Reviewerly/exHarmony huggingface dataset:
Brightness and Contrast Enhancement Method for Color Images Via Pairing Adaptive Gamma Correction and Histogram Equalization

For enhanced adaptability to poor light enhancement whilst achieving high image contrast, a new method for color image correction based on the advantages of non-linear function in grey transformation and histogram equalization techniques is proposed in this work.Firstly, the original red, green and blue (RGB) image is converted into the HSV color space, and the V channel is used for enhancement.An adaptive gamma generator is proposed to adaptively calculate gamma parameters in accordance with dark, medium, or bright image conditions.The computed gamma parameters are used to propose a cumulative distribution function that produces an optimized curve for illumination values.Next, a second modified equalization is performed to evenly correct the offset of the illumination curve values on the basis of the equal probability of the available values only.Finally, the processed V channel replaces the original V channel, and the new HSV model returns to the RGB color space.Experiments show that the proposed method can significantly improve the low contrast and poor illumination of the color image whilst preserving the color and details of the original image.Results from benchmark data sets and measurements indicate that the proposed method outperforms other state-of-the-art methods.
Self-Adapting Security Monitoring in Eucalyptus Cloud Environment

This paper discusses the importance of virtual machine (VM) scheduling strategies in cloud computing environments for handling the increasing number of tasks due to virtualization and cloud computing technology adoption.The paper evaluates legacy methods and specific VM scheduling algorithms for the Eucalyptus cloud environment and compare existing algorithms using QoS.The paper also presents a selfadapting security monitoring system for cloud infrastructure that takes into account the specific monitoring requirements of each tenant.The system uses Master Adaptation Drivers to convert tenant requirements into configuration settings and the Adaptation Manager to coordinate the adaptation process.The framework ensures security, cost efficiency, and responsiveness to dynamic events in the cloud environment.The paper also presents the need for improvement in the current security monitoring platform to support more types of monitoring devices and cover the consequences of multi-tenant setups.Future work includes incorporating log collectors and aggregators and addressing the needs of a super-tenant in the security monitoring architecture.The equitable sharing of monitoring resources between tenants and the provider should be established with an adjustable threshold mentioned in the SLA.The results of experiments show that Enhanced Round-Robin uses less energy compared to other methods, and the Fusion Method outperforms other techniques by reducing the number of Physical Machines turned on and increasing power efficiency.
The Cross-Cultural Teaching Model of Foreign Literature Under the Application of Machine Learning Technology

As globalization spreads, so does the world's ethnic makeup, leading to a surge in cultural diversity that has become a major issue for the educational systems of all countries.Many western countries advocate for cross-cultural education (CCE) as a means of dealing with cultural variety and promoting trust, tolerance, and interaction between individuals of different backgrounds.The way to achieve this goal is to work toward solving the issue while also fostering greater national unity.One of the newest developments in international education is the concept of CCE, which has also given rise to a whole new area of research within the subject of education.Too much time has passed since students were actively engaged in the learning process, and the new curriculum reform has seriously harmed the traditional approaches to teaching foreign literature.As a result, the proposed research has shown that around half of all education is dedicated to the FL cross-cultural teaching paradigm.Chinese students' data were first gathered for this study and divided into two groups: Control Class (CC) and Experimental Class (EC).The performance of the students in both groups is then forecasted using the extreme gradient boosting (XGBoost) technique, which is based on machine learning.Then, we use an optimization method known as the Flower Pollination Algorithm (FPA) to improve XGBoost's prediction performance.According to the descriptive findings, students who adhere to the suggested teaching strategy show more learning interest than those who adhere to existing strategies.
CYBERCRIME: A MULTIFACETED ONE

Cybercrime has caused widespread damage to personalities, companies, including the city council. Computers have unleashed an era of greater efficiency and creativity. Communication and connectivity have reached new heights in the last twenty years. The Internet started a new revolution, the online revolution. As the masses are accustomed and inclined to their daily activities online, most are looking for easy money and information. This criterion represents modern criminals who, in addition to the availability of extensive equipment to access almost all systems, enjoy the anonymity of boundless cyberspace and therefore take human error and system vulnerabilities for granted. The batch consists of these cyber criminals, malicious hackers, malicious hackers, and spammers. This paper attempts to understand cybercrime on a long continuum, starting with the doctrinal research method part. Research and analysis of the world's leading cybersecurity companies were integrated. Without delving into the actual instruments of manipulation, an attempt was made to visualize all progress as a series of activities.
A study on exploring factors for predicting business crisis universities using machine learning : Focusing on Random Forest Model

