Titlebook: Hightech goes Public; Zukunftstechnologien Hartmut G. Korn (Unternehmensberater) Book 2000 Betriebswirtschaftlicher Verlag Dr. Th. Gabler G

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ncreasingly realistic output. But what happens when models are fed with non-standard data, such as speech from a user with a speech impairment? We investigate how a recent voice conversion model performs on non-standard downstream voice conversion tasks. We use a simple but robust approach called k-

lesion

Dieter Hundtext-aware recommendation systems has previously only considered the sequential ordering of items as contextual information. However, there is a wealth of unexploited additional multi-modal information available in auxiliary knowledge related to items. This study extends the existing research by eval

Picks-Disease

Hans-Olaf Henkelial expressions have the potential to reveal nonverbal cues about a learner’s learning affect. However, most studies were limited in their analysis of learning affects exhibited by a learner with the possibility of providing appropriate feedback to teachers and learners. This work proposes an adapti

大雨

Hans Peter Stihland can range from simple conversations between family members and friends to complex interactions that represent the flow of money, information, or power. In our modern digital society, social media platforms present unique opportunities to study social networks through social network analysis (SNA

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omputer-aided systems have assisted security personnel to an extent. However, they require in the loop manipulation of X-ray baggage images to improve visibility of concealed prohibited items. Researchers have proposed several methods to detect threats automatically, but the occlusion problem is sti

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regale

Rüdiger von Rosendata is multi-style training (MTR), a form of data augmentation that attempts to transform the training data to be more representative of the testing data; and to learn robust representations applicable to different conditions. This task can be very challenging if the test conditions are unknown. We

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Ulrich Hockerdata is multi-style training (MTR), a form of data augmentation that attempts to transform the training data to be more representative of the testing data; and to learn robust representations applicable to different conditions. This task can be very challenging if the test conditions are unknown. We

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r morphologically-rich agglutinative languages such as the Southern African Nguni language group. In this paper, we investigate supervised and unsupervised models for two variants of morphological segmentation: canonical and surface segmentation. We train sequence-to-sequence models for canonical se

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