Artificial Intelligence
We introduce an English Twitter dataset designed for the detection of online drug use, comprising 112,057 tweets accompanied by metadata. This dataset underwent manual annotation by a team of expert annotators consisting of around 30 members, these annotators, possessing diverse multidisciplinary backgrounds and expertise, committed over six months to meticulously label each tweet.
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Our dataset encompasses a comprehensive collection of Azerbaijani news texts from the Azertac (https://azertag.az/) State Agency, drawn from a variety of news articles.
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The dataset and source code used in paper "Pick the Better and Leave the Rest: Leveraging Multiple Retrieved Results to Guide Response Generation".
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Recognizing and categorizing banknotes is a crucial task, especially for individuals with visual impairments. It plays a vital role in assisting them with everyday financial transactions, such as making purchases or accessing their workplaces or educational institutions. The primary objectives for creating this dataset were as follows:
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This dataset contains video-clips of five volunteers developing daily life activities. Each video-clip is recorded with a Far InfraRed (FIR) camera and includes an associated file which contains the three-dimensional and two-dimensional coordinates of the main body joints in each frame of the clip. This way, it is possible to train human pose estimation networks using FIR imagery.
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This compressed package is essential for KHDNN as it includes various components necessary for its functionality. Inside, you will find an Embedding, knowledge graphs, a user-item bipartite graph, and training records specifically tailored to the book-crossing dataset. These elements are crucial for KHDNN to operate effectively and provide accurate recommendations to users based on their preferences and interactions with books.
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Contains the benchmark Bayesian network dataset, which uses the seed of Bayesian networks from https://www.bnlearn.com. Some of the data comes from https://pages.mtu.edu/~lebrown/supplements/mmhc_paper/mmhc_index.html. And other datasets from the UCI that contain mixed data.
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