Reviewerly/exHarmony huggingface dataset:
Sistem Pakar Deteksi Dini Autisme Pada Anak Balita Menggunakan Metode Forward Chaining

Autism is a subtype of a condition that affects development and is referred to as autism spectrum disorder (ASD). Problems of a severe neurobiological nature that impair normal brain function have been implicated as a potential cause of autism. Children who have these disorders have a more difficult time engaging in social activities and effectively communicating with others, which is one of the signs of autism. Parents are able to use the system effectively by basing their decisions on the symptoms that their child is exhibiting, which makes early detection possible. In order to reduce the number of cases in which an incorrect diagnosis is made, there is a pressing need for the development of a system that would assist medical professionals in making simpler and more reliable diagnoses of autism. This system also needs to assist in the formulation of recommendations and the establishment of a diagnostic model that is able to evaluate each and every one of the obstacles and difficulties that youngsters are faced with. Utilizing an expert system as a means to carry out this implementation is one possible course of action. During the course of their examination, the researchers made use of a methodology known as forward chaining. This method entails tracing transitions beginning with the facts and seeking rules that fit the existing hypotheses all the way to the conclusions. The findings of the black box testing indicate that the functionality of the system is running effectively, while the results of the User Acceptance Test (UAT) indicate a value of 81.05% in the Very Good category. We now have the findings of the black box testing thanks to the fact that the construction of the expert system went off without a hitch.
Analyzing the Impact of Code Smells On Software Maintainability

The ease with which a software system can be modified or adapted to meet new requirements, fix defects, or improve its performance is referred to as software maintainability. Software maintainability is an essential quality that distinguishes high-quality software from low-quality software. Poor design or implementation choices are examples of code smells. These choices can have a negative impact on the software’s maintainability because they make the code more difficult to understand, modify, and test. In this article, we present a comprehensive analysis of the impact that code smells have on the maintainability of software. Our findings are based on a comprehensive review of the relevant literature as well as a meta-analysis of empirical studies. According to the findings of our study, code smells can significantly lower the maintainability of software, but the degree to which they do so varies depending on the nature and severity of the smell, as well as the programming language used, the method of software development, and the specifics of the software project. Refactoring, testing, documentation, and code reviews are some of the factors that we identify as being able to mitigate the negative effects of code smells. Our findings have important ramifications for software developers, managers, and researchers because they highlight the need for effective strategies to prevent, detect, and remediate code smells in software projects. These strategies can be found in software development environments.
Effects of Chinese Students' Participation In Exercise On Healthy Lifestyle Awareness

This study selected a total of 572 copies of Chinese international students in Korea over four weeks from September to October 2022 to investigate the effect of Chinese students' exercise participation on health life awareness.The results of the study were derived by performing frequency analysis, exploratory factor analysis, reliability verification, t-verification, one-way ANOVA, and multiple regression analysis using SPSS 26.0.First, as a result of analyzing the effect of exercise participation on physical health, which is a factor of healthy lifestyle, participation frequency (t=7.566,p<).001), participation period (t=6.625,p<.001), participation time (t=3.609,p<.001) was found to be a variable that explains physical health.Second, as a result of analyzing the effect of exercise participation on social health, which is a factor of healthy lifestyle, participation frequency (t=6.200,p<).001), participation period (t=7.678,p<.001), participation time (t=3.057,p<.01) was found to be a variable that explains social health.Third, as a result of analyzing the effect of exercise participation on mental health, which is a factor of healthy lifestyle, participation frequency (t=5.380,p<).001), participation period (t=9.262,p<.001) was found to be a variable that explains mental health.Fourth, as a result of analyzing the effect of exercise participation on intellectual health, which is a factor of healthy lifestyle, participation frequency (t=4.650,p<).001), participation period (t=7.121,p<.001), participation time (t=3.983,p<.001) was found to be an explanatory variable for intellectual health.Fifth, as a result of analyzing the effect of exercise participation on emotional health, which is a factor of healthy lifestyle, participation frequency (t=3.970,p<).001), participation period (t=3.686,p<.001), participation time (t=5.487,p<.001) was found to be a variable that explains emotional health.
The Prediction of Forward Reuse Distance Using a Classification Model for Write Buffer Management

