Reviewerly/exHarmony huggingface dataset:
Personalized Recommendation System for Online Learning: An Opportunity

In the world of education, learning management systems are currently widely used. The massively open online courses are accessible either via the web or mobile. The Learning Management System (LMS) is one of the learning media that is widely used today in online learning. An LMS with the use of a personalized recommendation system can provide items tailored to the interests and behavior of its users. Items in the recommendation system can be in the form of recommendations for learning models, ways of learning, learning patterns, or learning stages according to the characteristics and profile of the user. The purpose of this study is to identify opportunities for better online learning by utilizing a customized recommendation system based on the history of user interaction with the system, using several factors as determinants or variables. The method used is a review conducted from several stages. The result of this is knowing the factors that influence the user, and the opportunities that exist in the system recommendations on personalized development.
The Construction for Double Qualification Teacher Team Based on Cooperation Education Between Schools and Enterprises

The training of double qualification teacher is the primary training target of computer science in private colleges. This paper starts from the training of double qualification teacher, and constructs the training mode of double qualification teacher based on cooperation between schools and enterprises, which developed double qualification teacher that not only with a solid theoretical knowledge but also practical skills.
Fraud Detection of Credit Card Using Machine Learning

Abstract: This paper is focused on credit card fraud detection in real world scenarios. Nowadays credit card frauds are increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed paper we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions.
Evidence of Content Learning Through Animations Developed Based on the Cognitive Theory of Multimedia Learning

Background: Considering cognitive limitations is essential for learning, as it provides subsidies for developing methodologies that successfully facilitate and help the learning process. Objective: We start our research by asking: “Does a video lesson containing an animation developed based on the cognitive theory of multimedia learning promote positive effects on student learning in terms of content retention and transfer?” seeking to answer it based on data analysis. Design: Four animations containing the narration of two different contents were produced, two with and two without cognitive overload. The research team employed forms with questions about the animations to analyse the retention and transfer of the critical content to be learned in the animations. Setting and Participants: Undergraduate students were divided into two groups to watch the animations and answer the research questions. Data collection: After analysing the participants’ answers, we calculated the effect size (Cohen’s d). Results: The tests of retention and transfer of content were compared with the values obtained in the calculus of the effect size between the participating groups. Conclusions: The animations that followed the cognitive theory positively contributed to learning, collaborating with the thesis that one should seek to understand and regard cognitive processing.
Semiconductor Optical Amplifier (SOA)-Driven Reservoir Computing for Dense Wavelength-Division Multiplexing (DWDM) Signal Compensation

Optical signal processing (OSP) technology is a crucial part of the optical switching node in the modern optical-fiber communication system, especially when advanced modulation formats, e.g., quadrature amplitude modulation (QAM), are applied. However, the conventional on–off keying (OOK) signal is still widely used in access or metro transmission systems, which leads to the compatibility requirement of OSP for incoherent and coherent signals. In this paper, we propose a reservoir computing (RC)-OSP scheme based on nonlinear mapping behavior through a semiconductor optical amplifier (SOA) to deal with the non-return-to-zero (NRZ) signals and the differential quadrature phase-shift keying (DQPSK) signals in the nonlinear dense wavelength-division multiplexing (DWDM) channel. We optimized the key parameters of SOA-based RC to improve compensation performance. Based on the simulation investigation, we observed a significant improvement in signal quality over 10 dB compared to the distorted signals on each DWDM channel for both the NRZ and DQPSK transmission cases. The compatible OSP achieved by the proposed SOA-based RC could be a potential application of the optical switching node in the complex optical fiber communication system, where incoherent and coherent signals meet.
Studies on the EFL Assessment Framework Driven by Data Mining Technology

This essay looks at how data mining technology is used in the assessment of EFL instruction.The accuracy of traditional assessment methods are constrained because they rely on subjective indicators rather than objective criteria.Data mining technology can be utilized as a useful method to assess college English instruction and offer in-depth analysis of the procedure.By seeing patterns and trends in the data, it can assist in enhancing the assessment's accuracy and validity.Data mining techniques are used to find patterns in datasets and reveal correlations between variables.The literature on data mining and its applications to the evaluation of college English education is reviewed in this study.It also discusses the advantages of data mining and looks into the difficulties associated with its application.Additionally, this study suggests and effectively executes a way for data mining implementation in college English teaching assessment.It implies that data mining is a valuable technique that can enhance the timeliness and accuracy of the evaluation process.With regard to the potential of data mining for the evaluation of college English education, the research offers helpful insights for administrators and educators.
Analysis and Evaluation of Global Temperature Changes and the Prediction of Future Global Temperature

