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Images Connect Us Together: Navigating a COVID-19 Local Outbreak in China Through Social Media Images

Proceedings of the ACM on Human-computer Interaction 2024年第CSCW1期8卷 1-32页

作者： He, Changyang He, Lu Yang, Wenjie Li, Bo Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Zilber College of Public Health University of Wisconsin-Milwaukee Milwaukee United States

Social media images, curated or casual, have become a crucial component of communicating situational information and emotions during health crises. Despite its prevalence and significance in informational dissemination and emotional connection, there lacks a comprehensive understanding of visual crisis communication in the aftermath of a pandemic which is characterized by uncertain local situations and emotional fatigue. To fill this gap, this work collected 345, 423 crisis-related posts and 65, 376 original images during the Xi'an COVID-19 local outbreak in China, and adopted a mixed-methods approach to understanding themes, goals, and strategies of crisis imagery. Image clustering captured the diversity of visual themes during the outbreak, such as text images embedding authoritative guidelines and "visual diaries" recording and sharing the quarantine life. Through text classification of the post that visuals were situated in, we found that different visual themes highly correlated with the informational and emotional goals of the post text, such as adopting text images to convey the latest policies and sharing food images to express anxiety. We further unpacked nuanced strategies of crisis image use through inductive coding, such as signifying authority and triggering empathy. We discuss the opportunities and challenges of crisis imagery and provide design implications to facilitate effective visual crisis communication. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： COVID-19

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A Comprehensive Framework for Electronic Medical Records (EMRs) Leveraging Blockchain technology 1st

A Comprehensive Framework for Electronic Medical Records (EM...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Pandey, Shraiyash Alazzawi, Esraa Mohammed Pandey, Surendra Bhushan, Bharat Jamil, Akhtar Baniya, Pashupati Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Computer and Information Technology Tata Consultancy Services MariettaGA United States Department of Computer Science National University of Computer and Engineering Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031823763

In today’s healthcare landscape, where participation is widespread, the need for secure and efficient record management is evident. However, many healthcare organizations, particularly in regions like India, still rely on outdated paper-based methods for handling medical records. Unfortunately, these traditional approaches fall short in meeting the rigorous requirements for security, confidentiality, and efficiency in today’s digital age. This work presents a comprehensive framework that integrates modern technologies such as Smart Contracts, Ethereum Blockchain, and the Interplanetary File System (IPFS) to ensure secure storage, and seamless management of Electronic Medical Records (EMRs). By leveraging these emerging technologies, our goal is to lead a shift in electronic medical record management. Additionally, we explore recent advancements in the healthcare sector, with a focus on enhancing security protocols within Electronic Medical Records management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Multi-Scale Quantum Harmonic Oscillator Behaved Algorithm with Three-Stage Perturbation for High-Dimensional Expensive Problems

Multi-Scale Quantum Harmonic Oscillator Behaved Algorithm wi...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ye, Xinggui Li, Jianping Wang, Peng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Technology Southwest Minzu University Chengdu610225 China

ISBN: (纸本)9781665410205

Quantum perturbation plays an important role in quantum movement. This paper proposes a three-stage perturbation (TSP) framework to enhance the performance of multi-scale quantum harmonic oscillator algorithm (MQHOA). Three perturbations are adopted in the population initialization process with opposition-based learning (OBL), in quantum harmonic oscillator (QHO) process with ensemble of three differential evolution (DE) strategies and in multi-scale (M) process with a rollback mechanism to enhance the diversity of the particles and prevent from falling into local optima. The proposed approach has been evaluated on several high-dimensional expensive problems from 50-D to 500-D. The empirical data are compared with recent MQHOA variants and some state-of-the-art similar metaheuristic algorithms (MAs). The experimental results reveal the superiority or competitiveness of the the proposed approach. © 2024 IEEE.

