This dataset (MegaGeoCOV Extended), which is an extended version of MegaGeoCOV, was introduced in this paper: A Twitter narrative of the COVID-19 pandemic in Australia (the paper will appear in proceedings of the 20th ISCRAM conference, Omaha, Nebraska, USA May 2023). Please refer to the paper for more details (e.g., keywords and hashtags used, descriptive statistics, etc.).
This dataset and source code are related to knowledge tracing research called MonaCoBERT.
<p>Anonymized data used in the study of "<span style="font-family: Calibri, sans-serif; font-size: 11pt;">Administrative data processing, Clustering, classification, and association rules, Human factors and ergonomics, Machine learning"</span></p>
We provide two datasets extracted from Twitter, in Spanish and English, and annotate each one with approximately 1,500 users who have been diagnosed with one of nine different mental disorders (ADHD, Autism, Anxiety, Bipolar, Depression, Eating disoders, OCD, PTSD and Schizophrenia) along with 1,700 matched-control users.
The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods.
Unlicensed coexistence networks and spectrum sharing are two relatively new technological paradigms in cellular technology. These wireless systems are standardized and adopted to help cellular operators meet the ever-increasing mobile data demand by efficient utilization of unlicensed bands. However, several incumbents are already operational in these frequencies such as military, radar, and navy systems rendering the wireless environment extremely dynamic and unpredictable.
The project provides trained models of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for outdoor insulator detection and classification of the surface contamination, such as salt, snow, cement, soil and wet soil. The project is based on YOLOv3 implementation developed by Ultralytics/YOLOv3. The models were trained on custom insulator dataset consisting of 11816 images of different type insulators under various conditions.
Solar energy production has grown significantly in recent years in the European Union (EU), accounting for 12\% of the total in 2022. The growth can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives. The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources.
The liquid product H in stream 9 is considered as quality variable while 22 process variables XMEAS (1) – XMEAS (22) are selected as process variables. 960 samples are collected as training samples, and another 960 samples are collected as the test dataset in the same working condition.
The drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. Thus, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet).
