Reviewerly/exHarmony huggingface dataset:
A Conceptual Framework of a Detective Model for Social Bot Classification

Social media platform has greatly enhanced human interactive activities in the virtual community. Virtual socialization has positively influenced social bonding among social media users irrespective of one’s location in the connected global village. Human user and social bot user are the two types of social media users. While human users personally operate their social media accounts, social bot users are developed software that manages a social media account for the human user called the botmaster. This botmaster in most cases are hackers with bad intention of attacking social media users through various attacking mode using social bots. The aim of this research work is to design an intelligent framework that will prevent attacks through social bots on social media network platforms.
Spectral properties inspired image enhancement for crystalline polymer powder impurity detection

Crystalline polymer powder inevitably incorporates certain impurities, including decomposed polymers or foreign particles. An essential criterion for assessing the quality of polymers is the quantity of contaminants in the powder. However, it is challenging to discern powder contaminants through machine vision due to the poor quality of images taken at production sites. Inspired by the spectral properties of crystalline polymers, this paper proposes an efficient image-based impurity detection method, which seeks to precisely and robustly detect contaminant content. Based on the changes in absorbance during polymer decomposition, a highly selective channel-weighted image enhancement approach is designed to emphasize the difference between impurities and normal particles. Then, using the prior information on the powder’s attributes, an adaptive thresholding method is employed to categorize pixels belonging to impurities. Finally, a dataset of 119 12-megapixel photos from a chemical facility, where the average size of contaminants in images is 43 pixels, is used to evaluate the performance of the proposed algorithm. The results of the detection demonstrate that the proposed strategy for image enhancement has better selectivity to impurities than typical image enhancement methods.
THE EFFECT OF DEMOGRAPHIC CHARACTERISTICS OF THE RESEARCHERS ON THE PERFORMANCE OF RESEARCH INSTITUTIONS IN SRI LANKA

A countrys scientific research performance influences all aspects of a countrys future, its development, economic growth, and greater social well-being of the country. However, very few previous scientific studies have focused on the factors affecting a science research institutes research performance in Sri Lanka. The study focusses to identify whether the research performance of research institutions coming under the State Ministry of Skills Development, Vocational Education, and Research &amp; Innovation is affected by demographic characteristics. Purpose The purpose of this research was to identify whether the research performance of research institutions is affected by demographic characteristics. Design/Methodology/Approach To achieve the study aims, primary and secondary data were predominantly used. The study is carried out by perusing recent, major journals and papers on the topic that are published in reputable, high-quality journals. Data on academics research performance has been taken from NASTEC reports, the NIFS Research Repository, annual reports, and the questionnaire. This quantitative study, which employed a deductive methodology, was carried out using a slightly modified version of a previously validated and reliability-tested questionnaire. Findings The results have shown that researchers with age more than 60 years and more than 20 years of research experience, highest educational qualification of the researchers, university graduation (highest qualification), and the job category have significantly (P&lt; 0.05) associated with the research performance where gender and the research field do not influence the research performance. Keywords Research performance, Demographic characteristic
The Effect of Employee Motivation on Job Performance in the Banking Sector in Rwanda: Case of Bank of Kigali Headquarters

The Bank of Kigali was chosen as the case study to examine the effect of employee motivation on job performance. Findings showed that Intrinsic motivation has greatly enhanced job performance by a mean=3.5 and SD=1.219, and Style of management motivation had a positive effect on the degree of employee motivation by mean=3.5 and SD=1.289 while Extrinsic has a mean=3.39 and SD=1.267. Organizations make every effort to inspire their finest employees because they understand their crucial function and how it affects organizational success. Studies on motivation show that there are a number of ways to motivate employees. This study aims to offer explanations of why the employee of Bank of Kigali Rwanda do what they do, and examine to what level their activities affect job performance. Intrinsic motivation is a sort of internal incentive gained from the circumstance that propels workers to accomplish. This form of motivation develops because of the individual’s external effects received from environmental elements. These include good income, fringe benefits, empowering policies, and various types of supervision. Employers need to borrow a leaf from BK on what to do in extreme cases to sustain high productivity.
Research on the Influence, Value, and Development Strategy of Chinese University Teachers’ Leadership—Based on the Perspective of Distributed Leadership Theory