Temperatures have continued to rise in recent years, many areas have shown extreme temperatures, and global warming has been a perennial topic. We use various optimization prediction and optimization evaluation methods in this paper to determine the trends and causes of global temperature variation and make effective suggestions for them. We first constructed a first-order differential temperature model and found that the temperature rise in March 2022 was larger than that observed in the past 10 years. By constructing a prediction model (BP) based on BP neural network and a grey temperature prediction model (GAGM) optimized based on adaptive genetic algorithm, and by importing the data, we find that the average temperature in 2116 and 2145 will reach 20℃. In addition, if the correlation factor evaluation model (FAEM) is constructed in the country, and we conclude that there is a correlation between location, time and temperature. Next, we analyzed key environmental factors, such as volcanic eruptions, forest fires, and COVID-19. We imported the CO 2 concentration and temperature data into the FAEM evaluation model to obtain the results that natural disasters can lead to the temperature increase. Then nine environmental factors, including COND, NYKC, NYXH, GDP, PJRK, LJL, LZMJ, KJSP, and GJJY. Taking these factors as independent variables and temperature as the dependent variables, COND, NYXH, PJRK, LJMJ, JSL are the main causes of global temperature change and KJSP. This study provides an assessment of future global temperature changes, making recommendations for policymakers to optimize the energy mix and plant trees to effectively mitigate the global temperature rise.
A Comparative Analysis of Convolutional Neural Network and Recurrent Neural Network for Voice Disease Detection

Early diagnosis, monitoring, and effective diagnostics of abnormal speech can greatly benefit from computerized acoustic examination.This study presents a methodology for identifying voice pathology by employing Singular Value Decomposition (SVD) datasets for feature extraction.Acoustic measurements are utilized to assess the health of the voice, and the accuracy of these parameters influences speech noise detection methods.The extracted features are then processed through a system consisting of 27 neural network layers, comprising Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).A 10-fold validation technique is employed to split the dataset into training and testing datasets, enabling the evaluation of the system's performance.Experimental results demonstrate an observed accuracy of 87.11% for the CNN model and 86.52% for the RNN model.The classifier's performance is further assessed using 10-fold cross-validation.The implementation of the system's program is carried out in Python, utilizing the TensorFlow package, and computations are executed on a single NVidia Titan X GPU.The Linux operating system is employed for all computations.The proposed methodology offers promising potential in the early identification and monitoring of voice pathologies, providing a valuable tool for healthcare professionals in the field of speech analysis and diagnostics.
Challenges and Prospects of IFRS implementation: - listed Banks in Ethiopia.

This paper was explored Challenges and Prospects of International Financial Reporting Standards implementation in Listed Banks of Ethiopia.the research Approached, were Designed and Data analyzed, framed mixed method of investigation, both qualitative and quantitative were used.Data were collected using questionnaire were analyzed through descriptive statistics, frequency distribution, correlation and logistic regression using Statistical Package for the Social Scientists.The paper provides insight into the IFRS adoption process based on a questionnaire sent to those who had worked in Banking Industries and The population were considered according to the register of National Bank Ethiopia, there are More than 20 listed banks operating in the country and Out of this 18 are private banks while the remaining two are state owned banks and Employees were worked and randomly draw a sample from the total to get rich evidence Such as accountant, Auditor and Financial Manager professionals in Listed Banks of Ethiopia.235 responses received out of 250 and it indicate: a majority of respondents have indicated IFRS Enforcement capacity will be a key challenge for transition, the process is costly, complex, and burdensome, Institutional readiness require more attention for transition to IFRS, the more comprehensive the approach to conversion, the more respondents tend to agree with the factors influencing the transition, the complexity of IFRS as well as the lack of Implementation guidance and uniform interpretation are also key challenges in technical capacity area; the results of the questionnaire and interviews confirmed that, the transition to IFRS in Listed Banks of Ethiopia's will be challenging.The study also discusses the prospect of IFRS implementation in Listed Banks of Ethiopia's and the result showed that the transition plan to IFRS and its implications for preparers, users, educators and other stakeholder has to be effectively coordinated and communicated.In the conclusion part, the study brings out the ways through which these challenges can be addressed.
Synchronization of silicon thermal free-carrier oscillators

