Automating the process of analyzing the match footage is one of the important things that is around the football stakeholders. By using deep learning algorithms and architectures, we can have an advanced process of an...
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Corpus linguistics contributes significantly to numerous fields, including lexicography, it has a crucial and effective role in providing valuable linguistic data, and empirical evidence of language use, enabling the ...
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ISBN:
(纸本)9783031804373;9783031804380
Corpus linguistics contributes significantly to numerous fields, including lexicography, it has a crucial and effective role in providing valuable linguistic data, and empirical evidence of language use, enabling the construction of more accurate and comprehensive dictionaries, and reflecting how language is used in real-world contexts. Corpus linguistics has revolutionized the way dictionaries are constructed, compiled, and revised. Corpus linguistics has benefited from developments in computational linguistics, particularly in Natural Language Processing (NLP), providing a set of tools that greatly contribute to the processing, analysis, and exploration of language data. The primary objective of the paper is to demonstrate and highlight the effectiveness of using a textbook-based corpus in the development of school dictionaries. The research aims to explore how this corpus linguistics methodology, coupled with NLP tools, contributes to creating contextually relevant and pedagogically oriented dictionaries for educational purposes. This paper introduces a novel model for building corpora specifically designed for school dictionaries. The model's effectiveness is demonstrated by creating a corpus from a single Arabic language textbook through a detailed methodology and a real-world use case.
Deep learning-based models have been shown to outperform human beings in many computer vision tasks with massive available labeled training data in learning. However, humans have an amazing ability to easily recognize...
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Deep learning-based models have been shown to outperform human beings in many computer vision tasks with massive available labeled training data in learning. However, humans have an amazing ability to easily recognize images of novel categories by browsing only a few examples of these categories. In this case, few-shot learning comes into being to make machines learn from extremely limited labeled examples. One possible reason why human beings can well learn novel concepts quickly and efficiently is that they have sufficient visual and semantic prior knowledge. Toward this end, this work proposes a novel knowledge-guided semantic transfer network (KSTNet) for few-shot image recognition from a supplementary perspective by introducing auxiliary prior knowledge. The proposed network jointly incorporates vision inferring, knowledge transferring, and classifier learning into one unified framework for optimal compatibility. A category-guided visual learning module is developed in which a visual classifier is learned based on the feature extractor along with the cosine similarity and contrastive loss optimization. To fully explore prior knowledge of category correlations, a knowledge transfer network is then developed to propagate knowledge information among all categories to learn the semantic-visual mapping, thus inferring a knowledge-based classifier for novel categories from base categories. Finally, we design an adaptive fusion scheme to infer the desired classifiers by effectively integrating the above knowledge and visual information. Extensive experiments are conducted on two widely used Mini-ImageNet and Tiered-ImageNet benchmarks to validate the effectiveness of KSTNet. Compared with the state of the art, the results show that the proposed method achieves favorable performance with minimal bells and whistles, especially in the case of one-shot learning.
Exploratory dataanalysis provides visualexploration of data and also allows for the identification of trends and relationships between variables. The aim of this research is to study about different factors involved...
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The on-going evolution of technology of capturing and generation video, sound, RF, etc. signals results in very HDR data to be stored and processed. The analysis of human perception of different signals is showing tha...
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Recognition of human's emotional states is one of crucial issues in many Brain-Computer-Interface (BCI) applications. In the current study, the problem of emotion recognition based on EEG data will be addressed. T...
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In this paper we present a virtual Reality (vR) tool that facilitates the visualisation and exploration of context-based, multi-level annotations in archaeology. Indeed, thanks to photogrammetry and laser scanning tec...
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This study presents an innovative approach to emotion recognition by integrating RGB Kinect video data with physiological signals, including electroencephalography (EEG), electrocardiography (ECG), and galvanic skin r...
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Public databases such as the NCBI Gene Expression Omnibus (GEO) house millions of experimental gene expression datasets invaluable for transcriptome meta-analysis, enabling researchers to identify genes, pathways, and...
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The leather industry confronts persistent challenges in the maintenance of its product quality, often produced with defects that escape from the traditional conventional inspection methods. This research seeks the tra...
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