bluuebunny/crossref_metadata_2025 huggingface dataset:
Blended learning e a formação contínua e em serviço de professores de educação física
Authors: Cortez Luiz Claudio dos Santos , Palma José Augusto Victoria , Palma Ângela Pereira Teixeira Victoria
Published: March 2024

O estudo em questão, refere-se a parceria firmada entre a Secretária Municipal de Educação de Tamarana no Estado do Paraná, dois pesquisadores da Universidade Estadual de Londrina-Paraná e dois pesquisadores da Universidade Lusófona de Portugal, com intuito de conceber, planejar e concretizar um processo de formação contínua e em serviço na modalidade Blended Learning aos professores do município vinculados à construção e implementação do Projeto Pedagógico Curricular da Educação Física. A formação contínua e em serviço teve a duração de 20 meses e se encontra no âmbito das ações de inserção das Tecnologias Digitais de Informação e Comunicação e o processo de desenvolvimento profissional docente. Deste modo cumprirá as exigências legais do Conselho Nacional de Educação que homologou a Resolução nº 02/2017, que implementa a Base Nacional Comum Curricular em todo território brasileiro e considera a Educação Física como um dos componentes curriculares integrantes da área de linguagens. Com abordagem qualitativa elegemos a pesquisa descritiva, utilizando o referencial metodológico da pesquisa-ação, sendo nossa hipótese de trabalho que quando se pretende construir e implementar um projeto pedagógico curricular, e que também se pretenda que esse processo seja gerador de tomada de consciência em nível crítico-emancipatório sobre a educação escolarizada e à docência, ele deverá vir acompanhado de uma qualificação contínua na qual os envolvidos sejam levados, pelas ações que ali realizarem, a refleti-lo, a analisa-lo e compreende-lo, num contexto de prática crítico-reflexiva, que pode ser entendido como ambiente investigativo. Será o fazer e compreender. Uma das mais importantes características de um processo emancipatório para a formação/educação/capacitação envolve a produção/construção/reelaboração do próprio conhecimento pelos envolvidos, o que lhes permite assim, construir/reconstruir novos entendimentos e saberes.
Crianças e adolescentes: olhar sobre a pobreza, a qualidade de vida e o bem-estar subjetivo
Authors: Abate Elizabete Aparecida Bragatto , Stoltz Tania , D’Aroz Marlene Schüssler , Asinelli-Luz Araci , Bianchi Alessandra Sant’Anna
Published: March 2024

Acredita-se que a posição do indivíduo na vida, especialmente no contexto cultural e nos sistemas de valores, define a sua qualidade de vida e bem-estar. Investigou-se a qualidade de vida e o bem-estar subjetivo de crianças e adolescentes entre 10 e 12 anos, socioeconomicamente desfavorecidos, e a sua percepção de pobreza. Trata-se de estudo Quanti-Quali, utilizando o questionário Kidscreen-52 e entrevista acerca do entendimento sobre pobreza. Realizou-se análise estatística descritiva (quantitativo) e foram criadas categorias considerando as entrevistas (qualitativo). Nos resultados do Kidscreen-52, as dimensões sentimentos e auto percepção tiveram as maiores pontuações, indicando bons sentimentos e satisfação em relação a si mesmos. Em contrapartida, a dimensão estado emocional teve o menor valor,  sendo maior apenas que aspectos financeiros. Ainda assim, os resultados mostram que os participantes quase sempre estão bem emocionalmente, embora o desvio padrão elevado indique uma oscilação importante na amostra, uma vez que o menor resultado está relacionado com o sentimento de tristeza. Quanto à percepção de pobreza, foram encontradas três categorias: a aparência exterior determina a condição financeira ou de vida; a identificação com o outro está relacionada à aparência externa e o consumo diferencia o rico do pobre. Os participantes avaliaram as situações de pobreza e riqueza, considerando suas impressões do mundo, algumas vezes, movidos por estereótipos e, outras, pela própria observação das condições de vida, as quais estão submetidos; conseguem visualizar situações positivas em suas vidas, como o convívio com os pais e o sustento atendido por eles, observando-os como principal meio para conquistar aquilo que desejam. Concluiu-se que a qualidade de vida e o bem-estar subjetivo de crianças e adolescentes estão relacionados à percepção da pobreza, sendo necessário um novo olhar que valorize os aspectos físicos, econômicos e psicológicos, baseado na percepção dos próprios protagonistas.
Endowment Effect: Impact Factors and Influencing Mechanism
Authors: Duan Renhao
Published: March 2024

