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Raspberry Pi benchmarking dataset monitoring CPU, GPU, memory and storage of the devices. Dataset associated with "LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers" paper
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Several works attempted to establish procedures to individuate bugs, defects or anomalies during the different phases of software development, especially in the implementation phase. The mere detection of anomalies is not sufficient, though, at least until they get fixed. Corrective actions can be formulated to remove anomalies and enhance the software quality. Preventive actions are equally important in as much as they avoid the emergence and recurrence of anomalies in the future.
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Abstract— Objective: Recently, pupil oscillation synchronized with a steady visual stimulus was employed for an input of an interface. The system is inspired by steady-state visual evoked potential (SSVEP) BCIs, but it eliminates the need for contact with the participant because it does not need electrodes to measure electroencephalography. However, the stimulation frequency is restricted to being below 2.5 Hz because of the mechanics of pupillary vibration and information transfer rate (ITR) is lower than SSVEP BCIs.
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This dataset contains survey results collected from new recommendation system. This dataset asks about how the people accept recommendation systems from the AI trustworthiness and recommendation quality aspect.
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It is a combination of statistics from various cricket information sources like cricbuzz, espncricinfo, etc. The dataset has records right from the originating year of the IPL i.e., 2008 up to the previous year 2021.
The dataset has been encoded using a manual encoding technique where certain columns have been altered based on their value type
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Dataset includes the muscle activation results for each participant in the form of 3 CSV files, with one file each for unassisted, ankle-assisted, and hip-assisted muscle activations.
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With the large-scale adaptation of Android OS and ever-increasing contributions in the Android application space, Android has become the number one target of malware authors. In recent years, a large number of automatic malware detection and classification systems have evolved to tackle the dynamic nature of malware growth using either static or dynamic analysis techniques. Performance of static malware detection methods degrades due to the obfuscation attacks.
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This data set contains nifty 50 companies and all their details of Open, Volume, Close, Adj Close, and name of the company.
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The ANDON tower uses the low-cost NRF24L01 transceiver to send the experimental data from the sensor node to the coordinator node, with a computer connected to the coordinator node to store the data in a CSV file. The transceiver uses the SPI protocol for communication with the ANDON tower microcontroller. Its maximum range is 30 meters, however the maximum distance in the experimental scenario is 16 m. The transceiver works in the 2.4GHz free band and allows setting the transmission power in -18dBm, -12dBm, -6dBm and 0dBm.
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