总编辑评论

R. Maikala
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Although this article does not fit into the “design-related” scientific mission of the journal, the overarching message is an important one and relevant to our times as we continue to strive for inclusivity in scientific research and practice. I first learned about Dr. Lillian Gilbreth while pursuing my masters’ degree in industrial engineering. However, I credit my 10-year-old daughter for making me more aware of the exploits and heroism of Amelia Earhart, as she has been collecting stories on her life since first grade. As described in the Chong and Proctor article, Edward C. Elliott, the sixth president of Purdue University, hired industrial engineer Gilbreth and aviator Earhart in 1935 as part of his vision to encourage educational opportunities for women. He did so, even though at that time it was practically unthinkable to consider women for these roles. Elliott’s forward-thinking approach helped to foster scientific progress, and consequently, we have all benefitted from the contributions of these amazing women. The second article presented in this issue picks up the thread from past year’s special issue on “Machine Learning, Artificial Intelligence, and Human Factors Design” – a timely topic with regard to emerging developments in speech recognition, medical diagnosis, predictive analytics, data extraction, and so on. Kamaraj and Lee share their insight on machine learning through research that involves augmenting supervised learning with unsupervised learning and data visualization. The authors present a human-augmented machine learning aided framework to guide data analysts in classifying their data. They tested their framework on a subset of data from the Occupational Information Network database on tasks that might be potentially performed in an automated vehicle. The authors conclude that their augmented methodology could be used to classify big data in a meaningful fashion for optimal and informed decision making. The third article in this issue considers some of the human–technology interaction challenges presented by virtual reality – challenges that have become very apparent in recent years with the exponential growth of new HF/E technologies. In this article, Zhang touches upon the importance of evaluating the authenticity of virtual environments by evaluating three different extraction methods (scale invariant feature transform, local binary pattern, and histogram of oriented gradients) as input for a backpropagation neural network for classifying and recognizing different gestures. Based on the author’s analysis of gesture pictures, the authenticity obtained from the histogram of oriented gradients (such as recognition rate, false acceptance rate, and false rejection rate) was the closest to the original picture. Please watch out for In Practice in the coming issues. In Practice is a new feature of EID, edited by Dr. Anthony Andre. In Practice will showcase the work of HF/E practitioners and focus on delivering key insights, takeaways and lessons learned from their design or procedural applications, concept testing, user research and field work. I hope you find the articles presented in this issue inspiring and informative, and, as always, I welcome your feedback as we move forward in 2021. Best regards,","PeriodicalId":357563,"journal":{"name":"Ergonomics in Design: The Quarterly of Human Factors Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comment From the Editor-in-Chief\",\"authors\":\"R. 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Although this article does not fit into the “design-related” scientific mission of the journal, the overarching message is an important one and relevant to our times as we continue to strive for inclusivity in scientific research and practice. I first learned about Dr. Lillian Gilbreth while pursuing my masters’ degree in industrial engineering. However, I credit my 10-year-old daughter for making me more aware of the exploits and heroism of Amelia Earhart, as she has been collecting stories on her life since first grade. As described in the Chong and Proctor article, Edward C. Elliott, the sixth president of Purdue University, hired industrial engineer Gilbreth and aviator Earhart in 1935 as part of his vision to encourage educational opportunities for women. He did so, even though at that time it was practically unthinkable to consider women for these roles. 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Please watch out for In Practice in the coming issues. In Practice is a new feature of EID, edited by Dr. Anthony Andre. In Practice will showcase the work of HF/E practitioners and focus on delivering key insights, takeaways and lessons learned from their design or procedural applications, concept testing, user research and field work. I hope you find the articles presented in this issue inspiring and informative, and, as always, I welcome your feedback as we move forward in 2021. 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引用次数: 0