As a theoretical branch of behavioral economics, the endowment effect (EE) reflects the research method of behavioral economics, challenges the assumption that people are rational in traditional economics, and complements the deficiency of the Coase theorem by emphasizing that the phenomenon that people overvalue an item after owning, then it will affect the transaction of the item. In this paper, the author introduces the basic theories about the EE, analyzes the driving factors of the EE, and introduces the latest research findings on the basis of the loss aversion (LA) theory, providing readers with a new way to understand the EE. At the same time, as a link impressionable to the EE in the transaction, the author also analyzes and compares the research results of previous researchers on the influence of EE on the negotiation process, analyzes the reasons why the negotiation participants should avoid or reduce the influence of EE in the negotiation to achieve the goal of negotiation, and puts forward suggestions for the negotiators.
Research on Spatial Migration Patterns and Influencing Factors of Western Talents: Based on Data from "Double first-class" Universities in China
Authors: Zhao Wenrui , Feng Xuzhao , Chen Xingyue , Sheng Yuxue , Jiang Shujing
Published: March 2024

The western region's talent loss issue has received widespread attention. Promoting the construction of human resources in the west has become key to forming an important high-quality development pole in western China. This study analyzes the graduate migration data of first-class universities in western China between 2016 and 2020 and draws the following conclusions: Overall, the retention rate of college graduates in western China across seven provinces is high, with a relatively higher retention rate observed in the place of study in four southwestern provinces. The retention rate of different provinces' place of study showed different trends during the period of 2016-2020, and the retention rate of different provinces also showed different trends during the same period. In 2020, western college graduates continued to show a preference for employment in popular provinces such as Guangdong, Beijing, and Shanghai, while they showed a greater inclination towards choosing employment opportunities in the South and Central regions of China. The results based on the directed migration model show that economic factors and natural and educational resources in a city have a greater impact on employment decisions. Analysis of heterogeneity found that graduates from areas with a high sticking rate are more susceptible to population size, human resources, and economic factors, and that cultural resources and transportation resources also have a significant impact on talent migration. Policy recommendations include western provinces taking higher education as a key factor to utilizing employment retention advantages to attract and retain high-quality talents.
The Impact of Companion on the Consumption Behavior of Students in China
Authors: Yan Yiming
Published: March 2024

This study investigates the influence of peers on the consumption patterns of Chinese adolescents, synthesizes the existing research, and suggests coping strategies that may assist adolescents in achieving consumption patterns that are healthier and more sustainable in the face of peer pressure. According to the results, the guidance and influence of peers play a significant role in the purchasing decisions of Chinese adolescents. Peer influence has both positive and negative effects on individuals' consuming behaviors, including value perception, affective experience, and the desire to repurchase; the effect of perceived peer trust, control, and allure on inconsistent judgments. Peer influence has a negative impact on the consuming activities of individuals, including their value perception, affective experience, and desire to repurchase. The most apparent manifestation of peer influence is an increase in consumption knowledge and assistance in the process of developing one's own unique consumption patterns. Students struggling with consumer psychology and motivation in higher education should be referred to school-based psychological counseling services in order to receive the necessary psychological intervention and assistance.
Comparative Analysis of Reits Development in China and the USA
Authors: Xu Danni
Published: March 2024

This academic paper provides a comprehensive comparative analysis of the development of Real Estate Investment Trusts (REITs) in China and the United States. REITs have gained significant prominence as investment vehicles in the global real estate market, offering individuals and institutions the opportunity to access diversified real estate assets. The objective of this study is to investigate the historical background, legal frameworks, market characteristics, and performance indicators of REITs in China and the United States, with a focus on identifying similarities, differences, challenges, and potential opportunities for their development in each country. By examining these key aspects, this research aims to enhance the understanding of policymakers, investors, and researchers regarding the distinct dynamics and prospects of REITs in these two major economies. The findings of this analysis provide valuable insights for stakeholders interested in capitalizing on the potential of REITs as a viable investment option in China and the United States.
Exploring the Potential of Turnkey Asset Management Platforms in Transforming China’s Wealth Management Landscape: A Comparative Analysis with the United States
Authors: Su Lijin
Published: March 2024

Research background: China's wealth management landscape is evolving rapidly, driven by increased individual wealth accumulation and a shift towards comprehensive financial advisory services that emphasize personalized solutions and risk management. Research topic and method: This study examines the applicability of Turnkey Asset Management Platforms (TAMPs) in China by comparing their development and impact in the United States. Employing a comparative approach, the research combines literature review, case studies of notable TAMPs, and regulatory analysis to assess the feasibility and potential benefits of TAMP integration in the Chinese market. Research result: The analysis of TAMPs in the US highlights their role in reshaping wealth management by offering end-to-end services, from investment proposal generation to portfolio construction and reporting. TAMPs have proven effective in enhancing financial advisors' efficiency and client engagement. The study identifies key factors indicating TAMP relevance in China, including their potential to bridge advisor-client gaps, enhance investment decision-making, and provide scalable solutions. Research conclusion: This study suggests that TAMPs hold transformative potential for China's wealth management industry, addressing current advisor challenges and catering to diverse investor needs. However, successful implementation would necessitate adapting TAMP models to China's unique regulatory and market conditions. The research underscores the importance of considering TAMPs as a tool for enhancing efficiency, effectiveness, and client-centricity in China's evolving financial landscape.
Analysis of the Development and Prospects of the Baijiu Industry
Authors: Xia Yuhao
Published: March 2024

