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Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid have to produce to satisfy a given demand. Optimality is measured with respect to the cost that each generator incurs in producing this power. The OPF problem is non-convex due to the sinusoidal nature of electrical generation and thus is difficult to solve.

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Prosodic boundary is a crucial prosodic cue of prosodic phrasing. This research aims to build a prosodic boundary prediction model for improving the naturalness of the Viet- namese speech synthesis. This model can be used directly to predict prosodic boundaries in synthesis phase of the statisti- cal parametric speech synthesis (e.g. Hidden Markov Model - HMM, Deep Neural Network - DNN). It can also be used to improve the quality of the training phase in the end-to- end speech synthesis (e.g. Tacotron).

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This paper shows recent progresses on our Silent Speech Interface (SSI) that translates tongue motions into audible speech. In our previous work and also in the current study, the prediction of fundamental frequency (F0) from Ultra-sound Tongue Images (UTI) was achieved using articulatory-to-acoustic mapping methods based on deep learning. Here we investigated several traditional discontinuous speech-based F0 estimation algorithms for the target of UTI-based SSI system.

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This paper shows recent progresses on our Silent Speech Interface (SSI) that translates tongue motions into audible speech. In our previous work and also in the current study, the prediction of fundamental frequency (F0) from Ultra-sound Tongue Images (UTI) was achieved using articulatory-to-acoustic mapping methods based on deep learning. Here we investigated several traditional discontinuous speech-based F0 estimation algorithms for the target of UTI-based SSI system.

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An efficient, appliance-level approach for energy disaggregation, exploiting the benefits of Generative Adversarial Networks, is presented. The concept of adversarial training supports the creation of fine tuned disaggregators, which produce more detailed load estimations for a specific appliance, compared to state of the art deep learning models. The Generator and Discriminator of the model are appropriately adapted to fit the particularities of NILM problem, whereas a Seeder component is added to provide encoded compact input vectors to the Generator.

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Due to the widespread deployment of fingerprint/face/speaker recognition systems, attacking deep learning based biometric systems has drawn more and more attention. Previous research mainly studied the attack to the vision-based system, such as fingerprint and face recognition. While the attack for speaker recognition has not been investigated yet, although it has been widely used in our daily life.

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