Neural classifiers have achieved near human level performances when applied to several real-world tasks. Despite their successes, recent works have demonstrated their vulnerability to adversarial attacks. In particula...
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Alzheimer’s disease is a kind of dementia which leads in progressive loss of memory usually in elderly persons. Since there is not any cure for this condition it is vital to discover it as soon as possible. Machine l...
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Crowdsourcing has facilitated a wide range of applications by leveraging public workers to contribute large number of tasks. However, most prior works only considered static environments and overlooked the system dyna...
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Oxymoron is a figurative language which combines seemingly contradictory words in a short phrase. It is used to create an impression, enhance a concept or entertain the readers. In this work, we propose a novel task n...
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Bullying is a global issue that threatens the safety and wellbeing of children worldwide. While bullying is observed amongst children of all ages, the behavior peaks at ages 11–14 years. One intervention method ...
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An essential tool for diagnosing heart diseases is the electrocardiogram (ECG) signal. The accuracy of the diagnosis is impacted by the noise that occurs while this signal is being acquired. Denoising turns into a fou...
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Classifying cancer tissues has been a difficult task ever since computer Vision and Pattern Recognition were developed. Deep Learning, a modern, state-of-the-art method for texture categorization and localisation of c...
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Knowledge selection is the key component in knowledge-ground dialogues, which aims to choice correct knowledge based on external knowledge for dialogue generation. The quality of knowledge selection depend on knowledg...
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Ontologies have become the de-facto information representation method in the semantic web domain, but recently gained popularity in other domains such as cloud computing. In this context, ontologies enable service dis...
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Brain-computer interfaces (BCIs) are innovative systems that allow individuals to communicate with external devices without physical movements. These systems commonly use Event-Related Potentials (ERPs), particularly ...
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ISBN:
(数字)9783031442230
ISBN:
(纸本)9783031442223;9783031442230
Brain-computer interfaces (BCIs) are innovative systems that allow individuals to communicate with external devices without physical movements. These systems commonly use Event-Related Potentials (ERPs), particularly P300, as the signal control. However, despite their wide acceptance, there are still issues to be resolved, such as inter- and intra-subject variability. To address this challenge, we propose a novel approach based on post-processing the output of a Recurrent Neural Network using a Post-Recurrent Module (PRM). The PRM processes the temporal information extracted from the recurrent layer to make the final decision. This work shows that simple approaches, such as a reduce-max operation or a logistic regression layer, can improve the balanced accuracy by more than 9% compared to state-of-the-art results. Our findings also contribute to the interpretability of RNNs since we have deepened the internal mechanisms of the model through an extensive analysis of the PRM layer. Overall, this study enhances the performance of ERP-based BCIs.
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