Machine Learning
This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.
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Most text-simplification systems require an indicator of the complexity of the words. The prevalent approaches to word difficulty prediction are based on manual feature engineering. Using deep learning based models are largely left unexplored due to their comparatively poor performance. We have explored the use of one of such in predicting the difficulty of words. We have treated the problem as a binary classification problem. We have trained traditional machine learning models and evaluated their performance on the task.
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DataSet used in learning process of the traditional technique's operation, considering different devices and scenarios, the proposed approach can adapt its response to the device in use, identifying the MAC layer protocol, perform the commutation through the protocol in use, and make the device to operate with the best possible configuration.
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We present here one of the first studies that attempt to differentiate between genuine and acted emotional expressions, using EEG data. We present the first EEG dataset with recordings of subjects with genuine and fake emotional expressions. We build our experimental paradigm for classification of smiles; genuine smiles, fake/acted smiles and neutral expression. For the full details please refere to our paper entitled:
Discrimination of Genuine and Acted Emotional Expressions using EEG Signal and Machine Learning
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Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning.
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Game Building statistical analysis
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The data uploaded here shall support the paper
Decision Tree Analysis of ...
which has been submitted to IEEE Transactions on Medical Imaging (2020, September 25) by the authors
Julian Mattes, Wolfgang Fenz, Stefan Thumfart, Gerhard Haitchi, Pierre Schmit, Franz A. Fellner
During review the data shall only be visible for the reviewers of this paper. Afterwards this abstract will be modified and complemented and a dataset image will be uploaded.
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Gaming consoles are very common connected devices which have evolved in functionality and applications (games and beyond) they support. This diversity of traffic generated from these consoles has diverse quality of service (QoS) requirements. However, in order to offer diverse QoS, ISPs and operators must be able to classify this traffic. To enable research in traffic classification (Machine Learning based or other), we have generated and collected this dataset. This is a labelled dataset collected from a gaming console, PlayStation 4.
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This dataset includes PV power production measured on the SolarTech Lab, Politecnico di Milano, Italy. Data are freely available for scientific research purpose and further data validation.
In particular, the dataset is composed of the following variables and specifics, with a time resolution of 1 minute:
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