Artificial Intelligence

This paper is released with our paper titled “Annotated 3D Point Cloud Dataset for Traffic Management in Simulated Urban Intersections”. This paper proposed a 3D simulation based approach for generating an elevated LiDAR based 3D point cloud dataset simulating traffic in road intersections using Blender. We generated randomized and controlled traffic scenarios of vehicles and pedestrians movement around and within the intersection area, representing various scenarios. The dataset has been annotated to support 3D object detection and instance segmentation tasks.

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In this work, we study urban dynamics at the census tract level.

Information on census tracts and their corresponding geographic boundaries is available through the US Census Bureau survey. Each predetermined census tract is treated as a region. We use the GPS coordinates to compute the distance between two region nodes.

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Presented study introduces a novel distributed cloud-edge framework for autonomous multi-UAV systems that combines the computational efficiency of neuromorphic computing with nature-inspired control strategies. The proposed architecture equips each UAV with an individual Spiking Neural Network (SNN) that learns to reproduce optimal control signals generated by a cloud-based controller, enabling robust operation even during communication interruptions.

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162 Views

A new small aerial flame dataset, called the Aerial Fire and Smoke Essential (AFSE) dataset, is created which is comprised of screenshots from different YouTube wildfire videos as well as images from FLAME2. Two object categories are included in this dataset: smoke and fire. The collection of images is made to mostly contain pictures utilizing aerial viewpoints. It contains a total of 282 images with no augmentations and has a combination of images with only smoke, fire and smoke, and no fire nor smoke.

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410 Views

The Explainable Sentiment Analysis Dataset provides annotated sentiment classification data for Amazon Reviews and IMDB Movie Reviews, facilitating the evaluation of sentiment analysis models with a focus on explainability. It includes ground-truth sentiment labels, model-generated predictions, and fine-grained classification results obtained from various large language models (LLMs), including both proprietary (GPT-4o/GPT-4o-mini) and open-source models (DeepSeek-R1 full and distilled models).

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Meituan Bench (MTB) is an enterprise-level benchmarking tool designed for time-series forecasting in real-world business scenarios. Built upon an open-source dataset derived from 10,000 real-world services across various business units, MTB provides a standardized evaluation framework for time-series prediction models. The dataset includes 200 representative services, capturing diverse traffic patterns essential for assessing forecasting performance.

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The TripAdvisor online airline review dataset, spanning from 2016 to 2023, provides a comprehensive collection of passenger feedback on airline services during the COVID-19 pandemic. This dataset includes user-generated reviews that capture sentiments, preferences, and concerns, allowing for an in-depth analysis of shifting customer priorities in response to pandemic-related disruptions. By examining these reviews, the dataset facilitates the study of evolving passenger expectations, changes in service perceptions, and the airline industry's adaptive strategies.

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This data set contains all relevant data content required in the experiment, and all data are stored in.mat format. This format is a commonly used data file format in MATLAB software, which facilitates efficient data processing and analysis. Users can import these.mat files directly without additional data conversion or processing, saving time and improving productivity. In addition, the content in the dataset has been carefully curated to ensure the integrity and accuracy of the data, which is suitable for use in various experiments and research work.

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208 Views

Following the successful completion of two collaboration projects on AI, IERE has proposed a third initiative. We are now extending this invitation for the "Artificial Intelligence (AI) Collaboration Project" to all IERE members, inviting your participation in this exciting opportunity.

Please kindly confirm your participation by sending the attached Answer Sheet to IERE Central Office by March 10, 2025.

We look forward to your positive response and active participation in this project.

Last Updated On: 
Fri, 01/31/2025 - 09:52

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