This systematic literature review comprehensively examines the application of Large
Language Models (LLMs) in forecasting and anomaly detection, highlighting the current …

The advent of large language models (LLMs) revolutionized natural language processing
applications, and running LLMs on edge devices has become increasingly attractive for …

In the era of the COVID-19 pandemic, the surge in information sharing on social media,
particularly Twitter, necessitates a nuanced understanding of sentiments. Conventional …

With the rapid development of intelligent manufacturing, there are important and challenging
tasks in many aspects, especially in the calibration of 3D scanners. In order to improve the …

This paper focuses on algorithms for multi-stage stochastic linear programming (MSLP). We
propose an ensemble method named the “compromise policy”, which not only reduces the …

The broad sharing of research data is widely viewed as critical for the speed, quality,
accessibility, and integrity of science. Despite increasing efforts to encourage data sharing …

In order to deal with the problem of imbalance and complex feature relationship in network
data classification, this study proposes a machine learning classification method, combined …

Discovery of new knowledge is increasingly data-driven, predicated on a team's ability to
collaboratively create, find, analyze, retrieve, and share pertinent datasets over the duration …

We describe major improvements to the testing capabilities of SimOpt, a library of simulation-
optimization problems and solvers. Foremost among these improvements is a transition to …

Federated Learning (FL) is a distributed machine learning paradigm that allows clients to
train models on their data while preserving their privacy. FL algorithms, such as Federated …