Artificial Intelligence
Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic nature of the graph structure, resulting in sub-optimal node and graph representations. To address this limitation, we propose a novel \textbf{G}raph structure \textbf{P}rompt \textbf{L}earning method (GPL) to enhance the training of GNNs, which is inspired by prompt mechanisms in natural language processing.
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This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014.
This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).
These data have been reduced to extract the k-core, such that each of the remaining users and items have k reviews each.
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We propose a more challenging dataset known as Weibo23. By amalgamating all available fake news from the Weibo Management Community until March 2023 with existing samples from public datasets [1], we formed a comprehensive collection of fake news for Weibo23. Fabricated news articles were thoroughly examined and authenticated by certified experts.
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The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification.
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The steel tube dataset comprises comprehensive information on various attributes related to steel tubes, encompassing dimensions, material composition, manufacturing processes, and performance characteristics. This dataset facilitates in-depth analysis of steel tube properties, aiding researchers, engineers, and industry professionals in optimizing designs, ensuring structural integrity, and advancing materials science in the context of steel tube applications.
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The dataset contains 66376 image data of 124 pedestrians, each image contains two pedestrians. Each pedestrian has 6 sets of gait sequences (NM#01-NM#06). Each set of images is taken with the pedestrian's walking route parallel to the camera's perspective. The pedestrian is not carrying anything while walking. The gait sequences of each group of pedestrians are divided into training sets and test sets.The dataset consists of two parts, an image file and an XML file. The image folder contains all the image data, and the label folder contains all the label files.
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To make it possible for the model to distinguish the connection between requirements and the software architecture pattern during training using GAI, the expected response for a specific requirement was labeled with a software architecture pattern with the prefix “Software architecture pattern: ” and its explanation with the prefix “Explanation: ”.
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Zip includes commented Jupyter notebook with associated data files. Abstract for paper is:
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