Artificial Intelligence
The rapid growth of online shopping and e-commerce platforms has led to an explosion of product reviews. These reviews often contain valuable information about users’ opinions on various aspects of the products, including comparisons between different devices. Understanding comparative opinions from product reviews is crucial for manufacturers and consumers alike. Manufacturers can gain insights into the strengths and weaknesses of their products compared to competitors, while consumers can make more informed purchasing decisions based on these comparative insights.
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e used a mixed dataset\cite{ye2023geneface}in our experiments, where part of the data was referenced from the publicly available dataset provided by GaussianTalking\cite{li2025talkinggaussian}, and additional data was collected by ourselves. Specifically, we selected four high-definition talking video clips from the publicly available dataset, including two male portraits, "Macron" and "Obama" and one female portrait, "May". These video clips are centered on the subject, with an average length of 6500 frames and a frame rate of 25 FPS.
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The Tunnel Cable Fire dataset is derived experimentally, this dataset contains images of cable flames at different stages, different cable layers, and different wind speeds, with a special focus on computer vision tasks such as fire detection and segmentation. These images have been enhanced with mosaic data for a total of 1812 datasets, including single and double layer cable fire images in the case of no wind and wind speed of 2.7m/s.
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AI was begun to increasingly part of EFL education introducing novelties, issues and opportunities. Current studies explore many possibilities, such as employing federated learning as a protection of data privacy and the implementation of ChatGPT in multilingual learning. This article offers comprehensive analysis of how AI could transform pedagogy, improve writing skills and motivate students through evaluating the novelty, existing voids, need, and implications of many the most promising studies.
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This dataset contains human motion data collected using inertial measurement units (IMUs), including accelerometer and gyroscope readings, from participants performing specific activities. The data was gathered under controlled conditions with verbal informed consent and includes diverse motion patterns that can be used for research in human activity recognition, wearable sensor applications, and machine learning algorithm development. Each sample is labeled and processed to ensure consistency, with raw and augmented data available for use.
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This dataset is used for the automated assessment of open-ended exam questions in the online course Introduction to Software Engineering at Constantine the Philosopher University in Nitra. The dataset originates from the Moodle Learning Management System (LMS) and includes responses to eight open-ended questions centered on fundamental terminology related to the Scrum framework, a key methodology in agile software development.
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The constructed dataset comprises two representative scene types. The open-pit coal mining scenes (OCMS) include 201 original samples, which yielded 3247 standardized 224×224-pixel samples after target purification. The dataset is split 8:1:1 into training (2597 samples), validation (349 samples), and testing (301 samples). The composite coal-related scenes (CCS) include 272 original samples, which yielded 1531 standardized samples, also divided 8:1:1 into training (1223), validation (154), and testing (154).
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With the continuous improvement in the computational capabilities of edge devices such as intelligent sensors in the Industrial Internet of Things, these sensors are no longer limited to mere data collection but are increasingly capable of performing complex computational tasks. This advancement provides both the motivation and the foundation for adopting distributed learning approaches. This study focuses on an industrial assembly line scenario where multiple sensors, distributed across various locations, sequentially collect real-time data characterized by distinct feature spaces.
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A fact-checking dataset focused exclusively on quantitative claims. It includes 33,422 fact-checked claims featuring comparative, statistical, interval, and temporal entities. Each claim is accompanied by detailed metadata and supporting evidence, providing a robust foundation for automated verification. This dataset contains claims and their corresponding fact-checking details. It is provided in JSON format, with each entry containing information about a claim, its processed version, fact-checking results, and relevant metadata.
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This dataset extends the standard Myers-Briggs Type Indicator (MBTI) dataset, widely available on Kaggle, by incorporating advanced data augmentation techniques leveraging GPT-based Transformers. The augmentation addresses inherent class imbalance and data sparsity issues in the original dataset, significantly enriching the volume and diversity of textual samples while maintaining linguistic and contextual fidelity to the MBTI personality types.
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