关键词： Harmonic analysis

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Comprehensive Framework for Analyzing Grammar Error Correction with GPT -3.5

Comprehensive Framework for Analyzing Grammar Error Correcti...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Munda, Rajni Kant Kumar, Abhishek Burman, Ravi Kumar Kumar, Biresh Alam, Md. Shoaib Sinha, Anurag Jharkhand University of Technology Computer Science and Engineering Ranchi India Amity University Computer Science and Engineering Jharkhand Ranchi India Ignou School of Computing and Information Science New Delhi India

ISBN: (纸本)9798331508685

This investigate assesses the viability and exactness of an error-correction approach utilizing GPT-3.5 compared to routine instruments. Letter set rectification plays a vital part in making strides the quality of and improving clarity in different scholastic and proficient situations. The consider utilizes appraisal criteria such as precision, exactness, review, and F1 score to how well GPT-3.5 performs relative to other language structure redress devices. Also, the information incorporates client input and daze testing to gage the viable convenience of the adjustments. Discoveries demonstrate that GPT-3.5 outperforms conventional instruments in taking care of relevant blunders and client fulfilment, illustrating a momentous capacity to redress syntactic botches, indeed in complex sentences with inconspicuous subtleties. Be that as it may, it does confront a few challenges, such as affectability to input varieties and intermittent overcorrection. The think about recommends entering on moving forward relevant understanding and diminishing overcorrection to development these calculations assist. It too investigates potential applications of GPT-3.5 for upgrading composing back and languagelearning assets, highlighting conceivable future investigate ranges to optimize AI-driven language structure rectification models. © 2025 IEEE.

关键词： Instrument errors

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Reinforcement Learning with Quasi-Hyperbolic Discounting (Extended Abstract) 10

Reinforcement Learning with Quasi-Hyperbolic Discounting (Ex...

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10th Indian Control Conference, ICC 2024

作者： Eshwar, S.R. Motwani, Mayank Roy, Nibedita Thoppe, Gugan Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9798331517212

Human decision-making is better modeled via discounting schemes such as quasi-hyperbolic and hyperbolic than classical ones such as exponential or average reward. In [3], we initiated the study of reinforcement learning with Quasi-Hyperbolic (QH) discounting and proposed the first model-free algorithm for finding a type of policy called the Markov perfect equilibrium. This paper provides a summary of that algorithm and our other findings on its convergence. © 2024 IEEE.

关键词： Contrastive Learning

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HiLo Optical Sectioning Algorithm Based on the Rolling Ball Method 5

HiLo Optical Sectioning Algorithm Based on the Rolling Ball ...

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5th International Conference on Intelligent Computing and Human-computer Interaction, ICHCI 2024

作者： Ma, Rui Jiang, Jingrui Tao, Yuchuan Liu, Jiang School of Information Engineering Southwest University of Science and Technology Mianyang China School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350368284

Traditional structured illumination microscopy techniques still face issues such as complex hardware structures, high optical path costs, low efficiency of optical sectioning reconstruction algorithms, and long image acquisition times. To address these problems, this text designs and builds a high-speed portable three-dimensional optical sectioning microscopy system based on DMD and LED, which greatly simplifies the optical path structure and reduces hardware costs. Additionally, through in-depth analysis of traditional optical sectioning reconstruction algorithms, a HiLo optical sectioning reconstruction algorithm based on the rolling ball method (RB-HiLo) is proposed. Experimental results show that compared to the traditional RMS algorithm, this new algorithm improves image acquisition efficiency by 22.8%, enhances optical sectioning reconstruction efficiency by 20.7%, and demonstrates lower background intensity, more effective deblurring, and higher signal-To-noise ratio in the reconstructed optical sectioning images as indicated by the normalized intensity distribution curves. © 2024 IEEE.

关键词： Image reconstruction

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Blockchain Based Security System for Health Care 2

Blockchain Based Security System for Health Care

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Ankit Singh, Rishipal Guru Jambheshwar University of Science & Technology Dept. of Computer Science & Engineering Hisar India

ISBN: (纸本)9798350383652

Security in healthcare systems based on blockchain technology is the subject of ongoing research. Block Chain technology has the potential to revolutionize many different sectors since it provides a decentralized, incontestable, and publicly verifiable record of every transaction activity. Bitcoin's foundational mechanism is referred to as 'block chain technology.' This technology allows for completely decentralized transaction processing. Healthcare aims to diagnose, treat, relieve, and cure any and all bodily and mental health issues, as well as to promote health and prevent sickness. Since all transactions in a blockchain are encrypted, it provides the highest level of security possible. Blockchain networks provide the same assurance of security for medical records. The integrity of the network's ledgers may be checked by checking file signatures on each node to make sure no alterations have been performed. The signature will no longer be valid when the record has been modified. The future of blockchain and big data applications is promising. Big Data technology can manage any kind, amount, or speed of data, while blockchain enhances efficiency across all industries. It has been noticed that medical records are maintained on a blockchain for reasons of security. Proposed system has evaluated healthcare data for security. When compared to the results of cutting-edge research, however, the scalability and adaptability of traditional methods are clearly lacking. Better performance and security utilizing blockchain technology is essential, hence developing such a system is a priority. © 2024 IEEE.