More and more universities all over the world realize that improving the leadership of university teachers is very important for the development of students and schools. However, at present, most of the leadership models in China University are still based on the traditional leadership theory, and the cultivation of teachers’ leadership ability is neglected. In addition, China University seems to prefer centralized right-based management. So, what is the value and influence of teachers’ distributed leadership on China University? What can China University do in the future to better cultivate and develop teachers’ distributed leadership? This essay will make a detailed comment on the distributed leadership of teachers, the role and value of distributed leadership of teachers, and the obstacles to its implementation, so as to provide reference strategies for the future development of distributed leadership of teachers in China University.
Analisis Hambatan Belajar Siswa Kelas IV Pada Mata Pelajaran Matematika

The purpose of this study was to determine the learning barriers experienced by students in learning mathematics subject matter of multiples and factors of a number. Learning difficulties experienced by students when in the learning process are caused by gaps or distances between expected academic achievement and actual academic achievement. This study uses descriptive qualitative research methods by using data collection techniques through interviews, observation, and documentation as well as using descriptive analysis methods. With class IV A participants totaling 27 students. The results showed that there were learning barriers experienced by Class IV A in mathematics subject matter of multiples and factors of a number. Forms of learning barriers include; 1) lack of readiness of students in learning; 2) difficulty in solving questions; 3) limited understanding of students' concepts. Based on the results of research conducted, learning barriers were found to be found in ontogeny barriers as many as 5 students, didactic barriers as many as 2 students, and epistemological barriers as many as 2 students. By knowing the learning barriers experienced by students in learning the teacher can minimize student learning barriers so that there is no gap between academic achievement and the academic achievement to be achieved. Keywords: Learning Barriers, Mathematics, and Elementary Schools.
Determining Implementation Approaches of Effective Digital Competencies Training

Objective. To analyse approaches to the organization of user training that can be applied in distance learning of digital competencies. Mastering digital competencies by users is known to be one of the important tasks not only for higher education but also for the whole society. Therefore, we consider it important not only to provide quality training, but also to encourage more users. Methods. Empirical methods are applied. The article examines the "user roadmap" - the steps from user identification of necessary competences to quality mastery of competences. At every step, the user needs help. Results. Based on the analysis done, recommendations have been developed for organizing work in university libraries in this direction. The expected result is an increase in the number of persons trained and an increase in the effectiveness of user training in digital competencies. Conclusions. We believe that in order to obtain a high-quality result, it is necessary to provide comprehensive user support. Specialists in information departments in institutions, including university libraries, have the opportunity to do this successfully due to the following advantages: the possession of relevant knowledge and its constant improvement, communication with users who are motivated for successful learning.
The Relationship Between Corporate Governance Mechanisms and CSR Disclosure: Evidence from Jordanian’s Listed Firms

The purpose of the current study is to examine the relationship between corporate governance mechanisms and corporate social responsibility disclosure in the Jordanian context. Research is quantitative in nature, based on panel data of 78 firms listed on the Amman Stock Exchange (ASE) for 8 years from 2012 to 2019, with 624 observations, To deal with an econometric problems such as heteroscedasticity and autocorrelation, the panel corrected standard error (PCSE) regression was used to assess the relationship among variables. The study found that board size affects positively CSR disclosure, while board independence and managerial ownership affect negatively CSR disclosure. On another hand, the study found that the presence female in board, CEO duality, family ownership, and concentration ownership do not affect CSR disclosure. Because of the chosen sample, the research results may lack generalisability to other country. Therefore, researchers are encouraged to test another sample. The study contributes to the understanding of how good corporate governance practices affect CSR disclosure for both academics and particularly Jordanian policymakers. This study provided new findings to bridge the gaps in the general corporate governance literature relative to the lack of consensus on the relationship between corporate governance mechanisms and CSR disclosure. The finding contributes to knowledge by providing new and original evidence that some current corporate governance mechanisms are not effective in increasing CSR disclosure in a developing setting.
Optimisation of IT teaching materials for students of humanities and social sciences

The demands of the 21st century mean that without digital competence and basic IT skills we cannot thrive in society. This is particularly true for students leaving higher education. Today, it is not only students in engineering and science who need to acquire ICT and IT skills. Although the latest version of the National Curriculum sets the development of digital competences as a task, the previous curriculum did not require students leaving public education to have these competences. For this reason, it is particularly important in higher education in the humanities and social sciences to develop digital competences in addition to IT skills. The paper presents the history of the introduction of IT and computing in higher education, and the process of how these subjects have been replaced in the humanities as well as in technical higher education because of the Bologna process. In addition, it presents the elements of the implementation of an IT course in an LMS system. It presents good practices and tools to increase not only students' knowledge but also their motivation, digital competences and interest in IT content.
Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation

Deep Reinforcement Learning (DRL) algorithms have been widely studied for sequential decision-making problems, and substantial progress has been achieved, especially in autonomous robotic skill learning. However, it is always difficult to deploy DRL methods in practical safety-critical robot systems, since the training and deployment environment gap always exists, and this issue would become increasingly crucial due to the ever-changing environment. Aiming at efficiently robotic skill transferring in a dynamic environment, we present a meta-reinforcement learning algorithm based on a variational information bottleneck. More specifically, during the meta-training stage, the variational information bottleneck first has been applied to infer the complete basic tasks for the whole task space, then the maximum entropy regularized reinforcement learning framework has been used to learn the basic skills consistent with that of basic tasks. Once the training stage is completed, all of the tasks in the task space can be obtained by a nonlinear combination of the basic tasks, thus, the according skills to accomplish the tasks can also be obtained by some way of a combination of the basic skills. Empirical results on several highly nonlinear, high-dimensional robotic locomotion tasks show that the proposed variational information bottleneck regularized deep reinforcement learning algorithm can improve sample efficiency by 200–5000 times on new tasks. Furthermore, the proposed algorithm achieves substantial asymptotic performance improvement. The results indicate that the proposed meta-reinforcement learning framework makes a significant step forward to deploy the DRL-based algorithm to practical robot systems.
Penerapan Supply Chain Management (SCM) Dalam Pemantauan Stok Barang Berbasis Web

Umi Nala Shop store is engaged in selling toys, such as floats, laptop screens, construction sets, ambulance cars, small cars, jeep cars, and many more. The problem is that this store does not know the amount of inventory of goods in the warehouse. This store purchases goods from suppliers for the inventory of goods to be sold without knowing the available stock. This makes the inventory of goods to be sold by the store uncontrollable. The store records incoming and outgoing goods and transactions manually using paper so that data can be lost. The Umi Nala Shop has more than 200 resellers but is inactive and has ten suppliers to supply the goods sold. The sale of goods at the Umi Nala Shop is still offline, where customers come directly to the Umi Nala Shop. Marketing is carried out through social media networks such as Facebook, WhatsApp, and Instagram. In carrying out its business, it is necessary to pay attention to inventory management to provide for consumer needs and control inventory when there is a high demand for goods. For the store to compete, it is necessary to implement Supply Chain Management (SCM) to integrate suppliers, warehouse manufacturing, and storage so that goods are distributed in the correct quantity at the right time, to minimize costs and provide satisfaction to consumers. The purpose of this study is to build an SCM application that can make it easier for stores to find out the amount of inventory to ensure the availability of goods. The results of this study with the implementation of Supply Chain Management at the Umi Nala Shop store can help and make it easier for store owners to manage the supply chain of goods.
Self Organizing Maps (Kohonen) untuk Cluster Bidang Karya Ilmiah (Skripsi) Mahasiswa Berdasarkan Nilai-Nilai Matakuliah Pendukung Machine Learning

The time for the preparation of scientific papers is considered too fast and not suitable for students due to several things in the scope of its preparation. One of the reasons is that the student is unable to complete and the supporting aspects of the field of scientific work being done, many students whose topics of scientific work are in machine learning or the like, but these students are unable to complete their scientific work because they do not understand the theoretical supporting knowledge of the machine learning field. So that it can make the student depressed or even harder to repeat his scientific work the next semester. The scope of machine learning courses are statistics and probability, matrix and linear algebra, algorithms and programming, and data structures. This research was conducted to overcome the problems faced by students, namely knowing the suitability group for the field of student scientific work they will be working on. So that in its preparation students can be responsible in their trial and are of higher quality. The clustering test with the self organizing maps (SOM) algorithm is more stable because the input according to the data owned is not random, only the weighting is done randomly but based on the uniform low (mins) and high (max) limit values. The desired number of clusters is two namely cluster 0 is able to do scientific work based on machine learning and cluster 1 vice versa. The SOM process for 40 student data with a target of two clusters and the results are cluster 0 = 14 students, cluster 1 = 26 students. The result is obtained by increasing the radius = 1, which previously this achievement was not successful if radius = 0.
Pemodelan Klasifikasi Gaji Menggunakan Support Vector Machine

It is known that there are currently many types of work in the field. Creativity of the community and economic pressure that is felt makes people have to work hard to be able to meet the needs of life. One way that must be done to be able to continue to survive by working. By working someone can produce wages or salaries so that the necessities of life of a person can be met. Various work that exists raises a problem. In determining the salary or wages of a job. The salary given to someone must be in accordance with the criteria of the worker. Then we need a Machine Learning model to predict a person's salary. In this study, a classification model was made to determine a person to be categorized into salaries above 7 million and salaries below 7 million based on suitable criteria or attributes. This study uses the Python programming language and took 1000 samples from the dataset obtained from Kaggle. The Machine Learning method used is the Support Vector Machine. Then compared to the K-Nearest Neighbors method. In the SVM model the model accuracy was obtained of 87% and 86% for the KNN model. From the results of accuracy, it was found that the SVM model was better than the KNN model in conducting salary classifications based on existing jobs.
Eksperimen Pengujian Optimizer dan Fungsi Aktivasi Pada Code Clone Detection dengan Pemanfaatan Deep Neural Network (DNN)

