Reviewerly/exHarmony huggingface dataset:
Higher Expectations of Artificial Intelligence in Terms of Morality and Humanity

This article delves into the critical question of whether Artificial Intelligence (AI) should adhere to higher standards of morality and humanity. As AI systems infiltrate our everyday lives, their influence surfaces profound ethical and moral conundrums, primarily due to their intrinsic limitation in exercising moral judgments. The ethically sound deployment of AI requires a comprehensive understanding of these moral challenges and the construction of suitable safeguards. The article further scrutinizes the repercussions of AI on our conception of human nature, highlighting potential modifications in human relationships and the essence of our humanity. It contends that AI systems, given their decision-making prowess, should be governed by robust moral principles and human values to curtail harm and endorse fairness and justice. The piece accentuates the pivotal role of human guidance in AI development and application, asserting that these technologies should align with our values. It concludes with an emphasis on the need for continuous surveillance to ensure that AI technologies benefit society, underlining the importance of aligning AI with our moral and human expectations for sustainable societal progression.
Analisis Sentimen Program Mbkm Pada Media Sosial Twitter Menggunakan KNN Dan SMOTE

The Merdeka Belajar-Kampus Merdeka (MBKM) program is a relatively new program implemented in Indonesia since February 2020. Like a new program, the implementation of the MBKM program is also followed by various pro and con attitudes. Therefore, a sentiment analysis technique is needed to determine the public opinion towards the MBKM program. The purpose of this study is to determine the performance of the KNN method in performing sentiment classification optimized by the SMOTE method in overcoming the problem of unbalanced data and to determine the tendency of public sentiment towards the implementation of the MBKM program. Based on the research results, the KNN method optimized with the SMOTE method is proven to improve classification performance. From initially producing an accuracy value of 76.13%, precision of 76.03%, recall of 76.13% and f1-score of 76.01% there was an increase in accuracy value to 76.13%, precision to 76.03%, recall to 76.13%, and f1-score to 76.01%. In this study, it was found that community responses tended to be neutral towards the MBKM program. The community feels that the MBKM program is a program that can increase student experience. However, there are still program systems that are considered complicated and need to be evaluated.
Implementation of a Rabbit Disease Diagnosis Expert System using the Forward Chaining and Certainty Factor Method

An expert system is a system that adopts the expertise of an expert in a particular field into the system. Rabbits are one of the animals that are close to humans and have high body adaptability. However, rabbits are not free from the threat of disease. Treatment requires more costs so we need a way to find out the disease and the solution so that we can take the necessary actions. To overcome this, an expertise media was created that can be accessed by rabbit owners online using the Forward Chaining and Certainty Factor methods. The expert system must be able to work under uncertainty, so the Forward Chaining (FC) and Certainty Factor (CF) methods were added to overcome this problem. Forward Chaining is a decision making method that is commonly used in expert systems and the Certainty factor method is a method to prove the uncertainty of an expert's thinking. The system diagnosis results display possible diseases suffered by rabbits and provide treatment solutions for diseases with the largest CF values. With the calculation results, the level of confidence information based on the table is Definitely Yes. The accuracy of the system output is proven from the results of system validity with experts.
Analysis of Vocational Engineering Labor Education based on Vocational Core Competence

Labor education is an important part of China's education system, which directly determines the labor ability, labor spirit, labor habits and labor quality of the next generation of workers in China. It is an important content of vocational education teaching reform to integrate labor education into the curriculum system of higher vocational colleges. this paper analyzes the puzzles encountered in the labor education of higher vocational engineering, expounds the importance of the labor education of higher vocational engineering to the cultivation of vocational core competence. And explore ways of vocational engineering labor education based on vocational core competencies from aspects such as labor education content and labor education system.
Business Rules Driven Automated Quality Assurance Governance System for Higher Education

Although higher education institutes have paid serious attention to quality assurance, implementation issues have been reported frequently. Since the existing information systems do not adequately support automatic verification, manual verification of quality assurance compliances has become a challenge. Therefore, this study first analysed the prevailing higher education quality assurance context to identify the prevailing issues. Secondly, Quality Assurance Governance System integrated with a quality assurance rule manipulation mechanism was designed based on the business rules concept to address the identified issues. Thirdly, the key modules of the proposed Quality Assurance Governance System were developed as a proof-of-concept. Finally, the proposed quality assurance rules manipulation mechanism and modules of the Quality Assurance Governance System were evaluated. The study found that one of the main issues in the existing systems is the lack of integration among various modules and a rule manipulation mechanism. Therefore, the study concludes that quality assurance requirements could be better captured as rules using the proposed rule templates, while rule manipulations and compliance could be verified in real-time through the proposed Quality Assurance Governance System. Nevertheless, the results of the evaluation suggest that rigid rule implementations may make the system inflexible and create stakeholder resistance. Thus, this can be mitigated by implementing the proposed rule enforcement levels.
Analysis of Quasi-affixes in Cyber Language from the Perspective of Memetics

