Modern mobile devices have access to enormous amounts of user data including text, images, speech, etc., which can be utilized to train high-performance learning models and enhance the user experience. However, access...
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Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing *** the differences in LULC classification schemes,there is a lack of research on assessing ...
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Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing *** the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified ***,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification *** validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,*** FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,*** consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,*** contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern *** size was the most important factor for mapping *** area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and *** cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four *** results can provide valuable suggestions for selecting cropland products at the national or provinc
In the stage of rapid urban renewal, the renovation of old residential areas is moving along very slowly. Traditional methods of old residential areas assessment have the pain points of low efficiency and high consump...
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The paper presents a method to obtain Pythagorean fuzzy information (PFI) based on a bidirectional long short-term memory (BiLSTM) neural network. The method first uses the Word2Vec word embedding model for vectorized...
The paper presents a method to obtain Pythagorean fuzzy information (PFI) based on a bidirectional long short-term memory (BiLSTM) neural network. The method first uses the Word2Vec word embedding model for vectorized text pre-processing. Then, to solve the problems of one-way propagation and gradient explosion in traditional recurrent neural networks, BiLSTM is used to extract the global features of the comment information. Finally, the PFI is constructed based on the Softmax classifier and Pythagorean fuzzy set definition. Experimental results show that the proposed method can accurately capture online reviews’ satisfaction, dissatisfaction, and hesitation sentiment tendencies. Compared with other methods, such as RNN, CNN, LSTM, and GRU, the proposed method has higher accuracy, precision, and F1 values.
Open information extraction (OIE) is a key task in natural language processing. Compared to the flourishing development of OIE systems in English, there are very few high-quality Chinese OIE systems that are publicly ...
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Open information extraction (OIE) aim to extract surface relations and their corresponding arguments from natural language text, irrespective of domain. In this paper, we present an innovative OIE model, APRCOIE, tail...
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Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. R...
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(纸本)9798331314385
Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations. Our code is available at https://***/ml-stat-Sustech/Local-Perplexity-Ranking
Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain s...
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The "dual-carbon" objective presents a huge challenge for China and the world, with profound implications for the advancement of China’s eco-friendly economy. Additionally, informatization development has a...
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In this paper, our main aim is to investigate the strong convergence for a neutral McKean-Vlasov stochastic differential equation with super-linear delay driven by frac- tional Brownian motion with Hurst exponent H ∈...
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