Brain dynamics big data is of increasing promise for many applications like epilepsy detection and cognitive understanding, with the advancements of consumer technology. However, the deep-source brain measurement is d...
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ISBN:
(数字)9798331521165
ISBN:
(纸本)9798331521172
Brain dynamics big data is of increasing promise for many applications like epilepsy detection and cognitive understanding, with the advancements of consumer technology. However, the deep-source brain measurement is difficult. In this study, we target the brain electroencephalogram (EEG) application, and investigate the deep-source EEG generation from surface EEG towards convenient big data. The deep learning algorithm has been developed to mine different configurations of the surface EEG streams, including the single-channel and multi-channel cases, for deep-source EEG generation. Promising experiments on the epilepsy application have been conducted, demonstrating the great promise of deep-learning-empowered deep-source EEG generation. This study will greatly advance brain dynamics mining towards smart consumer technology.
3D human pose estimation (HPE) has improved significantly through Graph Convolutional Networks (GCNs), which effectively model body part ***, GCNs have limitations, including uniform feature transformations across nod...
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Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered ...
Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered in everyday scenarios such as services, healthcare, agriculture,construction, and numerous other fields. From the perspective of general robotic manipulation, the challenges arise from three factors.
Telerehabilitation systems leveraging depth video analysis provide an effective solution for remote physiotherapy, particularly for individuals with physical disabilities. This study presents an advanced exercise clas...
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Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed fo...
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This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects ap...
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Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain...
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The AlphaFold2 introduces an entirely new era in the field of computational biology by achieving outstanding results in protein structure predictions. Despite its remarkable performance, the model's prediction pip...
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Pretrained models have taken full advantage of monolingual corpora and achieved impressive results in training Unsupervised Neural Machine Translation (UNMT) models. However, when adapting UNMT models with in-domain m...
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Pretrained models have taken full advantage of monolingual corpora and achieved impressive results in training Unsupervised Neural Machine Translation (UNMT) models. However, when adapting UNMT models with in-domain monolingual corpora for domain-specific translation tasks, one of the languages may lack in-domain corpora, resulting in the unequal amount and proportion of in-domain monolingual corpora in each language. This problem situation is known as Domain Mismatch (DM). This study investigates the impact of DM in UNMT. We find that DM causes a translation quality disparity. That is, while in-domain monolingual corpora of a language can enhance the in-domain translation quality into that particular language, this enhancement cannot be generalized to the other language, and the translation quality into the other language remains deficient. To address this problem, we propose Domain-Aware Adaptation (DAA), which can be embedded in the vanilla UNMT model training process. By passing sentence-level domain information to the model during training and inference, DAA gives higher weight to in-domain data from open-domain corpora related to specific domains to alleviate domain mismatch. The experimental results on German-English and Romanian-English translation tasks specified in the IT, Koran, medical, and TED2020 domains demonstrate that DAA can efficiently exploit open-domain corpora to mitigate the quality disparity of translation caused by DM.
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