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This dataset integrates textual, financial, and macroeconomic indicators to support research on bank failure prediction and financial distress forecasting in Vietnam. It includes financial news from the BKAI News Corpus Dataset (2009–2023) and financial crisis data from "A Dataset for the Vietnamese Banking System (2002–2021)" (Tu Le et al., 2022), covering crisis-related events such as restructuring, special control, mergers, and acquisitions.
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This dataset supports the research paper "Synchronized Waveform Monitoring Unit Application: Lissajous DQ Curve to Improve Situational Awareness in Power Distribution Networks". It contains electromagnetic transient (EMT) simulation results from the IEEE 33-bus distribution system, including:
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This dataset contains high-resolution solar and wind measurement data collected from the Feni region, Bangladesh, spanning from 2017 to 2019. Logged at a 1-minute interval, the dataset provides a comprehensive record of atmospheric and meteorological conditions, essential for renewable energy analysis, climatological studies, and resource assessment.
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This dataset contains high-resolution wind measurement data collected from 22 channels at varying heights, providing valuable insights for wind energy assessment, atmospheric research, and meteorological studies. The dataset includes wind speed, wind direction, and environmental parameters measured at multiple altitudes ranging from 10m to 120m. Each channel records parameters such as average wind speed, standard deviation, minimum and maximum values, gust speed, and wind vane direction. Additionally, atmospheric parameters such as temperature, relative humidity, and pressure are included.
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Artificial Intelligence (AI) has increasingly influenced modern society, recently in particular through significant advancements in Large Language Models (LLMs). However, high computational and storage demands of LLMs still limit their deployment in resource-constrained environments. Knowledge distillation addresses this challenge by training a smaller language model (student) from a larger one (teacher). Previous research has introduced several distillation methods for both generating training data and training the student model.
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Artificial Intelligence (AI) has increasingly influenced modern society, recently in particular through significant advancements in Large Language Models (LLMs). However, high computational and storage demands of LLMs still limit their deployment in resource-constrained environments. Knowledge distillation addresses this challenge by training a smaller language model (student) from a larger one (teacher). Previous research has introduced several distillation methods for both generating training data and training the student model.
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This dataset contains anonymized Twitter data related to tourist activities in Bangkok, Thailand. It was collected to analyze travel behavior, activity preferences, and temporal patterns during events like the Songkran festival. The dataset includes timestamped activity classifications, geographic information at a generalized level, and extracted named entities relevant to tourism. The dataset is from 2019-04-05 to 2019-04-24.
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These are the daily closing prices of four stock indices including Shanghai Securities Composite Index (SSEC) and the Shenzhen Securities Component Index (SZI) from China, the Straits Times Index (STI) from Singapore, and the Standard & Poor 500 Index (SPX) from the United States. The SSEC data is from December 19,1990 to May 25, 2023. The data of SZI is from April 3, 1991 to May 25, 2023. The STI data is from December 3, 1990, to May 25, 2023, and the data of SPX is from December 3, 1990 to May 25, 2023.
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Māori enterprises are pivotal to the economic and cultural prosperity of Aotearoa, yet predictive analysis of business outcomes tailored to these enterprises remains underexplored. This research examines the application of recurrent neural networks (RNNs) and transformer architectures to forecast key performance indicators (KPIs) for Māori small and medium-sized enterprises (SMEs).
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This dataset comprises user-generated content from Stack Overflow, including post bodies, post tags, and user engagement metrics such as upvotes and downvotes. The data was collected from the stack exchange explorer based on user defined categories and other criteria like reputation and badges as explained in our work. It was collected to support research in technology and emotion analysis, focusing on understanding user interactions and sentiments within online communities.
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