Reviewerly/exHarmony huggingface dataset:
Case Study of Income Inequality, Governance, and Economic Growth in EMCCA Countries

The aim of this study is to examine the influence of governance and income inequality on economic growth in the six member nations of the Economic and Monetary Community of Central Africa (EMCCA) that are in the process of emerging. The poor performance of the sub-regional economy during the last few years can be explained in part by poor governance and unfair resource reallocation. Despite these nations' abundant natural riches, it has been proved that poverty is spreading swiftly. This has a greater impact on rural communities than on metropolitan places. The majority of theoretical and empirical surveys conducted provide contentious outcomes about the relationship between inequality and growth. The dynamic panel assessment results show that: (1) Income inequality has a negative impact on economic growth in EMCCA countries, (2) the composite index of governance has a positive impact on the growth rate of the effective Gross Domestic Product (GDP) individually, and (3) the interaction between income disparity and governance has a negative impact on economic growth.
Assessment of Students' Perceived Preparedness for E-learning in Ibadan, Nigeria

Background: The occurrence of the COVID-19 pandemic has made software developers come up with different e-learning tools to facilitate distance learning in institutions. These tools have eased the challenges brought about by limited contact among persons during that period and there could be variations in the level of preparedness to use these tools among different populations. This study thus aimed to assess the level of student’s preparedness to use these e-learning tools.&#x0D; Materials and Methods: A descriptive design was used to carry out this study among 203 respondents of the Federal Training Centre for Teachers of Health Sciences, in the University College Hospital, Ibadan. A simple random sampling technique was adopted to select the study participants from the population. A semi-structured questionnaire with 4 sections was used in the collection of data. Ethical considerations were adhered to and analysis was performed using the Statistical Package for Social Sciences (SPSS) software.&#x0D; Results: The results show that a high level of preparedness (71.4%) was recorded and this was evidenced by a greater proportion of availability of e-learning materials (76.8%) with a corresponding network system (82.8%). A majority (73.9%) of the respondents had utilized these e-learning tools for class assignments and virtual meetings in their respective environments. The recorded level of preparedness was reported to be faced with some constraints including poor availability of telecommunications networks (42.4%), lack of functional laptops (17.2%), and lack of e-learning facilities (10.3%).&#x0D; Conclusion: E-learning utilization preparedness was found to be good in this study but is faced with certain barriers limiting its use effectively. This research has been able to make a case for the effective development and sustenance of e-learning in institutions in Nigeria. Governments and necessary stakeholders need to prioritize the provision of infrastructure, training, and materials needed to enhance e-learning in the education system of Nigeria.
BIBLIOMETRIC ANALYSIS OF DIGITAL ACCOUNTING USING R BIBLIOSHINY

Digital technology significantly affects business processes, one of which is the accounting aspect that supports providing fast and accurate financial reports. The use of digital technology in accounting is often called digital accounting. This has prompted researchers to examine the impact of digital technology on accounting. Therefore, many publications have been detected examining this issue but have not mapped it well. This study aims to map research on digital accounting. This research was conducted on the dimensions.ai database from 2013 to mid-2022. Using the keyword “digital accounting”, 1,397 documents were obtained consisting of journal articles, book chapters, monographs, preprints, and proceedings. The R biblioshiny software found that the most relevant journals were Contributions to Management Science, IOP Conference Series Material Science and Engineering, and Plos One. Meanwhile, the one with the biggest impact is Plos One. In addition, the most relevant authors are Muravskyi and Shao. But the ones who had the biggest impact were Agrell, Karlsson, and Troshani. The distribution of words is dominated by ‘digital transformation’, ‘accounting information’, ‘digital technologies’, and ‘digital accounting’. Topics that have emerged in recent years are digital transformation, accounting information, digital accounting, blockchain technology, and accounting systems. Even blockchain technology is still emerging in 2022. These findings illustrate the importance of using digital technology in accounting, thus triggering researchers to continue to develop ideas and other significant findings related to digital accounting.
The Economic Impact of Banning Billionaires: An Analysis of Wealth Accumulation and Market Dynamics