The SSD write buffer is used to improve the latency of I/O requests and the lifetime of flash memory.Most algorithms for managing the SSD write buffer use past reference information such as LRU (Least Recently Used) or predict the future reference using deep learning with high computation cost.In this paper, we propose a technique for managing write buffers by efficiently predicting future reference patterns with a reasonable computation overhead using machine learning.If the future reuse distance, which means when the same data is re-referenced, is greater than the write buffer size, a strategy of directly flushing it in the flash memory without storing it in the buffer is used.However, since it is impossible to accurately predict the future reuse distance and is not essential for buffer management, we first group the future reuse distance into several levels considering the size of the write buffer.Then, a priority-based buffer management policy is proposed by taking the grouped future reuse distance as a priority for caching.As a result of the simulation, the proposed method showed a performance close to the optimal Belady's algorithm in terms of hit rate, while showing significant performance improvement compared to the existing LRU-based algorithm.
Exploration of Problems and Current Status of (Sexual Harassment/ Sexual Violence) Phenomenon in the Workplace : ( 2017-2022 ) Analysis of Media Articles using the Big Kinds of the Korea Press Foundation

This study pays attention to the problems of 'sexual harassment, sexual violence, and sex discrimination in the workplace' that appear in the news media every day, and seeks to explore the current status of incidents and problems that reflect the distorted gender sensitivity of corporate organizational culture.Research results First, of the total 1,353 articles related to 'bullying at work' over the past 6 years (January 1, 2017 -December 31, 2022), 'sexual harassment/sexual violence/sex discrimination' articles accounted for 40.9% with a total of 554 articles.Second, sexual harassment ranked first in articles related to workplace bullying before and after the enforcement of the revised Labor Standards Act on the prohibition of workplace bullying.Third, in the case of 'sexual harassment/sexual violence/sex discrimination in the workplace', institutions and organizations need to make active efforts to resolve the damage and establish a system to prevent recurrence.This study aims to provide basic data that will help improve related policies and systems by identifying the current status and problems of 'sexual harassment/sexual violence/sex discrimination in the workplace' before and after the enforcement of the revised Labor Standards Act through big data article analysis.
A Study on the Satisfaction with Non-face-to-face Online Liberal Art Classess of University Students due to COVID-19

This study attempted to derive implications related to class operation to improve the quality of non-face-to-face online liberal arts classes in the post-COVID-19 era by analyzing college students' class satisfaction with non-face-to-face online liberal arts classes.To this end, a study was conducted on 130 college students who took non-face-to-face online liberal arts classes in the second semester of 2022.Non-face-to-face online liberal arts essential classes were conducted as recorded video classes, and class satisfaction surveys were analyzed by dividing them into areas of class planning, class progress, class management, class data, and class evaluation.As a result of the study, university students' overall satisfaction with non-face-to-face online liberal arts classes due to COVID-19 was high, but class data and class evaluation were low, requiring improvement.Based on the research results, educational suggestions related to class operation to improve the quality of non-face-to-face online liberal arts classes are as follows.First, in order to improve the quality of non-face-to-face online liberal arts classes, overall, support for non-face-to-face online classes should be actively provided throughout the university, especially for professors in charge of large-scale lectures.Second, in order to improve the quality of non-face-to-face online liberal arts classes, fairness in class evaluation should be secured as to how it is fairest to evaluate large-scale students in non-face-to-face online class situations.Considering the non-face-to-face online class situation, various evaluation measures should be devised to ensure fair evaluation suitable for the online educational environment, such as the appropriateness of test questions, fair evaluation criteria, and rapid feedback on evaluation results.
A Study on Memory/Non-Memory Metacognitive Data Platform Based on Universal Middleware Supporting Terminal Service Clustering

Recently, as the scope of the industrial service supply chain has expanded to convergence terminal devices centered on data, service utilization that disposer various types of data queries occurring in existing industries is required.Data convergence cluster terminal service technology that exists between suppliers and consumers should be upgraded to a structure independent of the operating system and the real-time operating system by maximizing compatible data services.In this data service, as the complexity of convergence data processing of terminal devices increases, a platform structure was constructed to reconsider processor processing efficiency that supports clustering for non-memory-based relational data systems and object-oriented memory data models of convergence terminal systems.In order to process data queries and provide sustainable services, it is necessary to support convergence of various data models and establish a system-independent platform environment to process metacognitive data.Based on this, a dynamic context model with continuity and scalability is linked to a dynamic service to serve as a cycle of supply and demand between users and managers.This ring was designed as a data platform that enables simultaneous service resource management for communication convergence environment structure and application services based on universal middleware.The services should be provided by the data platform were divided into Object Space Data, Space Identity, and Object Oriented Memory Data Service, and these divided services are the provision of interaction, utilization, and flexibility based on modularization.As for the contents of the related study, first, a system that supports interaction by supporting the life cycle of scenario-based context objects was studied.Second, a case of using spatial cognitive training in the form of an application service was presented so that a data platform based on a real-time operating system could be applied to a terminal.Third, in order to support the flexibility of data queries, a convergence terminal system using a relational data model and a query-oriented Object Space Service that can process memory and non-memory data at the same time was established.
Automatic anomaly detection in engineering diagrams using machine learning

Abstract This study implements a method of automating anomaly detection in engineering diagrams by extracting patterns within graphs after recognizing graphs from Piping and Instrumentation Diagram (P&amp;ID). The framework consists of three parts: graph generation, subgraph extraction, and graph classification. Graphs are generated through symbol recognition and line recognition, and subgraphs are extracted using the frequent subgraph mining algorithm. The graph classification targets are divided into two categories according to the frequency of the main equipment of the extracted subgraph. If the frequency is low, it is classified through whether to include a user-defined subgraph, and if it is high, it is trained in a support vector machine (SVM) algorithm after vector embedding to generate a classification model. K-fold cross-validation is also applied to increase classification accuracy. The proposed framework shows 85% accuracy for a given test drawing through cross-validation. These outcomes contribute to the field of plant diagram analysis and have potential applications in various industries.
Bayesian regularized SEM: Current capabilities and constraints

An important challenge in statistical modeling is to balance how well our model explains the phenomenon under investigation with the parsimony of this explanation. In structural equation modeling (SEM), penalization approaches that add a penalty term to the estimation procedure have been proposed to achieve this balance. An alternative to the classical penalization approach is Bayesian regularized SEM in which the prior distribution serves as the penalty function. Many different shrinkage priors exist, enabling great flexibility in terms of shrinkage behavior. As a result, different types of shrinkage priors have been proposed for use in a wide variety of SEMs. However, the lack of a general framework and the technical details of these shrinkage methods can make it difficult for researchers outside the field of (Bayesian) regularized SEM to understand and apply these methods in their own work. Therefore, the aim of this paper is to provide an overview of Bayesian regularized SEM, with a focus on theoretical developments as well as available software implemen- tations. Through an empirical example, various open-source software packages for (Bayesian) regularized SEM are illustrated and all code is made available online to aid researchers in applying these methods. Finally, reviewing the current capabilities and constraints of Bayesian regularized SEM identifies several directions for future research.
Intent Negotiation Framework for Intent-driven Service Management

&lt;p&gt;To automate network operations and deployment of compute services, intent-driven service management (IDSM) is essential. It enables network users to express their service requirements in a declarative manner as &lt;em&gt;intents&lt;/em&gt;. To fulfill the intents, closed control-loop operations carry out required configurations and deployments without human intervention. Despite the fact that intents are fulfilled automatically, conflicts may arise between user’s and service provider’s intents due to limited resources availability. This triggers IDSM system to initialize an intent negotiation process among conflicting actors. Intent negotiation involves generating one or more alternate intents based on the current state of the underlying physical/virtual resources, which are then presented to the intent creator for acceptance or rejection. In this way, the quality of services (QoS) can be improved significantly by maximizing the acceptance rate of service requests in the scenario of limited resources. However, intent negotiation systems are still in their infancy. The available solutions are platform dependent which poses various challenges in their adoption to diverse platforms. The main focus of this work is to draft and evaluate a comprehensive and generic intent negotiation framework which can be used to develop intent ne- gotiation solutions for diverse IDSM platforms. In this work, we have identified and defined various processes that are necessary for intent negotiation. Furthermore, a generic intent negotiation framework is presented representing interactions among the identified processes, while conflicting actors engage in the intent negotiation. The results demonstrated that the proposed intent negotiation framework increases the intent acceptance rate by up to 38% with processing overheads less than 10%. &lt;/p&gt;
Continuous Internal Penetration Testing (CIPT)

&lt;p&gt;Continuous Internal Penetration Testing (CIPT) is a proactive cybersecurity practice aimed at assessing the security posture of an organization's internal network, systems, and applications. Unlike traditional penetration testing, which is performed periodically, CIPT involves ongoing and regular assessments to identify and remediate vulnerabilities continuously.&lt;/p&gt; &lt;p&gt;CIPT involves simulating real-world attacks and attempts to exploit potential weaknesses in an organization's infrastructure. It employs a combination of automated tools and manual testing techniques to detect vulnerabilities, misconfigurations, and security gaps that could be exploited by malicious actors.&lt;/p&gt; &lt;p&gt;The primary goal of CIPT is to uncover vulnerabilities before they can be leveraged by attackers, allowing organizations to proactively address and mitigate risks. By continuously monitoring and testing their internal environment, organizations can stay ahead of emerging threats, ensure compliance with security standards, and maintain a strong security posture.&lt;/p&gt; &lt;p&gt;CIPT not only helps organizations identify vulnerabilities but also provides valuable insights into their overall security effectiveness. The findings from CIPT assessments enable organizations to prioritize remediation efforts, implement necessary security controls, and improve their overall cybersecurity defenses.&lt;/p&gt; &lt;p&gt;By implementing CIPT as an ongoing practice, organizations can enhance their ability to detect and respond to potential threats promptly. It allows them to identify weaknesses, strengthen their security controls, and ultimately protect their sensitive data, systems, and reputation from evolving cyber threats.&lt;/p&gt;
Perkembangan Sistem Informasi pada Organisasi Dakwah