Recent exploration of collective phenomena in oscillator arrays has highlighted the potential to access a range of physical phenomena, from fundamental quantum many-body dynamics to the solution of practical optimization problems using photonic Ising machines. Spontaneous oscillations often arise in these oscillator arrays as an imbalance between gain and loss. Due to coupling between individual arrays, the spontaneous oscillation is constrained and leads to interesting collective behavior, such as synchronized oscillations in optomechanical oscillator arrays, ferromagnetic-like coupling in delay-coupled optical parametric oscillators, and binary phase states in coupled laser arrays. A key aspect of arrays is not only the coupling between the individuals but also their compliance toward neighbor stimuli. One self-sustaining photonic oscillator that can be readily implemented in a scalable foundry-based technology is based on the interaction of free carriers, temperature, and the optical field of a resonant silicon photonic microcavity. Here, we demonstrate that these silicon thermal free-carrier (FC) oscillators are extremely compliant to external excitation and can be synchronized up to their 16th harmonic using a weak seed. Exploring this unprecedented compliance to external stimuli, we also demonstrate robust synchronization between two thermal FC oscillators.
DEEPFAKE DETECTION USING DEEP LEARNING

Four billion images are uploaded to the internet every day, according to polls.With the widespread use of digital photography, new methods for modifying image content employing tools, apps, and editing software like Adobe's have emerged.Deepfake techniques were used to create a fake movie and photos, which has raised significant public concern.The majority of face-manipulation techniques used in videos today, such as Faceswap and Deepfake, have been created successfully.It has both benefits and drawbacks.On the one hand, it broadens the application to new fields (such as visual arts, visual studies, filmmaking, etc.), but on the other, it also fosters harmful users.Consequently, we can determine whether the video is real or not by applying Deep Learning algorithms.We're going to create a system that can identify this dangerous data in order to recognise it.
Data Mining Methods: K-Means Clustering Algorithms

A data warehouse is a straightforward definition of a database. Data mining technology can be used to process mountains of data in databases to uncover new, fascinating, and useful information.Clustering is an approach to data gathering. As one technique for grouping data into clusters or groups, the K-Means Clustering Algorithm algorithm divides the data into those that share the cluster's traits and those that don't. data into groups, and data into groups, so that data into groups, and data into groups, so that data has the same traits is grouped in the same cluster. Other clusters are formed from data and clusters with distinct properties. additional categories. The knowledge/information gathered in the groups or clusters is helpful to policy consumers in the decision-making process.mact of making decisions.
Pre-employment Education of Physical Education Teachers in Ethnic Local Universities Based on Teacher Education Certification

Based on the era background of teacher training professional certification, this paper analyzes the indicators such as the orientation of training objectives, employment status, curriculum system and condition guarantee of physical education majors in three colleges and universities in the western minority areas of Sichuan Province by using literature, interviews, mathematical statistics and other methods, and finds that there are the following problems: 1) The orientation of training objectives deviates from the original intention passively; 2) Low employment relevance reflects poor quality of training; 3) The curriculum system is difficult to support students' future needs; 4) The lack of conditions restricts the development of student movement ability.Corresponding countermeasures have been proposed to address the issues, hoping to provide reference for the evaluation and promotion of pre-service physical education teacher education in local universities in ethnic regions.
A Study of Teacher Attrition in Rural Primary and Secondary Schools under the Background of Rural Revitalization: A Survey in Y County, Yunnan Province

The key to rural revitalization lies in people, and the key to rural education revitalization lies in cultivating a rural teacher team that can be attracted and retained.However, there is still a serious problem of rural teacher loss in Yunnan province's rural education.After investigation, it was found that this is mainly due to external and internal factors.External factors include a large income gap between urban and rural teachers, and late salary payments.Rural teachers have low social status and occupational well-being.Internal factors include difficulties in adapting to rural life, lack of belongingness, excessive pressure, resulting in professional burnout and teacher loss.Therefore, in the author's opinion, attention should be paid to the introduction of talent for rural basic education.Through policy guidance, rural teacher income should be increased, model examples should be vigorously propagated to improve the social status of rural teachers, and promotion channels for teachers should be unblocked to enhance the attractiveness of rural talent acquisition.The goal is to create a rural teacher team that loves the countryside, is sufficient in quantity, has good quality, and is full of vitality, serving the purpose of rural revitalization.
Application of Game Teaching Method in Primary English Teaching

