Machine Learning

Egocentric vision is important for environment-adaptive control and navigation of humans and robots. Here we developed ExoNet, the largest open-source dataset of wearable camera images of real-world walking environments. The dataset contains over 5.6 million RGB images of indoor and outdoor environments, which were collected during summer, fall, and winter. 923,000 of the images were human-annotated using a 12-class hierarchical labelling architecture.

Time Scale Modification (TSM) is a well-researched field; however, no effective objective measure of quality exists.  This paper details the creation, subjective evaluation, and analysis of a dataset for use in the development of an objective measure of quality for TSM. Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales, while the testing component contains 20 source files processed using three additional methods at four time scales.

Since there is no image-based personality dataset, we used the ChaLearn dataset for creating a new dataset that met the characteristics we required for this work, i.e., selfie images where only one person appears and his face is visible, labeled with the person's apparent personality in the photo.

These datasets are used to detect Intrusions in Controller Area Network (CAN) bus. Intrusions are detected using various Machine Learning and Deep Learning algorithms.

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