arXiv subjects

Jinli Duan

Publications and source records attributed to Jinli Duan.

3 recordsLinked to original sources

arXiv2024

DataViz3D: An Novel Method Leveraging Online Holographic Modeling for Extensive Dataset Preprocessing and Visualization

DataViz3D is an innovative online software that transforms complex datasets into interactive 3D spatial models using holographic technology. This tool enables users to generate scatter plot within a 3D space, accurately mapped to the XYZ coordinates of the dataset, providing a vivid and intuitive understanding of the spatial relationships inherent in the data. DataViz3D's user friendly interface makes advanced 3D modeling and holographic visualization accessible to a wide range of users, fostering new opportunities for collaborative research and education across various disciplines.

cs.CV↗

arXiv2023

A Multimodal Approach for Advanced Pest Detection and Classification

This paper presents a novel multi modal deep learning framework for enhanced agricultural pest detection, combining tiny-BERT's natural language processing with R-CNN and ResNet-18's image processing. Addressing limitations of traditional CNN-based visual methods, this approach integrates textual context for more accurate pest identification. The R-CNN and ResNet-18 integration tackles deep CNN issues like vanishing gradients, while tiny-BERT ensures computational efficiency. Employing ensemble learning with linear regression and random forest models, the framework demonstrates superior discriminate ability, as shown in ROC and AUC analyses. This multi modal approach, blending text and image data, significantly boosts pest detection in agriculture. The study highlights the potential of multi modal deep learning in complex real-world scenarios, suggesting future expansions in diversity of datasets, advanced data augmentation, and cross-modal attention mechanisms to enhance model performance.

cs.CV↗

arXiv2023

Jup2Kub: algorithms and a system to translate a Jupyter Notebook pipeline to a fault tolerant distributed Kubernetes deployment

Scientific workflows facilitate computational, data manipulation, and sometimes visualization steps for scientific data analysis. They are vital for reproducing and validating experiments, usually involving computational steps in scientific simulations and data analysis. These workflows are often developed by domain scientists using Jupyter notebooks, which are convenient yet face limitations: they struggle to scale with larger data sets, lack failure tolerance, and depend heavily on the stability of underlying tools and packages. To address these issues, Jup2Kup has been developed. This software system translates workflows from Jupyter notebooks into a distributed, high-performance Kubernetes environment, enhancing fault tolerance. It also manages software dependencies to maintain operational stability amidst changes in tools and packages.

cs.DC↗