Calls for an alternative conceptualization of cognition for applied concerns retain the core commitment of the basic research community to abstract cognition detached from a physical environment. The present paper attempts to break out of the dominant, narrow view of cognition and cognitive domains, with a cognitive analysis of digging ditches for the utility industry. To illustrate knowledge-based cognition in manual labour excerpts are presented from the journal entries of a moderately experienced student working a summer job, organized with a representation that distinguishes between the goals and methods of work. The journal entries illustrate the functions of knowledge for interacting with a physical environment; knowledge enables the selection, execution and monitoring of work methods, the interpretation of perceptual information, the application of task completion criteria and the ability for explanation and generalization. To emphasize the generality of the functions of cognition in ditch digging, comparable functions are indicated in a domain rarely regarded as a form of manual labour: the practice of internal medicine. Discussion of the results includes the implications for cognitive theory as well as practical implications for productivity, training and task analysis.
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http://dx.doi.org/10.1080/0014013031000085626 | DOI Listing |
Med Biol Eng Comput
January 2025
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used.
View Article and Find Full Text PDFJ Clin Med
December 2024
Departments of Radiology, Eulji University Hospital, Eulji University College of Medicine, 95 Dunsanseo-ro, Seo-gu, Daejeon 35233, Republic of Korea.
It is known that the pituitary gland volume (PV) in idiopathic central precocious puberty (IPP) is significantly higher than in healthy children. However, most PV measurements rely on manual quantitative methods, which are time-consuming and labor-intensive. This study aimed to automatically measure the PV of patients with IPP using artificial intelligence to accurately quantify the correlation between IPP and PV, and to improve the efficiency of diagnosing IPP.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Large visual language models like Contrastive Language-Image Pre-training (CLIP), despite their excellent performance, are highly vulnerable to the influence of adversarial examples. This work investigates the accuracy and robustness of visual language models (VLMs) from a novel multi-modal perspective. We propose a multi-modal fine-tuning method called Multi-modal Depth Adversarial Prompt Tuning (MDAPT), which guides the generation of visual prompts through text prompts to improve the accuracy and performance of visual language models.
View Article and Find Full Text PDFSensors (Basel)
December 2024
KLEEMANN Group, 61100 Kilkis, Greece.
Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
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