Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries bu...
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Keyphrase extraction aims to extract important phrases that reflect the main topics of a document. Recently, deep learning methods are used to model semantic information and rank candidates based on the similarities b...
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Keyphrase extraction aims to extract important phrases that reflect the main topics of a document. Recently, deep learning methods are used to model semantic information and rank candidates based on the similarities between the n-grams and the document. However, existing keyphrase extraction methods mainly caused the keyphrase extraction task to be independent of the embedding. Based on the fact that phrases that are semantically closer to the document are more likely to become keyphrases, we propose a novel contrastive learning strategy for supervised keyphrase extraction by integrating local and global Information of the document. A pre-trained RoBERTa model is used to model contextual information of sub-words in the document. Then, the embedding vectors of n-grams and the document are calculated by the convolution neural layers. Finally, we propose a novel loss function for efficiently ranking candidate phrases by combining n-gram features and document embeddings during the training of the model.
This article deals with the accuracy check of the PQ analyzers using the OMICRON CMC 356 calibrator. This article describes the principle and concept of three applications used to test the accuracy of various quantiti...
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Adoption of cloud computing has led to increased demand by industry for cloud-ready graduates and pressure on universities to include cloud in their curriculum. While industry courses exist, these are almost all vendo...
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With the advancement of internet,there is also a rise in cybercrimes and digital ***(Distributed Denial of Service)attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant th...
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With the advancement of internet,there is also a rise in cybercrimes and digital ***(Distributed Denial of Service)attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital *** cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target *** a result,targeted services are inaccessible by the legitimate *** prevent these attacks,researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS ***,the challenge in using these techniques is the limitations on capacity for the volume of data and the required processing *** this research work,we propose the framework of reducing the dimensions of the data by selecting the most important features which contribute to the predictive *** show that the‘lite’model trained on reduced dataset not only saves the computational power,but also improves the predictive *** show that dimensionality reduction can improve both effectiveness(recall)and efficiency(precision)of the model as compared to the model trained on‘full’dataset.
Raman scattering and calorimetric measurements on lithium borate, (Li2O)x(B2O3)100-x, and sodium borate, (Na2O)x(B2O3)100-x, glasses, 0 2O)x(B2O3)100-x glasses, a wide square-well like variation of the enthalpy of rel...
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Large-scale transformer-based models like the Bidi-rectional Encoder Representations from Transformers (BERT) are widely used for Natural Language Processing (NLP) applications, wherein these models are initially pre-...
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To offer relevant and useful recommendations, the crucial role of recommender systems in e-commerce industry is to predict the users’ concern for various items by estimating items’ attributes and users’ preferences...
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To offer relevant and useful recommendations, the crucial role of recommender systems in e-commerce industry is to predict the users’ concern for various items by estimating items’ attributes and users’ preferences. The reliability of a recommender system is usually assessed through accuracy and speed of relevant recommendations for a variety of items. The matrix factorization-based stochastic gradient descent (SGD) methods proposed by researchers lack memory needed to capture the ratings history hidden in the previous iterations. Recently, sliding window-based SGD strategies designed for Recommender systems and Hammerstein nonlinear systems gained attention due to the improved performance in terms of convergence speed and estimated accuracy. The memory impact with regard to the historical information enhances the performance of sliding window-based SGD techniques. However, sliding window-based methods are deficient in capturing the ratings history based on the users’ rating patterns. Hence utilizing the same window length for the set of observed ratings rated by users. Therefore, we propose an improved sliding window-based SGD strategy to acquire historical information of the ratings with respect to a user’s rating patterns for efficient matrix factorization of recommender systems. The proposed strategy performs significantly by accomplishing fast convergence speed and accuracy for window sizes greater than 1. The accuracy of the suggested technique is verified for two benchmark datasets such as ML-100 K and Film-Trust. However, the authenticity of the proposed method as compared to the standard counterpart (window size = 1) is confirmed through Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The average improvements achieved by the proposed strategy in terms of RMSE and MAE over the baseline for ML-100 K dataset are 0.726% and 2.245% respectively. Whereas the proposed method accomplishes considerable average improvement of 7.89% and 9.41% for RMSE an
Fake news has become a significant problem in modern society, with the rapid dissemination of false information through social media and online platforms. This has led to an urgent need for effective methods to detect...
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The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems. From peripheral devices that monitor...
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