In information age, people have gradually integrated into the new era of network culture. In the process of Chinese communication online, quasi-affixes, a new type of affix, are being generated correspondingly. They enrich netizens’ life and boost the prosperity of Internet’s culture. Created by Richard Dawkins in The Selfish Gene, Memetics is a new theory to explain the law of cultural evolution based on Darwin's theory of evolution. Memes are the basic units of cultural information inheritance. The thesis intends to analyze the prevalent reasons of quasi-affixes in cyber language as well as their functions in daily communication and cultural transmission from the perspective of memetics by typical examples. To achieve it, the analysis will develop from three aspects, the form and meaning advantages of quasi-affixes and their roles playing in different scenarios. Finally, it will draw a conclusion.
Research on the Copyright Protection of the Artificial Intelligence Generated Products in China

With the change of the development stage of artificial intelligence makes the question of how to protect artificial intelligence-generated products imminent. Whether or not the AI-generated works can be included in the protection of copyright law is based on the prerequisite of what criteria are used to judge their originality. Based on the specificity of the subject matter of AI-generated materials in the law, it is more justifiable to judge their originality from objective criteria. In the allocation of the rights of AI-generated products, the rights should be attributed to the users in accordance with the priority of the agreement between the relevant right holders. In addition, the establishment of the registration system of AI generated products is conducive to cost saving and efficiency, and helps to promote the long-term development of AI technology.
Research on UAV Cluster Obstacle Avoidance Algorithm Based on Improved Artificial Potential Field Method

In view of the shortcomings of the traditional artificial potential field method for the obstacle avoidance of the UAV cluster, this paper solves the safety problem of obstacle avoidance during the flight of the UAV group, introduces the relative speed repulsion force field between the UAV group and the dynamic obstacle, and builds a virtual force field to solve the obstacle avoidance problem of the Calculate the force between the drones according to the distance between the drones. The obstacle generates repulsive force on the drone, the target generates gravity on the drone, and the combined force of gravity and repulsion acts on the drone to control the drone to avoid obstacles. The paper aims to realize multi-UAV obstacle avoidance, and improves the artificial potential field method. Through the design of the formation of UAVs and the UAV obstacle avoidance algorithm, the obstacle avoidance of UAVs is carried out. The simulation results show that the improved artificial potential field can meet the safety, real-time and accessibility requirements of the path planning of the UAV under the dynamic change of targets and obstacles, and improve the tracking and obstacle avoidance speed of the UAV in the dynamic environment.
Survey of AR Object Tracking Technology Based on Deep Learning

Augmented reality (AR) is the process of "augmenting" computer information into the real or physical world. This helps us better understand the process and has numerous application-level AR has an almost unlimited range of applications in today's world, and if implemented, will help solve many complex problems in a simple way. This paper presents a comprehensive survey of the different types of tracking technique. As used to develop object tracking algorithms for AR applications. This paper compares and analyses different types of sensor-based and vision-based tracking technologies, and finally draws conclusions. The mixed use of different types of tracking technologies is one of the best solutions to achieve accurate and powerful tracking to meet the strict requirements of AR applications.
Research on International Collaborative Protection Strategies for Sports Intellectual Property in the Digital Era

In the digital era, intellectual property infringement is showing a frequent trend, and sports intellectual property is also inevitable. In order to promote the safety and security of sports intellectual property rights worldwide, research suggests that it is necessary to strengthen international collaboration in order to achieve this, and the difficulties of international collaboration mainly manifest in the value conflicts of ideas, rule of law, and policies. The study proposes to strive for the uniformity of the application of sports intellectual property legal system; Actively utilizing technical measures to protect sports intellectual property rights; Vigorously promote the legal coordination of sports intellectual property protection.
An Overview of College English Writing Teaching Studies in China Between 2002 and 2022: Visualization Analysis Based on CiteSpace

