检索结果-内蒙古大学图书馆

AMIR: An Automated MisInformation Rebuttal System — A COVID-19 Vaccination datasets based Exposition

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Sharma, Shakshi Datta, Anwitaman Sharma, Rajesh School of Artificial Intelligence Bennett University Greater Noida India College of Computing and Data Science Nanyang Technological University Singapore Institute of Computer Science University of Tartu Estonia

Misinformation has emerged as a major societal threat in the recent years in general;specifically in the context of the COVID-19 pandemic, it has wrecked havoc, for instance, by fuelling vaccine hesitancy. Cost-effective, scalable solutions for combating misinformation are the need of the hour. This work explored how existing information obtained from social media and augmented with more curated fact checked data repositories can be harnessed to facilitate automated rebuttal of misinformation at scale. While the ideas herein can be generalized and reapplied in the broader context of misinformation mitigation using a multitude of information sources and catering to the spectrum of social media platforms, this work serves as a proof of concept, and as such, it is confined in its scope to only rebuttal of tweets, and in the specific context of misinformation regarding COVID-19. It leverages two publicly available datasets, viz. FaCov (fact-checked articles) [1] and misleading (social media Twitter) [2] data on COVID-19 Vaccination. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

Automated Injury Detection and Alert Systems in Public Transportation Integrating IoT with Convolutional Neural Networks 2

学校读者我要写书评

暂无评论

Automated Injury Detection and Alert Systems in Public Trans...

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vimal, S.P. Peter, John Benito Jesudasan Kavitha, U. Bhuvaneswari, G. Manikandan, G. Thamizhamuthu, R. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Capital One Services Llc RichmondVA United States Vel Tech RangarajanDr.Sagunthala R&d Institute of Science and Technology School of Commerce Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Innovative methods to improve public transportation safety and response mechanisms have emerged due to Internet of Things (IoT) and artificial intelligence (AI) technologies. IoT devices and Convolutional Neural Networks (CNNs), a class of deep learning algorithms analyzing visual imagery, are used to create an innovative Automated Injury Detection and Alert System (AIDAS) for public transportation. The proposed AIDAS architecture employs IoT sensors and cameras in buses and trains to monitor and evaluate passengers' physical health in real time. These sensors can detect unexpected impacts, odd motions, and distressing noises. A strong CNN model trained to detect passenger injury or bodily damage receives live images from the onboard cameras. An automatic alarm process is activated immediately upon injury detection. The closest emergency response teams and transportation system operators get complete event reports, including time, position (by GPS coordinates), and injury type. A quick reaction to accidents with this real-time alarm system might save lives and reduce public transportation injuries. AIDAS' use of IoT and CNN technology is innovative in public transportation safety. The technology significantly improved emergency response times and reduced unattended injuries in public transport networks. The system's excellent damage detection accuracy is due to CNNs' advanced image processing and pattern recognition skills and IoT devices' sensory inputs. It also addresses privacy issues and the necessity for strong data encryption and proposes solutions. Finally, it analyzes how AIDAS affects public transportation safety and future research and development in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

Research on the Application of Business data Fusion in the Engineering Management of Oil and Gas Storage and Transportation Station 23

学校读者我要写书评

暂无评论

Research on the Application of Business Data Fusion in the E...

Proceedings of the 2023 4th International Conference on Computer science and Management Technology

作者： Rui Xie Bo Fan Jianlin Ma Meng Pan Southwest Pipeline Ltd. China School of Computing and Artificial Intelligence Southwest Jiaotong University China Chengdu Aerospace Science & Industry Big Data Institute Ltd. China

ISBN: (纸本)9798400709517

Due to challenges such as complex contractor personnel, a large amount of scattered form data, a cumbersome business implementation process, low levels of digitalization and security, and difficult management in the engineering management business of oil and gas storage and transportation stations, this paper proposes a study on business data fusion application in the engineering management of oil and gas storage and transportation stations. Focused on the business processes and data of engineering management in oil and gas storage and transportation stations, this research establishes and analyzes an architecture for engineering management business in these stations. Using an Artifact-centric data fusion method, the research constructs an information model for the engineering management business, identifying the data topics for this area. Finally, the research applies and verifies the study of business data fusion through real-world business management scenarios. As researchers in the field of business data fusion, this study utilizes digital technologies and methods to enhance the overall safety management of engineering operations, thereby promoting the transformation and upgrading of engineering management business in oil and gas storage and transportation stations

关键词：

Development of a Sensor-Based Wearable Fetal and Maternal Health Monitoring System 1

学校读者我要写书评

暂无评论

Development of a Sensor-Based Wearable Fetal and Maternal He...