The problem of similarity (similarity) of program code can be one solution to the plagiarism detection approach. Plagiarism raises a form of action and consequences of plagiarism itself if the source used is not open source. Plagiarism is an act of deception of the work of others without the knowledge of the original author, which violates a Copyright and Moral Rights. With the increasing amount of data and data complexity, deep learning provides solutions for predictive analytics, with increased processing capabilities and optimal processor utilization. Deep learning shows success and improves the classification model in this field. On the other hand, clone detection code with massive, varied and high-speed data volumes requires feature extraction. With the potential of deep learning to extract better features, deep learning techniques are suitable for code clone detection. For this reason, it is necessary to develop a clone detection code that can process data from a programming language by utilizing deep learning. Based on the results of experiments conducted on 100 PHP program code data files, experimented with several types of activation function and optimizer methods. The average value of the resulting accuracy is good. With a variety of activation functions that we use such as Relu, Linear, Sigmoid, Softmax, Tanh, Elu, Selu, Softplus, Softsign, hard, and sigmoid, as well as variations of the optimizer used are Adagrad, RMSProp, SGD, Adadelta, Adam, Adamax and Nadam , the best attribute selection is in the Selu function and the RMSProp optimizer. The number of epochs used is 1000, the number of neurons per layer is 500 and the best number of hidden layers is 10, the average accuracy is 0.900
Sentiment Analysis on Twitter Against IndiHome Providers Using Chi-Square and Ensemble Bagging Methods

During the Covid-19 pandemic, internet usage has increased rapidly. Now the internet is used as a means in the online teaching and learning process and work from home. One of the internet service providers is IndiHome. IndiHome is an internet service provider company that has a huge number of users. A large number of IndiHome users causes frequent problems, and this is one of the factors that IndiHome users provide various kinds of opinions or responses. Sentiment analysis is used to see the opinion or opinion given by someone on a particular object or problem. This study conducted a sentiment analysis using the Chi-square and the Ensemble Bagging method with three base classifier methods, namely K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Naive Bayes (NB). Prediction results on labels obtained from each base classifier are combined using a hard majority vote. Tweet data collection was carried out in March 2022, and 6,962 tweets were collected. This study conducted two test scenarios. Scenario 1 is a scenario without oversampling with test results showing that Ensemble Bagging has the highest accuracy value of 83.32%, and in scenario 1 with hyperparameter tuning, Ensemble Bagging has the highest accuracy value of 83.93%. Scenario 2 is a scenario with oversampling, showing that Ensemble Bagging has the highest accuracy value of 84.51%, and scenario 2 with hyperparameter tuning also shows Ensemble Bagging has the highest accuracy value of 84.56%.
Penerapan Metode Certainty Factor Dalam Mendiagnosa Penyakit Kehamilan Berbasis Web

Diseases or disorders in pregnancy can cause death, especially for pregnant women and babies. This will also affect the infant mortality rate. Diseases that attack a woman's womb cannot be considered trivial and require the intervention of an expert, while an expert for this disease is still rare and certainly requires no small cost. In creating experts (human experts), it takes a relatively long time and costs are not small. One alternative effort to overcome this need is to create a web-based intelligent system. In this study, an expert system will be developed that can be used as a means to carry out early diagnosis of pregnancy diseases. The application of the system is equipped with a certainty factor calculation method that aims to show the level of confidence in the diagnostic results. From the symptoms experienced there is a 20% certainty level and for toxoplasma there are 80% that the patient has gestational anemia.
Prediction Retweet Using User-Based and Content-Based with ANN-GA Classification Method

Current technological advances have caused rapid dissemination of information, especially on social media, one of which is Twitter. Retweeting or reposting messages is considered an easily available information diffusion mechanism provided by Twitter. By finding out why a user retweets a tweet from another person and by making this prediction we can understand how information diffuses on Twitter. In this study, Artificial Neural Network – Genetic Algorithm is used in the classification process and uses user-based and Content-Based features. Evaluation result obtained in this study are 90% accuracy, 72% precision, 83% recall, and 65% F1-Score value on the model by Oversampling.