Artificial Intelligence
This dataset comprises Channel State Information (CSI) data collected from WiFi signals in six indoor environments, specifically designed for research in indoor intrusion detection. The dataset captures fine-grained variations in wireless signals caused by human, which are indicative of potential intrusions. CSI data, extracted from commercial WiFi chipsets, provides detailed amplitude and phase information across subcarriers, enabling robust detection of subtle environmental changes.
- Categories:
2 Views
Legal analysis utilizing natural language processing and machine learning technologies is a difficult undertaking that has recently sparked the interest of many academics and industries. Using a human-annotated dataset summarized into colloquial Thai from Supreme Court decisions, this work investigates a different combination of NLP, ML, and rule-based techniques for accurate legal case analysis as per Thai law, especially property-related offences, with the intuition to imitate the lawyer's cognitive process.
- Categories:
5 Views
The process of allocating the intestate inheritance among the statutory heirs is sophisticated yet occurs regularly. Many scholars have attempted to develop automated allocation systems to tackle this High task. However, most amply existing systems rely on conventional form-based input, which may overwhelm the general users. Furthermore, no existing system concerning intestate inheritance allocation according to the Civil and Commercial Code of Thailand is publicly available.
- Categories:
15 Views
This dataset is used to evaluate the effectiveness of the Growing-MoE learning framework. The dataset contains tasks across computer vision (CV) and natural language processing (NLP). The dataset includes CV tasks such as CIFAR, ImageNet, Cars, and Flowers, as well as NLP tasks including English Wikipedia and GLUE benchmark.
- Categories:
43 Views
We introduce a dataset comprised of energy consumption data from smart meters in French households, capturing detailed, disaggregated time series for various home appliances. This dataset covers a six-month period with a one-minute sampling rate across five different households. The objective of this dataset is to support the development of models that learn disentangled representations of time series energy data, which can significantly enhance model generalization across both in-distribution and out-of-distribution scenarios.
- Categories:
40 Views
This dataset contains audio recordings sourced from more than 57 TV shows provided by the Saudi Broadcasting Authority. The total number of hours published for these recordings is ~667 hours. The recordings are in Arabic, the majority are in Saudi dialects, and some are in other dialects. To enhance the usage of SADA, the dataset is split into training, validation, and testing sets. Each of validation and testing sets is around 10 hours in audio segments length while training set is 418 hours.
- Categories:
21 Views
we construct the fine-grained landmark dataset for real urban street scenes. and utilize this dataset for fine-tuning. Specifically, based on the GSV dataset\cite{Ali_bey_2022} from Google Street View, we obtain image data with landmark bounding boxes by having annotators outline common landmarks in the urban street view images, while also recording the types of landmarks.
- Categories:
4 Views
One of the leading causes of early health detriment is the increasing levels of air pollution in major cities and eventually in indoor spaces. Monitoring the air quality effectively in closed spaces like educational institutes and hospitals can improve both the health and the life quality of the occupants. In this paper, we propose an efficient Indoor Air Quality (IAQ) monitoring and management system, which uses a combination of cutting-edge technologies to monitor and predict major air pollutants like CO2, PM2.5, TVOCs, and other factors like temperature and humidity.
- Categories:
55 Views
This upload contains code related to the article and is intended to help IEEE DataPort users understand how to use and reproduce our research methods. The code implements Remote Sensing image change Detection Network (CGLCS-Net) based on deep learning, including global-local context-aware selector (GLCAS) and subspace Self-Attention Fusion module (SSAF).
- Categories:
41 Views
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.
- Categories:
44 Views