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Jayalath, Yugashi Maduranga, M.W.P. Tilwari, Valmik Wijayarathna, Wmsrb Wanniarachchi, Waam Sharma, Debashree School Of Pharmacy & Biomolecular Sciences Liverpool John Moores University Liverpool United Kingdom Sri Lanka Technology Campus Faculty of Computing and IT Department of Data Science Padukka Sri Lanka Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India General Sir Jhon Kotelawala Defence University Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9798350364590

According to the World Health Organization, approximately 360,000 pregnant women are registered annually in Sri Lanka. Among these, about 328,000 babies are born healthy, while 5,800 are born with birth defects, some of which are severe and impact daily life. To address the Sri Lankan stillbirth rate of 6.01%, continuous monitoring of fetal and maternal health is essential. Although commercial devices exist to fulfill this requirement, their high costs and safety issues often preclude regular use. To overcome these challenges, there is a need for a passive, wearable, and portable monitoring system for fetal and maternal health. This system should track key parameters such as fetal heart rate, fetal movements, and maternal heart rate to mitigate health risks for both the fetus and the mother. In this study, we developed a device using the Arduino, appropriate sensors, and a microcontroller to monitor these vital parameters. The device is user-friendly and suitable for regular use. MATLAB was utilized for the signal processing requirements of the proposed device. Test results demonstrated that fetal movement detection was accurate, although potential errors may arise during real-time testing. For pulse sensor readings, an average percentage difference of 3.025% was observed compared to pulse oximeter readings, likely due to placement errors and sensor instability in the proposed device. © 2024 IEEE.

关键词： Electronic health record

CLPCare: Bootstrapping Multimodal Large Language Models for Ultrasound Cleft Lip and Palate Recognition and Analysis 10

学校读者我要写书评

暂无评论

CLPCare: Bootstrapping Multimodal Large Language Models for ...

10th IEEE Smart World Congress, SWC 2024

作者： Wang, Jiaru Luo, Guofeng Yan, Haolun Huang, Shiwang Liao, Zhenghan Zhang, Shiyi Wu, Jinzhun He, Xiaoqin Chen, Longbiao Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen China Xiamen University National Institute for Data Science in Health and Medicine Xiamen China Xiamen University Women and Children's Hospital Xiamen China Xiamen University Xiamen Maternity & Child Healthcare Hospital Women and Children's Hospital Department of Ultrasound Xiamen China

ISBN: (纸本)9798331520861

Cleft lip and palate is a common congenital malformations in fetuses, and early diagnosis is crucial for reducing the physical and psychological impact on pregnant women and their families. Traditional image processing methods for recognizing and analyzing cleft lip and palate rely on manual annotation and have limited interpretability. To address this issue, we propose a method for ultrasound image recognition and analysis of cleft lip and palate based on multimodal large language models. Firstly, we design a specific ultrasound image findings instruction finetuning template and fine-tune the multimodal large language model to enhance the model's recognition capability. Secondly, we conceive a specific retrieval-enhanced prompt template aimed at improving the model's understanding of cleft lip and palate analysis. Lastly, we evaluate our framework using a real dataset from Xiamen Maternal and Child Health Hospital in China. Additionally, we develop a medical system for the recognition and analysis of cleft lip and palate. © 2024 IEEE.

