mask mandates. These measures had a substantial impact on dietary behavior, with individuals discussing more home-cooked meals and snacking on social media. Objective: The study explores pandemic-induced dietary behavior changes using Twitter images and text, particularly in relation to obesity, to inform interventions and understand societal influences on eating habits. Additionally, the study investigates the impact of COVID-19 on emotions and eating patterns. Methods: In this study, we collected approximately 200,000 tweets related to food between May and July in 2019, 2020, and 2021. We used transfer learning and a pretrained ResNet-101 neural network to classify images into 4 health categories: definitely healthy, healthy, unhealthy, and definitely unhealthy. We then used the state obesity rates from the Behavioral Risk Factor Surveillance System (BRFSS) to assess the correlation between state obesity rates and dietary images on Twitter. The study further investigates the effects of COVID-19 on emotional changes and their relation to eating patterns via sentiment analysis. Furthermore, we illustrated how the popularity of meal terms and health categories changed over time, considering varying time zones by incorporating geolocation data. Results: A significant correlation was observed between state obesity rates and the percentages of definitely healthy (r=–0.360, P=.01) and definitely unhealthy (r=0.306, P=.03) food images in 2019. However, no trend was observed in 2020 and 2021, despite higher obesity rates. A significant (P<.001) increase in the percentage of healthy food consumption was observed during (39.99% in 2020) and after the shutdown (39.32% in 2021), as compared w

关键词： COVID-19 dietary behaviors dietary pattern eating patterns food consumption obesity observational study social media social media data tweets Twitter

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High-Level Synthesis for Microfluidic Biochips Considering Actual Volume Management and Channel Storage

High-Level Synthesis for Microfluidic Biochips Considering A...

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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Chen Yuhan Zhu Zhen Chen Zhisheng Chen Genggeng Liu College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Fuzhou China School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

In recent years, microfluidic biochips have been widely applied in various fields of human society. The emergence of distributed channel-storage architecture enables fluid to be directly cached within the flow channels without being transported to the dedicated storage. However, the volume of the flow channels is limited, it is important to carefully consider the fluid volume to be cached to ensure it can be accommodated within the appropriate flow channels. In this paper, we propose a high-level synthesis method considering actual volume management and channel storage through integer linear programming, providing a rational analysis of fluid caching and the temporal relationships between different fluid-handling tasks. Experimental results conducted on multiple benchmark tests demonstrate the effectiveness of the proposed method in reducing the completion time of bioassay, the number of caches, the maximum fluid caching volume, and the number of fluid-handling tasks.

关键词： Fluids Biochips Estimation Cells (biology) Integer linear programming Benchmark testing Task analysis

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Enhancing Effective Learning Capability of SOAR Agent based Episodic Memory

Enhancing Effective Learning Capability of SOAR Agent based ...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Kuldeep Singh Kaswan Mahaveer Singh Naruka Jagjit Singh Dhatterwal School of Computing Science and Engineering Galgotias University Greater Noida India G L Bajaj Institute of Technology and Management Greater Noida India Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

In order to increase the agent's intelligence, the fundamental goal of the research paper is to learn from its experiences. In order to solve a problem, learning is accomplished with the aid of episodic memory, which stores previous experiences as episodes. This study illustrates the improvement in intelligence with the use of the application of an example in soar. Working memory elements (WMEs), which are episodes (past experiences) in the form, are discussed in this paper as a means of enhancing intelligence. The purpose of this work is to demonstrate that knowledge always leads to performance improvement, meaning that as an agent studies more, performance will extend.

关键词： Memory architecture Learning (artificial intelligence) Encoding

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Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference

arXiv

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arXiv 2023年

作者： Lin, Zhidi Sun, Yiyong Yin, Feng Thiéry, Alexandre Hoang The School of Science & Engineering The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China The School of Science & Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China The Department of Statistics & Data Science National University of Singapore Singapore117546 Singapore

The Gaussian process state-space models (GPSSMs) represent a versatile class of data-driven nonlinear dynamical system models. However, the presence of numerous latent variables in GPSSM incurs unresolved issues for existing variational inference approaches, particularly under the more realistic non-mean-field (NMF) assumption, including extensive training effort, compromised inference accuracy, and infeasibility for online applications, among others. In this paper, we tackle these challenges by incorporating the ensemble Kalman filter (EnKF), a well-established model-based filtering technique, into the NMF variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO). Moreover, owing to the streamlined parameterization via the EnKF, the new GPSSM model can be easily accommodated in online learning applications. We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting. We also provide detailed analysis and fresh insights for the proposed algorithms. Comprehensive evaluation across diverse real and synthetic datasets corroborates the superior learning and inference performance of our EnKF-aided variational inference algorithms compared to existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Parameterization

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Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture

Image-Based Soil Health Analysis Using Deep Learning for Pre...

