Reviewerly/exHarmony huggingface dataset:
Rancang Bangun Aplikasi Android Si-Raja sebagai Upaya Memantau Pertumbuhan Janin pada Ibu Hamil

Pregnancy is fertilization or union of spermatozoa and ovum, followed by nidation or implantation. Determining estimated fetal weight (TBJ) is vital in antenatal care, counseling, diagnosis and delivery approaches. Accurate predictions of a baby's birth weight are one of the most crucial metrics in pregnancy until the commencement of labor. The health application for pregnant women has been produced in prototype form that may be run on the Android smartphone operating system. Pregnant women used the application. Apart from that, the Si-RAJA Pregnant Application was a development of the Si-RAJA application version 1. This application was built so that pregnant ladies can use it to find out the estimated weight of their fetus. This program was provided with functions to compute weight gain, upper arm circumference, and hemoglobin level status, as well as health information about fetal growth that pregnant women need to know. One of the prerequisites that needed to be met before beginning the application was to install it on your smartphone via Google Playstore. The Si-Raja Bumil program comprises of the primary menu: Calculate predicted fetal weight, how to enhance TBJ, Calculate upper arm circumference, calculate weight gain, calculate Hb levels, as well as information about the size of the baby. The program is expected to go through significant enhancements, enabling pregnant women to effectively engage with midwives to transmit complaints, examination findings, and pregnancy-related information.
Veri Merkezli Güvenlik ve Uyumluluk Platformu Geliştirilmesi (Resepsiyon), Uygulama Sonuçlarının Değerlendirilmesi

This study focuses on some of the challenges faced by energy, banking and information technology companies in information systems. In particular, the following problems come to the fore in systems in these sectors: incompatibilities between different systems, repetition or contradiction of the same data, problems of not being able to use the same language in different systems, and uncertainty about who controls the data. As a solution to these problems, techniques such as authorization-based dynamic data hiding, data monitoring and user behavior analysis are combined to provide information to detect malicious activities. That is, the study considers problems in information systems in these sectors and proposes the combination of certain technical approaches to solve these problems.
Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

This article outlines the design and development process of an automatic product categorization software intended for use in e-commerce and online marketplace platforms. The project aims to tackle the urgent issue of effectively classifying a wide range of products within digital markets. By utilizing artificial intelligence (AI) and machine learning methodologies, the software effectively analyzes product descriptions, enabling users to seamlessly incorporate products through automated categorization. The significance of the project is rooted in its ability to guarantee the accuracy and consistency of category hierarchies, automate the process of categorizing, and improve the overall user experience. The main goals involve the process of project planning, data collecting, and preparation. Machine learning models have been built and subsequently incorporated to facilitate the study of product descriptions. Through strict evaluation and optimization processes, a high level of accuracy and efficiency is achieved, resulting in several anticipated benefits. These benefits cover automated product categorization, enhanced user experience, and the potential for online platforms to gain a competitive edge. The key elements of innovation involve AI-driven textual analysis, learning methodologies grounded in data, and the ability to adapt to diverse industry contexts. Precautions and backup strategies are implemented to tackle technical issues, including the selection of machine learning libraries and algorithms, ensuring data quality, and integrating with various platforms. The success criteria encompass the objective of achieving a minimum prediction accuracy rate of 90%, optimizing business efficiency, enhancing user pleasure, and ensuring smooth system functioning. This project is a significant contribution to the field of product categorization inside the digital marketplace, as it provides automation, accuracy, and efficiency, ultimately resulting in an enhanced user experience.
Comparative Analysis of Baseline Vnet and Unet Architectures on Pancreas Segmentation

The pancreas is one of the vital organs in the human body. It has an essential role in the digestive system and endocrine system. Diseases such as cancer, diabetes, hormonal problems, pancreatitis, and digestive problems occur in pancreatic disorders. In detecting pancreatic disorders, first blood and urine tests are requested. If further examination is needed, CT (Computed Tomography), MR (Magnetic Resonance), and EUS (Endoscopic Ultrasonography) imaging methods are used. Pancreas segmentation is generally the process of defining and drawing the lines of the pancreas from medical images such as CT and MRI. The size and shape of the pancreas varies from person to person. Manual segmentation of the pancreas is time-consuming and varies between physicians. Recently, deep learning-based segmentation methods that achieve high-performance results in organ segmentation have become trendy. In this study, Unet and Vnet architectures were comparatively analyzed on the NIH-CT-82 dataset. As a result of the ablation studies, a validation sensitivity of 0.9978 and a validation loss of 0.041 were obtained in the Unet architecture. In the training with the Vnet architecture, 0.9975 validation sensitivity and 0.046 validation loss values were obtained, respectively.
Computational and Corpus-Based Phraseology

