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Conversation summarisation is the transformation of long conversational texts into concise and accurate summaries, the importance of which lies in improving the user experience and information filtering. As an important natural language processing task, conversation summarisation can provide concise and accurate information and avoid repetition and redundancy. In the dialogue summarisation task, pre-trained language models can be used to summarise long conversations and generate concise and accurate summaries. The aim of this paper is to investigate the possibility of using bidirectional and auto-regressive transformer models for dialogue summarisation tasks. In our experiments, we analysed the characteristics of the Query-based Multi-domain Meeting Summarization (QMsum) dialogue summarisation dataset, proposed a dialogue summarisation model based on the Bidirectional and Auto-Regressive Transformer model, and designed evaluation experiments to compare its performance with other methods in the dialogue summarisation task. The experimental results show that the results of this thesis are important for facilitating the development of dialogue summarisation tasks and the application of the Bidirectional and Auto-Regressive Transformer model.
From 2020 to 2023, SARS-CoV-2 destroyed much of our society, while few treatments were available due to the time required for drug discovery. However, with recent advancements in artificial intelligence, it is now ready to fight viruses such as SARS-CoV-2. Chemprop, a machine-learning backbone for molecular properties prediction, can be used to discover novel antiviral drugs by training a classifier model with hundreds of thousands of data points that include molecular information represented by SMILES strings and the observed efficacy in inhibiting SARS-CoV-2 in laboratory tests. The resulting model predicts the effectiveness of untested molecules, which then can be manually tested, minimizing tedious hunting traditionally done by human scientists. With promising performance, the proposed method pushes the boundary of machine learning’s involvement in drug research. The trained model achieved a high accuracy in predicting the effectiveness of drugs against SARS-CoV-2 with an AUC score of 0.8455. However, the model loses accuracy when predicting the effectiveness of drugs against SARS-CoV, a different strand of coronavirus, with an AUC of 0.7302. The model was then run on one of the data sets to locate the molecule most likely effective against COVID-19, demonstrating its applicability. The result was a molecule with SMILES string CN1CCN(CC1)C(=O)COC=2C=CC(C)=CC2 also called 1-(4-Methyl-piperazin-1-yl)-2-p-tolyloxy-ethanone. Then the model DrugChat was utilized to determine the properties of the molecule. The model’s ability to find likely drugs can hasten drug research drastically, potentially saving countless lives during future pandemics.
The project's objective is to simulate movement in the study and analysis of motion simulation problems and to propose simulation algorithms based on numerical calculation methods. It has many practical application values. In engineering and science, it is often necessary to simulate and analyze the motion of the fold to study the motor and dynamic properties of the fold, providing the theoretical basis and solutions to practical problems. With the support of modern computers and numerical computing technology, the problem of motion simulation has become a popular research direction. Many scholars and engineers have proposed different numerical calculation methods and simulated algorithms to simulate the motion process of the fold and analyze its motion laws. This article introduces the basic knowledge of physics and the formulas of motion, as well as some important concepts and theories related to motion. The motion simulation algorithm was then analyzed and discussed in detail. Subsequently, numerical calculations were prepared using MATLAB software, and simulated experiments were conducted using examples to analyze dynamic changes. Finally, the prospects for the future direction of research are presented. Therefore, if the initial speed is the same, the width and length of the time will increase.
Nowadays, credit payment is a very common way to pay, such as credit cards, loans, many people can use their credit as a guarantee to borrow money from the bank, however some people will default. So we have to predict whether the borrower will pay on time, it is known as credit risk assessment. In this paper, we analyze a data set on credit risk to predict whether individuals will be late on their payments, helping financial firms improve their earnings and reduce their losses. We not only made predictions on the data, but also analyzed the relationship between the variables that affect the overdue probability to find some specific associations. Specifically, we performed ANOVA analysis and found that married people borrowed significantly more than other groups, and the delinquency rate of people with higher education was lower, and the delinquency rate of married people was higher than that of unmarried people. In addition, we conducted a binary logistic regression and found that gender had no significant impact on the prediction results, but an individual’s amount of bill statement, amount of previous payment, past repayment situation and Amount of the given credit had an impact on the prediction results. Other variables, such as marital status and education, can also impact the predicted results. Our research puts forward more factors affecting credit risk and also different angles that can be used to analyzes individual credit risk. This has a guiding role for financial firms like banks and other companies in the financial industry, providing more ways to help them analyze the credit risk of borrowers.