Artificial Intelligence
This dataset is made of the Channel Impulse Response (CIR) data collected in 9 different environments in Ghent city, Belgium. These environments include:
1. Fourth floor at iGent Tower in the premises of Gent University
2. Zwijnaarde Open Area
3. Stadhuis Street and Nearby
4. Zuid Mall
5. Portus Ganda
6. Sint-Pieters Railway Station
7. Krook library
8. Citadel Park
9. Graffiti Straat
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Cars, mobile phones, and smart home devices already provide automatic speech recognition (ASR) by default. However, human machine interfaces (HMI) in industrial settings, as opposed to consumer settings, operate under different conditions and thus, present different design challenges. Voice control, arguably the most natural form of communication, has the potential to shorten complex command sequences and menu structures in order to directly execute a final command.
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This dataset and source code are related to knowledge tracing research called MonaCoBERT.
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In view of blast furnace ironmaking process (BFIP), the co-existence of dynamics and nonstationarity causes it extremely difficult to build an effective fault detection model for securing safety and reliability. First, to explore the hybrid properties in the dynamic nonstationary system more explicitly, we established an inferential observation decomposition strategy by combining independent nonstationary, static, and dynamic components.
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Fabdepth HMI is designed for hand gesture detection for Human Machine Interaction. It contains total of 8 gestures performed by 150 different individuals. These individuals range from toddlers to senior citizens which adds diversity in this dataset. These gestures are available in 3 different formats namely resized, foreground=-background separated and depth estimated images. Additional aspect is added in terms of video format of 150 samples. Researchers may choose their combination of data modalities based on their application.
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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.
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High quality perception is essential for autonomous driving (AD) systems. To reach the accuracy and robustness that are required by such systems, several types of sensors must be combined. Currently, mostly cameras and laser scanners (lidar) are deployed to build a representation of the world around the vehicle. While radar sensors have been used for a long time in the automotive industry, they are still under-used for AD despite their appealing characteristics (notably, their ability to measure the relative speed of obstacles and to operate even in adverse weather conditions).
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There are five types of data in the dataset, namely NORMAL, DoS, Probe, R2L and U2R. A total of 20,000 training samples were used during the experiment (5 classifications in total, 4000 samples for each classification). There are 4047 samples in the validation dataset, including 1000 samples each of NORMAL, DoS, and Probe types. 995 samples of R2L and 52 samples of U2R.
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This data is the embedding of abstracts of articles on echocardiography in Pubmed with the models of BERT, BioBERT, and SciBERT.
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This data is the embedding of abstracts of articles on artificial intelligence in Pubmed with the models of BERT, BioBERT, and SciBERT.
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