关键词： Ultrasonic imaging

Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Bo Xu, Zhiwei Yun, Jing Wang, Jiatai College of Data Science and Application Inner Mongolia University of Technology Huhhot China Haihe Lab of ITAI Tianjin China Institute of Computing Technology Chinese Academy of Sciences Beijing China

This paper study one challenging issue in incomplete multiview clustering, where valuable complementary information from other views is always ignored. To be specific, we propose a framework that effectively balances Complementarity and Consistency information in Incomplete Multi-view Clustering (CoCo-IMC). Specifically, we design a dual network of delayed activation, which achieves a balance of complementarity and consistency among different views. The delayed activation could enriches the complementarity information that was ignored during consistency learning. Then, we recover the incomplete information and enhance the consistency learning by minimizing the conditional entropy and maximizing the mutual information across different views. This could be the first theoretical attempt to incorporate delayed activation into incomplete data recovery and the balance of complementarity and consistency. We have proved the effectiveness of CoCo-IMC in extensive comparative experiments with 12 state-of-the-art baselines on four publicly available datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

Staggered Routing in Autonomous Mobility-on-Demand Systems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Coppola, Antonio Hiermann, Gerhard Paccagnan, Dario Schiffer, Maximilian School of Management Technical University of Munich Germany Department of Computing Imperial College London United Kingdom School of Management Munich Data Science Institute Technical University of Munich Germany

In autonomous mobility-on-demand systems, effectively managing vehicle flows to mitigate induced congestion and ensure efficient operations is imperative for system performance and positive customer experience. Against this background, we study the potential of staggered routing, i.e., purposely delaying trip departures from a system perspective, in order to reduce congestion and ensure efficient operations while still meeting customer time windows. We formalize the underlying planning problem and show how to efficiently model it as a mixed integer linear program. Moreover, we present a matheuristic that allows us to efficiently solve large-scale real-world instances both in an offline full-information setting and its online rolling horizon counterpart. We conduct a numerical study for Manhattan, New York City, focusing on low- and highly-congested scenarios. Our results show that in low-congestion scenarios, staggering trip departures allows mitigating, on average, 94% of the induced congestion in a full information setting. In a rolling horizon setting, our algorithm allows us to reduce 90% of the induced congestion. In high-congestion scenarios, we observe an average reduction of 66% as the full information bound and an average reduction of 56% in our online setting. Surprisingly, we show that these reductions can be reached by shifting trip departures by a maximum of six minutes in both the low and high-congestion scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Integer programming

Beyond the Limit of Weight-Sharing: Pioneering Space-Evolving NAS with Large Language Models

学校读者我要写书评

暂无评论

Beyond the Limit of Weight-Sharing: Pioneering Space-Evolvin...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiu Su Shan You Hongyan Xu Xiuxing Li Jun Long Yi Chen Chang Xu Big Data Institute Central South University School of Computer Science Faculty of Engineering The University of Sydney SenseTime Research School of Computer Science and Engineering University of New South Wales Institute of Computing Technology Chinese Academy of Sciences School of Computer Science Hong Kong University of Science and Technology

Large language models (LLMs) offer impressive performance across diverse fields, but their increasing complexity raises both design costs and the need for specialized expertise. These challenges are intensified for Neural Architecture Search (NAS) methods reliant on weight-sharing techniques. This paper introduces GNAS, a new NAS method that boosts the search process with the aid of LLMs for efficient model discovery. With insights from existing architectures, GNAS swiftly identifies superior models that can adapt to changing resource constraints. We provide a mathematical framework to facilitate the transfer of knowledge across different model sizes, thereby improving search efficiency. Our experiments conducted on ImageNet, NAS-Bench-Macro, and ChannelBench-Macro confirm the effectiveness of GNAS across both CNN and Transformer architectures.

关键词：

An In-Context Schema Understanding Method for Knowledge Base Question Answering

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Liu, Yantao Li, Zixuan Jin, Xiaolong Guo, Yucao Bai, Long Guan, Saiping Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. Recently, Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. In doing so, a major challenge for LLMs is to overcome the immensity and heterogeneity of knowledge base schemas. Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details. Then, an extra module is used to inject schema information to these drafts. In contrast, in this paper, we propose a simple In-Context Schema Understanding (ICSU) method that enables LLMs to directly understand schemas by leveraging in-context learning. Specifically, ICSU provides schema information to LLMs using schema-related annotated examples. We investigate three example retrieval strategies based on raw questions, anonymized questions, and generated SPARQL queries. Experimental results show that ICSU demonstrates competitive performance compared to baseline methods on both the KQA Pro and WebQSP datasets. © 2023, CC BY.

关键词： Knowledge based systems