Patrons of the Revolution

Crowther‐Heyck, Hunter

doi:10.1086/508075

Cited by 71 publications

(4 citation statements)

References 13 publications

(1 reference statement)

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“…(Read, for example, the preface to Erving Goffman's Asylum s.) No doubt, the lion's share of NIH funding still went toward laboratory and clinical medicine. Still, NIH funded a sizeable amount of social and behavioral research in diverse disciplines, especially through the National Institute of Mental Health and the National Institute of General Medical Sciences (Crowther‐Heyck 2006).…”

Section: Rights Harms and Responsibilities In Historical Contextmentioning

confidence: 99%

Victims in Our Own Minds? IRBs in Myth and Practice

Stark¹

2007

Law Soc Rev

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Section: Rights Harms and Responsibilities In Historical Contextmentioning

confidence: 99%

Victims in Our Own Minds? IRBs in Myth and Practice

Stark¹

2007

Law Soc Rev

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“…In [13] use a computer vision-primarily based device that includes both hardware and software to assess first-rate of the product to replace manual grading. Based totally on the goods we grade, hardware consisting of a digicam, conveyor device belt, sensing devices, and variable pace sensors are required.…”

Section: Literature Surveymentioning

confidence: 99%

Artificial Intelligence and Deep Learning-Based System for Agri-Food Quality and Safety Detection

Shah

Kumar

Akgül

2023

WJCMS

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Deep Learning (DL) has emerged as a highly effective technique for analyzing large volumes of data across various domains, including image processing, speech recognition, and pattern recognition. Recently, DL has also found applications in the field of food science and engineering, a relatively novel area of research. This paper provides a concise introduction to DL and delves into the architecture of a typical Convolution Neural Network (CNN) structure, as well as AI and IoT (Internet of Things) data training methodologies. Our research involved an extensive review of studies that utilized DL as a computational approach to address food-related challenges, such as food recognition, calorie computation, and safety detection of various food types like fruits, potatoes, meats, and aquatic products, as well as food supply chain management and food borne illness detection. Each study examined different problems, datasets, preprocessing techniques, network architectures, and evaluation metrics, comparing their results with alternative solutions. Furthermore, we explored the role of big data in the field of food quality assurance, uncovering compelling trends. Based on our analysis, DL consistently outperforms other approaches, including manual feature extractors and traditional machine learning algorithms. The findings highlight the tremendous potential of DL as a promising technology for food safety inspections and related applications in the food industry

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“…28 It also conditioned Miller's rejection of behaviourism and his move to adopting a cognitive-psychological view of human nature. 29 The focus on tools as a means of improving their fields was a common strategy among social scientists as well as their patrons in mid-twentieth-century America. At a conference funded by the Carnegie Corporation devoted to analysing the state of the social sciences, Samuel Stouffer argued that the national strategy for improving the social sciences should revolve around the invention of research tools.…”

Section: Working Across Departmental and Disciplinary Lines : Toolingmentioning

confidence: 99%

Instituting the science of mind: intellectual economies and disciplinary exchange at Harvard's Center for Cognitive Studies

Cohen-Cole¹

2007

Br J Hist Sci

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Focusing on Harvard's Center for Cognitive Studies as a case, this article uses economies of research tool exchange to develop a new way of characterizing cross-disciplinary research. Throughout its life from 1960 to 1972, the Center for Cognitive Studies hosted scholars from several disciplines. However, there were two different research cultures at the Center. With its directors and patrons committed to a philosophy that equated creative science with eclectic search for and invention of new tools, the Center's initial interdisciplinary research culture emphasized the exchange of ideas and methods. Several years later, once its work was well under way, the Center's culture became multidisciplinary. Rather than emphasizing the sharing, invention, location, discussion and stabilization of new research techniques, the Center's multidisciplinary economy involved researchers working in parallel.When the psychologists Jerome Bruner and George Miller founded the Center for Cognitive Studies at Harvard in 1960, they conceived of it as an interdisciplinary institution. The Center, regarded by practitioners as the first institutional location for the cross-disciplinary field of cognitive science, 1 included people from the fields of psychology,

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Patrons of the Revolution

Cited by 71 publications

References 13 publications

Victims in Our Own Minds? IRBs in Myth and Practice

Victims in Our Own Minds? IRBs in Myth and Practice

Artificial Intelligence and Deep Learning-Based System for Agri-Food Quality and Safety Detection

Instituting the science of mind: intellectual economies and disciplinary exchange at Harvard's Center for Cognitive Studies

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