Artificial Intelligence

This study presents a English-Luganda parallel corpus comprising over 2,000 sentence pairs, focused on financial decision-making and products. The dataset draws from diverse sources, including social media platforms (TikTok comments and Twitter posts from authoritative accounts like Bank of Uganda and Capital Markets Uganda), as well as fintech blogs (Chipper Cash and Xeno). The corpus covers a range of financial topics, including bonds, loans, and unit trust funds, providing a comprehensive resource for financial language processing in both English and Luganda.

In order to realize intelligent and accurate campus risk detection, this paper proposes an improved YOLOv10 algorithm that integrates Self-Calibrated Illumination algorithm. The algo-rithm optimizes the loss function by introducing an auxiliary bounding box, and accelerates model convergence. StarNet is employed to enhance the original network structure, feature extraction capability, and decrease parameter count and calculations.

Underwater acoustic target classification (UATC) aims to identify the type of unknown acoustic sources using passive sonar in oceanic remote sensing scenarios. However, the variability of underwater acoustic environment and the presence of complex background noises create significant obstacles to improving accuracy of UATC. To address these challenges, we develop an innovative deep neural network (DNN) algorithm integrated by  multiscale feature extractor and efficient channel attention mechanism.

This data ia small sample set from the purchase order data catalog of state of california publicaly available at https://catalog.data.gov/dataset/purchase-order-data. From this source, we take a sample of 50,000 entries. The data provides comprehensive details about various purchase orders issued during this period. The dataset comprises 32 columns, capturing information about each purchase order. This dataset is rich in information and provides a valuable resource for item UNSPSC categorization.