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International Conference on Communication and Electronics Systems (ICCES)

作者： V. S. Saranya Saikiran Mangali K Srinija Galeiah Medabalimi Meena Devi R Venkata Ramana N Ajanthaa Lakkshmanan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Department of CSBS B V RAJU INSTITUTE OF TECHNOLOGY Medak Telangana Department: Data science MLRIT DUNDIGAL Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Bhimavaram Andhra Pradesh India Department of electrical and electronics engineering KCG college of technology Chennai Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management.

关键词： Precision agriculture Deep learning Moisture Soil Thermal sensors Nutrients Feature extraction Real-time systems Convolutional neural networks Monitoring

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Red Tide Monitoring in the Bohai Sea Based on Domestic High-Resolution data 24

Red Tide Monitoring in the Bohai Sea Based on Domestic High-...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Chen, Chao Wang, Jianping Bai, Linyan Feng, Jianzhong Gao, Xiangwei Liu, Ruixiang School of Marine Technology and Geomatics Jiangsu Ocean University Lianyungang China Shandong Marine Resource and Environment Research Institute Yantai China International Research Center of Big Data for Sustainable Development Goals China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China

ISBN: (纸本)9798400716720

In recent years, red tide disasters occur frequently in China's sea areas. In 2022,17 red tide events occurred in the Bohai Sea area, which had a significant impact on the local environment. This paper focuses on the red tide event that occurred in the Qinhuangdao sea area from May 13th to May 15th, 2022. The event were extracted the red tides by using SDGSAT-1 and GF1-WFV data, and analyzed the accuracy by using the GF1-RI Red Tide Detection Index algorithm, the Dual Band Ratio method, and the Maximum Likelihood Classification method. Additionally, the temporal characteristics of this red tide event were analyzed using HJ2A/B data. The results indicate that the area of red tide detected by SDGSAT-1 data using the GF1-RI Red Tide Detection Index algorithm was 17.46 km2, while the area extracted by the GF1-WFV data using the Dual Band Ratio method was 18.51 km2. These two methods produced results that were close to the actual area, while the maximum likelihood classification method led to larger error. Hence, for small-scale red tide monitoring, priority should be given to the GF1-RI red tide detection algorithm and the two-band ratio method. © 2024 ACM.

关键词： Maximum likelihood

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Perceived MOOC satisfaction: A review mining approach using machine learning and fine-tuned BERTs

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Computers and Education: Artificial Intelligence 2025年 8卷

作者： Chen, Xieling Xie, Haoran Zou, Di Cheng, Gary Tao, Xiaohui Lee Wang, Fu School of Education Guangzhou University China School of Data Science Lingnan University Hong Kong Department of English and Communication The Hong Kong Polytechnic University Hong Kong Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong School of Mathematics Physics and Computing University of Southern Queensland Australia School of Science and Technology Hong Kong Metropolitan University Hong Kong

This study investigates the application of machine learning and BERT models to identify topic categories in helpful online course reviews and uncover factors that influence the overall satisfaction of learners in massive open online courses (MOOCs). The research has three main objectives: (1) to assess the effectiveness of machine learning models in classifying review helpfulness, (2) to evaluate the performance of fine-tuned BERT models in identifying review topics, and (3) to explore the factors that influence learner satisfaction across various disciplines. The study uses a MOOC corpus containing 102,184 course reviews from 401 courses across 13 disciplines. The methodology involves three approaches: (1) machine learning for automatic classification of review helpfulness, (2) BERT models for automatic classification of review topics, and (3) multiple linear regression analysis to explore the factors influencing learner satisfaction. The results show that most machine learning models achieve precision, recall, and F1 scores above 80%, 99%, and 89%, respectively, in identifying review helpfulness. The fine-tuned BERT model outperforms baseline models with precision, recall, and F1 scores of 78.4%, 74.4%, and 75.9%, respectively, in classifying review topics. Additionally, the regression analysis identifies key factors affecting learner satisfaction, such as the positive influence of "Instructor" frequency and the negative impact of "Platforms and tools" and "Process". These insights offer valuable guidance for educators, course designers, and platform developers, contributing to the optimization of MOOC offerings to better meet the evolving needs of learners. © 2025

关键词： Adversarial machine learning

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Child-Centred AI Design: Definition, Operation, and Considerations

