Artificial Intelligence

Simulated dataset for deriving parametric constraints for Bayesian Knowedge Tracing. The classical Expectation-Maximization method results in degenerate parameters (i.e., parameters that violate the conceptual interpretation of the model, such as by saying that a learner with no knowledge of a skill is more likely to get an answer correct than a learner with knowledge). A novel approach based on Newton's method rescues these paramters using mathematically derived constraints on the parameter space. 



We introduce a novel dataset consisting of approximately 5,700 video files, specifically designed to enhance the development of real-time traffic accident detection systems in smart city environments. It encompasses a diverse range of traffic scenarios, captured through Traffic/Surveillance Cameras (Trafficam) and Dash Cameras (Dashcam), along with additional external data sources. The dataset is meticulously organized into three segments: Training, Validation, and Testing, with each segment offering a unique blend of traffic and dashcam footage across different scenarios.


The D-Faust dataset [26] consists of 41k scanned human body models represented as 3D point clouds and triangular meshes. These models are obtained through scanning human bodies in various poses, allowing for the inclusion of various defects such as noise, holes, missing body parts, and occasional artifacts caused by reflections. This dataset is essential for research on human body reconstruction and provides a rich resource for studying variations in human body shape and pose.


Fingerprint recognition is crucial for device and data

security, especially with the widespread use of capacitive sensors

in mobile devices. However, denoising wet fingerprints from

these sensors poses challenges due to small fingerprint areas,

limited features, and significant moisture-induced dark regions.

Our ”DRB-FD” method combines a Featured Discriminator (FD)

and a Deformed Residual Block (DRB) with attention mechanisms,

drop-out layers, and pre-activation. In experiments using the Nasic9395


The data was constructed using the publicly available C-MAPSS dataset,  from which 14 effective degradation indicators were selected,  and the health state of the engine was described by the knowledge graph,  and the health state knowledge graph was constructed.

1:The first column in the data represents the engine ID, and the second column indicates the current health status label of the engine.


This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014.

This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

These data have been reduced to extract the k-core, such that each of the remaining users and items have k reviews each.



We propose a more challenging dataset known as Weibo23. By amalgamating all available fake news from the Weibo Management Community until March 2023 with existing samples from public datasets [1], we formed a comprehensive collection of fake news for Weibo23. Fabricated news articles were thoroughly examined and authenticated by certified experts.


The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification.


We introduce a novel multi-modal dataset comprising point cloud data from a mmWave radar, RGB and depth images from an RGB-D camera, collected from 23 human subjects.


The steel tube dataset comprises comprehensive information on various attributes related to steel tubes, encompassing dimensions, material composition, manufacturing processes, and performance characteristics. This dataset facilitates in-depth analysis of steel tube properties, aiding researchers, engineers, and industry professionals in optimizing designs, ensuring structural integrity, and advancing materials science in the context of steel tube applications.