&#x0D; Information systems have a very important role, the more rapidly the development of a company or organization, the information system also has an increasingly important role. The demand for an increasingly good information system is the result of demands for company development, technological developments, government policies, changes in procedures and demands for information needs. Information system development is often referred to as the system development process (system development). Information system development is defined as an activity to produce computer-based information systems to solve organizational problems or take advantage of opportunities that arise. System development can mean compiling a new system to replace the old system as a whole or repairing an existing system, this is done because the previous system has problems, inefficient operations, and so on. Methods In this study using a qualitative approach to the method of interviewing informants who are considered related to the problem. Data collection by providing a number of questions that can answer the formulation of the problem in this mini research. In writing the report on the results of this mini research using various references related books and journals.&#x0D; Keywords :information, organization, dakwah
Penggunaan Diksi dan Konjungsi dalam Teks Prosedur Karya Siswa Kelas XI SMA Negeri 1 V Koto Kampung dalam Kabupaten Padang Pariaman

The objective of this research is to describe the use of diction and conjunctions in procedural texts written by students of class XI at SMA Negeri 1 V Koto Kampung Dalam, Padang Pariaman Regency. This research is qualitative in nature, using a descriptive method. The data for this research consists of qualitative data in the form of diction and conjunctions used by the students in procedural texts. The data source for this research is the procedural texts written by students of class XI at SMA Negeri 1 V Koto Kampung Dalam, Padang Pariaman Regency. The researcher is the instrument for this research. The data collection technique used is documentation. The data validation technique used in this research is triangulation. From the 29 research data, a total of 3,002 instances of diction were found, consisting of 2,753 instances of accurate diction usage and 249 instances of inaccurate diction usage. The procedural texts written by students of class XI at SMA Negeri 1 V Koto Kampung Dalam, Padang Pariaman Regency tend to be constructed with structured diction, indicating that the students have a good command of appropriate diction. Secondly, the research found a total of 423 instances of conjunctions from the 29 data, consisting of 363 instances of accurate conjunction usage and 60 instances of inaccurate conjunction usage. The procedural texts written by students of class XI at SMA Negeri 1 V Koto Kampung Dalam, Padang Pariaman Regency tend to be constructed with structured conjunctions, indicating that the students have a good command of appropriate conjunctions, especially in their writing. Therefore, students of class XI at SMA Negeri 1 V Koto Kampung Dalam, Padang Pariaman Regency tend to use standard diction and conjunctions that are not yet accurate in writing procedural texts.
Using Objectives and Key Results (OKRs) and Slack: A Case Study of Coordination in Large-Scale Distributed Agile

Today, many large-scale software projects have members working from home, which has changed the way teams coordinate work. To better understand coordination in this setting, we conducted a case study through which we examined two teams in a large-scale agile project by observing meetings and conducting 17 interviews. Through the lens of Relational Coordination Theory (RCT), we analyzed the use of the goal-setting framework Objectives and Key Results (OKRs) and the collaboration tool Slack. Slack was used for frequent, timely, and problem-solving communication and, and its use decreased the number of planned meetings. However, discussions often started on Slack and continued in virtual ad-hoc meetings. The use of OKRs facilitated knowledge sharing, helped the teams align their goals, and provided inter-team insights. The main implication of our research is that projects using OKRs need to support project members, especially in formulating the key results that align and motivate the teams to work toward the same mission.
Experiment ended

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Re-Label By Data Pattern For Knowledge Driven Deep Learning

&lt;p&gt;In industry deep learning application, we should fix the badcase by human evaluation after we achieve more than 95% accuracy at dev dataset. The badcase reason is from the wrong rule/knowledge of human labeling and will cause low accuracy under human evaluation. In this paper, we propose the pattern-based method to fix the badcase for industry application inference. We propose the pipeline to solve the problem and improve the accuracy of human evaluation. The experiment results verify our idea, which means label-edit/data-edit is the method to implement controllable deep learning application.&lt;/p&gt;