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The dataset contains Moodle Log Reports of two batches of students. They used Moodle platform for their solo and team activities. The column includes Date, Time, User full name, Affected User, Event Context, Component, Event Name, Description, Origin and IP Address. The sensitive data like User name and IP address are removed in this Draft version dataset. Pivot table is used for filtering the data and visual charts and graphs are applied for understanding the data.
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284 Views
Wild-SHARD presents a novel Human Activity Recognition (HAR) dataset collected in an uncontrolled, real-world (wild) environment to address the limitations of existing datasets, which often need more non-simulated data. Our dataset comprises a time series of Activities of Daily Living (ADLs) captured using multiple smartphone models such as Samsung Galaxy F62, Samsung Galaxy A30s, Poco X2, One Plus 9 Pro and many more. These devices enhance data variability and robustness with their varied sensor manufacturers.
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536 Views
The prototype of the calibration is verified with a 12-bit SAR ADC manufactured in 28-nm standard CMOS process. It is based on non-binary weights differential SAR ADC with bottom-plate sampling. This data was captured using a logic analyzer. The data for fast Fourier transform (FFT) is an input 1 MHz sine wave at 50MS/s. The signal input amplitude is 15dbm. The sampling points are 131072. The MATLAB code includes both the original weight and the calibration weight.
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19 Views
Our dataset comes from the paper called "XBlock-ETH: Extracting and exploring blockchain data from Ethereum", the datasets are the on-chain data obtained by running all nodes of Ethereum. For the purpose of the experiment, we only selected block transactions from 0-2,000,000 blocks. These datasets are sufficient to support the experiments. You can get more details and analysis from the paper called "XBlock-ETH: Extracting and Exploring Blockchain Data from Ethereum". The citation of the paper as follows: P. Zheng, Z. Zheng, J. Wu, and H.-N.
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230 Views
The synthetic data is generated loosely following the concepts developed by Skomedal and Deceglie (2020)
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233 Views
The dataset is based on the latent faults detected by the popular OSS static code analysis tool, sonarQube Community Edition. The dataset is populated using the latent faults found in popular Java software from the open source repository GitHub . This dataset was specifically developed to identify the significant latent faults that affect the reliability of Java programs. This dataset can be used in its current form to conduct experiments with machine learning algorithms and to infer new reliability characteristics of Java programs. Please refer to the documents associated with sona
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7 Views
Classifying the driving styles is of particular interest for enhancing road safety in smart cities. The vehicle can assist the driver by providing advice to increase awareness of potential dangers. Accordingly, dissuasive measures, such as adjusting insurance costs, can be implemented. The service is called Pay-As-You-Drive insurance (PAYD), and to address it, the paper introduces a method for constructing a database of simulated driver behaviors using the Simulation of Urban MObility Simulation of Urban MObility (SUMO) simulator.
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314 Views
This data reflects the prevalence and adoption of smart devices. The experimental setup to generate the IDSIoT2024 dataset is based on an IoT network configuration consisting of seven smart devices, each contributing to a diverse representation of IoT devices. These include a smartwatch, smartphone, surveillance camera, smart vacuum and mop robot, laptop, smart TV, and smart light. Among these, the laptop serves a dual purpose within the network.
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1495 Views
This dataset focuses on the redevelopment and psychometric evaluation of the Adversity Response Profile for Indian Higher Education Institution (ARP-IHEI) students, emphasizing its importance in understanding how individuals respond to adversity. The data were gathered from a sample of 122 second year students at school of computing, MIT Art, Design and Technology University. The psychometric properties were rigorously examined using factor analysis.
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64 Views
The rise in Generative Artificial Intelligence technology through applications like ChatGPT has increased awareness about the presence of biases within machine learning models themselves. The data that Large Language Models (LLMs) are trained upon contain inherent biases as they reflect societal biases and stereotypes. This can lead to the further propagation of biases. In this paper, I establish a baseline measurement of the gender and racial bias within the domains of crime and employment across major LLMs using “ground truth” data published by the U.S.
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354 Views