Machine Reasoning: Technology, Dilemma and Future

Duan, Nan; Tang, Duyu; Zhou, Ming

doi:10.18653/v1/2020.emnlp-tutorials.1

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…Neural networks are robust but struggle with interpretability and generalisability (Duan et al, 2020), which is of particular importance for automated claim validation. Underwhelming model interpretability may induce an increased probability of models making the right prediction based on the wrong evidence.…”

Section: Model Interpretabilitymentioning

confidence: 99%

Automated fact‐checking: A survey

Zeng

Abumansour

Zubiaga

2021

Language and Linguist. Compass

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As online false information continues to grow, automated fact‐checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact‐checking datasets, devising automated fact‐checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact‐checking covering both the claim detection and claim validation components.

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Section: Model Interpretabilitymentioning

confidence: 99%

Automated fact‐checking: A survey

Zeng

Abumansour

Zubiaga

2021

Language and Linguist. Compass

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“…Model Intepretability Neural networks are robust but struggle with interpretability and generalisability [Duan et al, 2020], which is of particular importance for automated claim validation. Underwhelming model interpretability may induce an increased probability of models making the right prediction based on the wrong evidence.…”

Section: Claim Validationmentioning

confidence: 99%

Automated Fact-Checking: A Survey

Zeng¹,

Abumansour²,

Zubiaga³

2021

Preprint

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As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets, devising automated fact-checking pipelines and proposing NLP methods to further research in the development of different components. This paper reviews relevant research on automated fact-checking covering both the claim detection and claim validation components.

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“…Shallow learning methods rely on manual features that need to be created manually, thus negatively impacting detection accuracy [23]. In other words, deep learning can automatically extract sophisticated features from data with multiple levels of end-to-end representation compared to shallow learning approaches [24,25].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision Process Development regarding Worker’s Safety Harness and Hook to Prevent Fall Accidents: Focused on System Scaffolds in South Korea

Lim

Jung

Park

et al. 2022

Advances in Civil Engineering

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In South Korea, industrial accidents continue to increase in frequency, with construction accidents accounting for more than a third of all industrial accidents. Specifically, by preventing fall accidents, the death rate from accidents can be reduced by 50%. Fall protection is required to prevent fall accidents, and investigating the reinforcement of the worker’s safety harness and hook fastening becomes imperative. This requires automation of computer vision confirmation of the safety harness and hook fastening. As the accident risk can be reduced by an effective safety culture in the system, it is necessary to monitor safety on site through a construction safety automation system. Therefore, the objective of this study is to develop a computer vision process for safety harness and hook for preventing fall accidents in South Korea’s construction industry. This study focuses on system scaffolds that are widely used at construction sites. The application of this methodology to sample sites and field cases established its applicability. The proposed computer vision application methodology will serve as the foundation for visualization research in the construction industry, and image recognition will help reduce the safety accident rate. Accidents caused by failure to use a safety harness and hook can be reduced in South Korea as well as globally. Additionally, this methodology is applicable to roof construction, tower crane installation and dismantling work, as well as steel tower installation and dismantling.

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Machine Reasoning: Technology, Dilemma and Future

Cited by 4 publications

References 36 publications

Automated fact‐checking: A survey

Automated fact‐checking: A survey

Automated Fact-Checking: A Survey

Computer Vision Process Development regarding Worker’s Safety Harness and Hook to Prevent Fall Accidents: Focused on System Scaffolds in South Korea

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