Reviewerly/exHarmony huggingface dataset:
Strategy Management of Waterboom Bali Tourism Object In The New Normal Tourism Era

&lt;p&gt;&lt;em&gt;The Pandemic COVID-19 has had a huge impact on the tourism industry. Waterboom Bali tourist attraction as one of the largest water recreation facilities in Bali has also not escaped the impact of the pandemic. In order to continue to operate and maintain the sustainability of tourism objects, they must be able to live in peace with the Covid-19 virus. This study aims to determine the application of health protocols and tourism management strategies in the new normal era of tourism. Methods of data collection are done by observation, interviews, literature study, and documentation. The sampling technique used was Accidental Sampling. The data analysis method uses data triangulation, namely the triangulation method. The results of this study are that the implementation of the CHSE health protocol has been very good for tourists and workers. Implementation of health protocols in the form of check-in for eligibility to enter tourist destinations on the PeduliLindungi application, checking body temperature, using complete personal protective equipment, and Sanitation Team for periodic environmental cleaning. The management strategy in the new normal of tourism is in the form of promoting ticket prices according to tourist classifications and operating schedules only on weekends.&lt;/em&gt;&lt;/p&gt;
Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data

Abstract Explainable artificial intelligence aims to interpret how the machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, the understandings of the applicability of these model explainers to biological data are still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pretrained models of predicting tissue types from transcriptomic data, and by identifying top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.
Penerapan Data Mining Untuk Menganalisis Kepuasan Pegawai Terhadap Pelayanan Bidang SDM dengan Algoritma C4.5

Employee satisfaction includes the difference between the level of importance and perceived performance or results, and is an alternative evaluation that exceeds employee expectations. There are 5 dimensions to measure service quality based on expectations and perceived performance by employees, namely career development, leadership in HR, policy and law enforcement, building a work atmosphere and providing salaries and rewards. Five dimensions are very influential in the progress of STIKOM Tunas Bangsa, using data mining methods can be found important trends for campuses. Employee satisfaction assessment is based on a questionnaire filled out by the employee. The results of the questionnaire were processed using the c4.5 algorithm. The c4.5 algorithm is a classification method and produces a decision tree. C4.5 turns large facts into decision trees that represent rules. Rules are easy to understand in natural language. Based on the results of the research that has been done, the use of the C4.5 algorithm can help the campus in improving services according to the results of the questionnaire. The results of the calculation, there are two variables satisfied employee questionnaire. Meanwhile, the employee questionnaire was not satisfied with the three variables. The highest gain value is the variable to build a work atmosphere with a value of 0.20619372. The indicator of the variable of building a work atmosphere that has the highest entropy value is a fairly good indicator with a value of 1. The total of questionnaires filled in are 65 questionnaires, 44 people stated they were satisfied and only 21 people said they were not satisfied.
Examples on the Analysis of Statements Trough Design

The first example concerns the analysis of two statements on which very few things are known, with the goal of establishing which one is truer, and what should be previously known in order to capture their respective meanings.
An Automatic Convolution Neural Network-Based Framework for Robust Classification of Breast Cancer Histopathological Images

Histopathological images are the standard for breast cancer diagnosis. Throughout assessment some dozens of them are obtained for a solitary enduring. Conservative, image-based categorization organizations create the supposition that all the patient’s imagery encompasses similar tag as the patient, which is hardly ever established in rehearsal since classification the information is costly. The presentation of mainly conservative categorization systems relies on suitable information symbol and a great deal of the labors are devoted to characteristic manufacturing, a hard and long process that uses previous specialist area information of the statistics to create helpful kind. On the previous dispense, deep knowledge can take out and systematize the discriminative in order from the statistics not require the plan of characteristic extractors by an area specialist. Convolution Neural Network (CNN) is an exacting type of deep, feed presumptuous system that contains gain notice from investigating neighborhood and manufacturing to achieve experiential success in everyday jobs such as language credit, signal dispensation, object credit, natural language processing, and move knowledge. This scheme aims to behavior experiment by means of the deep knowledge move toward to categorize breast cancer histopathological images as of IDC_regular dataset (the breast cancer histology image dataset) as of Kaggle.
CANCEL CULTURE: THE SCARLET LETTER IN GABRIELA PUGLIESI'S CASE