Baijiu is one of the important representatives of Chinese liquor culture. It plays an irreplaceable role in banquets and is often used as a gift in Chinese society. Baijiu production and the number of baijiu companies in the past ten years show that the market is saturated and shrinking. On the other hand, comparing the revenue and profits between listed baijiu companies and the entire industry, it can be found that the baijiu industry has obvious monopoly characteristics and structural prosperity. These two phenomena can be divided into political and economic reasons. Politically, the Chinese government promulgated restrictions on government spending and family planning policies, which directly reduced the consumption group of baijiu, leading to the shrinkage of the baijiu industry. Economically, China's rapid development has increased people's disposable income, and people began to care more about their health and pursue high-end baijiu, leading to a monopoly. To broaden the market, the baijiu industry can consider e-commerce channels and international expansion.
Analysis of the Fifth Set of listing standards of China Science and Technology Innovation Board
Authors: Lu Xinyu
Published: March 2024

With the landing of micro-electrophysiology on the Science and Technology Innovation Board, the fifth set of Science and Technology Innovation Board listing rules appeared and hoped to promote the development of new things in domestic capital production. The fifth set of Science and Technology Innovation Board listing rules reflects the inclusiveness of the Science and Technology Innovation Board because this set of listing rules is for some unprofitable or small-profit research and development technology, innovation, and future development of enterprises listed standards. Including the core technology, production products and stage results, good market trends, and innovative technological advantages. Especially for most types of biological drug development or medical technology companies. Today, almost all the companies that use the fifth set of Science and Technology Innovation Board to go public have commercialized their products, gained operating income of 100 million yuan, and some companies have even earned profits. As time goes on, the fifth set of Science and Technology Innovation Board of the development is getting better and better. There are many successful cases for other companies with the same enterprises to reference, and the future trend is also on the rise. However, due to the special characteristics of the Technology Innovation Board, the liberalization of IPO listing also brings great risks to the auditing work.
Digital Inclusive Finance Helps Developing Countries Narrow the Urban-rural Income Gap Analysis——Taking China as an Example
Authors: Duan Guanyu
Published: March 2024

With the rapid development of digital technology, finance, and technology integration today, a new type of large-scale financial digitization has emerged, increasing the breadth and depth of services, increasing the comfort of the financial services sector, and enabling residents to participate equally in the financial system and obtain necessary financial resources. Extensive digital financing has enabled information exchange and reduced costs. It can promote better management. The concept of digital fusion funding is an innovation in the development of traditional financing. This is a modern development vision focusing on the interests of the group. Their honesty and tolerance are of great importance to achieve social equality. Many studies have shown that the development of digital economic integration may effectively assist developing countries to reduce the revenue gap in the village. China is the largest developing country with the world's largest population. For example, China can summarize how the integration of digital economy affects urban-rural income gaps, identify problems and signs of experience in developing countries.
Optimization Strategy for Industrial Transformation in Hebei Province
Authors: Tang Xuan
Published: March 2024

Hebei province has implemented an ambitious optimization strategy to enhance its industrial landscape and promote sustainable economic growth. This paper examined the multifaceted approach adopted by Hebei province, focusing on high-tech integration, innovation, and addressing talent challenges. The foundation of Hebei’s strategy lies in transforming traditional industries, directing them towards high-value sectors such as new energy and intelligent manufacturing. This strategic pivot aligned with global trends and emphasized environmental preservation through a green, low-carbon economy. However, a pressing challenge surfaced in the form of high-tech talent scarcity. To address this obstacle, the paper introduced numerous suggestions. This research underscored the significance of region-specific strategies for driving industrial transformation. It contributed to the broader discourse on achieving balanced and sustainable economic development, highlighting the importance of nurturing a skilled workforce that can adapt to the demands of a rapidly evolving technological era. As Hebei Province advanced its optimization strategy, continued dedication to talent development and seamless integration of high-tech advancements would be pivotal in ensuring successful policy execution. This study encapsulated the essence of Hebei’s journey toward economic revitalization, emphasizing the need for forward-looking strategies that harmonized economic growth and environmental consciousness.
Research on the Business Environment in the Post-pandemic Era: Taking Shenzhen as an Example
Authors: Jin Zi
Published: March 2024

