The data set includes three sub-data sets, namely the DAGM2007 data set, the ground crack data set, and the Yibao bottle cap defect data set, which are divided into a training set and a test set, in which the positive and negative samples are unbalanced.

Computer vision can be used for environment-adaptive control of robotic exoskeletons and prostheses. However, small-scale and private training datasets have impeded the development of image classification algorithms (e.g., convolutional neural networks) to recognize the walking environment. To address these limitations, we developed ExoNet, a large-scale 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}