Artificial Intelligence

The Narrative question answering (QA) problem involves generating accurate, relevant, and human-like answers to questions based on the comprehension of a story consisting of logically connected paragraphs. However, this problem remains unexplored for the Arabic language because of the lack of Arabic narrative datasets. To address this gap, we present the Arabic-NarrativeQA dataset, which is the first dataset specifically designed for machine-reading comprehension of Arabic stories.


ScanReferr facilitates a clear correspondence between expressions and instances in 3D point cloud scenes, enabling effective identification of target objects. However, the explicit mention of the target object in the expression creates a shortcut that filters out negative samples, aiding model learning. In order to mitigate overreliance on this shortcut, we conducted manual processing of the ScanReferr dataset. Specifically,  we replaced the name of the referring object with the term ``object'' while preserving the names of other objects.


Dataset description:

This contains ten categories of gas data, each category contains 5 concentrations, 10, 20, 30, 40, 50ppm.

There are 160 groups of 10, 20, 30, 40, each group contains 6000 sampled voltage signals, and the sampling frequency is 10HZ.

There are only 80 groups for 50ppm concentration, and each group also contains 6000 sampled voltage signals.

The label corresponding to each gas includes category and concentration, which can be split by gas category and concentration.


This dataset is centered around high school students and comprises 40 features that encompass demographics, behavioral attributes, and curriculum-related information. It offers significant insights into the intricate interplay among students' characteristics, behaviors, and their Spatial Intelligence.

Last Updated On: 
Thu, 08/24/2023 - 03:42

Los datos empleados en el análisis del estudio fueron obtenidos del sistema SAP del Departamento Comercial de la Compañía Nacional de Electricidad (CNEL EP) Unidad de Negocio Esmeraldas. Estos datos consisten en registros originales de consumo mensual de energía eléctrica facturada (expresada en kilovatios-hora, kWh) durante un periodo de 25 meses (enero de 2021 a enero 2023). Estos registros pertenecen a 136218 clientes aproximadamente de del sector residencial de la provincia de Esmeraldas.



The dataset is obtained through the transformation of mathematical tools and image processing techniques based on TTPLA. The original TTPLA dataset consisted of aerial wire data captured through pinhole cameras. After our conversion, we obtained the corresponding fisheye aerial wire data. It includes both the original images and annotated images, significantly reducing the annotation workload for fisheye wire data. We now make it publicly available for researchers to study and learn from.


This manuscript proposes an approach to fuzzing test based on basic block vulnerabilities. Existing directed fuzzing test techniques rely on manual intervention to identify vulnerabilities and lack automated localization methods or are not efficient enough for localization.


We have created a new in-Air Signature dataset using Smart Phone that we called IASSP dataset. Forty participants voluntarily took part in each of the two databases’ construction. Each participant signs in the air five signatures and imitates five signatures of five other participants.

The participants were seated in a comfortable chair, with their dominant hand placed approximately 7 cm away from the camera of a smartphone, which was directly in front of them.

The data recorded on two files:


Dataset Description:

Based on some real-world events, the dataset offers a synthetic representation of 5G network states and metrics during a high traffic event, such as a major sports gathering in a city. Each row corresponds to a unique record capturing the attributes of the network at a particular moment, and each column corresponds to a specific feature or attribute.



Please cite the following paper when using this dataset:

N. Thakur, K. A. Patel, I. Hall, Y. N. Duggal, and S. Cui, “A Dataset of Search Interests related to Disease X originating from different Geographic Regions”, Preprints 2023, 2023081701, DOI: