Reviewerly/exHarmony huggingface dataset:
Classification the Characteristics of Traffic Accident Victims in Pariaman Using the Chi-square Automatic Interaction Detection Algorithm

Traffic accidents are incidents that occur when motor vehicles collide on the road, resulting in damage to vehicles and road infrastructure, as well as the potential for material losses, injuries, physical damage, and even death for those involved. Data from the Indonesian National Police show that the number of traffic accident victims between 2010 and 2020 ranged from 147.798 to 197.560 people, with fatalities predominantly occurring among individuals aged 15-34. The high number of traffic accident victims has negative impacts on various aspects of life, ranging from material losses to physical damage to the victims. Classification is a technique used to group objects or data into pre-defined classes or categories based on their attributes or features. One method in the field of classification is Chi-Square Automatic Interaction Detection (CHAID). The results of the classification using this method indicate that the age of the victims and the type of accident are the most significant variables influencing the condition of traffic accident victims. The evaluation of the model using a confusion matrix yielded an accuracy rate of 92%. This indicates that the model performs well in overall data classification.
A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier

An increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component analysis to find the normal vectors of the points, and then generates a certain number of random point sets around the selected training points for training the classifier. The trained, improved Naive Bayes classifier is based on point normal vectors and is able to automatically remove noise points due to various reasons in conjunction with the knee point algorithm, realizing high-precision extraction of the discontinuity sets. Subsequently, the individual discontinuities are segmented using a hierarchical density-based spatial clustering method with noise application. Finally, the PCA algorithm is used to complete the orientation by plane fitting the individual discontinuities. The method was applied in two cases, Kingston and Colorado, and the reliability and advantages of the new method were verified by comparing the results with those of previous research, and the discussion and analysis determined the optimal values of the relevant parameters in the algorithm.
EVALUATION OF THE APPLICATION OF THE ASSURE MODEL BASED ON THE COUNTENANCE STAKE MODEL ON RUKHSAH MATERIAL IN BUILDING EFFECTIVE LEARNING

The learning process determines student success in learning, so teachers need to build effective learning that supports these achievement goals. This study aims to analyze the evaluation of the application of the ASSURE model based on the Countenance Stake model on rukhsah material in building effective learning at SMP Hikmah Teladan. This research uses a qualitative approach using a descriptive analysis method. The results and conclusions of this study show that the application of the ASSURE model to rukhsah material in SMP Hikmah is exemplary based on the results of evaluation using the countenance stake model has been applied very well and supports building effective learning; the average good student learning outcomes show this.
Negation Detection Techniques in Sentiment Analysis: A Survey

Negation is a linguistic phenomenon that can cause sentences to have their meanings reversed. It frequently inverts affirmative sentences into negative ones, affecting the polarity; therefore, the sentiment of the text also changes accordingly. Negation can be expressed differently, making it somewhat challenging to detect. As a result, detecting negation is critical for Sentiment Analysis (SA) system development and improvement and will increase classifier accuracy, but it also poses a significant conceptual and technical challenge. This paper aims to survey and gather the most recent research related to detecting negation in SA. Many researchers have worked and performed methods, including algorithmic, machine, and deep learning approaches such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Bidirectional Long Short-Term Memory (BiLSTM), and other hybrid methods such as rule-based and machine learning, lexicon and machine learning, machine learning, and deep learning. It addresses and tries to identify the gaps in the current studies, laying the foundation for future studies in this field.
Sentiment Analysis of DANA Application Reviews on Google Play Store Using Naïve Bayes Classifier Algorithm Based on Information Gain

DANA is a digital payment platform that provides various features to make it easier for users to make payments, transfers, and balance replenishment online. DANA application users provide a variety of reviews that include both constructive and critical opinions, which can be valuable input for DANA application developers. The purpose of this research is to evaluate the results of sentiment classification of DANA application user reviews on the Google Play Store service using the Naïve Bayes Classifier method and Information Gain feature selection. In addition, this study aims to assess the effect of applying IG feature selection on the performance of the resulting model. In this study, reviews are divided into two categories, namely positive and negative based on lexicon-based labeling. Furthermore, data weighting, feature selection, and data division are carried out with a proportion of 80% train data and 20% test data before model building. There are two models, namely a model without feature selection (NBC model) and a model with feature selection (NBC-IG model). The evaluation results showed that the NBC model with 1106 features performed well, with 82.91% accuracy, 83.96% precision, and 90.23% recall. Meanwhile, the NBC-IG model with 536 features showed higher performance, with 85.09% accuracy, 85.79% precision, and 92.09% recall. The application of IG feature selection with the IG value limit parameter &gt; 0.01 in the NBC model successfully reduced the number of features by 570, and improved model performance with an increase in accuracy by 2.18%, precision by 1.83%, and recall by 1.86%.
Application Analysis of Nursing Students' Grades in Course Relevance Based on Association Rule Mining Algorithm Apriori

By analyzing the correlation between courses in students’ grades, we can provide a decision-making basis forthe revision of courses and syllabi, rationally optimize courses, and further improve teaching effects. With the help of IBM SPSS Modeler data mining software, this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine, and to explore the correlation between professional basic courses and professional core courses. Lastly, according to the detailed analysis of the mining results, valuable curriculum information will be found from the actual teaching data.
Is There A Relationship Between Motivational Components in Foreign Language Learning?

Motivation is the main determining factor to ensure successful achievement in foreign language acquisition. Ever since influential Canadian Psychologists, Gardner and Lambert introduced the term motivation in foreign and second language learning in 1959, numerous researchers investigated the nature of motivation, however, the area on relationships between motivational components is still understudied until now, especially in foreign language acquisition area. A random sampling of 131 participants from a public university in Malaysia responded to the survey. This quantitative study attempted to explore the relationships between motivational components, which have been identified as value components, expectancy components and affective components. Motivational scale by Pintrich & De Groot (1990) is used to compose the questionnaire, which examined the students’ motives in learning foreign language. The learners answered four sections, consist of Demographic Profile, Value Components, Expectancy Components and Affective Components by using a 5-point Likert scale survey. Analysis by using SPSS has been done to discover results in the form of mean scores and correlations scores. The findings revealed that the three motivational components have strong relations with students’ motivation. Additionally, the correlation analyses revealed interesting discoveries; Value and expectancy components showed favourable correlations, whereas there were negative correlations between expectancy and affective components and also between value and affective components. These findings are useful for teachers and curriculum writers since contribution of this study will clearly provide teachers and curriculum writers the foundation ideas to design and produce authentic lesson plans. The implementation of ideas from this study will motivate the students to learn foreign language skills based on value and expectancy components.
Mitigating Overfitting in Extreme Learning Machine Classifier Through Dropout Regularization

Achieving optimal machine learning model performance is often hindered by the limited availability of diverse datasets, a challenge exacerbated by small sample sizes in real-world scenarios. In this study, we address this critical issue in classification tasks by integrating the Dropout technique into the Extreme Learning Machine (ELM) classifier. Our research underscores the effectiveness of Dropout-ELM in mitigating overfitting, especially when data is scarce, leading to enhanced generalization capabilities. Through extensive experiments on synthetic and real-world datasets, our findings consistently demonstrate that Dropout-ELM outperforms traditional ELM, yielding significant accuracy improvements ranging from 0.19% to 16.20%. By strategically implementing dropout during training, we promote the development of robust models that reduce reliance on specific features or neurons, resulting in increased adaptability and resilience across diverse datasets. Ultimately, Dropout-ELM emerges as a potent tool to counter overfitting and bolster the performance of ELM-based classifiers, particularly in scenarios with limited data. Its established efficacy positions it as a valuable asset for enhancing the reliability and generalization of machine learning models, providing a robust solution to the challenges posed by constrained training data.
Analysis of Students' Critical Thinking Ability Profile Using HOTS-Based Questions

Critical thinking includes higher processes, such as analysis, synthesis, evaluation, conclusion, and reflection. This research aims to determine the profile level of students' critical thinking abilities. The research sample consisted of 28 students taken by random sampling consisting of 2 classes. Data was obtained through test and non-test techniques which included a critical thinking ability test using 20 questions. Meanwhile, the non-test uses an observation sheet on students' critical thinking abilities. Analysis of data obtained from the results of students' critical thinking skills tests was then analyzed using analysis items with the Rasch model. The results of the profile analysis of students' critical thinking skills were classified as moderate. This is proven by test results and observations in basic biology courses. Based on the results of tests on students' critical thinking skills, it is known that there are 16 students who have high levels of critical thinking skills, 5 students with moderate critical thinking skills, and 6 students with weak critical thinking skills. Meanwhile, the results of observations of the critical thinking abilities of Bima Muhammadiyah University students in basic biology courses are in the moderate critical thinking ability category with a category score of around 68.06%.
A Study on Teaching Competency of Teacher Educators in Colleges of Education

The research is in the form of a Descriptive survey method. The purpose of this study is to determine the Teaching Competency of Teacher Educators in Colleges of Education. It also contrasts Teaching Competency of Teacher Educators with two types of background variables, such as Gender and Locality. The study's findings reveal that the Male and Female Teacher Educators have similar mean scores of Teaching Competency. As well as Rural and Urban Teacher Educators, have similar mean scores of Teaching Competency.
A Case Study of Teacher’s Practices in Village-Linked Curriculum Implementation- from the Perspective of Community Sustainability -

This study analyzed teacher practices in village-linked curriculum implementation from the perspective of sustainability of communities. A village-linked curriculum is a school curriculum that aims to realize the vision of a village education community and is based on the virtuous circle perspective that local capacity can be mobilized to improve the quality of education, which in turn can improve the community. This study analyzed teacher practices from the perspective of sustainability to provide an in-depth understanding of how a village-linked curriculum enhances local sustainability and to suggest policy measures to enhance teacher practices. For this purpose, an analytical case study was conducted to analyze the experiences of a typical teacher who had been implementing a village-linked curriculum for more than six years. Various materials, such as literature, interviews, photos, and videos, were analyzed; supplementary interviews were conducted; and validity was ensured through member checks and peer verification. The study found that in terms of the education system, the teacher aimed to improve the welfare system, engage in governance activities and build social capital, and create a virtuous cycle of education and society. In terms of curriculum implementation, the teacher provided lessons that were designed to foster their students’ sense of local identity and community belonging, as well as the students’ agency for local development. The policy recommendations for enhancing teacher practices include building a school-centered welfare system, improving the system for educational autonomy, building a local education―career―vocation linkage system, budgeting for school equality, and building an educational system focused on the lesson.
Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %，8.17 %, and 1.27 %, the RMSE - 47.83 %，6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.
Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases

Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital microscopy system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store, and analyze BMA samples remotely but is also supported by an Artificial Intelligence (AI) pipeline that accelerates the differential cell counting process and reduces interobserver variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.
Study of Web Security Using Blockchain Technology and Its Application in Education

Present paper discussed about Blockchain Technology and its application in Education. Cybersecurity offers protection from cyberattacks for protected data on internet-connected devices as well as for hardware and software. A set of regulations, guidelines, and laws that apply to hardware, software, programmers, and secure information provide defense against dangers from without, such as destruction, theft, manipulation, and unauthorized access [1]. The words "cyber" and "security" are combined to form the compound word "cybersecurity." The network of protected devices, services, networks, and data is referred to as "cyber". On the other hand, security is concerned with safeguarding data, networks, applications, and systems. The application of blockchain technology to improve data security, dependability, and device security has increased significantly in a number of industries in recent years [3]. A number of cryptographically chain blocks can be eagerly decoded from the blockchain technology.
Instance-Specific Model Perturbation Improves Generalized Zero-Shot Learning

Abstract Zero-shot learning (ZSL) refers to the design of predictive functions on new classes (unseen classes) of data that have never been seen during training. In a more practical scenario, generalized zero-shot learning (GZSL) requires predicting both seen and unseen classes accurately. In the absence of target samples, many GZSL models may overfit training data and are inclined to predict individuals as categories that have been seen in training. To alleviate this problem, we develop a parameter-wise adversarial training process that promotes robust recognition of seen classes while designing during the test a novel model perturbation mechanism to ensure sufficient sensitivity to unseen classes. Concretely, adversarial perturbation is conducted on the model to obtain instance-specific parameters so that predictions can be biased to unseen classes in the test. Meanwhile, the robust training encourages the model robustness, leading to nearly unaffected prediction for seen classes. Moreover, perturbations in the parameter space, computed from multiple individuals simultaneously, can be used to avoid the effect of perturbations that are too extreme and ruin the predictions. Comparison results on four benchmark ZSL data sets show the effective improvement that the proposed framework made on zero-shot methods with learned metrics.
Mergers and Acquisition of Deposit Money Banks in Nigeria: Empirical Analysis of Contribution to the Economic Growth of Nigeria (2006-2021)

Despite impressive results declared regularly by banks in Nigeria, their contribution to the economic growth of Nigeria has been a subject of mixed results as economic growth has been inconsistent for some time and historically been characterised by oscillations in both positive and negative directions. The Central Bank of Nigeria embarked on a major Mergers and Acquisition of Banks in 2005. The study evaluated the effect of Mergers and Acquisition of Deposit Money Banks’ contribution to Economic Growth of Nigeria. The study adopted ex-post facto research design. Total yearly data for Economic Growth, Total Commercial Bank Lending and Total Commercial Bank Assets were extracted from the Central Bank Statistical Bulletin for the period 2006 to 2021 and they are considered valid and reliable. The study adopted descriptive and inferential statistics and the Auto-Regressive-Distributed Lag-ARDL model was adopted for data analysis.The elasticity of Total Bank Lending- upsurges pertaining to Gross Domestic Product (GDP)- is 0.9951, indicating that ceteris paribus, a 1% rise in is expected to increase (Economic Growth ) by 0.995. The positive nexus was statistically significant at 5% (p = 0.009), showing that Total Bank Lending ( ) impact economic growth positively ( ) in the long run. Furthermore, the study revealed that the Total Bank Assets- depicted a positive nexus with GDP- . The elasticity of pertaining to is 0.5773, indicating that ceteris paribus, a 1% rise in is expected to increase Economic Growth- by 0.5773%. The positive nexus was statistically significant at 0.05 (p = 0.018), hence denoting that Total Bank Assets have a significant individual impact on Economic Growth ( ) in Nigeria in the long run. The study concluded that Total Commercial Bank Lending and Total Commercial bank Assets jointly influenced Economic Growth positively. It is recommended that the policy makers should pay attention to Mergers and Acquisition of Banks and continue to ensure that banks are strong enough to support Economic Growth.
Investigation of Causal Linkages Between Financial Development and Economic Growth: Evidence from Pakistan

Financial development is a driving factor for economic growth and is important in fostering growth. An efficient and effective financial system contributes to economic growth and fosters the performance of other economic measures. This study aims to investigate the influence of financial innovation on economic expansion to understand the correspondence between financial development and economic growth in Pakistan. Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) are used for stationarity. Auto Regressive Distributed Lag (ARDL) and Error Correction Model are employed to analyze the correlation between economic growth and financial development. The finding shows that financial development stimulates economic growth. The research explains that foreign direct investment and personal remittances adversely affect GDP growth. These understandings recommend that although financial development generally promotes, its results can vary extensively and depend on specific context-dependent conditions. Furthermore, applying CUSUM AND CUSUM of square tests to measure model stability convincingly confirms a long-term relation between financial development and economic expansion, disproving the null hypothesis of no co-integration and reinforcing that financial development is essential to domestic economies. Policymakers need to practice these understandings in actionable plans by highlighting financial innovations that advance access to credit and efficiently allocate resources so investment in education and agriculture can maximize economic growth.