This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This ch...
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This study addresses the need for scenario-specific drone simulators to enhance research, education, pilot training, and performance evaluation across diverse mission contexts. A gamified simulator was developed for t...
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Heterogeneous Multi-Processor System on Chips (HMPSoCs) combine several types of processors on a single chip. State-of-the-art embedded devices are becoming ever more powerful thanks to advancements in the computation...
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This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit cu...
This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..
Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literat...
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Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source...
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Reports on spin Hall magnetoresistance, magnonic spin currents from thermal gradients, and spin transfer-torque magnetic random-access memory using compensated ferrimagnets largely discuss bulk magnetization but lack ...
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Reports on spin Hall magnetoresistance, magnonic spin currents from thermal gradients, and spin transfer-torque magnetic random-access memory using compensated ferrimagnets largely discuss bulk magnetization but lack consideration of depth profiles or interfacial characteristics. Here, magnetic and structural characterization of profiles and interfaces was performed for nearly compensated gadolinium iron garnet (GdIG) thin films. X-ray diffraction and reciprocal space maps show that sputter deposited GdIG on Si is polycrystalline with the desired cubic garnet phase, and GdIG on gadolinium gallium garnet (GGG) is epitaxial with <0.06% compressive strain. Temperature-dependent magnetometry confirms the compensation temperatures of GGG/GdIG and Si/GdIG to be 285 and 260 K, respectively, both near room temperature. Interestingly, these measurements suggest the presence of unsaturated rare-earth moments, which result in a characteristic hysteresis between heating and cooling sequences in the magnetization-temperature curves at zero field. Depth-profile measurements from polarized neutron reflectometry (PNR) indicate up to 91% volume fraction in GdIG on Si. At the interface, PNR reveals a region containing magnetized Fe-doped GGG, a low-density GdIG at the GGG/GdIG interface, and a thin magnetically dead layer at the Si/GdIG interface. Cross-sectional transmission electron microscopy and energy dispersive x-ray spectroscopy confirm the assessment of PNR. The magnetic characteristics of interfacial regions are attributed to intermixing of Fe-Ga at the GGG/GdIG interface and the presence of amorphous Fe-Si at the Si/GdIG interface.
With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Betwe...
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Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source...
Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source code and are commonly adopted by existing DL-based vulnerability detection methods. However, the existing methods are still limited by the fact that GNNs are essentially difficult to handle the connections between long-distance nodes in a code structure graph. Besides, they do not well exploit the multiple types of edges in a code structure graph (such as edges representing data flow and control flow). Consequently, despite achieving state-of-the-art performance, the existing GNN-based methods tend to fail to capture global information (i.e., long-range dependencies among nodes) of code graphs. To mitigate these issues, in this paper, we propose a novel vulnerability detection framework with grAph siMplification and enhanced graph rePresentation LEarning, named AMPLE. AMPLE mainly contains two parts: 1) graph simplification, which aims at reducing the distances between nodes by shrinking the node sizes of code structure graphs; 2) enhanced graph representation learning, which involves one edge-aware graph convolutional network module for fusing heterogeneous edge information into node representations and one kernel-scaled representation module for well capturing the relations between distant graph nodes. Experiments on three public benchmark datasets show that AMPLE outperforms the state-of-the-art methods by 0.39%-35.32% and 7.64%-199.81% with respect to the accuracy and F1 score metrics, respectively. The results demonstrate the effectiveness of AMPLE in learning global information of code graphs for vulnerability detection.
The development of game technology has opened up new possibilities in learning methods through educational games. However, in Indonesia, the application of this concept is lacking. One lesson that can benefit from edu...
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