Combining games into elementary school English education can change the conventional learning mode, increase the fun of classroom education, promote students' interest in English learning, and improve the effectiveness of elementary school English learning.In this article, we look at the significance of combining games in primary English teaching.It is necessary for elementary school English teachers to give full attention to and understand the game teaching method when teaching, and try to apply this teaching method to assist students to learn English knowledge, so that students can feel English knowledge better and do better in learning knowledge.For this reason, this paper will study and discuss the application of English game teaching method in elementary school from the following three aspects for reference.
The Dilemma and Countermeasures of Online College English Teaching Based on the Deep Learning Field Theory

In online classroom learning, students' sense of presence and constraint is relatively insufficient.How to promote their deep learning is a key issue that should be considered in teaching design.The explicit goal of achieving deep learning is to construct a deep learning field.This article takes the construction of an online classroom deep learning field as the research goal.Starting from the connotation of the deep learning field, based on the analysis of the elements forming the online classroom deep learning field, it constructs an online classroom deep learning field.Taking college English courses as an example, it analyzes the implementation methods of the online classroom deep learning field.
Exploration on the Mode of College English Language Teaching under the Background of Informatization

Due to the rapid growth of information technology (IT), the network has already penetrated into all fields of social life.In this situation, the traditional English language teaching (ELT) can no longer meet the needs of the times.It is an urgent task for ELT in universities to effectively integrate network resources and integrate IT into ELT.Based on this, this article explores and innovates the ELT mode in universities under the background of informationization.This article analyzes the positive influence of information background on university ELT.This article probes into the English learning situation in universities under the condition of informationization; It also puts forward some ideas on the construction of university ELT mode in the information age.Information-based teaching in English classroom can create a good learning atmosphere and communication environment for students, and arouse their initiative to participate in the classroom.In order to promote the sustainable growth of college information-based ELT.
Construction of a Mixing Mode of Online and Offline College English Deep Learning in the Context of the New Liberal Arts

Based on the interpretation of the goal of cultivating college foreign language talents in the context of the new liberal arts, it is proposed that college English teaching guided by the concept of deep learning is one of the feasible ways to achieve this goal.From the three objective dimensions of cognition, interpersonal relationship, and self, the construction of an online and offline mixing mode of college English deep learning integrating mutual evaluation, self-evaluation, and teacher evaluation is divided into three stages: before class, during class, and after class.With the aim of providing an operable action guidance framework for deep teaching and learning, it proposes alternative paths for achieving college English teaching objectives in the context of the new liberal arts.
An Analysis on the Globalization of Criminal Law Legislation from the Perspective of Big Data

The globalization of criminal law refers to the multiple extension and developments of different countries' criminal laws to the absorption of other countries' criminal laws.Based on the perspective of big data in the information age, this model is basically feasible.Criminal law is a legal form that mainly stipulates crime and criminal responsibility.There is a necessary connection between criminal responsibility and specific criminal behavior.Therefore, in criminal law legislation, whether the punishment of criminal behavior is reasonable and appropriate is particularly important.This article mainly discusses the globalization of criminal law legislation from the perspective of big data for reference.
An Innovative AdaBoost Process Using Flexible Soft Labels on Imbalanced Big Data

DNNs (Deep Neural Networks) have been proved to be a successful technique in many areas. Understanding the reasons behind DNN is, however, quite important in assessing trust, which is fundamental if one plans to take action based on leaning results, or when choosing whether to deploy a new model. Lack of insights into the model can be an obstacle that hinders the development and application of the DNN. AdaBoost is a well-known boosting learning technique with better interpretability, combining many relatively weak and inaccurate rules to increase the performance. And as it modified sample weights in the training process, it shows excellent adaptability even in complex cases as it can alleviate overfitting. In the paper, we propose a method training the nets by the AdaBoost based on the soft label, which we call flexibly soft-labeling AdaBoost (FSL AdaBoost). Our soft labels are made in a novel and sequential way, adding further interpretability and adaptability to the learning process. Experimental results on several well-known datasets have validated the effectiveness and novelty of FSL AdaBoost.