关键词： Electronic health record

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Improve Dense Passage Retrieval with Entailment Tuning

Improve Dense Passage Retrieval with Entailment Tuning

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Dai, Lu Liu, Hao Xiong, Hui China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798891761643

Retrieval module can be plugged into many downstream NLP tasks to improve their performance, such as open-domain question answering and retrieval-augmented generation. The key to a retrieval system is to calculate relevance scores to query and passage pairs. However, the definition of relevance is often ambiguous. We observed that a major class of relevance aligns with the concept of entailment in NLI tasks. Based on this observation, we designed a method called entailment tuning to improve the embedding of dense retrievers. Specifically, we unify the form of retrieval data and NLI data using existence claim as a bridge. Then, we train retrievers to predict the claims entailed in a passage with a variant task of masked prediction. Our method can be efficiently plugged into current dense retrieval methods, and experiments show the effectiveness of our method. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

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Multimedia Tools and Applications 2024年 1-17页

作者： Mohan, G. Bharathi Kumar, R. Prasanna Elakkiya, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Birla Institute of Technology and Science Dubai345055 United Arab Emirates

Pre-trained language models have significantly advanced text summarization by leveraging extensive pre-training data to enhance performance. Many cutting-edge models undergo an initial pre-training phase on a large corpus before being fine-tuned specifically for text summarization tasks. The challenge arises when the limited data available for these summarization tasks and the high complexity of the models lead to aggressive fine-tuning, which can cause the models to over fit the training data and fail to generalize well to new, unseen data. To address these challenges, the study proposes a multi-objective based Genetic Algorithm (MOGA) approach for optimizing pre-trained models for summarization The proposed approach first compares the performance of various pre-trained models by fine-tuning them with the CNN/Daily Mail dataset. Secondly, the proposed Multi-Objective Genetic Algorithm (MOGA) is applied to all validated pre-trained models and compared using a statistical approach. Experimental results reveal that Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-sequence (PEGASUS) model performs best among the pre-trained models. Further optimization with the proposed MOGA method yields even better results, with the optimized model achieving a Recall-Oriented Understudy for Gisting Evaluation) ROUGE-1 score of 0.514, a ROUGE-2 score of 0.4, and a ROUGE-L score of 0.5. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Reinforcement Learning with Quasi-Hyperbolic Discounting 63

Reinforcement Learning with Quasi-Hyperbolic Discounting

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Eshwar, S.R. Motwani, Mayank Roy, Nibedita Thoppe, Gugan Indian Institute of Science Department of Computer Science and Automation Bengaluru India Indian Institute of Technology Department of Computer Science and Engineering Bombay India

ISBN: (纸本)9798350316339

Reinforcement learning has traditionally been studied with exponential discounting or the average reward setup, mainly due to their mathematical tractability. However, such frameworks fall short of accurately capturing human behavior, which has a bias towards immediate gratification. Quasi-Hyperbolic (Q H) discounting is a simple alternative for modeling this bias. Unlike in traditional discounting, though, the optimal QH-policy, starting from some time t1, can be different to the one starting from t2. Hence, the future self of an agent, if it is naive or impatient, can deviate from the policy that is optimal at the start, leading to sub-optimal overall returns. To prevent this behavior, an alternative is to work with a policy anchored in a Markov Perfect Equilibrium (MPE). In this work, we propose the first model-free algorithm for finding an MPE. Using a two-timescale analysis, we show that, if our algorithm converges, then the limit must be an MPE. We also validate this claim numerically for the standard inventory system with stochastic demands. Our work significantly advances the practical application of reinforcement learning. © 2024 IEEE.

关键词： Contrastive Learning

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