Artificial Intelligence

Computer vision can be used for environment-adaptive control of robotic leg prostheses and exoskeletons. However, small-scale and private training datasets have impeded the development and dissemination of image classification algorithms (e.g., convolutional neural networks) to recognize the walking environment. To address these limitations, we developed ExoNet, a large-scale hierarchical dataset of wearable camera images (i.e., egocentric perception) of real-world walking environments.

The PPI datasets were collected from four different sources: DIP, MIPS, Gavin, and Krogan. All self-interactions and repeated interactions were filtered.  The essential proteins were collected from the following four different databases: MIPS,SGD,DEGand SGDP (http://www.sequence.stanford.edu/group/). Gene expression data were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) with accession number GSE3431.

We conduct experiments with the datasets of SemEval 2014 task4 to evaluate our model, the SemEval 2014 datasets consist of reviews in two categories: Restaurant and Laptop, and the reviews contains three labels of sentiment polarity: {positive, negative, neutral}

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.