Artificial Intelligence

RITA (Resource for Italian Tests Assessment), is a new dataset of academic exam texts written in Italian by second-language learners for obtaining the CEFR certification of proficiency level.
In addition to the tests, RITA provides a variety of speech elements, annotations, and statistics, including phraseological units and their syntactic dependencies. The dataset consists of two corpora: one containing the task assignment and the other containing the texts elaborated by the learners in response to the assignment. This work describes the


To thoroughly investigate the non-overlapping registration problem, we created our own datasets: Pokemon-Zero for zero overlap and Pokemon-Neg for negative overlap. In this section, we describe the process of dataset creation. 


This dataset contains the Supplementary Information of the article "Discovering Mathematical Patterns Behind HIV-1 Genetic Recombination: a new methodology to identify viral features" (Manuscript DOI: 10.1109/ACCESS.2023.3311752).


SYPHAXAR dataset is a dataset for Arabic text detection in the wild. It was collected from Tunisia in “Sfax” city, the second largest Tunisian city after the capital. A total of 3078 images were gathered through manual collection one by one, with each image energizing text detection challenges in nature according to real existing complexity of 15 different routes along with ring roads, intersections and roundabouts. These annotated images consist of more than 31000 objects, each of which is enclosed within a bounding box.


This dataset comprises data created during research on AI-generated code, with a focus on software engineering use-cases. The purpose of the research was to investigate how AI should be integrated into university software engineering curricula.



The dataset under consideration is a comprehensive compilation of code snippets, function descriptions, and their respective binary representations aimed at fostering research in software engineering. It contains a variety of code functionalities and serves as a valuable resource for understanding the behavior and characteristics of C programs. This data is sourced from the AnghaBench repository, a well-documented collection of C programs available on GitHub.


Columns and Data Types


The "Queue Waiting Time Dataset" is a detailed collection of information that records the movement of waiting times in queues. This dataset contains important details such as the time of arrival, the start and finish times, the waiting time, and the length of the queue. The arrival time denotes the moment when customers enter the queue, while the start and finish times track the duration of the service process. The waiting time measures the time spent waiting in the queue, and the queue length shows the number of customers in the queue when a new customer arrives.


The 33-, 119-, and 136-bus datasets are commonly used in the field of power systems and electrical engineering to train reinforcement learning-based algorithms for distribution network reconfiguration. Distribution network reconfiguration involves altering the topology of the electrical distribution grid by opening or closing switches to optimize certain objectives, such as minimizing power losses, improving voltage profiles, or enhancing overall system efficiency. This process is essential for maintaining a reliable and cost-effective power distribution system.


In primary education in China, mathematics, science, and Chinese are commonly considered as the core subjects. This emphasis is primarily due to their significance in providing a strong foundation for students' overall academic development in their whole life. Mathematics cultivates logical thinking, problem-solving skills, and numerical proficiency, which are essential in various disciplines. Science education fosters scientific literacy, critical thinking, and an understanding of the natural world.


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.