Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to a...
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This study utilizes the game rules of a falling-puzzle game, developed as a consumer-oriented digital game, in programming education for young people. When digital games are used in programming education, they are oft...
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The development of satellite communications has received considerable attention in recent years. Early satellite communications were dominated by voice and low-speed data services, but now they must support high-speed...
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Accessible software for picture processing and editing is widely available, facilitating the modification and manipulation of digital photographs. Crucial elements can now be added or removed from an image without lea...
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The domain adaptation method effectively mitigates the negative impact of domain gaps on the performance of super-resolution (SR) networks through the guidance of numerous target domain low-resolution (LR) images. How...
Predicting stable crystal structures for complex systems that involve multiple elements or a large number of atoms presents a formidable challenge in computational materials science. A recent study presents an efficie...
Low-Rank Adaptation (LoRA) is a widespread parameter-efficient fine-tuning algorithm for large-scale language models. It has been commonly accepted that LoRA mostly achieves promising results in single-task, low-resou...
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Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multisp...
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Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multispectral data,hinders the classification accuracy from being further improved and tends to meet the performance *** this reason,we develop a novel superpixel-based subspace learning model,called Supace,by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC *** can learn a common subspace across multimodal RS data,where the diverse and complementary information from different modalities can be better combined,being capable of enhancing the discriminative ability of to-be-learned features in a more effective *** better capture semantic information of objects in the feature learning process,superpixels that beyond pixels are regarded as the study object in our Supace for *** experiments have been conducted on two popular hyperspectral and multispectral datasets,demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.
This paper investigates the muti-trip vehicle routing problem (MTVRP) with considerations of vehicle capacity and time constraints. The problem aims to determine a set of trips and assign each trip to a vehicle in a p...
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Cervical cell segmentation is a significant task in medical image analysis and can be used for screening various cervical diseases. In recent years, substantial progress has been made in cervical cell segmentation tec...
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