The following paper presents definitions of the cancel culture phenomenon on the internet, bringing concepts based on shame, public humiliation, and reputation according to Solove (2007) and Ng (2020). Focused on digital influencers, draws parallels between public humiliation practices adopted in the Middle Ages - such as a scarlet letter embroidered on clothes – and current ways to promote virtual lynching in social media websites. Through the study of mediatic coverage of Gabriela Pugliesi’s canceling, presents an initial framework for the process of canceling digital influencers, taking into consideration the practices of these content creators as it relates to the relationship with the brands and the transformation of private space into public space.
The Real Un?verse Model

The developed cosmogonic theory and laws explain the objective solution of the problem of cosmogony - the universe, antiquity, solar system, planets and their satellites the origin and development, as well as their structure and chemical composition. At the same time, objective reasons for the formation of water and life in the solar system are given.
Enhanced LPQ Based Two Novel Blur Invariant Face Descriptors in Light Variations

Among numerous local descriptors persisting in literature, LPQ is one of most influential blur invariant descriptor. But when high content blurring is mixed with light variations (moderate and high) then LPQ and many others fails to achieve required robustness to declare the efficient face descriptor. This gap is filled by introducing advance versions of LPQ so-called Enhanced LPQ1 (ELPQ1) and ELPQ2. Precisely, blurred affected image (with light variations), by using Gaussian low pass filtering, is passed to 2 novel pre-processing approaches before LPQ feature extraction. In former approach edge oriented (enhanced) images are produced by 3 methods i.e. Sobel (magnitude gradient), Image sharpening and Kirsch (magnitude gradient). In latter approach, Sobel (magnitude gradient) is replaced by more robust magnitude gradient, called Sobel + Prewitt (magnitude gradient), produced by using Sobel horizontal gradient and Prewitt vertical gradient. Rest 2 methods remains similar in both the pre-processing approaches. Further LPQ is imposed for feature extraction. As each pre-processing approach contains 3 methods therefore 3 transformed images are produced after LPQ. Histograms under respective category are integrated to build feature size of 2 robust blur invariant representations ELPQ1 and ELPQ2. Compressed feature size is attained from FLDA and classification is assisted from SVMs. Experiments on GT and EYB proves efficacy of ELPQ1 and ELPQ2 against benchmark methods.
Understanding Differences Between Human Language Processing and Natural Language Processing by the Synchronized Model

Chat applications using Artificial Intelligence (AI) based on Natural Language Processing (NLP) platforms have been reported to be gradually accepted by people.This research aims to investigate differences between human language processing and Natural Language Processing (NLP) system, which is the core technology of most chat applications, using the synchronized language model.To achieve this objective, this research first distribute and collect questionnaires with questions such as the frequency and motivation of using AI chatbots among university students.The study then evaluate the selected chatbot with linguistic method and knowledge through semantics and pragmatics.Practically, this study proposes valid approaches to perfect existing chatbots.This study suggests that AI chatbots based on NLP can be applied to complete tasks but differ apparently from the human language processing system.The conclusion drawn from this study is that if the AI chatbot is developed to recognize misspelled words and their vocabulary is expanded, it will enhance the applicability of AI chatbots and fit them into people's lives.
Didactic Possibilities of Critical Thinking Strategies as a Tool for Implementing Media Education Technology for Developing Key Skills of Students

The relevance of the study lies in the fact that intense social changes are transforming not only society, but also affect the main reference points in education, which prompts the teaching community to consider current trends in the development of the European school. The purpose of the study is to generalize and systematize scientific, methodological, pedagogical and psychological aspects of the learning process and the development of key skills of students through the use of didactic capabilities of media education technologies. The theoretical and methodological basis of the research consists of methodologies of the following approaches: didactic, general scientific, comparative. In the process of research it was determined that in a changing environment, only individuals who are able to adapt to constant rapid change, solve complex problems, think critically about the circumstances, compare alternative points of view and make balanced decisions can navigate and act actively. It is confirmed that the main goal of modern education is not to provide students with important information, but to develop a critical view of the perception and research of information. Media education technologies were found to help prepare an applicant of education for life in an information society, choose the main thing among the incessant flow of information, critically examine the information received, and cooperate with others. In scientific research, media education is considered as one of the tools that helps teachers replace passive listening of a student or primitive retelling with active participation in the educational process, and thereby increase the effectiveness of classes. The introduction of media-educational technologies in practice was proved to solve this issue by applying strategies for the development of critical and systematic thinking. The practical significance lies in the implementation and systematization of critical thinking mechanisms as a tool for introducing media education technology for the development of key skills of students
6. Administrasi Sarana dan Prasarana

The purpose of writing this article is to find out how important facilities and infrastructure greatly affect the goals and processes in education. School infrastructure is an important component in the education system. Sometimes people even assess the quality of education in a school by looking at infrastructure. Although it is not entirely true, it cannot be denied that the success of the Teaching and Learning Process is more or less influenced by the condition of pre-educational facilities available. Administrative activities in the field of educational facilities and infrastructure include; goods procurement planning, partner prequalification, goods procurement, storage, inventory, distribution, maintenance, rehabilitation, elimination and removal and control. The role of teachers in the administration of school infrastructure facilities: involved in planning the procurement of teaching aids, involved in the use and maintenance of teaching aids used by teachers, supervision in the use of practical tools by students.
Multi-hop entanglement swapping in quantum networks based on polization-space hyperentanglement

Entanglement swapping (ES) based multi-hop quantum information transmission is a fundamental way to realize long-distance quantum communication. However, in the conventional quantum networks, the entanglement in one degree of freedom (DOF) of photon system is usually used as a quantum channel, showing disadvantages of low capacity and susceptibility to noise. In this paper, we present an efficient multi-hop quantum hyperentanglement swapping (HES) method based on hyperentanglement, which utilizes the entangled photos in polarization and spatial-mode DOFs to establish the hyperentangled multi-hop quantum channel. Taking long-distance hyperentanglement based quantum teleportation for example, we first describe a basic hop by hop HES scheme. Then, in order to reduce the end-to-end delay of this scheme, we propose a simultaneous HES (SHES) scheme, in which the intermediate quantum nodes perform hyperentangled Bell state measurements concurrently. On the basis of this scheme, we further put forward a hierarchical SHES (HSHES) scheme that can reduce the classical information cost. Theoretical analysis and simulation results show that the end-to-end delay of HSHES is similar to that of SHES, meanwhile, the classical information cost of HSHES is much lower than that of SHES, showing a better tradeoff between the two performance metrics. Compared with the traditional ES methods, the scheme proposed in this paper is conductive to meeting the requirements for long-distance hyperentanglement based quantum communication, which has positive significance for building more efficient quantum networks in the future.
Knowledge Graph

Wikipedia defines knowledge as a familiarity, awareness or understanding of someone or something such as facts, skills or objects. It provides graphical, textual, audio information and combinations of these as its representation. The connection of data elements are provided semantically using knowledge graph. The importance and its applications are discussed in this chapter. The detailing of its implementation using ontology, RDF, SPARQL is introduced in this chapter. The open source libraries, datasets and tools are provided in the last section of the book.
administrasi pendidik dan tenaga kependidikan

The purpose of this article is to add reader knowledge about student admin and educational articles using the method first, collecting study material from book sources and journal articles, as well as other sources of administrative information and other sources, literary methods which are being discussed can help and facilitate the formulation of research problems, which method is also referred to by using the SELR method (systematic literature literature) after the next step material needs to be known knowledge of research studies and materials collected and then analyzed and analyzed as well as the conclusions of educator administration and educational staff is the overall activity to organize and direct students to achieve the goals that have been set. and there are several steps in the administrative process of educators and education personnel, including procurement which includes the types of employees needed, requirements, registration deadlines and address of placement, placement and orientation and educators and education personnel also receive welfare in the form of basic salary is the right in receive civil servants in accordance with applicable regulations, allowances are bonuses received by employees outside the basic salary of the allowance divided into special allowances, functional and rewards, educators and education personnel also get a leave of absence, among others: annual leave, large leave, leave and leave maternity
Assessment of the Effect of Re-organization Strategy on Performance of Department of Immigration and Citizen Services in Kenya

