CybAI news

arXiv:2408.04651v1 Announce Type: new Abstract: This project investigates the efficacy of Large Language Models (LLMs) in understanding and extracting scientific knowledge across specific domains and to create a deep learning framework: Knowledge AI. As a part of this framework, we employ pre-trained models and fine-tune them on datasets in the scientific domain. The models are adapted for four key Natural Language Processing (NLP) tasks: summarization, text generation, question answering, and named entity recognition. Our results indicate that domain-specific fine-tuning significantly enhances model performance in each of these tasks, thereby improving their applicability for scientific contexts. This adaptation enables non-experts to…

OverviewThis blog post is a follow-up to the session From Supernovas to LLMs at Data + AI Summit 2024, where I demonstrated how anyone can consume and process publicly available NASA satellite data from Apache Kafka.Unlike most Kafka demos, which are not easily reproducible or rely on simulated data, I will show how to analyze a live data stream from NASA's publicly accessible Gamma-ray Coordinates Network (GCN) which integrates data from supernovas and black holes coming from various satellites.While it's possible to craft a solution using only open source Apache Spark™ and Apache Kafka, I will show the significant advantages…

arXiv:2408.04839v1 Announce Type: new Abstract: Deep learning-based industrial anomaly detection models have achieved remarkably high accuracy on commonly used benchmark datasets. However, the robustness of those models may not be satisfactory due to the existence of adversarial examples, which pose significant threats to the practical deployment of deep anomaly detectors. Recently, it has been shown that diffusion models can be used to purify the adversarial noises and thus build a robust classifier against adversarial attacks. Unfortunately, we found that naively applying this strategy in anomaly detection (i.e., placing a purifier before an anomaly detector) will suffer from a high anomaly…