This paper employs CiteSpace to conduct a visualization analysis of literature on college English writing teaching researches published in core journals from the CNKI database and CSSCI journals between 2002 and 2022. It aims to explore the characteristics of researches and future directions on college English writing teaching. The present study yielded the following major findings: the field primarily focuses on innovative writing teaching models and methods, the integration of traditional classroom teaching and information technology, and instructional strategies to enhance students' writing skills. The future research is anticipated to involve a hybrid writing teaching approach combining online and offline teaching methods, leveraging the "Internet+" digital platform, aiming to elevate students' writing proficiency. This paper also presents a prospective outlook for college English writing teaching research in China.
The Speech Act of Apology by Lenovo on Facebook

New media platforms such as Twitter, Facebook, and Instagram have allowed brands to connect with the public. However, negative comments can damage their image, so brands should use clear apology strategies. Despite the COVID-19 pandemic, the growth of the electronics market is closely related to the connection between brands and customers. This article examined the apology strategies used by Lenovo, a company with international brand influence, in response to negative messages or complaints on its official Facebook account from April 30, 2023 to June 9, 2023. The study used a mixed research method to identify responses containing common apology words on Lenovo's public account through IFIDs and obtained all posts using the two most frequently used apology words in the text. Under the guidance of Olshtain and Cohen (1983) and Page (2014)'s apology strategies, the study examined the apology strategies and combinations used by Lenovo in its posts.The results showed that Lenovo often used the following six apology strategies: "expression of apology", "explanation or account of the situation", "acknowledgment of responsibility", "an offer of repair", "follow-up moves: questions and imperatives", and "greetings, closings, and terms of address". Furthermore, "greetings", "expression of apology", and "follow-up moves: questions and imperatives" were the most frequently used semantic combinations.
Workplace deviance &amp; Predictors of Workplace deviance: A Systematic Review of Literature

This study examines recent conceptual advances in workplace deviance constructs through a systematic literature review (SLR). This study also focuses on particular types of deviant behavior at work which have been studied more frequently since last seven years and whether they include interpersonal or organizational forms of workplace deviance. The study also investigated mediating and moderating variables of workplace deviance. An SLR included Scopus and Web of Science database articles published between 2015 and 2022. This evidence suggests that interpersonal forms of deviant work behavior, rather than organizational forms, have received more attention in recent studies. In terms of antecedents, the findings indicate that personality factors remain prevalent. The study has both theoretical and practical limitations.
Professional Development for Young Teachers in Higher Education

With the implementation of university enrollment expansion policy, many young teachers have joined universities to play their academic value. This group of young teachers gradually as an important role in the university faculty, their professional level directly affects the school's teaching quality and scientific research level. However, their professional development path in schools is constrained by many factors such as low salary, promotion difficulties and no perfect training mechanism. This paper analyzes the current development situation of young teachers in China, describes their difficulties and dissatisfaction, and puts forward suggestions for reforming the development path of young teachers in universities.
The Bigger the Better?

This article looks at a controversy over the ‘better’ architecture for conversational AI that unfolds initially along the question of the ‘right’ size of models. Current generative models such as ChatGPT and DALL-E follow the imperative of the largest possible, ever more highly scalable, training dataset. I therefore first describe the technical structure of large language models and then address the problems of these models which are known for reproducing societal biases or so-called hallucinations. As an ‘alternative’, computer scientists and AI experts call for the development of much smaller language models linked to external databases, that should minimize the issues mentioned above. As this paper will show, the presentation of this structure as ‘alternative’ adheres to a simplistic juxtaposition of different architectures that follows the imperative of a computable reality, thereby causing problems analogous to the ones it tried to circumvent.
Would Bitcoin Relace World Currency in The Future: Analysis Based on Marx's Theory of Monetary Function

Bitcoin has received a lot of attention as a cryptocurrency in recent years. The paper focuses on determining if Bitcoin would replace world currency in the future, which compares Bitcoin with the US dollar and gold. The article considers qualitative analysis to discuss the bitcoin's characteristics based on Karl Marx's five different categories in his book Das Kapital: measure value, means of circulation, means of hoarding, means of payment, and universal currency. The paper presents the advantages and disadvantages related to Bitcoin compared to the US dollar and gold. The article finds that Bitcoin is more secure in saving and privacy. Also, it wouldn't be affected by inflation. However, disadvantages are also present when applying Bitcoin in the market. The volatility and value of storing would be a challenge to solve. The lack of population using it and the limitations of transactions would also be problems. The research illustrates that Bitcoin can't replace world currency in the short term, but in the long term, Bitcoin would be the mainstream currency to use in real life due to the performance of people, governments, and the world economy. The article is the first research to utilize Marx's theory to analyze whether Bitcoin could replace world currency in the existing literature. This paper recommends that governments of all countries establish a unified regulatory policy and security mechanism on a global scale to ensure the legitimacy, stability, and security of Bitcoin so that Bitcoin can truly become a world currency.
Q-Learning: Applications and Convergence Rate Optimization