This article examines the issue of banning billionaires and provides a comprehensive analysis of potential policy implications. First, the article discusses the possible negative impact of banning billionaires on innovation and the spirit of adventure, highlighting their important role in funding support, venture capital and high-risk projects. Second, the article analyzes the potential adverse effects of banning billionaires on charitable giving and philanthropy, as well as the possible impacts on economic growth and job opportunities. Finally, the article points out that the prohibition of billionaires is not the same as eliminating the upper class of society, stressing that solving social inequality requires more comprehensive policies and systems Sex reform.
Research on Emotional Analysis of Online Book Reviews Based on Word2Vec Method

At present, emotional analysis in the field of natural language processing is gradually becoming a popular research direction on social media. With the rise of artificial intelligence technology and deep learning algorithms, word vector representation methods based on neural networks have emerged and emerged in the field of sentiment analysis. According to the current research results of experts, the Word2Vec model has become the mainstream word vector representation method. The algorithm model is based on continuous optimization and iteration of random initialization word vectors, ultimately obtaining a stable performance word vector representation. If the research goal is to obtain sentence vectors, weighting processing is also required. Therefore, after systematically summarizing and analyzing the representation techniques of initialized word vectors and solving sentence vectors through word vector weighting, this article proposes corresponding improvement methods. This article improves the initialization algorithm of its word vector based on the Word2Vec model. On the basis of the original randomly generated word vector, the correlation information of words is added to change the distribution characteristics of the initial word vector, thereby enhancing the performance of the word vector. In the comparative experiment of the user comment dataset, the improved model showed a 1% to 3% improvement in sentiment polarity classification accuracy compared to the other model.
Using machine learning and optical imaging to identify malignant tumor cells

Employing multiple advanced technologies offers an approach for identifying and phenotyping circulating tumor cells in blood samples.
Purchasing, Sales and Inventory Information System Design at UD. Monza Collection

The information system is also one of the most important parts for companies in increasing productivity, both in obtaining information, processing, and using the information, especially for the benefit of the company. UD. Monza Collection is a form of business that is engaged in distributors for women's clothing such as shorts, leggings, tank tops, and others and then sells them wholesale. The problem with the store is that the store often has difficulty in knowing the amount of raw materials by the distribution staff quickly and the calculation of the number of finished goods by the warehouse staff who are very prone to errors, this will result in delays in distribution and sales activities. The system design methodology used is the System Development Life Cycle (SDLC) methodology. The design of the input system using Microsoft Visual Studio 2015, The design of the system output using Crystal Report and data storage using Microsoft SQL Server 2014. The result of this writing is a desktop design that makes it easy to find out the available stock and makes it easy to make the required reports quickly. So that the results of this information system design can help the store in managing information and computerized business processes.
Information Systems to Enhance Focus in the Learning Process Using Design Thinking Methodology

This study adopts the Design Thinking methodology to investigate the habits, needs, and challenges faced by students when attempting to maintain focus during activities that require concentration. The research surveyed students, with a significant proportion (approximately 53% of all participants) reporting frequent loss of focus during tasks. External disturbances, such as an unconducive environment, gadget notifications, and enticing distractions, were identified as the primary triggers for this lack of focus, resulting in procrastination and hindrance in achieving their main goals. In response to these findings, this research proposes a User Interface / User Experience (UI/UX) design approach to develop an application aimed at enhancing focus and learning performance to boost overall productivity. The design aims to ensure the application's interface is user-friendly and easily comprehensible. By designing an application interface that can organize focused sessions for the activities to be carried out by the users. The UI/UX design of this application holds the potential to empower students in elevating their learning experience, thereby fostering higher productivity and goal achievement. The results of this research show that the use of the Design Thinking methodology achieved a score of 70% from the overall users who tested the application design using the Maze tool.
Performance Evaluation of Light Weight Cryptographic CLEFIA Algorithm

We have become more cognizant of communication since the 19th century. However, we have known how important communication is for daily life for the last three or four decades. It is merely a method of transmitting data from one end to the other. A transmitter and a receiver are the two ends. Both of these components must be present for communication to succeed. As the application of this process continues to grow, new techniques or tactics have been developed. The human being then learned that communication is crucial, but so is keeping that communication safe. Claimed to be a trustworthy cipher is CLEFIA. The CLEFIA specs and algorithm design can be assessed for performance and security by cryptographers and the general public. For ISO/IEC lightweight cryptography, the CLEFIA cipher is a popular choice. It is a generalised Feistel network with four nodes. This architecture requires little room, both physically and virtually. The Diffusion Switching Mechanism in CLEFIA shields the system from serious assaults. Additionally, the primary scheduling and data processing components of CLEFIA perform comparable tasks, which decreases the gate size. In this study, the execution times of the Low Weight Cryptographic CLEFIA old and modified algorithms are discussed. This calls for using a product from Texas Instruments (TI), the MSP-EXP430FR5994 LaunchPad Development Kit from the MSP430 family. The CLEFIA supports 128- bit blocks and offers 192-bit and 256-bit key sizes.
Development of an Intelligent Walking Aid for Fall Detection