Child-Centred AI Design: Definition, Operation, and Consider...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in computing Systems, CHI EA 2023

作者： Wang, Ge Sun, Kaiwen Atabey, Ayça Pothong, Kruakae Lin, Grace C. Zhao, Jun Yip, Jason Department of Computer Science University of Oxford United Kingdom School of Information University of Michigan United States School of Law Centre for Data Culture & Society University of Edinburgh United Kingdom Department of Media and Communications The London School of Economics and Political Science 5Rights Foundation United Kingdom Scheller Teacher Education Program The Education Arcade The Massachusetts Institute of Technology United States Information School University of Washington United States

ISBN: (纸本)9781450394222

AI systems and related algorithms are starting to play a variety of roles in the digital ecosystems of children - being embedded in the connected toys, smart home IoT technologies, apps, and services they interact with on a daily basis. Going forward, AI systems will, in all likelihood, become even more pervasive in children's applications simply due to their sheer usefulness in creating compelling, adaptive, and personal user experiences. Yet, understanding the ways that AI-driven systems used by children operate, and how AI could be designed to better anticipate and respond to children's diverse requirements is still a new and emerging area of investigation. Our goals of this workshop are to (1) extend the current critically constructive dialogue around the meaning of child-centred AI design and (2) explore ways to operationalise such child-centred AI design in practice, and finally (3) further expand and foster a community for those who are interested in designing and developing child-centred AI systems. © 2023 Owner/Author.

关键词： Automation

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Privacy Preservation Fully Homomorphic Encryption for Cloud-assisted Biometric Identification with Multi-key

Privacy Preservation Fully Homomorphic Encryption for Cloud-...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Shenghui Peng Peiheng Jia Jinbo Xiong Liehuang Zhu Ximeng Liu School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China College of Computer and Cyber Security Fujian Normal University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Biometrics is a technology that utilizes individual physiological or behavioral characteristics for identity authentication and identification, ensuring secure and accurate identity verification by comparing biometric information with samples in a database. However, traditional biometric technology raises concerns about privacy leakage, which has led to the emergence of privacy-preserving biometric technology. This study introduces a cloud-assisted privacy-preserving biometric authentication system based on multi-key full homomorphic encryption. Our scheme employs a TFHE encryption scheme to encrypt user biometrics and utilizes a cloud server to assist in computation. We design secure computing protocols based on multi-key TFHE and develop a privacy-preserving biometric system based on a single cloud server. Privacy analysis and performance evaluation demonstrate that the proposed solution is efficient and feasible. Our experimental findings indicate that the proposed scheme exhibits a superior accuracy rate to current schemes. Compared to the best solution, our method has increased the iris recognition accuracy by approximately 68.42%. Although the experimental results demonstrate high accuracy and feasibility, implementing TFHE in biometric models could be improved by its execution time, presenting challenges for broad adoption. Nevertheless, through optimization and technological advancements, this protocol is anticipated to elevate privacy protection standards in practical biometric recognition systems moving forward.

关键词： Biometrics Privacy Cloud computing Accuracy Protocols Biometric identification Servers Homomorphic encryption Protection Standards

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Don't Swim in data: Real-Time Microbial Forecasting for New Zealand Recreational Waters

SSRN

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SSRN 2025年

作者： Cheena, Asif Juzar Dost, Katharina Straathof, Nina Wicker, Jörg Simon Sarris, Theo School of Computer Science The University of Auckland Auckland New Zealand Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Statistics & Data Environment Canterbury Christchurch New Zealand Institute for Environmental Science and Research Christchurch New Zealand

Traditional water quality monitoring, reliant on infrequent sampling and 48-hour laboratory delays, often fails to capture rapid contamination fluctuations, exposing recreational water users to significant health risks. We propose two novel machine learning frameworks for real-time forecasting of Enterococci concentrations in Canterbury, New Zealand. The Probabilistic Forecasting Framework uses an ensemble of quantile regression models with a gradient boosting meta-learner and Conformalized Quantile Regression (CQR) to produce accurate point forecasts and calibrated 90% prediction intervals. In parallel, the Matrix Decomposition Framework employs Non-negative Matrix Factorization (NMF) to decompose spatio-temporal data into interpretable latent factors, modeled via multi-target Random Forests to enhance generalizability. Evaluated on data from 15 sites (2021–2024, 1047 samples, 100 exceedance events), our frameworks exceed USGS guidelines, achieving exceedance sensitivities of 67.0% and 61.0%, with high precautionary sensitivities of 77.0% and 74.0%, respectively, and competitive performance relative to state-of-the-art systems such as Auckland’s Safeswim. © 2025, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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