artificial intelligence
FormAI is a novel AI-generated dataset comprising 112,000 compilable and independent C programs. All the programs in the dataset were generated by GPT-3.5-turbo using dynamic zero-shot prompting technique and comprises programs with varying levels of complexity. Some programs handle complicated tasks such as network management, table games, or encryption, while others deal with simpler tasks like string manipulation. Each program is labelled based on vulnerabilities present in the code using a formal verification method based on the Efficient SMT-based Bounded Model Checker (ESBMC).
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This dataset consists of “.csv” files of 4 different routing attacks (Blackhole Attack, Flooding Attack, DODAG Version Number Attack, and Decreased Rank Attack) targeting the RPL protocol, and these files are taken from Cooja (Contiki network simulator). It allows researchers to develop IDS for RPL-based IoT networks using Artificial Intelligence and Machine Learning methods without simulating attacks. Simulating these attacks by mimicking real-world attack scenarios is essential to developing and testing protection mechanisms against such attacks.
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A CNC adapter was utilized together with the software established as part of the GRBL project to operate the CNC adapter, and two data sets were produced for the physical model in order to build the linear and circular motion models. The parameters for motion quantity, motion duration, and feed rate are in the data set.
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The advancements in the field of telecommunications have resulted in an increasing demand for robust, high-speed, and secure connections between User Equipment (UE) instances and the Data Network (DN). The implementation of the newly defined 3rd Generation Partnership Project 3GPP (3GPP) network architecture in the 5G Core (5GC) represents a significant leap towards fulfilling these demands. This architecture promises faster connectivity, low latency, higher data transfer rates, and improved network reliability.
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The signals of a recording belonging to the Frontside task are shown, the first two corresponding to the electromyography sensors while the others correspond to the infrared and red LED readings of the pulse oximetry sensor.
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The "Noisy Imperfect Partial Symbolic Sketches" (NIPSS) dataset provides symbolic sketches generated from the texture extraction method described in [1] when the inputs are associated with, either Imagenet animals [2] that have been preprocessed by [3], or the CelebAMask-HQ person faces described in [4].
Any sketch is an image containing a multichannel (artificial color) binary sequence of information, where artificial colors have consisted in concatenating the results of different regularizers from [1].
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This dataset is created by an experimental setup of a DC-PV -Battery-based grid-connected distributed generation system. This dataset is split into four parts such as irradiance, and temperature, which were measured by a meteorological station, and lastly, PV output current and voltage acquired by an inverter. Furthermore, we can have a chance to obtain the output PV power by multiplying current and voltage. The dataset has 288 elements for one day as a time series since the station obtains the data within five minutes. However, the whole dataset has three days of data with 864 elements.
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BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).
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A dataset of Global Positioning System (GPS) spoofing attacks is presented in this article. This dataset includes data extracted from authentic GPS signals collected from different locationsto emulate a moving and a static autonomous vehicle using a universal software radio peripheral unit configured as a GPS receiver. During the data collection, 13 features are extracted from eight-parallel channels at different receiver stages (i.e., acquisition, tracking, and navigation decoding).
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