Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identify...
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In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high c...
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Recently monkeypox outbreak has raised concerns due to its increasing number of cases and diverse dermatological symptoms in 2024, which can complicate early diagnosis due to similarities with other viral infections s...
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The Music to Score Conversion (MSC) project focuses on bridging the gap between auditory and visual representations of music. It uses signal processing techniques for the conversion such as pitch estimation, onset det...
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The paper presents a novel idea of proposing the application of Variational Autoencoders (VAEs) in crime detection for predicting face aging and deaging, which is one of the potential challenge of forensic science. VA...
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Despite the various advances in medical technology, heart disease continues to rank among the leading causes of mortality in the world, killing millions each year. There is hope that the risks involved with heart dise...
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In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems,...
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In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.
The rapid advancement of digital media technologies has given rise to DeepFake videos, synthetic content generated using deep learning algorithms that can convincingly mimic real individuals' appearances and actio...
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With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. ...
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With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.
Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning ***,data privacy and security are always a challenge in every field wher...
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Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning ***,data privacy and security are always a challenge in every field where data need to be uploaded to the *** learning(FL)is an emerging trend for distributed training of *** primary goal of FL is to train an efficient communication model without compromising data *** traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic *** paper presents FL-based traffic flow prediction with spatio-temporal *** work uses a differential privacy(DP)scheme for privacy preservation of participant's *** the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP *** proposed framework trains the data locally at the client-side with *** then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained *** results of the proposed work show that the FedGCN model accurately predicts the *** scheme at client-side helps clients to set a budget for privacy loss.
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