This study seeks to find the criteria for preparing government and social countermeasures by using machine learning to explore predictive factors for universities in business crisis. Between 2012 and 2020, universities with low evaluation results in the government's university restructuring evaluation are defined as universities in business crisis. The goal was to discover factors that could predict a university in business crisis by using data such as students, faculty, and financial data, which are university components. As a result of the analysis, when using the Random Forest, it was possible to accurately identify universities in business crisis compared to the Logistic Regression model. The results of predicting universities in business crisis through shap analysis and statistical (t-test) are as follows. First of all, the ROC-AUC value that can evaluate the predictive power was checked by applying the logistic regression model and random forest to the same data for the prediction of universities in business crisis, and the logistic regression model was evaluated as 0.819 for universities and 0.854 for colleges. , Random Forest was evaluated as 0.927for universities and 0.943 for colleges. In the case of universities, the factors that have a positive(+) influence on the prediction of universities in business crisis are the dropout rate, region_Gyeongnam. On the other hand, the factors that have a negative(-) effect on the prediction of universities in business crisis are education expenses per student, education expense reimbursement rate, enrollment rate, maintenance recruitment rate, new student recruitment rate, employment rate, operating expense burden rate, and research expenses per full-time faculty member. In the case of colleges, the factors that have a positive (+) influence on the prediction of universities in business crisis are the carryover rate, teacher retention rate, region_Gangwon, dropout rate and debt ratio. On the other hand, the factors that have a negative(-) effect on the prediction of universities in business crisis are education expenses per student, student recruitment rate, university size, education expense reimbursement rate, maintenance recruitment rate, operating expense burden rate, new student recruitment rate, scholarship payment rate, and research expenses per full-time faculty member. The contribution of this study is to propose a new methodology to predict universities in business crisis using machine learning techniques, which are widely used recently.
Attestation of Improved SimBlock Node Churn Simulation

Node churn, or the constant joining and leaving of nodes in a network, can impact the performance of a blockchain network. The difficulties of performing research on the actual blockchain network, particularly on a live decentralized global network like Bitcoin, pose challenges that good simulators can overcome. While various tools, such as NS-3 and OMNet++, are useful for simulating network behavior, SimBlock is specifically designed to simulate the complex Bitcoin blockchain network. However, the current implementation of SimBlock has limitations when replicating actual node churn activity. In this study, the SimBlock simulator was improved to simulate node churn more accurately by removing churning nodes and dropping their connections, and increasing additional instrumentation for validation. The methodology used in the study involved modeling the Bitcoin node churn behavior based on previous studies and using the enhanced SimBlock simulator to simulate node churn. Empirical studies were then conducted to determine the suitability and limitations of the node churn simulation. This study found that the improved SimBlock could produce results similar to observed indicators in a 100-node network. However, it still had limitations in replicating node churn behavior accurately. It was discovered that SimBlock limits all nodes to operate as mining nodes and that mining is simulated in a way that does not depict churn accurately at any time but only at specific intervals or under certain conditions. Despite these limitations, the study’s improvements to SimBlock and the identification of its limitations can be useful for future research on node churn in blockchain networks and the development of more effective simulation tools.
Realtime face matching and gender prediction based on deep learning

&lt;span lang="EN-US"&gt;Face analysis is an essential topic in computer vision that dealing with human faces for recognition or prediction tasks. The face is one of the easiest ways to distinguish the identity people. Face recognition is a type of personal identification system that employs a person’s personal traits to determine their identity. Human face recognition scheme generally consists of four steps, namely face detection, alignment, representation, and verification. In this paper, we propose to extract information from human face for several tasks based on recent advanced deep learning framework. The proposed approach outperforms the results in the state-of-the-art.&lt;/span&gt;
Risk management framework in Agile software development methodology