The coronavirus broke out in 2019, which quickly swept the world. In such a global environment, the economies of various countries are very sluggish, and the business environment is quite depressed. Now, the world is entering the post-pandemic era. In order to better study the business environment, this paper selects Shenzhen as an example to analyze. Firstly, the paper explored the current situation with a number of official data, including negative aspects, while some progress has also been witnessed. The problems in the business process were also demonstrated by examples and statistics. Some suggestions were provided, hoping to contribute to the future development of Shenzhen's business environment. The paper focused on accurately analyzing the current status and issues of Shenzhen’s business environment, providing a better atmosphere for the development of enterprises and promoting the legal and administrative system.
Evaluation of the Influence of 2008 Scoping Plan to Carbon Emissions in California by DID
Authors: Ding Chuhan
Published: March 2024

Global warming has been a crucial issue these years as the development of human society and technologies. The main factor is the greenhouse gas emissions, including water vapor, methane, carbon dioxide and nitrous oxide in the earth atmosphere. There are many countries with different kinds of policies to reduce greenhouse gas emissions to slow down the speed of global warming and protect the world environment, such as the California 2008 Scoping Plan. Difference-in-Difference is an estimation method to evaluate the effectiveness of a policy change on a group of individuals. This study selects the transportation sector as the indicator of carbon emissions in California and uses the data from 2004 to 2014 in all 51 states in the U.S. to estimate the influence of 2008 Scoping Plan on carbon emissions in California. The result shows the policy had a positive effect on reducing the carbon emissions in the transportation sector in California, which means that the Scoping Plan is worth continuing to implement and improve to achieve the goal of decreasing greenhouse gas emissions to protect the global environment.
Relationship Between Minimum Wage and the Unemployment Rate
Authors: Zhou Siwen
Published: March 2024

The following issues are carefully considered in minimum wage discussions: There is considerable interest in the minimum wage across the country. State-level minimum wages are also set by each state in addition to the federal minimum wage, which is decided by the federal government. The importance of the minimum wage in the job market is clear. The minimum wage and its relationship to the unemployment rate are both topics covered in this article along with the factors affecting minimum wages. Workers experience good effects from increasing minimum salaries in the beginning but negative effects at a certain point. On the other side, businesses initially respond favorably to increases in the minimum wage, but they turn around once they reach a certain barrier and must incur too much capital investment. As a result, they tend to hire fewer workers in the long run, which lowers unemployment. The essay states distinct responses from employers and employees to various minimum wage changes, illustrates a labor supply curve that bends backward, and then introduces the accompanying unemployment oscillations.
Social Media Fake Information Identification Method Based on LSTM
Authors: Liu Shuhan
Published: March 2024

False information is spreading like wildfire on social media platforms, causing unnecessary confusion and mistrust. Most people do not have the ability to judge for themselves whether information is true or false that is why it is important to use machine learning models to help people identify false information on the web. LSTM model is a good choice for dealing with identifying social media disinformation. The key steps in identifying social media disinformation using LSTM include data preparation and preprocessing, building a model structure adapted to LSTM, model training and evaluation, and subsequent hyper-parameter tuning and model deployment. These steps constitute a complete process that enables LSTM to effectively identify false information and provide credible prediction results through the processing of text data and model training. After model training and validation, LSTM has an accuracy of 99%, AUC of 100% and successfully identifies false information on social media, providing reliable classification results for both true and false information.
Knowledge Graph Construction of Near-Homonymy and Near-Synonymy English Words
Authors: Shen Yushu
Published: March 2024

This paper investigates the issue of near-homonymy and near-synonymy in English vocabulary learning and explores the significance and practical value of addressing this problem. Through data analysis, we emphasize the widespread demand for English language acquisition and the importance of the English language. Furthermore, we cite research in the field of education to provide evidence of the positive impact of near-homonymy and near-synonymy on word memorization. The main research work of this paper includes the construction of a graph database and the measurement of near-homonymy and near-synonymy. We describe in detail the workflow of building the graph database, including the definition of nodes and edges, as well as edge filtering methods. Additionally, we introduce the mathematical principles and formulas used to calculate near-homonymy and near-synonymy. The experimental section presents the data source and the process of constructing the graph database, including data preprocessing, similarity calculation, and parameter settings. We establish a graph database using neo4j and showcase the intermediate results of near-homonymy and near-synonymy, as well as visualization results. Finally, we summarize the main findings of this paper and discuss the prospects for applying similarity calculations based on word origins.
Feature Selection in House Price Prediction
Authors: Guo Jia
Published: March 2024

