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Machine Learning

The dataset analyzed in this study is the result of a systematic literature review and a crowdsourced mini-project that aimed to identify and validate metrics relevant to maternal and neonatal healthcare examinations. The study involved a diverse group of participants, including 193 registered medical personnel from reputable institutions and 161 non-medical individuals who were active on various social media platforms related to maternal and neonatal healthcare.

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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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We build a large-scale dataset for term name generation, which contains the GO terms about Homo sapiens (humankind and yeast). We collect the term ID, term name and the corresponding genes’ ID from \href{http://geneontology.org/}{Gene Ontology Consortium}.

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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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 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.

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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. 

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