Machine Learning
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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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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We employed a case study research approach to gather the factors for troubled software projects from the existing literature to generate an innovative dataset. A comprehensive dataset that serves as a foundational reference for future investigations. We extracted incidents from case study data, generated open codes, and organized these open codes into 18 problem categories and 27 solution categories. The mapping between open codes, axial codes and phases is documented in dataset. The codes encapsulate the behavioral patterns or actions of a team that initiate or cause is
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ARImulti-mic: real-world speech recordings on a humanoid robot (ARI)
This dataset includes “real-world” experiments. A recording campaign was held in the acoustic laboratory at Bar-Ilan University. This lab is a [6×6×2.4]m room with a reverberation time controlled by 60 interchangeable panels covering the room facets.
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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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The dataset, titled "SensorNetGuard: A Dataset for Identifying Malicious Sensor Nodes," comprises 10,000 samples with 21 features. It is designed to facilitate the identification of malicious sensor nodes in a network environment, specifically focusing on IoT-based sensor networks.
General Metrics
§ Node ID: The unique identifier for each node.
§ Timestamp: The time at which data or a packet is sent or received.
§ IP Address: Internet Protocol address of the node.
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RITA (Resource for Italian Tests Assessment), is a new NLP dataset of academic exam texts written in Italian by second-language learners for obtaining the CEFR certification of proficiency level.
RITA dataset is available for automatic processing in CSV and XML format, under an agreement of citation.
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