Speech emotion recognition (SER) is a Machine Learning (ML) topic that is now receiving a lot of research attention. This can be attributed to its growing capacity, improvements in algorithms, and utilization in pract...
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In this article, we present the mathematical analysis of the convergence of the linearized Crank–Nicolson Galerkin method for a nonlinear Schrödinger problem related to a domain with a moving boundary. The conve...
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Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature o...
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Lossy video compression introduces visual artifacts that degrade video quality, where deep neural networks (DNNs) are effective in enhancement. However, conventional DNN-based methods often focus on a single video com...
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ISBN:
(数字)9798331522124
ISBN:
(纸本)9798331522131
Lossy video compression introduces visual artifacts that degrade video quality, where deep neural networks (DNNs) are effective in enhancement. However, conventional DNN-based methods often focus on a single video compression standard, limiting their deployment in multiple cases. To overcome this issue, this study introduces a multi-domain video quality enhancement architecture based on the Spatio-Temporal Deformable Fusion (STDF) technique. This method enables the model to enhance videos compressed with multiple codecs, maintaining reliable performance across standards. After trained, the proposed architecture was tested with videos compressed by the High Efficiency Video Coding (HEVC) encoder, the Versatile Video Coding (VVC) encoder, the VP9 codec and the AOMedia Video 1 (AV1) codec. Results show an average Peak Signal-to-Noise Ratio (PSNR) improvement between 0.228 dB and 0.787 dB.
In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality....
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Software projects are affected by technical knowledge as well as the personality of the team. Such factors can reduce or increase the software quality and development speed. For successful task allocation, it is essen...
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作者:
Ximenes, PabloMello, Patricia
School of Cybersecurity and Privacy College of Computing Atlanta United States
Computer Science Graduate Program Fortaleza Brazil
This paper uses the Diamond Model of intrusion analysis to discuss the intricacies and unfoldings of the cyberattack that enabled Operation 'Car Wash' leak (nicknamed 'VazaJato'), one of the most signi...
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One of the most important new tools of the Versatile Video Coding (VVC) standard is the Affine Motion Estimation (AME). The AME contribution to the coding efficiency comes with a high computational cost, especially fo...
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This paper proposes a model for accurately exploring approximate adder compressors (AxAC) utilizing gates-free logic. As case studies, we explore energy-efficient architectures of approximate 3–2 (Ax3-2), 4–2 (Ax4-2...
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ISBN:
(数字)9798350377200
ISBN:
(纸本)9798350377217
This paper proposes a model for accurately exploring approximate adder compressors (AxAC) utilizing gates-free logic. As case studies, we explore energy-efficient architectures of approximate 3–2 (Ax3-2), 4–2 (Ax4-2), and 5–2 (Ax5-2) adder compressors (AC), focusing exclusively on gates-free in the critical path. This model for the accuracy analysis of AxACs is essential for estimating the optimal arrangements with repetition (AR) of inputs that are the way outputs for these compressors. For instance, the exact 4–2 AC has five inputs (A, B, C, D, and Cin) and three outputs (Cout, Carry, and Sum). Specifically, the Ax3-2 AC generates nine possible architectural-space gates-free AR, the Ax4-2 AC generates 125, and the Ax5-2 AC generates 2, 401 possible output AR. To estimate all output AR, we implemented the accuracy-exploration model in Python. After identifying the best AR, we created a Pareto front addressing the trade-offs between accuracy, energy, and area. The Ax3-2, Ax4-2, and Ax5-2 proposals demonstrate substantial energy and area efficiency improvements while maintaining high accuracy.
We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN construct...
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