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.

With the development of audio synthesis techniques, the most state-of-art synthesis methods based on  Generative Adversarial Network(GAN) have been proposed. Whether the automatic speaker verification (ASV) systems are vulnerability to the GAN based synthesized audios is urgently needed to be verified. We present a publicly available set of GAN based synthesized audios generated by some open source schemes (WaveGAN,TifGAN,GANSynth,MelGAN), which allows researches to verify impact of the GAN-synthetic audio on security of ASV systems.