摘要

多样性、平等和包容一直是人因与工效学学会(HFES)的核心原则。这一承诺在女科学家和女院士的贡献中尤为明显,多年来,她们担任了hes前任主席、杰出研究员和执行委员会成员等重要职位。在过去,在一系列题为“HFES的第一夫人”的文章中,前主席Francis T. Durso强调了HFES女性成员的杰出职业生涯,她们首先担任了学会的领导职务,其中包括三位前人体工程学设计(EID)主编。为了向妇女对HFES和人为因素与人体工程学(HF/E)领域的贡献表示敬意,本期以Chong和Proctor关于Lillian Gilbreth和Amelia Earhart的成就的一篇文章开始。虽然这篇文章不符合该杂志的“与设计相关”的科学使命,但其总体信息是重要的,与我们的时代有关,因为我们继续努力在科学研究和实践中实现包容性。我第一次认识莉莲·吉尔布雷斯博士是在攻读工业工程硕士学位的时候。然而,我要感谢我10岁的女儿,她让我更加了解阿米莉亚·埃尔哈特的功绩和英雄主义,因为她从一年级开始就一直在收集她的生活故事。正如Chong和Proctor的文章所描述的那样,1935年,普渡大学第六任校长爱德华·c·埃利奥特(Edward C. Elliott)聘请了工业工程师吉尔布雷斯(Gilbreth)和飞行员埃尔哈特(Earhart),作为他鼓励女性受教育机会的愿景的一部分。他这样做了,尽管当时考虑女性担任这些角色实际上是不可想象的。艾略特的前瞻性思维有助于促进科学进步,因此,我们都从这些了不起的女性的贡献中受益。这期的第二篇文章从去年的特刊“机器学习、人工智能和人为因素设计”中获得了线索,这是一个关于语音识别、医疗诊断、预测分析、数据提取等新兴发展的及时主题。Kamaraj和Lee通过研究分享了他们对机器学习的见解,包括用无监督学习和数据可视化来增强监督学习。作者提出了一个人工增强的机器学习辅助框架来指导数据分析师对数据进行分类。他们在职业信息网络数据库中的数据子集上测试了他们的框架,这些数据子集是关于可能在自动驾驶汽车中执行的任务的。作者得出结论,他们的增强方法可以用于以有意义的方式对大数据进行分类,以实现最佳和明智的决策。本期的第三篇文章考虑了虚拟现实带来的一些人机交互挑战——近年来随着新的高频/电子技术的指数级增长,这些挑战变得非常明显。在这篇文章中,Zhang通过评估三种不同的提取方法(尺度不变特征变换、局部二值模式和定向梯度直方图)作为反向传播神经网络分类和识别不同手势的输入,谈到了评估虚拟环境真实性的重要性。根据作者对手势图片的分析,由方向梯度直方图(如识别率、错误接受率、错误拒绝率)得到的真实性最接近原始图片。请留意在实践中,在未来的问题。在实践中是EID的一个新功能,由Anthony Andre博士编辑。在实践中,将展示高频/电子从业者的工作,并重点介绍从他们的设计或程序应用、概念测试、用户研究和现场工作中获得的关键见解、要点和经验教训。我希望你能发现本期文章具有启发性和知识性,并一如既往地欢迎你的反馈,因为我们将在2021年向前迈进。最好的问候,
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comment From the Editor-in-Chief
Diversity, equality, and inclusion have long been central tenets of the Human Factors and Ergonomics Society (HFES). This commitment is especially evident in the contribution of women scientists and academicians who, over the years, have held prominent positions as HFES past presidents, distinguished fellows, and executive council members. In the past, in a series of articles titled, “First Ladies of HFES,” Past President Francis T. Durso highlighted the distinguished careers of women HFES members who were first to serve in leadership positions for the Society, including, I might add, three former Ergonomics in Design (EID) editors-in-chief. In tribute to the contribution of women to HFES and to the field of human factors and ergonomics (HF/E), this issue opens with an article by Chong and Proctor on the achievements of Lillian Gilbreth and Amelia Earhart. Although this article does not fit into the “design-related” scientific mission of the journal, the overarching message is an important one and relevant to our times as we continue to strive for inclusivity in scientific research and practice. I first learned about Dr. Lillian Gilbreth while pursuing my masters’ degree in industrial engineering. However, I credit my 10-year-old daughter for making me more aware of the exploits and heroism of Amelia Earhart, as she has been collecting stories on her life since first grade. As described in the Chong and Proctor article, Edward C. Elliott, the sixth president of Purdue University, hired industrial engineer Gilbreth and aviator Earhart in 1935 as part of his vision to encourage educational opportunities for women. He did so, even though at that time it was practically unthinkable to consider women for these roles. Elliott’s forward-thinking approach helped to foster scientific progress, and consequently, we have all benefitted from the contributions of these amazing women. The second article presented in this issue picks up the thread from past year’s special issue on “Machine Learning, Artificial Intelligence, and Human Factors Design” – a timely topic with regard to emerging developments in speech recognition, medical diagnosis, predictive analytics, data extraction, and so on. Kamaraj and Lee share their insight on machine learning through research that involves augmenting supervised learning with unsupervised learning and data visualization. The authors present a human-augmented machine learning aided framework to guide data analysts in classifying their data. They tested their framework on a subset of data from the Occupational Information Network database on tasks that might be potentially performed in an automated vehicle. The authors conclude that their augmented methodology could be used to classify big data in a meaningful fashion for optimal and informed decision making. The third article in this issue considers some of the human–technology interaction challenges presented by virtual reality – challenges that have become very apparent in recent years with the exponential growth of new HF/E technologies. In this article, Zhang touches upon the importance of evaluating the authenticity of virtual environments by evaluating three different extraction methods (scale invariant feature transform, local binary pattern, and histogram of oriented gradients) as input for a backpropagation neural network for classifying and recognizing different gestures. Based on the author’s analysis of gesture pictures, the authenticity obtained from the histogram of oriented gradients (such as recognition rate, false acceptance rate, and false rejection rate) was the closest to the original picture. Please watch out for In Practice in the coming issues. In Practice is a new feature of EID, edited by Dr. Anthony Andre. In Practice will showcase the work of HF/E practitioners and focus on delivering key insights, takeaways and lessons learned from their design or procedural applications, concept testing, user research and field work. I hope you find the articles presented in this issue inspiring and informative, and, as always, I welcome your feedback as we move forward in 2021. Best regards,
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