Inferring Cognitive State of Pilot’s Brain Under Different Maneuvers During Flight

Wu, Edmond Q.; Cao, Zhengtao; Sun, Poly Z. H.; Li, Dongfang; Law, Rob; Xu, Xin; Zhu, Limin; Yu, Mengsun

doi:10.1109/tits.2022.3189981

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…e use of big data is prevalent across all academic elds, with particular emphasis on computer software and applications, automation technology, macroeconomic management, mathematics, telecommunications technology, enterprise economy, and Internet technology. Data mining (DM) [7,8] is the process of removing from data the interesting, potential, and available knowledge of individuals and expressing it in a way that users can comprehend. One of the most active research areas in DM is association rule mining, which reflects the intriguing correlation between items in a lot of data.…”

Section: Introductionmentioning

confidence: 99%

Educational Evaluation Information System Model of Political Ideological Big Data in Colleges and Universities

Cao

2022

Mobile Information Systems

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This study presents the fundamental guidelines for developing an evaluation index system for political thought education in light of the shortcomings of the current system, including its limited evaluation objectives, singular evaluation methods, lack of pertinence of evaluation indexes, and subjectivity of evaluation standards. In parallel, big data-related technologies are being used to determine the indicators and index weight coefficients of the evaluation system of political thought teaching in institutions of higher learning. This is done on the basis of earlier research on big data and teaching evaluation. The decision tree method is used in this study to create a prediction model for teaching evaluation, which is then pruned based on “relative support” to categorize the sample data. The decision tree method is aimed at a large number of teachers’ information databases. The model is validated at the end. According to the experimental findings, this educational evaluation system is stable to a degree of 94.6 percent, and its evaluation accuracy can reach a level of 95.33 percent. The validity and reliability of the educational evaluation system developed in this study are confirmed by this result. Big data can therefore be used to evaluate political thought instruction in higher education institutions, which offers some fresh perspectives for related research.

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Section: Introductionmentioning

confidence: 99%

Educational Evaluation Information System Model of Political Ideological Big Data in Colleges and Universities

Cao

2022

Mobile Information Systems

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“…We also note that inferring a user's cognition is currently an active area of research (Akula et al 2022;Shergadwala, Panchal, and Bilionis 2022;Wu et al 2022). While some human-centric requirements may point to the user's desire to be "understood" by a system in real-time, it is currently not practically feasible to so.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Human-Centric Perspective on Model Monitoring

Shergadwala¹,

Lakkaraju

Kenthapadi³

2022

HCOMP

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Predictive models are increasingly used to make various consequential decisions in high-stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that these models make accurate predictions, are robust to shifts in the data, do not rely on spurious features, and do not unduly discriminate against minority groups. To this end, several approaches spanning various areas such as explainability, fairness, and robustness have been proposed in recent literature. Such approaches need to be human-centered as they cater to the understanding of the models to their users. However, there is little to no research on understanding the needs and challenges in monitoring deployed machine learning (ML) models from a human-centric perspective. To address this gap, we conducted semi-structured interviews with 13 practitioners who are experienced with deploying ML models and engaging with customers spanning domains such as financial services, healthcare, hiring, online retail, computational advertising, and conversational assistants. We identified various human-centric challenges and requirements for model monitoring in real-world applications. Specifically, we found that relevant stakeholders would want model monitoring systems to provide clear, unambiguous, and easy-to-understand insights that are readily actionable. Furthermore, our study also revealed that stakeholders desire customization of model monitoring systems to cater to domain-specific use cases.

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Background Subtraction Based on Visual Saliency

Zhang

Huang

et al. 2022

Communications in Computer and Information Science

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Inferring Cognitive State of Pilot’s Brain Under Different Maneuvers During Flight

Cited by 6 publications

References 19 publications

Educational Evaluation Information System Model of Political Ideological Big Data in Colleges and Universities

Educational Evaluation Information System Model of Political Ideological Big Data in Colleges and Universities

A Human-Centric Perspective on Model Monitoring

Background Subtraction Based on Visual Saliency

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