The Indonesian government is furnishing help in the form of" Bantuan Langsung Tunai(BLT)" or the Direct Cash backing program in order to make up for the recent increase in the price of energy oil painting(BB...
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Although millimeter wave (mmWave) mesh networks provide large bandwidth to deliver IoT packets, link failure caused by non-light-of-sight (NLoS) condition degrades network reliability. With the assistance of software ...
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Generative AI models for music and the arts in general are increasingly complex and hard to *** field of ex-plainable AI(XAI)seeks to make complex and opaque AI models such as neural networks more understandable to **...
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Generative AI models for music and the arts in general are increasingly complex and hard to *** field of ex-plainable AI(XAI)seeks to make complex and opaque AI models such as neural networks more understandable to *** ap-proach to making generative AI models more understandable is to impose a small number of semantically meaningful attributes on gen-erative AI *** paper contributes a systematic examination of the impact that different combinations of variational auto-en-coder models(measureVAE and adversarialVAE),configurations of latent space in the AI model(from 4 to 256 latent dimensions),and training datasets(Irish folk,Turkish folk,classical,and pop)have on music generation performance when 2 or 4 meaningful musical at-tributes are imposed on the generative *** date,there have been no systematic comparisons of such models at this level of com-binatorial *** findings show that measureVAE has better reconstruction performance than adversarialVAE which has better musical attribute *** demonstrate that measureVAE was able to generate music across music genres with inter-pretable musical dimensions of control,and performs best with low complexity music such as pop and *** recommend that a 32 or 64 latent dimensional space is optimal for 4 regularised dimensions when using measureVAE to generate music across *** res-ults are the first detailed comparisons of configurations of state-of-the-art generative AI models for music and can be used to help select and configure AI models,musical features,and datasets for more understandable generation of music.
In this paper, we propose a method of fundamental solution for two-dimensional doubly periodic problems, especially potential flow problems with a doubly periodic array of obstacles. In the proposed method, we approxi...
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This study focuses on developing and evaluating a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) based deep learning model for detecting fake voice recordings. The proposed model addresses the c...
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Global anxiety and depression have become 25% more prevalent, with teenagers and women being the most affected. Approximately 280 million people suffer from depression. Doctors and psychologists are able to diagnose d...
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The development of Artificial Intelligence (AI) technology is used to minimize the risk of maternal disorders during pregnancy. Maternal health needs to be monitored so as not to cause problems during the baby's b...
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Empowering each vehicle with four dimensional (4D) situational awareness, i.e., accurate knowledge of neighboring vehicles' 3D locations over time in a cooperative manner (instead of focusing only on self-localiza...
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Empowering each vehicle with four dimensional (4D) situational awareness, i.e., accurate knowledge of neighboring vehicles' 3D locations over time in a cooperative manner (instead of focusing only on self-localization), is fundamental for improving autonomous driving performance in diverse traffic conditions. For this task, identification, localization and tracking of nearby road users is critical for enhancing safety, motion planning and energy consumption of automated vehicles. Advanced perception sensors as well as communication abilities, enable the close collaboration of moving vehicles and other road users, and significantly increase the positioning accuracy via multi-modal sensor fusion. The challenge here is to actually match the extracted measurements from perception sensors with the correct vehicle ID, through data association. In this paper, two novel and distributed Cooperative Localization or Awareness algorithms are formulated, based on linear least-squares minimization and the celebrated Kalman Filter. They both aim to improve ego vehicle's 4D situational awareness, so as to be fully location aware of its surrounding and not just its own position. For that purpose, ego vehicle forms a star like topology with its neighbors, and fuses four types of multi-modal inter-vehicular measurements (position, distance, azimuth and inclination angle) via the linear Graph Laplacian operator and geometry capturing differential coordinates. Moreover, a data association strategy has been integrated to the algorithms as part of the identification process, which is shown to be much more beneficial than traditional Hungarian algorithm. An extensive experimental study has been conducted in CARLA, SUMO and Artery simulators, highlighting the benefits of the proposed methods in a variety of experimental scenarios, and verifying increased situational awareness ability. The proposed distributed approaches offer high positioning accuracy, outperforming other state-of-the-art c
Stereotypes constitute a widely used technique for creating user models. This paper explores the potential of stereotype-based models in virtual environments in order to enhance user engagement and learning outcomes. ...
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This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised ...
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