*.csv

The synthetic data is generated loosely following the concepts developed by Skomedal and Deceglie (2020) 

"https://doi.org/10.1109/JPHOTOV.2020.3018219" , 

"https://github.com/asmunds/simulate_pv_time_series"

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.

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.

Recently, contactless hand biometrics authentication has become increasingly popular among biometric researchers. These systems offer several advantages over traditional hand identification systems, including ease of capture and affordability, as they do not require the user’s hand to make direct contact with the sensor.

The Mobile Hand Biometrics (MHB) dataset includes images of fingerprint, palmprint, and hand geometry. These images are captured with a mobile camera without any physical contact, with no lighting conditions, and in free positions.