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Sensor, Signal, and Imaging Informatics in 2017.
ObjectiveāTo summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.MethodsāPubMedĀ® and Web of ScienceĀ® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.ResultsāThe selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics
Metacognitive Development and Conceptual Change in Children
There has been little investigation to date of the way metacognition is involved in conceptual change. It has been recognised that analytic metacognition is important to the way older children acquire more sophisticated scientific and mathematical concepts at school. But there has been barely any examination of the role of metacognition in earlier stages of concept acquisition, at the ages that have been the major focus of the developmental psychology of concepts. The growing evidence that even young children have a capacity for procedural metacognition raises the question of whether and how these abilities are involved in conceptual development. More specifically, are there developmental changes in metacognitive abilities that have a wholescale effect on the way children acquire new concepts and replace existing concepts? We show that there is already evidence of at least one plausible example of such a link and argue that these connections deserve to be investigated systematically
Sensor Signal and Information Processing II [Editorial]
This Special Issue compiles a set of innovative developments on the use of sensor signals and information processing. In particular, these contributions report original studies on a wide variety of sensor signals including wireless communication, machinery, ultrasound, imaging, and internet data, and information processing methodologies such as deep learning, machine learning, compressive sensing, and variational Bayesian. All these devices have one point in common: These algorithms have incorporated some form of computational intelligence as part of their core framework in problem solving. They have the capacity to generalize and discover knowledge for themselves, learning to learn new information whenever unseen data are captured
Toward an integrative approach of cognitive neuroscientific and evolutionary psychological studies of art
This paper examines explanations for human artistic behavior in two reductionist research programs, cognitive neuroscience and evolutionary psychology. Despite their different methodological outlooks, both approaches converge on an explanation of art production and appreciation as byproducts of normal perceptual and motivational cognitive skills that evolved in response to problems originally not related to art, such as the discrimination of salient visual stimuli and speech sounds. The explanatory power of this reductionist framework does not obviate the need for higher-level accounts of art from the humanities, such as aesthetics, art history or anthropology of art
Exploring AI Tool's Versatile Responses: An In-depth Analysis Across Different Industries and Its Performance Evaluation
AI Tool is a large language model (LLM) designed to generate human-like
responses in natural language conversations. It is trained on a massive corpus
of text from the internet, which allows it to leverage a broad understanding of
language, general knowledge, and various domains. AI Tool can provide
information, engage in conversations, assist with tasks, and even offer
creative suggestions. The underlying technology behind AI Tool is a transformer
neural network. Transformers excel at capturing long-range dependencies in
text, making them well-suited for language-related tasks. AI Tool has 175
billion parameters, making it one of the largest and most powerful LLMs to
date. This work presents an overview of AI Tool's responses on various sectors
of industry. Further, the responses of AI Tool have been cross-verified with
human experts in the corresponding fields. To validate the performance of AI
Tool, a few explicit parameters have been considered and the evaluation has
been done. This study will help the research community and other users to
understand the uses of AI Tool and its interaction pattern. The results of this
study show that AI Tool is able to generate human-like responses that are both
informative and engaging. However, it is important to note that AI Tool can
occasionally produce incorrect or nonsensical answers. It is therefore
important to critically evaluate the information that AI Tool provides and to
verify it from reliable sources when necessary. Overall, this study suggests
that AI Tool is a promising new tool for natural language processing, and that
it has the potential to be used in a wide variety of applications
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