Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and *** paper introduces a generative adversarial network model that incorporates an attention mechanis...
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Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and *** paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price ***,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock *** discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock ***,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation *** results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
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Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
Slot filling,to extract entities for specific types of information(slot),is a vitally important modular of dialogue systems for automatic *** responses can be regarded as the weak supervision of patient *** this way,a...
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Slot filling,to extract entities for specific types of information(slot),is a vitally important modular of dialogue systems for automatic *** responses can be regarded as the weak supervision of patient *** this way,a large amount of weakly labeled data can be obtained from unlabeled diagnosis dialogue,alleviating the problem of costly and time-consuming data ***,weakly labeled data suffers from extremely noisy *** alleviate the problem,we propose a simple and effective Co-WeakTeaching *** method trains two slot filling models *** two models learn from two different weakly labeled data,ensuring learning from two ***,one model utilizes selected weakly labeled data generated by the other,*** model,obtained by the Co-WeakTeaching on weakly labeled data,can be directly tested on testing data or sequentially fine-tuned on a small amount of human-annotated *** results on these two settings illustrate the effectiveness of the method with an increase of 8.03%and 14.74%in micro and macro f1 scores,respectively.
Transformers have been widely studied in many natural language processing (NLP) tasks, which can capture the dependency from the whole sentence with a high parallelizability thanks to the multi-head attention and the ...
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Transformers have been widely studied in many natural language processing (NLP) tasks, which can capture the dependency from the whole sentence with a high parallelizability thanks to the multi-head attention and the position-wise feed-forward network. However, the above two components of transformers are position-independent, which causes transformers to be weak in modeling sentence structures. Existing studies commonly utilized positional encoding or mask strategies for capturing the structural information of sentences. In this paper, we aim at strengthening the ability of transformers on modeling the linear structure of sentences from three aspects, containing the absolute position of tokens, the relative distance, and the direction between tokens. We propose a novel bidirectional Transformer with absolute-position aware relative position encoding (BiAR-Transformer) that combines the positional encoding and the mask strategy together. We model the relative distance between tokens along with the absolute position of tokens by a novel absolute-position aware relative position encoding. Meanwhile, we apply a bidirectional mask strategy for modeling the direction between tokens. Experimental results on the natural language inference, paraphrase identification, sentiment classification and machine translation tasks show that BiAR-Transformer achieves superior performance than other strong baselines.
Databases play a vital role in data management in many fields,such as finance,government,telecommunications,energy,electricity,transportation,*** the database management system has become a core foundational *** is an...
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Databases play a vital role in data management in many fields,such as finance,government,telecommunications,energy,electricity,transportation,*** the database management system has become a core foundational *** is an enterprise-grade open-source database,a product of deep integration of research and development from Huawei,Tsinghua University,and China Mobile in the past decade.
Multimodal sentiment analysis (MSA) seeks to understand human affection by leveraging signals from multiple modalities. A core challenge in MSA is the effective extraction of sentimental relations between these signal...
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Using the semiclassical ensemble model,the dependence of relative amplitude for the recollision dynamics in nonsequential double ionization(NSDI)of neon atom driven by the orthogonally polarized two-color field(OTC)la...
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Using the semiclassical ensemble model,the dependence of relative amplitude for the recollision dynamics in nonsequential double ionization(NSDI)of neon atom driven by the orthogonally polarized two-color field(OTC)laser field is theoretically *** the dynamics in two typical collision pathways,recollision-impact-ionization(RII)and recollisionexcitation with subsequent ionization(RESI),is systematically *** results reveal that the V-shaped structure in the correlated momentum distribution is mainly caused by the RII mechanism when the relative amplitude of the OTC laser field is zero,and the first ionized electrons will quickly skim through the nucleus and share few energy with the second *** the relative amplitude increases,the V-shaped structure gradually disappears and electrons are concentrated on the diagonal in the electron correlation spectrum,indicating that the energy sharing after electrons collision is symmetric for OTC laser fields with large relative *** studies show that changing the relative amplitude of the OTC laser field can efficiently control the electron–electron collisions and energy exchange efficiency in the NSDI process.
Cohesive subgraph search is a fundamental problem in bipartite graph *** integers k andℓ,a(k,ℓ)-biplex is a cohesive structure which requires each vertex to disconnect at most k orℓvertices in the other ***(k,ℓ)-biple...
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Cohesive subgraph search is a fundamental problem in bipartite graph *** integers k andℓ,a(k,ℓ)-biplex is a cohesive structure which requires each vertex to disconnect at most k orℓvertices in the other ***(k,ℓ)-biplexes has been a popular research topic in recent years and has various ***,most existing studies considered the problem of finding(k,ℓ)-biplex with the largest number of *** this paper,we instead consider another variant and focus on the maximum vertex(k,ℓ)-biplex problem which aims to search for a(k,ℓ)-biplex with the maximum *** first show that this problem is Non-deterministic Polynomial-time hard(NP-hard)for any positive integers k andℓwhile max{k,ℓ}is at least *** by this negative result,we design an efficient branch-and-bound algorithm with a novel *** particular,we introduce a branching strategy based on whether there is a pivot in the current set,with which our proposed algorithm has the time complexity ofγ^(n)n^(O(1)),whereγ<*** addition,we also apply multiple speed-up techniques and various pruning ***,we conduct extensive experiments on various real datasets which demonstrate the efficiency of our proposed algorithm in terms of running time.
Ground-based tests are important for studying hypervelocity impact(HVI)damage to spacecraft pressure vessels in the orbital debris *** analyzed the damage to composite overwrapped pressure vessels(COPVs)in the HVI tes...
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Ground-based tests are important for studying hypervelocity impact(HVI)damage to spacecraft pressure vessels in the orbital debris *** analyzed the damage to composite overwrapped pressure vessels(COPVs)in the HVI tests and classified the damage into non-catastrophic damage and catastrophic *** proposed a numerical simulation method to further study non-catastrophic damage and revealed the characteristics and mechanisms of non-catastrophic damage affected by impact conditions and internal *** fragments of the catastrophically damaged COPVs were collected after the *** crack distribution and propagation process of the catastrophic ruptures of the COPVs were *** findings contribute to understanding the damage characteristics and mechanisms of COPVs by HVIs.
Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar...
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Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.
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