Artificial Intelligence

In this dataset we release the data of a sequence of boxes that go through a physical binary sorter that acts as a load balancer between warehouses. This particular physical binary sorter works in real-time operating 4.5 million boxes per year. This is a particular example for a company with hundreds of \textit{physical binary sorters} that are central to the internal logistics of the business.

As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard, including signature-based and machine learning (ML)-based systems. More recently, researchers are considering deep learning (DL)-based anomaly detection approaches.

This dataset comprises high-resolution imaging data of biological porcine, clinically approved porcine and bovine, and chick embryo heart tissues. The dataset includes comprehensive anatomical and structural details, making it valuable for research in cardiovascular biology, tissue engineering, and computational modeling. The porcine and bovine heart samples are clinically approved, ensuring relevance for translational and preclinical studies. The chick embryo heart data provides insights into early cardiac development.

This dataset is the outcome of an observation on Millet traits under seed coating and covering. For covering we rely on Germination Percentage (FGP), Germination Index (GI),Mean Germination Time (MGT), Seedling Length( SL) and Seedling Vigour Index (SVI) and Abnormal Seedling have been measured.  Moreover, different enzyme levels including catalase, peroxidase, and  Malondialdehyde (MDA) are measured. 

This dataset consists of 737 documents from the BBC Sport website, corresponding to sports news articles in five topical areas from 2004-2005. The class labels are divided into five categories: athletics, cricket, football, rugby, and tennis. The datasets have been pre-processed using the Porter stemming algorithm, stop-word removal, and filtering out terms with low frequency (count < 3).

Livox-3DMatch contains 11 scenes with 33 scans. Livox-3DMatch augments the original 3DMatch training data from 14,400 pairs to 17,700 pairs (a 22.91% increase). By training on this augmented dataset, the performance of the SOTA learning-based method SGHR is improved by 2.90% on 3DMatch,4.29% on ETH, and 22.72% (translation) / 11.19% (rotation) on ScanNet.

Efficient and realistic tools capable of modeling radio signal propagation are an indispensable component for the effective operation of wireless communication networks. The advent of artificial intelligence (AI) has propelled the evolution of a new generation of signal modeling tools, leveraging deep learning (DL) models that learn to infer signal characteristics.