This study aims to construct a model to choose effective features and use them to predict the market price of an exact house, which can help people with house pricing and property evaluating. This paper preliminarily constructs several machine learning models like Linear Regression, SVM, KNN and compares their accuracy on this problem to choose the best-fit one for improving. After using parameter tuning to optimize this model, this study tries to use and recursive feature elimination and genetic algorithm to select features to improve simple SVM model. After feature selection, this study re-evaluated the accuracy of the model and compared which features had a greater impact on the predictions. After comparison, this study found that in this particular case, features have closer connections with living condition, traffic condition and sale condition will have a huge impact on the house price.
Problems and Solutions of Quantitative Analysis in Financial Market
Authors: Gu Yihao
Published: March 2024

This paper explores the role and challenges of quantitative analysis in financial markets. It provides an overview of the applications of quantitative analysis in the financial market, including risk management, portfolio optimization, asset pricing, algorithmic trading, and market prediction. The paper also identifies three main challenges in using quantitative analysis: limited data, lack of human intuition, and overreliance on data. Limited data can lead to inaccurate or biased predictions, and the lack of human intuition in quantitative models can limit their ability to capture complex human behavior and unexpected events. Overreliance on data is also a potential risk that may lead to false correlations. Solutions to these problems are proposed, including incorporating domain expertise, qualitative information, interpretability, transparency, and careful consideration of the data's context. Financial professionals can make more informed and effective decisions using quantitative analysis by addressing these challenges.
Credit Default Analysis and Prediction Based on Machine Learning
Authors: Lai Yuxiang
Published: March 2024

The Credit Default Prediction project aims to develop an efficient machine learning model to accurately predict loan default risk on the Lending Club platform. The project is based on historical loan data, including borrower personal information, financial metrics, and credit history records, with the goal of building a robust predictive model. The project's workflow encompasses several critical steps, including data preprocessing, exploratory data analysis, feature engineering, model selection, and performance evaluation. In terms of model selection, the project employs two primary machine learning algorithms, namely Artificial Neural Networks (ANN) and Random Forest. These algorithms are renowned for their outstanding performance in handling extensive borrower data and providing reliable risk predictions. Model training and evaluation are conducted using a substantial amount of historical data to ensure accurate predictions across various scenarios. Furthermore, the project conducts feature importance analysis to identify factors that significantly influence loan default risk. These insights contribute to enhancing Lending Club's risk assessment process and supporting more informed decisions regarding loan approval and pricing strategies. By combining data-driven predictive modeling with in-depth data analysis, this project aims to enhance the efficiency of Lending Club's loan operations and elevate its risk management capabilities, ultimately providing more dependable financial services to investors and borrowers.
The Analysis of Human Development in China
Authors: Lu Ziqi
Published: March 2024

These few years, China has had specific developments in science and technology. In the 21st century, with the rapid development of science and technology, human happiness and quality of life have been rapidly improved, and infrastructure has also been rapidly built with the efforts of people and countries. However, there is a big difference between economic growth and economic development. Economic growth mainly measures the output of a region or a country, which is mainly reflected in the economic growth. Economic development is measured by many aspects, including human development, human happiness, sustainability and social fairness. In China, there are many plans based on economic development, and this idea is also accompanied by China's aging society. Based on the background of China's economic development plan and aging society, this paper will analyze the impact of the plan and the situation in China. China has done something about aging, the most typical of which is family planning. This plan played some role in the initial stage of implementation, but it could not maintain a long-term plan and could not guarantee the sustainability of a country's economic development. This paper put forward new opinions on the advantages and disadvantages of this policy.
The Main Difficulties and Countermeasures of Small and Medium-sized Enterprises under the COVID-19
Authors: Wang Meng
Published: March 2024

The COVID-19 epidemic broke out suddenly and swept the world rapidly in 2020, which had a great impact on China's economy and even the global economy. Under the pressure of enterprises stopping production and increasing personnel costs, many small and medium-sized enterprises are facing bankruptcy. This paper analyzes the difficulties and opportunities faced by small and medium-sized enterprises under the epidemic crisis, and the role and challenges of Internet technology in helping small and medium-sized enterprises to transform and upgrade. This study holds that Internet technology can provide small and medium-sized enterprises (SMEs) with more innovation space, broaden sales channels, improve the effect of customer drainage and other advantages. At the same time, it also needs SMEs to change their business ideas, strengthen the use of information technology, and strengthen the talent pool. The paper puts forward some concrete suggestions, such as strengthening the awareness of Internet management, accelerating the transformation of industrial model, and strengthening the talent pool, so as to help small and medium-sized enterprises realize innovative development by using the Internet. Based on relevant data and cases, this paper explores and analyzes how small and medium-sized enterprises use the Internet to achieve innovative development.
Investigation of generative capacity related to DCGANs across varied discriminator architectures and parameter counts: A comparative study
Authors: Yi Fan
Published: March 2024

