Abstract: In this study, we present a scalable method for modeling and visualizing large-scale industrial sensor networks using Graph Neural Network (GNN) principles. By applying the K-Nearest ...

AI tools are frequently used in data visualization — this article describes how they can make data preparation more efficient ...

We introduce a novel dataset of large depreciations worldwide since 1971. First, we use a multi-step approach to accurately pinpoint large depreciation events on monthly data. We then construct large ...

Q. I work with large spreadsheets. These spreadsheets have hundreds or even thousands of rows and often 10 or more columns. It’s so much to process that I become confused and make mistakes. Does Excel ...

# Two signals with a coherent part at 10Hz and a random part s1 = np.sin(2 * np.pi * 10 * t) + nse1 s2 = np.sin(2 * np.pi * 10 * t) + nse2 ...

Series 1 uses 'smallX' and 'smallY' as its dataset source, where the small dataset consists of only 3 points. Series 2 uses 'largeX' and 'largeY' as its dataset source, where the large dataset ...

On the CMeEE dataset, GPT-4.0 achieved an F1-score of 65.42 using few-shot learning, surpassing traditional models such as BERT-CRF (62.11) and Med-BERT (60.66). Building upon this, we compiled a ...

Abstract: As the processing of large-scale graphs on a single device is infeasible without partitioning, graph partitioning algorithms are essential for various algorithms and distributed computing ...

Close to 12,000 valid secrets that include API keys and passwords have been found in the Common Crawl dataset used for training multiple artificial intelligence models. The Common Crawl non-profit ...