Using photographs as data, which involves making observations from images and organizing them into variables to answer statistically investigative questions, is recommended for K-12 level statistics education. Researc...
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Using photographs as data, which involves making observations from images and organizing them into variables to answer statistically investigative questions, is recommended for K-12 level statistics education. Research is needed to support pre-service mathematics teachers' experiences with exploring such image-based data. With this task-based interview study, the goal was to shed light on (1) the pre-service mathematics teachers' data-ing actions during identifying and generating variables in relation to data familiarization, question posing, and data organization components and (2) how pre-service mathematics teachers identified and generated variables in the process of data-ing. data from video recordings, transcripts of the interview sessions for each pair, and their work with photos on the shared online document, that is, groupings and questions posed, were analyzed using a progressive focusing approach. The results showed that the data-ing actions during identifying and generating variables with data familiarization, question posing, and data organization included observing, interpreting, conjecturing, inferring, comparing, grouping, ordering, questioning/question posing, relating variables, categorizing variables, and measuring variables. The pairs used various combinations of multiple actions while data-ing. There were two types of variable identification: (1) observational variables based on visual judgment or metadata and (2) inferential variables based on personal interpretation. The latter type presented a tension between the variable defined and how to measure it objectively, as well as challenges in writing clearly defined variables when posing questions.
In recent times, the integration of chatbots into customer interaction systems has transformed the way businesses engage with their customers. As businesses worldwide seek to implement chatbot systems to improve custo...
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In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, vis...
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In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77-0.78, RMSE: 8.31-9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light in-tensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in sta-tionary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging tech-niques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.
Disease severity prediction is essential in clinical diagnosis nowadays, as correct understandings of the onset and progression of disease are priceless in treatment planning. In this study, dementia disease, one of t...
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Disease severity prediction is essential in clinical diagnosis nowadays, as correct understandings of the onset and progression of disease are priceless in treatment planning. In this study, dementia disease, one of the most severe non-communicable diseases worldwide, is focused. A novel dementia disease severity prediction scheme is proposed using new big ranking and learning techniques. To be specific, arterial spin labeling, an emerging functional-magnetic resonance imaging technique, is adopted to provide image-based clinical data. There are two steps composed of the whole scheme. First, a single-pixel based method is presented to correct the partial volume effect in arterial spin labeling images. The advantage of this method is that, problems of blurring and brain detail loss can be well tackled. Second, novel big pair-wise ranking and learning techniques is proposed to realize the dementia disease severity prediction task using arterial spin labeling images after partial volume effects correction. Extensive experiments using a big database composed of images acquired from 350 real demented patients are carried out with several conventional methods being compared. Comprehensive statistical analysis is performed and it suggests that the new scheme is promising in dementia disease severity prediction. (C) 2015 Elsevier B.V. All rights reserved.
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