Reseña de Computational and Corpus-Based PhraseologyGloria Corpas Pastor y Ruslan Mitkov (Eds.)Cham (Suiza), Springer, 2022, 252 pp.
A Study on the Current Status of Education on Korean Speech Act of Greeting

Speech act of greeting is crucial in daily life, but Korean language learners often encounter difficulties due to the strong cultural characteristics reflected in the speech act of greeting. This study examines the characteristics of speech act of greeting, taking into account the elements of context of situation, and investigates how it is taught in Korean language education through the curriculum and textbooks. &#x0D; The variables of the context of situation that are typically observed in speech act of greeting include the characteristics of the 'setting' where the conversation takes place and the 'participants' involved, which can be further divided into the 'relationship between the speaker and the listener' and the 'relationship of meeting '. This study explores the role of these variables in speech act of greeting.&#x0D; By examining the position of the speech act of greeting within the Korean language education framework, focusing on the 'International Korean Standard Curriculum' and the 'Korean Standard Curriculum,' it is revealed that the speech act of greeting is presented in the first-level overarching goal, aimed at educating learners across different levels, incorporating the content of both the 'Functions and Tasks Area' and the 'Cultural Area.' However, the cultural area of speech act of greeting in the currently used textbooks, developed by the National Institute of the Korean Language, varies significantly depending on the textbook. Moreover, since it is primarily introduced at the beginner level, it may not fully align with the content prescribed in the curriculum.
Role of Research Data Repositories and Open Data Sharing with Specific Reference to Open Science Framework (OSF)

In the world of research and academia, in the current era of data-driven research, the availability and accessibility of research data have become crucial for scientific research, collaboration and advancement. Consequently, sharing of data is becoming a more crucial aspect of fostering open science and reproducible research. The practice of research data sharing not only enables transparency in research and collaboration but also allows other researchers to build upon existing work to reproduce research, accelerating the pace of scientific discovery. Various research data repositories have emerged to facilitate this process, providing a platform for researchers to deposit and access research data. And, research data repositories, specifically generalist research data repositories, have become vital platforms facilitating the storage, management, and sharing of research data across various disciplines. One such prominent research data repository is the Open Science Framework (OSF), which has gained prominence as a powerful and versatile platform for its comprehensive set of features and commitment to open science and collaborative research practices. This article examines the concept of research data repositories and their features and functions. Further, it sheds light on the NIH’s GREI initiative and the role of Generalist Research Data Repositories (GRDR) and explores the Open Science Framework (OSF) with its key features and advantages with an emphasis on reproducible research and data sharing.
A Critical Review of the Concepts of ‘Education’ presumed in the KEDI Comprehensive Report &lt;The Current Situation and Countermeasures of Education for the Entrance Examination&gt;

This study, as the first phase of the “Educational Concept Analysis Project for countering ‘education for the entrance examination’”, critically examined the concept of ‘education’ presumed in the Korean Educational Development Institute(KEDI) research report that dealt with the current situation and countermeasures of education for the entrance examination. The problematic issues associated with the concepts of education can be classified into four separate categories. First, the concept of education assumed in the KEDI report was revealed to consider education as merely a means for school entrance or preparation for school entrance and have a distorted perspective on the subject of education, based on the common sense that all aspects within schools constitute education. Second, the KEDI report, by reducing education to the external contexts, specifically within the realms of psychology and sociology, concealed educational phenomena, inevitably resulting in confusing the context. Third, in this report, issues were identified where education was defined only in relation to the results that education is expected to bring out. This approach defers the construction of the conceptual system and the value of education and leads to the divergence in causality and contradictions between concepts. Fourth, while the report emphasized the importance of the “essence of education” and its “intrinsic value” as an alternative to overcome the shortcomings of education for the entrance examination, it paradoxically renders the argument empty due to the neglect of a clear understanding of the conceptual system of education. The issues outlined above originate from the confusion between entrance-centric schooling and education. While entrance assessments and educational phenomena should be distinctly demarcated within the school context, the report is deficient in establishing the requisite conceptual boundaries and theoretical underpinnings necessary for their coexistence. Addressing these limitations is a challenge that must be overcome through the supplementation of appropriate theories distinguishing education from entrance examinations.
An optimal principal component analysis method for carrier removal in Fourier transform profilometry

