Artificial Intelligence
The analysis suggests various innovative ideas to improve English instruction, with an emphasis on current technologies and an inclusive approach. These include using AI as a peer tutor, exploring virtual reality to create immersive learning environments, analyzing data to create customized learning materials, integrating local cultural values into instructional materials, implementing a technology-based inclusive learning model, implementing a policy for digital advancement in education, and making the most of contemporary learning resources.
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Our paper presents a novel approach to enhance vulnerability descriptions using code-based methods for predicting missing phrase-based concepts. We leverage Large Language Models (LLMs) integrated with 1-hop relationship analysis to address hallucination issues. The code processes Textual Vulnerability Descriptions (TVDs) and extracts relevant security-related concepts such as vulnerability type, root cause, and impact. Our methodology involves generating predictions, verifying these with external knowledge sources, and filtering inaccurate results.
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This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJong Competition Rules. This dataset provides 98208 self-play history data of a MahJong agent using MahJon
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All the healthcare facilites in this dataset were collected from the MOH 2018 list of Uganda healthcare facilites (https://library.health.go.ug/sites/default/files/resources/National%20Health%20Facility%20MasterLlist%202017.pdf) Additional features were scraped using the Google Maps API and additionally from some of the websites of the healthcare facilities themselves.
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This paper describes a dataset of droplet images captured using the sessile drop technique, intended for applications in wettability analysis, surface characterization, and machine learning model training. The dataset comprises both original and synthetically augmented images to enhance its diversity and robustness for training machine learning models. The original, non-augmented portion of the dataset consists of 420 images of sessile droplets. To increase the dataset size and variability, an augmentation process was applied, generating 1008 additional images.
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The datasets used for TKDE manuscript. These datasets are widely recognized and validated within the summarization research community, and using these well-established datasets ensures that our results are comparable with the vast majority of related work.In each dataset, we use 80% as the training set, while the remaining 20% as the test data. The range of datasets used for collecting knowledge is also consistent with the above. For the large dataset, we only use a part of it because collecting knowledge is time-consuming.
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Imaging, Pathology, and Psychological Research on Neuroimmune Gastrointestinal Diseases
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The Hindi Spam SMS Dataset comprises 3,894 messages, each labeled as either spam or ham. This dataset was meticulously curated with contributions from students who encountered these messages daily. The messages were collected from their experiences and those shared by friends and peers, ensuring a diverse and realistic representation of SMS communication in Hindi. It offers a representative sample of real-world Hindi text messages for analysis. The dataset primarily contains messages written in Hindi, reflecting its origin's linguistic and cultural context.
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The lack of an objective and ready-to-use tool for preoperative planning in C2 pedicle screw placement surgery is notable. We developed C2-Net, a deep learning model for rapidly and accurately assessing C2 pedicle screw placement feasibility from CT images. C2-Net incorporates image segmentation and screw placement probability assessment modules.
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The dataset used in this study is a comprehensive, publicly available dataset designed for research purposes, encompassing various features related to the public sector. It includes data on all genders (male and female) from diverse regions, age groups, and educational levels. The dataset was sourced from CAPMAS reports (2018-2023), which provide quarterly updates on the labor force, including details on unemployment assessments categorized by geographical distribution.
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