The increasing presence of robots and unmanned systems as a result of technological breakthroughs and falling costs has increased the demand for robust and scalable multi-agent control systems. We developed a multi-UA...
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The use of transformer models in natural language processing(NLP) has gained significant attention in recent years due to their exceptional performance in various language tasks. This paper explores the application of...
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The use of transformer models in natural language processing(NLP) has gained significant attention in recent years due to their exceptional performance in various language tasks. This paper explores the application of transformer models in rumor detection, the relevant research on rumor detection, the use of transformer models, and the techniques used to boost the model's performance. Ultimately, the purpose of this paper is to provide insight into the potential of transformer models in detecting rumors on social media. Unlike other rumor-detecting models,the author adds a sentiment analysis model as a supplement to rumor detection. Also, to address the issue of insufficient information in early-stage comments on rumors, this paper proposes a decision-level fusion method before the output layer, which effectively utilizes information from different sources and minimizes the negative impact of insufficient data sources. The early-stage rumor detection accuracy of the model is greatly enhanced by this method, therefore, the article's main contributions can be regarded as follows: First, this paper proposes a combination of an aspect level text sentiment analysis method according to syntactic features, gated recurrent units,and a self-attention mechanism. Experimental findings demonstrate that, compared to the original model without taking the sentiment analysis method into account, the proposed network model has advantages in accuracy and Macro F1 evaluation indexes. Second, a cross-text rumordetecting method based on Decision-level fusion is proposed. Its advantage is that when the cross-text data source is incomplete and a certain text is missing, another text can be used to continue the analysis. Experimental findings show the effectiveness of this method in improving the accuracy of emotion recognition by integrating data from different modes. Third, a comparison is conducted between the performance of the Transformer-sentiment model and other related models, Text
One of the major achievements of the modern theory of the non-linear dynamical systems is a conclusion about the opportunity of the existence of a dissipative structures with a high degree of ordering of a movement su...
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One of the major achievements of the modern theory of the non-linear dynamical systems is a conclusion about the opportunity of the existence of a dissipative structures with a high degree of ordering of a movement such as Tailor's whirls and Bernard's cells. As experiment shows this is not a single dynamic phenomenon in which a high degree order of motion is observed when the dynamic system is far from steady state of dynamic balance and at the same time there is a large transfer of energy passing through it. Here a new kind of dissipative structure connected with the phenomenon of carrying an impulse from the vibrating wall of a thin cylinder to bulky block through a layer of a liquid is described as a new result. From the authors point of view the results of the experiment and computer modelling have all attributes of a dissipative structure.
To address the complexities and incompleteness of manual electroencephalogram (EEG) feature extraction, this paper proposes a fatigue detection method based on a multi-feature fusion convolutional neural network with ...
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The technique that allows for researchers to read and convert the genetic information found in the DNA of any organisms is called Genome Sequencing. Genome Sequencing involves determining the order of the nucleotide s...
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ISBN:
(纸本)9781509059607
The technique that allows for researchers to read and convert the genetic information found in the DNA of any organisms is called Genome Sequencing. Genome Sequencing involves determining the order of the nucleotide subunits found in DNA, which consists of a small number of bases called short reads. The human genome is approximately 3 billion bases in length, which would take months or years to be processed on a single machine. So large numbers of short reads are available in such sequencing. In these cases, the first step in the data analysis pipeline is the short read mapping problem. Speed is becoming significantly important and challenging due to the huge volume of data. In this paper, we have proposed an approach that will take a dataset of DNA sequencing as an input and split them across the cluster machine by applying MapReduce implementation of Hadoop to make the search efficient for large scale genome sequencing applications.
Online learning platforms come with a number of difficulties. To identify the student who does not do the given assignment within the allotted time. Researchers have been attempting to solve this issue in the literatu...
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