Generating lifelike images through generative models poses a significant challenge, where Generative Adversarial Networks (GANs), particularly Deep Convolutional GANs (DCGANs), are commonly employed for image synthesis. This study focuses on altering the DCGAN discriminators structure and parameter count, investigating their effects on the characteristics of the resulting generated images. Assessment of these models is carried out using the Frchet Inception Distance (FID) score, a metric that gauges the quality of generated image samples. The research specifically involves substituting some convolutional layers with fully-connected layers, and the ensuing outcomes are thoroughly compared to discern the impact of these structural changes. Furthermore, dropout was used to study the number of the parameters influence. This study compared the FID score of the models when the probability is 0, 0.2, 0.4, 0.6 and 0.8. Experimental results showed that the DCGAN with the fully-connected layers generated ability was stronger than the original one. Besides, when the probability of the dropout is 0.6, the images generated was the most realistic. Finally, the paper explained the possible reasons for the difference and proposed a better generative model based on DCGAN.
The application of federated learning in face recognition: A systematic investigation of the existing frameworks
Authors: Xu Chuanzhi
Published: March 2024

This paper presents a thorough examination of the recent progress made in applying federated learning to the field of face recognition. As face recognition technology continues to gain widespread adoption across various sectors, issues related to data privacy and efficiency have taken center stage. In response, federated learning, characterized by its decentralized machine learning approach, has emerged as a promising solution to tackle these pressing concerns. This review categorises the current federated learning frameworks for face recognition into four main purposes: Training Efficiency, Recognition Accuracy, Data Privacy, and Spoof Attack Detection. Each category is explored in-depth, highlighting the principles, structures, applicability, and advantages of the frameworks. The paper also delves into the challenges faced in the integration of federated learning and face recognition, such as high computational overhead, model inconsistency, and data heterogeneity. The review concludes with recommendations for future research directions, emphasising the need for model compression, asynchronous communication strategies, and techniques to address data heterogeneity. The findings underscore the potential and challenges of applying federated learning in face recognition, paving the way for more secure and efficient facial recognition systems.
An analysis of BERT-based model for Berkshire stock performance prediction using Warren Buffet's letters
Authors: Yu Geyang
Published: March 2024

The objective of this study is to discover and validate eective Bidirectional Encoder Representations from Transformers (BERT)-based models for stock market prediction of Berkshire Hathaway. The stock market is full of uncertainty and dynamism and its prediction has always been a critical challenge in the nancial domain. Therefore, accurate predictions of market trends are important for making investment decisions and risk management. The primary approach involves sentiment analysis of reviews on market performance. This work selects Warren Buetts annual letters to investors and the year-by-year stock market performance of the Berkshire Hathway as the dataset. This work leverages three BERT-based models which are BERT-Gated Recurrent Units (BERT-GRU) model, BERT-Long short-term memory (BERT-LSTM) model, and BERT-Multi-Head Attention model to analyse the Buetts annual letters and predict the Berkshire Hathways stock price changes. After conducting experiments, it could be concluded that all three models have a certain degree of predictive capability, with the BERT-Multi-Head Attention model demonstrating the best predictive performance.
Exploration, detection, and mitigation: Unveiling gender bias in NLP
Authors: Zhang Chunxiao
Published: March 2024

Natural Language Processing (NLP) systems have a mundane impact, yet they harbour either obvious or potential gender bias. The automation of decision-making in NLP models even exacerbates unfair treatment. In recent years, researchers have started to notice this issue and have made some approaches to detect and mitigate these biases, yet no consensus on the approaches exists. This paper discusses the interdisciplinary field of linguistics and computer sciences by presenting the most common gender bias categories and breaking them down with ethical and artificial intelligence approaches. Specific methods for detecting and minimizing bias are shown around biases present in raw data, annotator, model, and the linguistic gender system. In this paper, an overview of the hotspots and future perspectives of this research topic is presented. Limitations of some detection methods are pinpointed, providing novel insights into future research.
Effectiveness of finetuning pretrained BERT and deBERTa for automatic essay scoring
Authors: Zhong Wentao
Published: March 2024

