Artificial Intelligence

This dataset contains both the artificial and real flower images of bramble flowers. The real images were taken with a realsense D435 camera inside the West Virginia University greenhouse. All the flowers are annotated in YOLO format with bounding box and class name. The trained weights after training also have been provided. They can be used with the python script provided to detect the bramble flowers. Also the classifier can classify whether the flowers center is visible or hidden which will be helpful in precision pollination projects.

The public UA-DETRAC data set was used to train the vehicle target detection model in this study. This data set was captured predominantly in the Beijing-Tianjin-Hebei area. Using a Canon EOS 550 camera, 10 hours of video were captured at 25 frames per second, and then the footage was processed. Each frame had a resolution of 960*540 pixels. The UA-DETRAC data set contained 8,259 manually labeled vehicles, with a total of 1,210,000 labeled vehicles. The labeled types included cars, buses, trucks, etc.

This dataset includes input dynamics (keystroke, touch, and mouse), affect data (physiological measurements), video, and text data collected from research participants aged 6 and older. The dataset includes data from a diverse set of participants, identifying as Asian, White, Middle Eastern or North African, Black or African American, and Hispanic, Latino, or of Spanish origin). Additionally, participants represent both iOS and Android users.

This work selects two real datasets, namely Diginetica and Yoochoose. Diginetica is employed in CIKM Cup 2016, which contains user's transaction data. Yoochoose is released in Recsys Challenge 2015 as a public dataset and mainly consists of click data within six months. For better recommendation, we follow previous work and preprocess the real datasets.