Artificial Intelligence

The Deepfake-Synthetic-20K dataset significantly contributes to digital forensics and deepfake detection research. It comprises 20,000 high-resolution, synthetic human face images generated using the advanced StyleGAN-2 architecture. This dataset is designed to support the development and evaluation of machine-learning models that can differentiate between real and artificially synthesized human faces. Each image in the dataset has been meticulously crafted to ensure a diverse representation of age, gender, and ethnicity, reflecting the variability seen in global human populations.

<p>Mixed critical applications are real-time applications that have a combination of both high and low-critical tasks. Each task set has primary tasks and two backups of high-critical tasks. In the work carried out, different synthetic workloads and case studies are used for extracting the schedule and overhead data on a real-time operating system. The utilization of the task sets lies between 0.7 to 2.4.The Linux kernel is recompiled with Litmus-RT API to monitor the scheduling decisions and also measure the overheads incurred.

 Publicly available dataset weibo_senti_100k, which consists of Weibo comments, verify the validity of the model. We have assigned the label of 0 to negative semantics, 1 to neutral statements, and 2 to positive semantics in the dataset,comments data is divided into a training set, a test set and a validation set, distributed in a ratio of 3:1:1 to facilitate the training and evaluation of our machine learning model.

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We're excited to present a unique challenge aimed at advancing automated depression diagnosis. Traditional methods using written speech or self-reported measures often fall short in real-world scenarios. To address this, we've curated a dataset of authentic depression clinical interviews from a psychiatric hospital.