With the growing importance of summary writing skills in the educational system and the inherent complexity of manual assessment, there is an urgent need for automated summary scoring solutions. Pre-trained models are popular nowadays, such as Bidirectional Encoder Representations from Transformers (BERT) and Decoding enhanced BERT with disentangled attention (deBERTa). The performance of direct use with trained models on specific tasks still needs to be improved. This paper focuses on the impact on the performance of summary scoring systems after adding linear and dropout layers to these pre-trained models for feature extraction and dimensionality reduction operations. The paper details the optimization for the particular task of summary scoring automation after using the pre-trained models. This paper focuses on adding linear and dropout layers to perform feature extraction and dimensionality reduction operations. The aim is to make the model more adaptable to this specific educational task. Ultimately, it is hoped that these studies will enhance the pedagogical toolkit for educators and enrich the academic experience for students.
Performance evaluation of Latent Dirichlet Allocation on legal documents
Authors: Ogundare A. O. , Saleh A. U. , James O. A. , Ajayi E. E. , Gostoji S.
Published: March 2024

Latent Dirichlet Allocation (LDA) is an algorithm with the capability of processing large amount of text data. In this study, the LDA is used to produce topic modelling of topic clusters from corpus of legal texts generated under 4 topics within Nigeria context Employment Contract, Election Petition, Deeds, and Articles of Incorporation. Each topic has a substantial number of articles and the LDA method proves effective in extracting topics and generating index words that are in each topic cluster. At the end of experimentation, results are compared with manually pre-annotated dataset for validation purpose and the results show high accuracy.
The LDA output shows optimal performance in the word indexing processing for Election Petition as all the documents annotated under the topic were accurately classified.
Sentiment analysis of Twitter user text based on the BERT model
Authors: Zhou Chenyang
Published: March 2024

Deep Neural Networks (DNNs) utilizing Recurrent Neural Network (RNN) architectures have found extensive application in text sentiment analysis. A prevailing notion suggests that augmenting the model's capacity can significantly improve accuracy and overall model performance. Building upon this premise, this paper advocates the adoption of a larger BERT model for text sentiment analysis. Bidirectional Encoder Representations from Transformers (BERT) is a sophisticated pre-trained language comprehension model that leverages Transformers as feature extractors. However, as the amount of model data increases, exceeding the memory limitations of a single GPU, algorithm optimization becomes crucial. Therefore, this paper employs two methods, namely data parallelism and GPipe parallelism, to accelerate and optimize the BERT model. Compared to a single GPU, training speed almost linearly increases with the addition of more GPUs. In addition, this research investigates the accuracy of the most advanced language model, chatgpt, by reannotating the dataset. During training, it was observed that the accuracy of the chatgpt-annotated dataset significantly declined in both RNN and BERT models. This indicates that chatgpt still exhibits some errors in sentiment text analysis.
Analysis of computational power as a potential breakthrough in advancing AI technology
Authors: Chen Yezhen
Published: March 2024

The term Artificial Intelligence(AI) has become more common in view recently. The performance and fame of ChatGPT brought a new AI fever to present industries with multiple big companies announcing their upcoming AI plans. People are arguing about whether AI will help them or steal their jobs. It seems that AI will be, if not have been, walking into peoples daily lives. As a result, this article analyzes the current challenges and potential future breakthroughs of Artificial intelligence by focusing on one of the most fundamental factors that support the development and operation of AIcomputational power. This article analyzes the relationship between AI performance and computer performance from different eras particularly. It summarizes and analyzes several sources published in related fields. The primary purpose of this article is to provide people with a better overview of present AI technology by taking a close look at the history, present difficulties, and potential solutions of AI and computational power and concludes with possibilities of each solution and expected futures of the AI industry. This paper concludes that the development of AI relies on the computer performance acquired by the industry. Finding a way to obtain better computer performance at a lower cost might be the next breakthrough in the AI industry.
Comparison between tendon-driven and soft pneumatic actuators in soft robotic hands
Authors: Ye Qianli
Published: March 2024

Bionic robotic hands have huge potential for applications. Traditional rigid robotic hands have the disadvantages of complex control and poor compliance, while soft robotic hands can well overcome these difficult drawbacks. Tendon-driven actuators and soft pneumatic actuators are two main actuating methods in soft robotic hands which are increasingly used due to their good compliance. This article first reviews the basic principles and structures of these two actuating methods in order to develop a basic understanding of different actuating methods. Then through comparing the dexterity and grasp performance of two specific designs, this article analyzes the characteristics of both methods in terms of their structures. Through discussion of structural differences, the systematic impact of the two actuating methods on function is analyzed and a general conclusion about differences in applicability is drawn. This article aims to help researchers have a better understanding of the two main actuating methods for soft robotic hands before they choose a specific actuating method, which can contribute to better completion of expected functions.
Vehicle detection in complex scenarios based on YOLOv5
Authors: Lin Xingyue , Zhu Zhaoting
Published: March 2024

