Artificial Intelligence
The Explainable Sentiment Analysis Dataset provides annotated sentiment classification data for Amazon Reviews and IMDB Movie Reviews, facilitating the evaluation of sentiment analysis models with a focus on explainability. It includes ground-truth sentiment labels, model-generated predictions, and fine-grained classification results obtained from various large language models (LLMs), including both proprietary (GPT-4o/GPT-4o-mini) and open-source models (DeepSeek-R1 full and distilled models).
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Meituan Bench (MTB) is an enterprise-level benchmarking tool designed for time-series forecasting in real-world business scenarios. Built upon an open-source dataset derived from 10,000 real-world services across various business units, MTB provides a standardized evaluation framework for time-series prediction models. The dataset includes 200 representative services, capturing diverse traffic patterns essential for assessing forecasting performance.
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The TripAdvisor online airline review dataset, spanning from 2016 to 2023, provides a comprehensive collection of passenger feedback on airline services during the COVID-19 pandemic. This dataset includes user-generated reviews that capture sentiments, preferences, and concerns, allowing for an in-depth analysis of shifting customer priorities in response to pandemic-related disruptions. By examining these reviews, the dataset facilitates the study of evolving passenger expectations, changes in service perceptions, and the airline industry's adaptive strategies.
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This data set contains all relevant data content required in the experiment, and all data are stored in.mat format. This format is a commonly used data file format in MATLAB software, which facilitates efficient data processing and analysis. Users can import these.mat files directly without additional data conversion or processing, saving time and improving productivity. In addition, the content in the dataset has been carefully curated to ensure the integrity and accuracy of the data, which is suitable for use in various experiments and research work.
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Contains 80 questions of LeetCode weekly and bi-weekly contests released after March 2024.
Each question contains an average of 644 test cases, as well as programming solutions in Python language collected from the official LeetCode website. The input fields of the data set contain function headers and natural language descriptions, which are mainly used to evaluate the ability of large models to solve programming problems according to requirements.
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Heart Rate Variability (HRV) parameters provide valuable insights into the autonomic nervous system’s regulation of the cardiovascular system in response to various physiological conditions, particularly during dynamic tasks. This dataset consists of ECG recordings obtained during the execution of three dynamic tasks based on the three-dimensional movement of the upper limb. The data is available in both .XLSX and .CSV formats, containing 166 rows and 21 columns, all of which correspond to HRV parameters such as RR intervals, heart rate, and frequency-domain measures.
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This dataset can be used for vulnerability detection. This repository is devised to explain vulEmbedding,
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First "altKlasörTaraTahmin.R" file is for searching code files to generate suitable numeric matrix,
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createKeywordMatrix.R is for generating keyword matrix, thereby checking vulnerabilities,
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sphericalLabeling.R is for generating spherical labeling.
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Final you can run deepnetVersion2.R to produce vulnerability prediction.
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This dataset comprises 2,052 .jpeg image samples from 74 students, offering a comprehensive portrayal of student life. Capturing academic, extracurricular, and social dimensions, it provides insights into diverse learning environments, activities, and interactions. From classrooms to sports fields, cultural events to social gatherings, the dataset encapsulates the multifaceted nature of student experiences. Researchers can utilize these images to explore educational dynamics, analyze social behaviors, and develop algorithms for image recognition and analysis.
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This study includes three commercially available 3D printers for soft material printing based on the Material Extrusion (MEX) AM process. The samples are 3D printed for six different AM process parameters obtained by varying layer height and nozzle speed. The novelty part of the methodology is incorporating an AI-based image segmentation step in the decision-making stage that uses quality inspected training data from the Non-Destructive Testing (NDT) method.The performance of the trained AI model is compared with the two software tools based on the classical thresholding method.
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