The purpose of this study was to assess the relationship between reorganization strategies and the performance of department of immigration and citizen services in Kenya. The study was anchored under Contingency Theory of Structural Adaptation to Regain Fit Theory, it adopted an exploration configuration approach targeting a population of 124 representatives of the Department of Immigration and Citizen Services. The specialist utilized a straightforward separated arbitrary examining technique to choose a sample size of 74 respondents. Polls were utilized which had open finished and shut finished inquiries to gather information which were then investigated utilizing descriptive analysis, correlation and regression model to establish the relationship between the variables. The study established a positive correlation between the reorganization strategies and organizational performance (ρ = 0.638, p- value=0.000) at 0.01 level of significance, the study further revealed a statistically significant strong positive linear relationship between Reorganization strategies and organizational Performance (β = 0.687, P- value = 0.000).
Optimization of software defects prediction in imbalanced class using a combination of resampling methods with support vector machine and logistic regression

The main problem in producing high accuracy software defect prediction is if the data set has an imbalance class and dichotomous characteristics. The imbalanced class problem can be solved using a data level approach, such as resampling methods. While the problem of software defects predicting if the data set has dichotomous characteristics can be approached using the classification method. This study aimed to analyze the performance of the proposed software defect prediction method to identify the best combination of resampling methods with the appropriate classification method to provide the highest accuracy. The combination of the proposed methods first is the resampling process using oversampling, under-sampling, or hybrid methods. The second process uses the classification method, namely the Support Vector Machine (SVM) algorithm and the Logistic Regression (LR) algorithm. The proposed, tested model uses five NASA MDP data sets with the same number attributes of 37. Based on the t-test, the &lt; = 0.0344 &lt; 0.05 and the &gt; = 3.1524 &gt; 2.7765 which indicates that the combination of the proposed methods is suitable for classifying imbalanced class. The performance of the classification algorithm has also improved with the use of the resampling process. The average increase in AUC values using the resampling in the SVM algorithm is 17.19%, and the LR algorithm is at 7.26% compared to without the resampling process. Combining the three resampling methods with the SVM algorithm and the LR algorithm shows that the best combining method is the oversampling method with the SVM algorithm to software defects prediction in imbalanced class with an average accuracy value of 84.02% and AUC 91.65%.
Intelligent Information Retrieval System

An Intelligent Information Retrieval (IIR) is a machine learning system used by users to retrieve the massive volume of information available through internet.IIR offers personalization and efficiency in the current internet development allowing users to examine and acquire appropriate information.The paper will focus on the keyword searching issues of the current digital library system.The paper will analyze a model that solves the issue using metadata case-based and concept-based approach.The objective is to evaluate a set of categories and concepts in the domain field that demonstrates some relations between them to elaborate how poor quality of information retrieval in digital library system can be solved.The paper further argued that the already developed domain-specific ontology can be efficient for query advancement.Many researchers have used semantic retrieval technology using concepts to solve problems that lack semantics in traditional retrieval technology.Using concepts in ontology enhances search results
RETHINKING ROBUSTNESS IN MACHINE LEARNING: USE OF GENERATIVE ADVERSARIAL NETWORKS FOR ENHANCED ROBUSTNESS

Machine learning (ML) is increasingly being used in real-world applications, so understanding the uncertainty and robustness of a model is necessary to ensure performance in practice.This paper explores approximations for robustness which can meaningfully explain the behavior of any black box model.Starting with a discussion on components of a robust modelthis paper offers some techniques based on the Generative Adversarial Network (GAN) approach to improve the robustness of a model.The study concludes that a clear understanding of robust models for ML allows to improve information for practitioners, and helps to develop tools that assess the robustness of ML.Also, ML tools and libraries could benefit from a clear understanding on how information should be presented and how these tools are used.
Analisis Data Mining Pesebaran Siswa Smp Di Pematangsiantar Dengan Metode Algoritma K-Means Clustering

Data Mining is an automatic analysis of large or complex data with the aim of finding important patterns or trends that are usually not realized. However, the student data has not been utilized optimally, making it difficult for the school to carry out student development at school. Each student will make a comparison between the desired service with the service received. The K-Means algorithm is an iterative cluster technique algorithm. This algorithm starts with random selection, which is the number of clusters that you want to form. The attributes needed in processing are the student's hometown, the student's school origin, and the student's grade. Based on these attributes, the data will then be grouped into junior high school students based on city origin, school origin, and student grades using the K-Means Clustering algorithm. The results of processing this data will greatly help the junior high school, so that it is known that the distribution of each junior high school student is the most. In this study, the data used were data from junior high school students from the 2018-2019 class in Pematangsiantar as many as 300 data samples