13 docs tagged with "Release 2025-07"

This repository provides a python script to generate synthetic location and vehicle data (csv files) for developers and researchers aiming to model and solve agricultural logistics problems such as:

ArrayMorph is a software to manage array data stored on cloud object storage efficiently. It supports both HDF5 C++ API and h5py API. The data returned by h5py API is numpy arrays. By using h5py API, users can access array data stored on the cloud and feed the read data into machine learning pipelines seamlessly.

The Camera Traps application is both a simulator and IoT device software for utilizing machine learning on the edge in field research. The first implementation specializes in applying computer vision (detection and classification) to wildlife images for animal ecology studies. Two operational modes are supported: "simulation" mode and "demo" mode. When executed in simulation mode, the software serves as a test bed for studying ML models, protocols and techniques that optimize storage, execution time, power and accuracy. It requires an input dataset of images to act as the images that would be generated an IoT camera device; it uses these images to drive the simulation.

The Cyberinfrastructure Knowledge Network (CKN) is an extensible and portable distributed framework designed to optimize AI at the edge—particularly in dynamic environments where workloads may change suddenly (for example, in response to motion detection). CKN enhances edge–cloud collaboration by using historical data, graph representations, and adaptable deployment of AI models to satisfy changing accuracy‑and‑latency demands on edge devices.

This component implements a time cost estimator for distributed training of large language models (LLMs). It is used to predict the time required to train one batch across multiple GPUs. The predictor module only requires at least a CPU. The computation sampling module needs one or more GPUs, while the communication sampling module requires multiple GPUs, depending on your computing platform.

API access to the US Bureau of Transportation Statistics' Freight Analysis Framework dataset

This is intended as a helpful front end to a REST API to the US Bureau of Transportation Statistics (BTS) Feight Analysis Framework (FAF) dataset. It has been developed by the Data To Insight Center (D2I) at Indiana University as part of the NSF ICICLE AI Institute and in collaboration with the US Bureau of Transportation Statistics. See FAF-API-ICICLE. This project was generated with Angular CLI version 18.2.12.

Overview

Harvest is a tool designed to allow multiple types of stake holders in the digital agriculture space further their own unique goals from research to increases of the bottom line. Harvest allows for the creation of pipelines where users can preprocess their data, train models on HPC resources, infer on models to get insights on farm fields, and some visualizations to give an at a glance understand of what is happening on the field.

This repository contains code for training and evaluating models that classify organizations into Standard Industrial Classification (SIC) codes based on different types of descriptive text. This model is designed for researchers and data scientists who need to categorize unknown or newly listed organizations by business type. It can be applied to tasks such as food systems research, analyzing supply chains, and regional economic mapping, particularly in scenarios where structured corpora are unavailable. Given only an organization’s name and its description, the model predicts a high-level SIC category.

The Patra Knowledge Base is a system designed to manage and track AI/ML models, with the objective of making them more accountable and trustworthy. It's a key part of the Patra ModelCards framework, which aims to improve transparency and accountability in AI/ML models throughout their entire lifecycle. This includes the model's initial training phase, subsequent deployments, and ongoing usage, whether by the same or different individuals.

The Patra Toolkit is a component of the Patra ModelCards framework designed to simplify the process of creating and documenting AI/ML models. It provides a structured schema that guides users in providing essential information about their models, including details about the model's purpose, development process, and performance. The toolkit also includes features for semi-automating the capture of key information, such as fairness and explainability metrics, through integrated analysis tools. By reducing the manual effort involved in creating model cards, the Patra Toolkit encourages researchers and developers to adopt best practices for documenting their models, ultimately contributing to greater transparency and accountability in AI/ML development.

ScienceAgent Interface provides a web interface for conducting data-driven scientific tasks using ScienceAgent. The interface connects to a Python backend which allows users to execute generated programs in an isolated Docker environment and view the results.