Conveners
Artificial Intelligence (AI)
- Ludek Matyska (CESNET)
Artificial Intelligence (AI)
- Simon C. Lin (ASGC)
Artificial Intelligence (AI)
- Ludek Matyska (CESNET)
Abstract:
In scientific applications, integrating artificial intelligence (AI) and machine learning (ML) has revolutionized research methodologies and workflows. This study delves into an innovative application of cloud-based OpenAI's Large Language Models (LLMs) in developing a conversational AI chatbot, drawing exclusively from the culturally significant Legacy of Slavery (LoS) datasets...
This paper investigates the effect of pretraining and fine-tuning for a multi-modal dataset. The detaset used in this study is accumulated in a garbage disposal facility for facility control and consists of 25000 sequential images and corresponding sensor values. The main task for this dataset is to classify the state of garbage incineration from an input image for the combustion state...
ABSTRACT:
The efficient manifold ranking (EMR) algorithm has been widely applied in content-based image retrieval (CBIR). For this algorithm, each image is represented by low-level features, describing color, texture, and shape. The characteristics of low-level features include the ability to quickly detect differences in color, texture, and shape, and the invariance to rotations and...
As human-wildlife conflicts escalate in our area and around Japan, safeguarding crops and farmers from animal intrusions becomes paramount. This research introduces a deep learning approach to prototype a prevention system against monkey trespassing in sweet potato fields. The proposed system was motivated by the idea of developing wildlife identification and assisting local farmers in...
Researchers at INFN (National Institute for Nuclear Physics) face challenges from basic to hard science use cases (e.g., big-data latest generation experiments) in many areas: HEP (High Energy Physics), Astrophysics, Quantum Computing, Genomics, etc.
Machine Learning (ML) adoption is ubiquitous in these areas, requiring researchers to solve problems related to the specificity of...
Machine learning is today’s fastest-developing and most powerful computer technology, finding applications in
nearly every domain of science and industry, such as natural language processing, visual object detection, autonomous driving, stock market prediction, medical applications, and many more. For machine learning to be effective, a large amount of high-quality training data is essential....
INFN CNAF data center provides a huge amount of heterogeneous data through the adoption of dedicated monitoring systems. Having to provide a 24/7 availability, it has started to assess artificial intelligence solutions to detect anomalies aimed to predict possible failures.
In this study, the main goal is to define an artificial intelligence framework able to classify and predict anomalies...
Self-supervised learning speeds up the representation learning process in lots of computer vision tasks. It also saves time and labor of labelling the dataset. Momentum Contrast (MoCo) is one of efficient contrastive learning methods, which has achieved positive results on different downstream vision tasks with self-supervised learning. However, its performance on extracting 3D local parts...
The INFN CNAF User Support unit plays the role of the first interface to the user of the data center, which provides computing resources to over 60 scientific communities in the fields of Particle, Nuclear and Astro-particle physics, Cosmology and Medicine.
While its duties span from repetitive tasks to supporting complex scientific-computing workflows, many of them can be automatized or...
