In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource lan...
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This study addresses the imperative challenge of enhancing school enrolment in South Africa by investigating the dynamics of mobile learning technologies, internet access, and key socio-economic variables. Spanning 35...
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This study addresses the imperative challenge of enhancing school enrolment in South Africa by investigating the dynamics of mobile learning technologies, internet access, and key socio-economic variables. Spanning 35 years (1998–2022), the research draws from the National Digital and Future Skills Strategy, aiming to provide insights into the factors influencing educational access. Employing an analytical framework that integrates Autoregressive Distributed Lag (ARDL), dynamic ARDL (dynARDL) simulations, and Kernel-based Regularized Least Squares (KRLS) machine learning, the study finds that economic prosperity, as represented by real GDP per capita, positively influences secondary school enrolment. Mobile phone subscribers emerge as a significant driver, emphasizing the transformative potential of digital technologies. Surprisingly, an inverse relationship between internet users and enrolment prompts a reassessment of the role of internet access in education. The dynARDL simulations introduce counterfactual shocks, highlighting the positive impact of a 10% increase in mobile subscribers and the nuanced consequences of changes in internet users. KRLS analysis reinforces the significance of economic indicators, digital technologies, and human development in shaping enrolment. Drawing policy implications, the study advocates targeted investments in digital infrastructure, strategic approaches to internet access optimization, and policies fostering sustainable economic growth.
While Large Language Models (LLMs) excel in zero-shot Question Answering (QA), they tend to expose biases in their internal knowledge when faced with socially sensitive questions, leading to a degradation in performan...
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Stunting is a condition where a child's height significantly falls below the average for their age, primarily due to prolonged malnutrition and inadequate nutrient intake. This condition poses long-term challenges...
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ISBN:
(数字)9798331513320
ISBN:
(纸本)9798331513337
Stunting is a condition where a child's height significantly falls below the average for their age, primarily due to prolonged malnutrition and inadequate nutrient intake. This condition poses long-term challenges, affecting both physical growth and cognitive development. This study focuses on developing a predictive model using the C4.5 Decision Tree algorithm to classify the stunting status of children under five. The dataset comprises nutritional status data of toddlers collected from the Berastagi Health Center. Incorporating attributes such as gender, birth weight and height, age in months, weight, height, and the height-to-age ratio. The model aims to enhance the accuracy of stunting classification to support targeted prevention and treatment strategies. When compared to Naive Bayes, the Decision Tree C4.5 demonstrated superior performance, achieving an F1 score and average accuracy of 86%. This reflects a robust balance between precision and recall, underscoring its reliability in identifying children at risk of stunting. The research highlights the critical role of data-driven methodologies in public health. With its high accuracy, the model serves as a promising tool for healthcare providers, enabling more effective interventions to reduce stunting prevalence and improve nutritional outcomes among children under five years of age.
Previous benchmarks for evaluating large language models (LLMs) have primarily emphasized quantitative metrics, such as data volume. However, this focus may neglect key qualitative data attributes that can significant...
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In the educational domain, extracting insights from student-written text has shown to be valuable for instructors. Efficiently summarizing students' reflections in a course offers instructors valuable insights to ...
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ISBN:
(纸本)9798400705328
In the educational domain, extracting insights from student-written text has shown to be valuable for instructors. Efficiently summarizing students' reflections in a course offers instructors valuable insights to enhance students' learning experience. Therefore, quickly understanding students' impressions about the course could be very helpful to instructors for in-time and/or personalized one-on-one discussions. Achieving this often involves using natural language processing (NLP) techniques Understanding capabilities of LLMs through a series of comparative experiments involving prompt engineering is the goal of this work. We compare the summarization outputs of GPT-4 with an experimentally optimized temperature of 0.75 through a variety of experiments that include different levels of prompts, starting with base level and proceeding to increase context in the prompt. We evaluate and compare the outputs of these summaries based on a rubric from literature, evaluated by human annotators. Our findings suggest that providing more detailed context prompts help LLMs uncover less frequent and obvious student challenges and provide more detailed explanations. One notable finding showed how sensitive the LLM approach is to the distribution of the challenge types in students' reflections. In other words, all prompts regardless of their contextual details faced issues due to this misrepresenting of student challenges distributions, sometimes overstating their occurrence frequency. Therefore, further study is required to refine the data distribution impact. Despite this, the approach shows much potential to extract useful knowledge quickly. It offers valuable insights to instructors and could help in supporting students more effectively.
Background : Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biol...
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Background : Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation. Results : Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability. Conclusions : Our empirical study show
This research investigates the application of Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, paired with gradient-based optimization techniques for dynamic pricing in e-commerce...
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Augmented reality (AR) is increasingly being integrated into the home renovation sector, yet current AR solutions often fail to address the challenges of soft decoration design. This study introduces SmartDe...
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This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning m...
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