Most state-Of-The-Art(sOTA) Neural Machine Translation(NMT) systems today achieve outstanding resultsbased only on large parallel *** large-scale parallel corpora for high-resource languages is easily ***,the transla...
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Most state-Of-The-Art(sOTA) Neural Machine Translation(NMT) systems today achieve outstanding resultsbased only on large parallel *** large-scale parallel corpora for high-resource languages is easily ***,the translation quality of NMT for morphologically rich languages isstill unsatisfactory,mainly because of the data sparsity problem encountered in Low-Resource Languages(LRLs).In the low-resource NMT paradigm,Transfer learning(TL) has been developed into one of the most efficient *** is difficult to train the model on high-resource languages to include the information in both parent and child models,as well as the initially trained model that only contains the lexicon features and word embeddings of the parent model instead of the child languages *** this work,we aim to address this issue by proposing the language-independent Hybrid Transfer learning(HTL) method for LRLs by sharing lexicon embedding between parent and child languages without leveraging back translation or manually injecting ***,we train the High-Resource Languages(HRLs) as the parent model with its ***,we combine the parent and child language pairs using the oversampling method to train the hybrid model initialized by the previously parent ***,we fine-tune the morphologically rich child model using a hybrid ***,we explore some exciting discoveries on the original TL *** resultsshow that our model consistently outperforms five sOTA methods in two languages Azerbaijani(Az) and Uzbek(Uz).Meanwhile,our approach is practical and significantly better,achieving improvements of up to 4:94 and 4:84 BLEU points for low-resource child languages Az ! Zh and Uz ! Zh,respectively.
With the use of artificial intelligence techniquessuch as deep learning, thisproject can analyze the traffic conditions of a designated area, including the length of traffic queues and the number of pedestrians wait...
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Facial expression recognition is an intriguing and demanding subject in the realm of computer vision. In this paper, we propose a novel deep learning-basedstrategy to address the challenges of facial expression recog...
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Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling isstill a ch...
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Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling isstill a challenging problem due to its modeling difficulties and low optimization *** paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based ***,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway ***,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast *** experiments on various delay scenarios are *** results demonstrate the effectiveness of the proposed method in comparison to other popular methods.
This paper presents a scheme for intelligent advice generation with the help of a fuzzy rule base, meant for a self-paced learner in an e-learning environment. It employs a fuzzy machine that is fed with performance p...
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The stable operation of transmission lines under severe weather conditions is critical to the reliability of power systems. Accurately identifying the state of transmission lines during snowy and other extreme weather...
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The authors present some of the analytical and design foundations for an integrated analysisstructure for knowledge acquisition and design. Concept maps, IDEF (integrated computer-aided manufacturing definition), and...
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ISBN:
(纸本)078030084X
The authors present some of the analytical and design foundations for an integrated analysisstructure for knowledge acquisition and design. Concept maps, IDEF (integrated computer-aided manufacturing definition), and storyboards are combined into an integrated structure for knowledge representation, called cognitive maps, and a hypermedia-based analytical framework is established using graphical spreadsheets. The authors describe the preliminary results of a project to define this hypermedia environment and integrated software.
Emotion-Cause joint extraction is to extracting both the emotion and its corresponding cause from the given text, which has a wide range of application scenarios. Previous work only considered emotion extraction, caus...
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Visual camera localization can be defined as a computer vision problem, that aims to retrieve the pose of a camera given a query image which was originally captured by the camera. This paper proposes a new deep-learni...
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Thisstudy presents a novel, integrated modeling framework that combines machine learning (ML) techniques with physics-based approaches to forecast both CO 2 emissions and global temperature anomalies. Unlike prior re...
Thisstudy presents a novel, integrated modeling framework that combines machine learning (ML) techniques with physics-based approaches to forecast both CO 2 emissions and global temperature anomalies. Unlike prior research that typically addresses these components in isolation, this work concurrently applies and compares five advanced ML models—Long short-Term Memory (LsTM), Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), Facebook Prophet, and a hybrid CNN-LsTM—alongside two physics-based models: a zero-dimensional Energy Balance Model (EBM) and a simplified General Circulation Model (GCM) adapted from NAsA's GIss *** monthly global datasets from January 2000 to April 2024, obtained from the National Oceanic and Atmospheric Administration (NOAA) and the scripps Institution of Oceanography, the models are evaluated based on predictive accuracy (RMsE, MsE, MAE, R 2 ), scalability, and interpretability. Prophet demonstrated the highest accuracy for CO 2 emission forecasting (RMsE = 0.035), while LsTM achieved the best performance in temperature anomaly prediction (RMsE = 0.086). Physics-based models provided interpretable and computationally efficient long-term projections but lacked short-term *** facilitate reproducibility and practical application, we developed ClimateChange-ML, an open-source software package that implements all proposed models, includes trained weights, and provides full documentation and visualization *** novelty of this work lies in its dual-modeling strategy and comprehensive comparative evaluation, highlighting the complementary strengths of data-driven and physically grounded methods. This integrated approach offers a more holistic framework for climate forecasting across multiple temporal scales, providing valuable insights for both scientific understanding and climate policy planning.
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