As an important algorithm of artificial intelligence technology, Q-learning algorithm plays a significant part in a number of fields, such as driverless technology, industrial automation, health care, intelligent search, game, etc. As a classical learning algorithm in reinforcement learning, with the help of an experienced action sequence in a Markov environment, an agent can learn to select the best course of action using the model-free learning technique known as Q-learning. This paper mainly discusses the addition of received signal strength (RSS) to the Q-learning algorithm to navigate unmanned aerial vehicle (UAV), summarizes the main content and results of the neural Q-learning algorithm helping UAV avoid obstacles, adaptive and random exploration (ARE) method is proposed to address the issue in UAV planning a route tasks, summarizes the content and results of route designing of moving robot using obstacle characteristics as Q-learning states and actions, the Q-learning algorithm employs a novel exploration technique that combines ε-rapacious exploring with Boltzmann theory. to help mobile robot to plan its path, and analyzes the convergence speed of the algorithm for planning a route of stage Q-learning and the path planning algorithm of traditional Q-learning. When there are many states and actions, the operation efficiency of the Q-learning algorithm will be greatly reduced, so it is necessary to study in depth how to reduce the operation time of the algorithm for Q-learning and increase the convergence velocity of algorithm for Q-learning.
Financial Integration, Trade Integration and Economic Growth in the East African Community

This study sought to investigate the influence of macroeconomic volatility and trade integration on the relationship between financial integration and economic growth in the EAC member states. Specifically, the study aimed at determining the influence of trade integration on the relationship between financial integration and economic growth in the EAC. To achieve this objective, the following hypothesis was developed: There is no significant intervening effect of trade integration on the relationship between financial integration and economic growth. The study adopted a positivistic research philosophy and casual research design. Diagnostic tests were carried out to meet the requirements for conducting correlation and regression analysis on panel data. These include; Multicollinearity tests, Im- Pesaran-Shit Test (IPS) panel unit root test and Hausman test for fixed effects and random effects models. Descriptive statistics such as the mean, standard deviation, coefficient of variation as well as correlation analysis were conducted as the preliminary statistical analysis. Generalized-two stage least squares instrumental variable regression model (G2SLSIV) was then conducted to test the hypotheses. The findings of the study showed that: there is no significant intervening effect of trade integration on the relationship between financial integration and economic growth. These findings contribute to knowledge in the sense that, the positive and significant correlation between financial integration and economic growth confirms that, an increase in gross capital flows is accompanied by increase in economic growth. It also contributes to knowledge by revealing that, financial deepening contributes positively to financial integration which further contributes to accelerating economic growth. Therefore, the study is useful to the governments of respective member states in formulating policies aimed at achieving similarity in economic structures and ensuring the quality .....
Machine Learning based Data Analysis for Wordle Game

As wordle game is becoming more and more popular all over the world, the study of the intrinsic patterns and problem-solving techniques of the game has become a popular topic. In this paper, we obtained daily result files of wordle over a period of time, which included data such as words of the day, the number of people who reported their scores on the day, and the number of players who entered the difficult mode, and further analyzed the data. In order to predict the results reported on a future day, this paper first eliminates the abnormal data in the dataset, and then builds the LSTM model and ARIMA model. On the test set, the MAPE (mean absolute error) of the LSTM model is 5.643, and the LSTM is significantly better than the ARIMA model. Secondly, in order to predict the distribution of word results for a given future date, the 12 features of the network training data were first subjected to PCA dimensionality reduction, and the results showed that the percentages were consistent with time by performing the Shapiro-Wilk normalization test on the correlation percentages. Based on this observation, we built a BP-LSTM parallel model to extract word attributes and extract percentage features over time. The model has a MAPE of 6.09, which outperforms a BP neural network that can only extract word attributes.
Crime Online

Abstract: The majority of property crimes are now cybercrimes—including identity theft, credit card fraud, and vari_ous kinds of cyber attacks—and they are on the rise. This development raises serious political questions. What are our digital rights, and how are they upheld? When we’re online, who’s in charge of protecting us? How are our institutions addressing the social transition to the internet?