The development of critical thinking, creativity, and communication abilities is facilitated by the acquisition of writing skills, which are significant for students. Nevertheless, a considerable number of students pr...
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I think black people are the faces at the bottom of societal well, that Whites-most of Whites in America who are only one level above, also denied opportunity, also oppressed in a certain way, are fascinated in lookin...
I think black people are the faces at the bottom of societal well, that Whites-most of Whites in America who are only one level above, also denied opportunity, also oppressed in a certain way, are fascinated in looking down on us, rather than looking back at the top to see where the folk at the top are manipulating both groups. Only if they, in effect, let down their ropes, join with us, can both groups ever climb up and challenge and confront those at the top who make all the money, who have all the opportunity.
Personalised Integrated Healthcare Monitoring System (PIHMS) has received major attention from the healthcare industry. With large population and strong growth of Indonesian middle class, PIHMS is certainly a big mark...
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The aim of this study is to present an analysis of continued word association responses in Mandarin through the selection and validation of CKIP CoreNLP in Academia Sinica for grammatical category, word frequency and ...
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In this paper, we introduce an approach for recognizing and classifying gestures that accompany mathematical terms, in a new collection we name the "GAMT" dataset. Our method uses language as a means of prov...
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ISBN:
(纸本)9798350365474
In this paper, we introduce an approach for recognizing and classifying gestures that accompany mathematical terms, in a new collection we name the "GAMT" dataset. Our method uses language as a means of providing context to classify gestures. Specifically, we use a CLIP-style framework to construct a shared embedding space for gestures and language, experimenting with various methods for encoding gestures within this space. We evaluate our method on our new dataset containing a wide array of gestures associated with mathematical terms. The shared embedding space leads to a substantial improvement in gesture classification. Furthermore, we identify an efficient model that excelled at classifying gestures from our unique dataset, thus contributing to the further development of gesture recognition in diverse interaction scenarios.
Introduction The Commonwealth Partnerships for Antimicrobial Stewardship (CwPAMS) programme, funded by the UK Department of Health and Social Care's Fleming Fund and managed by the Commonwealth Pharmacists Associa...
Introduction The Commonwealth Partnerships for Antimicrobial Stewardship (CwPAMS) programme, funded by the UK Department of Health and Social Care's Fleming Fund and managed by the Commonwealth Pharmacists Association (CPA) and Tropical Health and Education trust (THET), currently supports 24 health partnerships (HPs) developing and implementing antimicrobial stewardship (AMS) interventions in 73 health facilities across eight African countries: Ghana, Kenya, Malawi, Nigeria, Sierra Leone, Tanzania, Uganda, and Zambia. This study assesses the barriers and challenges experienced by HPs in designing and implementing AMS interventions. Method A qualitative case-study design was adopted, with data collection undertaken between June 2023 and March 2024 by in-person (THET and CPA) visits. Data collection methods included non-participant observations, formal and informal feedback from key stakeholders, semi-structured interviews and focus groups. Interview guides were designed based on the Consolidated Framework for Implementation Research to encourage systematic data collection and reporting, track progress and identify barriers encountered during implementation of AMS interventions. Qualitative data underwent thematic analysis using NVivo 14®. Results Since the introduction of CwPAMS, a total of 15 AMS committees, 52 Point Prevalence Surveys (PPS) on antimicrobial use, and 21 AMS action plans have been developed and established across 73 health facilities, with 884 healthcare professionals completing AMS training. Key barriers reported by HPs included: challenges in PPS data collection due to lack, or inadequacy, of electronic patient record systems; delays in permissions and approvals (e.g. ethical approval); competing priorities (e.g. institutional priorities vs quality improvement initiatives); limited funding; challenges with procurement of medicines/reagents; over-ambitious projects as well as monitoring and evaluation plans. Health facilities also reported obstacle
This research explored the interrelationship among Taiwanese high school students' conceptions of learning science (COLS), self-regulated learning science (SRLS), and science learning self-efficacy (SLSE). A total...
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This research explored the interrelationship among Taiwanese high school students' conceptions of learning science (COLS), self-regulated learning science (SRLS), and science learning self-efficacy (SLSE). A total of 309 students participated in the study, and the self-report survey data were collected to measure these three constructs. Four COLS factors (Testing, Calculating and practicing, Application, and Understanding and seeing in a new way), two SRLS dimensions (Preparatory SRLS [task definition, goal setting, planning] and Enactment SRLS [controlling, monitoring, reflecting]), and two SLSE factors (Conceptual understanding and Higher-order cognitive skills), which adhere to the cognitive learning dimensions, were included for analysis. The results revealed a direct relationship between Testing and SLSE without going through any of the SRLS constructs. However, no direct relationship was built among other COLS components and the two SLSE dimensions. There are direct relationships among Calculating and practicing, Application, and the two SRLS constructs, but Understanding and seeing in a new way solely links to Enactment SRLS and not to Preparatory SRLS. In the end, the two SRLS constructs are directly associated with the students' SLSE dimensions. These results have the important implication that learners' COLS have a significant impact on their SRL engagement, which eventually leads to their beliefs about their cognitive abilities in learning the abstract concepts and critical thinking tasks in science.
Recently, there has been a growing interest in applying machine learning (ML) and deep learning to medical big data and smart healthcare. However, it can be challenging to possess both domain knowledge of medical data...
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Recently, there has been a growing interest in applying machine learning (ML) and deep learning to medical big data and smart healthcare. However, it can be challenging to possess both domain knowledge of medical data and expertise in ML necessary for conducting research in this area. In this regard, an automated machine learning (AutoML) framework can be a solution, which automates an ML pipeline-building process from data cleansing and preprocessing to model building, optimization, and performance evaluation. Since AutoML can reduce domain dependency in ML and quickly build high-performance models, it has a high potential for use in the medical and healthcare fields. Therefore, in this paper, we aim to validate the effectiveness of various AutoML frameworks using structured and unstructured medical data such as electronic medical records, medical images, and signal data. More specifically, we compare the performance of representative AutoML frameworks and simple handcrafted models in terms of various metrics such as the area under the curve, accuracy, and F1 score. Our experimental results show that AutoML effectively handles structured and unstructured data in general, but it still needs to be further improved to deal with data that is imbalanced or requires special preprocessing, such as signal data.
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