To improve the reconstruction accuracy in the fringe projection profilometry (FPP), a bidimensional empirical mode decomposition and principal component analysis (BEMD&PCA) algorithm are proposed to directly acquire the object phase from fundamental-frequency component which includes the carrier phase and object phase. BEMD is employed to remove the zero frequency to reduce the spectrum overlapping. A reduced-size spectrum is adopted to limit the size of the spectrum for principal component analysis. PCA is used to decompose the phase into several principal components and extract the carrier phase from the first dominant component, and the object phase can be acquired by multiplying with the carrier phase conjugation. The experiment and analysis results illustrate that the proposed method has the merit of less error distribution and single-frame acquisition.
Enhancing Image Content Analysis in B2C Online Marketplaces

The automation of image analysis in Business-to-Consumer (B2C) online marketplaces is critical, especially when managing vast quantities of supplier-uploaded product images that may contain various forms of objectionable content. This study addresses the automated detection of diverse content types, including sexual, political, and disturbing content, as well as prohibited items like alcohol, tobacco, drugs, and weapons. Furthermore, the identification of competing brand logos and related imagery is examined for competition and ethical reasons. The research integrates custom transfer learning models with the established Microsoft and Google Vision APIs to enhance the precision of content analysis in e-commerce settings. The introduced transfer learning model, trained on a comprehensive dataset, exhibited a significant improvement in identifying and categorizing the specified content types, achieving a notable true positive rate that surpasses traditional API performances. The findings reveal that the “Pazarama Model”, with its transfer learning framework, not only delivers a more accurate and cost-effective content moderation solution but also demonstrates enhanced efficiency by reducing the image processing time and associated costs. These results support a shift toward specialized transfer learning models for content moderation, advocating for their adoption to maintain content integrity and enhance user trust within e-commerce platforms. The study advocates for continued refinement of these models, suggesting the integration of multimodal data to further advance the content analysis capabilities in B2C environments
Advancing Workplace Safety: A Proactive Approach with Convolutional Neural Network for Hand Pose Estimation in Press Machine Operations

Press machine operations are integral to goods production across industries, yet worker safety faces significant risks. Machine misuse and non-compliance with safety standards contribute substantially to these incidents. This study addresses the mounting concerns regarding workplace incidents through a proactive solution—a Convolutional Neural Network (CNN) model crafted to prevent press machine misuse by monitoring workers' hand placement during operation. The model that we suggest ensures adherence to safety standards. The CNN model does not replace the role of human operators but acts as a supportive layer, providing instant feedback and intervention when deviations from safety standards are detected. In conclusion, this research endeavors to pave the way for a safer and more secure industrial environment by leveraging the capabilities of advanced technology. The proposed CNN model addresses current concerns and sets a precedent for future advancements in ensuring workplace safety across diverse industries.
Artificial Intelligence Based Store Management

The project proposes innovative ideas such as personalized customer interactions through a mobile application and optimizing queues through the sliding checkout model. It also leverages existing kiosks for digital customer connections. The project's methodology is based on comprehensive needs analysis, consultations with industry experts, and the identification of processes suitable for automation. It also prioritizes Research and Development (R&amp;D) in retail merchandising by securing R&amp;D licenses from industry giants. The project's technological infrastructure is designed for the Azure cloud environment, ensuring operational efficiency and seamless integration with various systems. A robust logging infrastructure is in place to maintain an uninterrupted connection between artificial intelligence support and the backend architecture. The project also develops a mobile application with user-friendly interfaces and cross-platform functionality using Flutter. The anticipated benefits of the project include time savings for store managers, data-driven decision-making, and experimental positioning for testing and implementing novel methods in store operation. Overall, the "Artificial Intelligence Based Store Management" project aims to set new industry standards by integrating artificial intelligence and machine learning into retail merchandising.
Data Mining, Weka Decision Trees

Nowadays, computer technologies are increasing rapidly. Thanks to the development of computer technologies, large and complex raw data sets can be transformed into useful information with different analysis techniques. Different algorithms developed thanks to computer technologies can offer different solutions to scientists and users working in different branches of science, especially engineering sciences, mathematics, medicine, industry, financial/economic fields, marketing, education, multimedia and statistics. Thanks to these solutions, it is possible to easily achieve the desired goals and objectives. Thus, by correctly managing and analyzing existing data in large and complex raw data datasets, accurate predictions can be made to be used in similar problems in the future. Data sets are analyzed and evaluated using different methods. It is also possible that the classification of data during the analysis and evaluation stages of data sets significantly affects the decision-making process regarding the work to be done. Classification of data can be done by statistical method or data mining method. Decision trees, which can be used to classify numerical and alphanumeric data, generally provide a great advantage for decision makers in terms of easy interpretation and understandability compared to other classification techniques. For these reasons, in this study, decision trees, one of the most used classification techniques in data mining, are mentioned.
Effectiveness of training on Employee Motivation and Job Satisfaction with Mediating Role of E-learning in Branchless Micro Finance Banking

