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.
A Chinese dataset for table-to-text generation named WIKIBIOCN which inculeds 33,244 biography sentences with related tables from Chinese Wikipedia (July 2018).
The dataset is divided into training set (30,000), verification set (1000) and test set (2,244).
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.
These datasets are used to detect Intrusions in Controller Area Network (CAN) bus. Intrusions are detected using various Machine Learning and Deep Learning algorithms.
With the development of audio synthesis techniques, the most state-of-art synthesis methods are based on Generative Adversarial Network(GAN)。 To imporve the detection algorithm to discriminate genuine audio from spoofed audio produced using GAN based audio synthesis shcemes, we make this public dataset.
ArPC is an Arabic paraphrase identification corpus. It consists of 1331 sentence pairs along with their binary score that indicates weather the pairs are paraphrase or not. The corpus has been manually annotated by three Arabic native speakers.
Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection.