In software projects that use the Agile methodology, the focus is on development in small iterations to allow both frequent changes and client involvement. This methodology affects the risks that may happen in Agile software projects. Hence, these projects need a clear risk management process to reduce risks and address the problems before they arise. Most software production methodologies must use a framework for risk management, but currently, there is no such framework for the Agile methodology. Therefore, we present a risk management framework for projects that use the Agile methodology to help the software development process and increase the likelihood of the project’s success. The proposed framework states the necessary measures for risk management according to the ISO31000 standard at each stage of the Agile methodology. We evaluated the proposed framework in two running software projects with an Agile methodology by a number of expert experts. The results show that using our proposed framework increases the average positive risk reaction score by 49%.
Speaking Skills Analysis of 4th Grade Students in Indonesian Language Object

This study aims to describe the implementation of learning, analyze students' speaking skills, the factors that can influence them and the efforts to improve speaking skills in class VI MIN 1 Purwakarta. This research is motivated by the low participation of students in the Indonesian language learning process. The approach used in this study is a qualitative approach with descriptive methods and the type of data is qualitative data. The results of this study, it is known that the speaking skills of 4 th grade students at the school are included in the low category with an average score of 59. Clearly, on the linguistic factor most of the students have been able to be skilled in speaking such as phoneme clarity and intonation accuracy, while on the linguistic factor non-linguistically most of the students have not been able to be skilled in speaking. The factors that affect the speaking skills of class VI students at the school are divided into two factors, namely internal factors that come from within the students such as concentration problems, fear of making mistakes, embarrassment to express, low motivation to learn, and external factors come from learning facilities, and the environment outside the school. Keywords : Elementary School, External Factors, Indonesian Language, Internal Factors, Speaking Skills.
Graph learning model for saliency detection in thermal pedestrian videos

Thermal imaging is becoming popular recently due to its all-weather and all-time capability. However, video saliency detection, an active topic in computer vision, has not been well studied for thermal videos. This paper proposes an effective saliency detection method for thermal pedestrian videos. Unlike graph-based saliency detection methods with fixed graph structures, we introduce a saliency-guided graph learning model (SGL), which integrates a multi-view graph and saliency to achieve joint graph learning and saliency diffusion. Then, we apply SGL to thermal video saliency detection by constructing the objectness descriptor based motion saliency and a multi-view spatiotemporal graph. Furthermore, a challenging thermal pedestrian video dataset IPV with 67 sequences and 1462 frames in total is built to compensate for the insufficient scale and complexity of existing datasets. Extensive experiments on a public dataset and our newly built dataset demonstrate the superior performance of our method. Compared to 11 state-of-the-art (SOTA) video saliency detection methods, the proposed approach achieves the best performance of 74.0 %, 75.1 %, and 1.8 % in F-measure, S-measure, and MAE respectively, on the IPV dataset. It also achieves better performance than SOTA on the public dataset GTFD with 69.9 %, 75.6 %, and 1.1 %, respectively.
MODERN EDUCATION IN THE CONTEXT OF V.I. VERNADSKY’S IDEAS OF THE NOÖSPHERE

The paper provides a review of the existing common education in Russia in the context of V.I. Vernadsky’s ideas of the noösphere. An analysis was made of the compliance of the content of educational standards and exemplary educational programs for the common education school with V.I. Vernadsky’s main ideas of the noösphere.
SISTEM APLIKASI ANDROID SERVICE PROVIDER BIZNET PADA PT SUPRA PRIMATAMA NUSANTARA

The purpose of this research is to develop an Android application system for service provider Biznet at PT Supra Primatama Nusantara using Java programming language. This application is designed to help users with various activities such as registering for services, managing accounts, choosing packages, paying bills, and filing customer complaints. This study uses the waterfall system development method, which consists of needs analysis, system design, implementation, testing, and maintenance. The result of this research is an Android application for Biznet service providers that can be used by users to access Biznet services easily and efficiently. This application has been successfully implemented and tested, and is able to overcome the problems faced by users in accessing Biznet services. This application also has a user-friendly interface that can be operated easily. The conclusion of this research is that the development of the Android application service provider Biznet at PT Supra Primatama Nusantara using the Java programming language was successfully carried out using the waterfall method. This app can help users efficiently and easily access Biznet services. This research also shows that using the Java programming language can provide benefits in developing effective and efficient Android applications.
A novel watermarking scheme for medical image using support vector machine and lifting wavelet transform