The research investigated the effectiveness of training on employee work motivation and job satisfaction and the mediating role of e-learning in branchless banking. In every organization Employees are the major assets. The success of the organization depends on the performance of the employee. For that reason, the higher level officials realize the importance of spending in training and E-learning for the betterment and development of employee motivation and job satisfaction also keep them to get the challenges of the today’s spirited environment of the business. The importance of E-learning system on organizations have been discussed at length but not much substantially towards the proportion of E-learning, the current research has targeted to see the “Impact of training with mediating role of E-learning on employees’ job satisfaction and work motivation in micro finance banks in which training was considered as independent variable, whereas dependent variables are work motivation and job satisfaction and E-learning is mediator. A self-administrated questionnaire with the help of convenient sampling technique has been carried out by survey of employees. Four hypotheses were formulated, which narrated unswervingly to the questions of the research. A statistical software SPSS is used to perform descriptive, reliability, regression and correlation analysis. Results of the research showed that there is a significant positive relationship between training and employee performance job satisfaction and work motivation with mediating role of E-learning on the variables.
Enhancing Wireless LAN Security : A Comprehensive Analysis on Threatsand Vulnerabilities

These days, wireless LANs are found everywhere—from large corporate networks to homes - because they are simple to set up, convenient for employees, don't require expensive wiring, and support continuous mobility. However, the greater availability of wireless LANs means increased danger from attacks and increased challenges to an organization, IT staff, and IT security professionals. We have implemented a time-based one-time password for wireless security lans to protect a network.
A Secure and Optimization Based Clustering for Vertical and Horizontal Fragmentation in Distributed Database Management System

Distributed database systems are increasingly important due to the massive data output, and their effectiveness is largely based on their design. Two key processes, fragmentation and allocation, are used to improve the efficiency and efficacy of these systems. Effective data fragmentation requires both horizontal and vertical categorization of tuples. Advanced optimization techniques are used for both fragmentations, such as the Enhanced Arithmetic Optimization (EAO) algorithm with Opposition-based Learning (OBL) and Levy Flight Distributer (LFD) for vertical fragmentation and the hybrid Aquila Optimizer (AO) with Artificial Rabbit Optimization (ARO) algorithm for horizontal fragmentation. The fragmented data is securely transmitted using the Fully Homomorphic Encryption (FHE) algorithm. The implementation is executed using the Python language, and the performance of the proposed algorithms is evaluated using different performance parameters. The execution time analysis shows that the proposed EAOA algorithm consumes 5.5 seconds for vertical fragmentation, while the hybrid AARO algorithm takes 5.9 seconds for horizontal fragmentation. The vertical fragmentation is found to be better than the horizontal one in DDBMS.
Container Sizing for Microservices with Dynamic Workload by Online Optimization

Over the past ten years, many different approaches have been proposed for different aspects of the problem of resources management for long running, dynamic and diverse workloads such as processing query streams or distributed deep learning. Particularly for applications consisting of containerized microservices, researchers have attempted to address problems of dynamic selection of, for example: types and quantities of virtualized services (e.g., IaaS/VMs), horizontal and vertical scaling of different microservices, assigning microservices to VMs, task scheduling, or some combination thereof. In this context, we argue that online optimization frameworks like simulated annealing are highly suitable for exploration of the trade-offs between performance (SLO) and cost, particularly when the complex workloads and cloud-service offerings vary over time. Based on a macroscopic objective that combines both performance and cost terms, annealing facilitates light-weight and coherent policies of exploration and exploitation. In this paper, we first give some background on simulated annealing and then experimentally demonstrate its usefulness for container sizing using microservice benchmarks. We conclude with a discussion of how the basic annealing platform can be applied to other resource-management problems, hybridized with other methods, and accommodate user-specified rules of thumb.
An Interacting Multiple Model Algorithm Based On Cubature Kalman Filter

The interacting multiple model(IMM) based on different motion models can mix the target state estimation,and be proved to be the most effective target state estimator.Based on the interacting multiple model composed of the constant velocity (CV) model,the adaptive coordinated turn(ACT) model and the current statistical(CS) model,this paper adopts the cubature kalman filter(CKF) to realize the nonlinear filter. The simulation of a maneuvering target tracking shows that the algorithm can estimate the rate of turn in real time,has the ability to recognize maneuvers, and has a high tarcking accuracy.