With the acceleration of urbanization, traffic congestion, accident risk, and other issues have become increasingly prominent, so vehicle recognition and optimization technology has become particularly important. Based on the YOLOv5 deep learning model, this study proposes a vehicle recognition and optimization method to address key issues in urban traffic management and intelligent driving. This study first tested YOLOv5 and trained and optimized the YOLOv5 model to enhance the model's ability to detect vehicles in various complex scenarios, such as rainy days and the coexistence of people and vehicles, which have a large number of interference factors. Through experimental verification, our improved model improves the accuracy of detection in such scenarios in vehicle recognition tasks, and finds the deficiencies in the experiment and proposes corresponding solutions.
Application of deep learning algorithms in predicting the exchange rate of Chinese yuan against the US dollar
Authors: Luo Yiyang
Published: March 2024

This paper mainly studies the feasibility of using deep learning algorithms to predict the exchange rate between the Chinese yuan and the US dollar. Firstly, this study chooses the Long Short Term Memory Network (LSTM) as the main algorithm, which is a deep learning model suitable for processing time series data. Then, the study collects a large amount of data, including historical exchange rate data, macroeconomic indicators, and political events that may affect exchange rates. These data provide sufficient information for the model to learn and predict exchange rate fluctuations. The study found that the deep learning model can accurately predict the volatility of the RMB US dollar exchange rate by contrasting the calculated results of the pattern with effective exchange rate record, and its predicted results have high stability. Deep learning has great potential in the field of exchange rate forecasting, providing more accurate predictive information for financial transactions.
Machine learning classification methods for battery cathode material crystal system
Authors: Wu Zhengli
Published: March 2024

The optimization of lithium-ion battery material design is becoming increasingly pivotal in the context of the transition to new energy sources. This research delves into the classification of the crystal structures of 339 sets of lithium silicate cathode materials, depending on the chemical and physical parameters of the materials to classify the crystal system type. Five classification algorithms were employed in this study, and the classification accuracy was enhanced through algorithm optimization. Notably, the accuracy of the random forest model with PCA (Principal component analysis) =5 reached 74% accuracy with 80% of the data used for training. Simultaneously, this study optimized the model accuracy by fine-tuning parameters such as depth, k-fold, and learning rate. Through this research, the classification accuracy of cathode materials in batteries was further elevated, bearing significant implications for the development of new, suitable battery materials. This research aids in expediting the screening process for researchers and facilitating industrial-scale experimentation, thereby accelerating the application of next-generation batteries.
Data mining models in telecom churn prediction
Authors: Xing Zekai
Published: March 2024

Customer attrition has become one of the biggest problems facing the telecommunications industry due to its fast growth. According to telecom studies, acquiring new customers is more expensive than keeping current ones. Telecom companies may use the information gleaned from telecom data to understand the causes behind customer churn and take steps to maintain their current clientele. This study explores the popular data mining methods for spotting trends in loss of clients. Principal component analysis is used in the survey to reduce the dimension of the attributes. Some conclusions about the relationship between costumer usage data their churn can be summarized through the exploratory data analysis. And three prediction techniques (Logistic Regression, SVM Regression, Random Forest Regression) are applied in the customer churn prediction. This paper compares the accuracy and performance of these models. The result shows, among these models, SVM regression has the highest accuracy. It has an accuracy of 0.92 and a precision of 0.48.
Stock price prediction of PayPal by Linear Regression, SVM, Random Forest and LSTM
Authors: Zhang Xueer
Published: March 2024

This research paper undertakes an extensive and detailed examination of four distinct machine learning models, specifically Linear Regression, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). The stock values of PayPal are forecast employing these techniques, and Python serves as the fundamental tool in facilitating a comprehensive and thorough evaluation of their efficacy in predicting trends in the financial market. In the realm of methodology, this study encompasses a multifaceted approach. Python forms the cornerstone for data analysis, model development, and testing. The data collection process encompasses historical stock price data for PayPal, alongside an array of relevant economic indicators. The core of the study lies in the comparative analysis of the four machine learning models. Each model is rigorously tested against historical data, allowing for a nuanced understanding of their strengths and weaknesses. Linear Regression, remarkably, emerges as the standout performer in terms of predictive accuracy and consistency. This firmly establishes Linear Regression as the optimal choice for forecasting stock prices. The significance distinction of this research extends beyond its findings. It advances the discipline of financial forecasting by illuminating the comparative effectiveness of different models, offering valuable insights for guiding future research initiatives within this domain. The integration of advanced methodologies and the clear-cut conclusion regarding Linear Regression's superiority underscore the pivotal role of this study in enhancing the precision of financial market trend forecasting.