Digital image watermarking has become a valuable tool for preventing unauthorized use and alteration of digital images due to technological advancements. A fundamental difficulty in digital image watermarking is to provide resilience against geometrical assault while retaining a sufficient degree of imperceptibility and security. This study presents an efficient authentication scheme for digital image watermarking on medical images benefiting from both techniques: Support Vector Machine (SVM) and Lifting Wavelet Transform (LWT). In this article, we use two strategies, where SVM is used first to separate the Region of Interest (ROI) from the Non-Region of Interest (NROI) in the medical image. Then LWT is applied to embed watermark information within the NROI part of the medical image (Cover Image). Moreover, we have applied a shared secret key to enhancing the robustness of the proposed scheme. The method is tested on an extensive image database to see how it performs under different situations. The research looked into the various experimental analyses to establish the acceptability of the existing scheme. The simulation is performed to measure the imperceptibility and robustness using various evaluation metrics.
Development of STEM-Based Student Worksheets on Virus Material to Improve Student Science Literacy

This study aims to design and produce Student Activity Sheets based on Science Technology Engineering and Mathematics (STEM) on Virus material. The research design used is a type of research and development. The subjects in this study were Material Experts, Media Experts, Instrument Experts, Biology teachers and students of class X-IPA-2 MAS-AL-Washliyah 12 Perbaungan. Data collection was carried out with instruments in the form of material expert validation assessment sheets, media experts, teacher assessment instrument experts and student responses. Data analysis used quantitative and qualitative descriptive analysis. The design of STEM-based student worksheets is carried out using the 4D development model because in this model each stage is revised until a better student worksheet product is obtained. The results showed that the design of STEM-based student worksheets based on the assessment of material experts obtained an average percentage of 98.2 % with very valid criteria, the assessment of media experts obtained an average percentage of 84 % with very valid criteria, and the assessment of instrument experts obtained an average percentage 100 % with very valid criteria. The results of using student worksheets based on the STEM approach designed to foster scientific literacy skills obtained an average score of 8.6.3 % with a total of 19 students who completed. STEM-based student worksheets on Virus material that have been designed to obtain the assessment criteria of "Very Practical " and have fulfilled the requirements for effective use in improving scientific literacy skills and are suitable for use in the Biology learning process on virus material
A Study on the Experience of Writing a Korean Contemporary History Textbook of Preservice History Teachers

This study aimed to deeply understand the preservice history teachers’ experiences and meanings of “Korean Contemporary History Textbook Writing Project” performed in the < Theory of History and Education > of S university. The researcher performed an action research, and utilized all the outcomes and texts written in the process of producing the outcomes, by total 36 students who took the relevant course, as the research data.Even though the preservice history teachers perceived “if the understanding of the current life has been improved” as the historical standard, and composed the Korean contemporary history textbook within the narrative called “development of state”, they attempted to consider such various and complex aspects of history through various descriptions and materials. The preservice history teachers faithfully reflected the external·compositional characteristics of the existing history textbooks as ‘familiarity pitfall’.The preservice history teachers estimated the level of learners in their own methods, and also aimed to help the learners’ understanding through detailed descriptions based on the criticism saying that the description of the existing history textbooks was fragmentary and segmental. The learners’ understanding was an important element for the preservice history teachers to write the textbook, which was mostly concentrated on the formal part and also realized the elements of workbooks familiar with the preservice history teachers.Also, as the historical description that they tried to exceed the standard in which the narrative description should be “objective and neutral”, they made selections in the perspective of learners’ interest while emphasizing that the Korean contemporary history textbooks should be “observation of apprenticeship”.Also, as the historical description that they tried to exceed the standard in which the narrative description should be “objective and neutral”, they made selections in the perspective of learners’ interest while emphasizing that the Korean contemporary history textbooks should be “observation of apprenticeship”. Like this, the preservice history teachers’ experience of writing the Korean contemporary history textbook was a meaningful and challenging experience of learning as it was, which could be used as a foundation for the development of curriculum in teacher training institutions.