Abstract The medical field has made significant improvements over the past decades, leading to an older and healthier population. One of the main core problems being tackled is the detection of falling events. The quick detection of a fall contributes greatly to the fast arrival of help and prevents further damages. Therefore an intelligent crutch was developed with the purpose of detecting a fall and the following call for help if needed, as well as conveying the location of the user to relatives and care givers. The crutch was evaluated in a series of experiments during which all falls were identified correctly and alerting was initiated.
Lymphoma Cell Nuclei Classification using Color and Morphology Features

Abstract Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of Non-Hodgkin’s Lymphoma, presenting a great challenge for treatment due to its highly heterogeneous nature. DLBCL is diagnosed based on microscopy images of patient tissue samples. To help gain a better understanding of DLBCL, we developed an automated computer vision method to analyze morphological and color-based information within patient biopsies. We analyzed a dataset of whole slide images of DLBCL by segmenting individual cells and representing cell morphologies through a set of engineered features. The features were evaluated using a variety of visualization and machine learning (ML) classification techniques. Current state-of-the-art deep learning methods use images as the input in classification tasks achieving high performance but lacking in interpretability. A big challenge lies in finding out what features the pixel-based deep learning methods utilize in prediction. Here, we present a technique that not only yields high prediction accuracy but also provides insights into which of the features are key for prediction. We show that the color-based features have the highest importance for cell classification, allowing for the accurate identification of various cell types with an accuracy of 84% in a multi-class and 91% in a binary classification. Our results provide valuable insights for exploring cell image datasets to gain an in-depth view of the tumor microenvironment.
An approach to privacy-aware image analysis on edge devices using CNNs

Abstract This paper presents our approach to a privacy preserving person detection algorithm on edge computing devices. It utilizes a commonly used neural network architecture (VGG16) to encode an image by passing it through a fixed number of layers of the network on the edge device. The resulting feature vector can then be transmitted over the network to a more powerful computer (e.g. in the cloud) to be passed through the remaining layers of VGG16. As a result, the transmitted feature vector is only an abstract representation of the image. However, our research has shown that it is in fact possible to reconstruct most images by their respecting feature vectors if a potential attacker can tap into the network transmission by using a decoder network. Our approach to mitigate that risk is to preprocess the transmitted feature vector randomly by different manipulation methods. We used methods like mean filter or random null value insertion to manipulate the feature vector before it gets transmitted and show that those methods are able to counter the reconstruction capabilities of a decoder network while still preserving the capabilities of the original VGG16 network. The actual VGG16 network was used for object detection.We used the faces in the wild dataset and utilized two different approaches to confirm our approach. First, we used the VGGFace-Network on the decoded images and tried to let it identify people from the former mentioned dataset. The second approach used a group of 26 participants who had to match the decoder image to one of five images. In our experiments, we found different combinations of manipulation and number of layers on the edge device to preserve the detection capability of VGG16 (e.g. object detection) while preventing VGGFace and the participating group from identifying the shown people.
Analysis of Learners’ Satisfaction and Needs for Basic Writing Classes at D University

This study examined learners’ satisfaction and expectations of a writing course, titled “Basic Writing,” at D University, using a questionnaire survey administered to 126 students taking this course. The findings revealed a high level of student satisfaction with the class. Additionally, the overall class evaluation by students, the extent of assistance provided to improve students’ thinking ability and expression skills, and the need for writing classes were all positively perceived. Students felt that the professor’s expertise contributed to their positive perceptions and satisfaction levels. Students who had somewhat adapted to online classes due to the coronavirus disease preferred it to the current face-to-face mode, which the university has reverted to after the pandemic. Their expectations regarding the class timings and study mode are more related to operational rather than educational aspect. While the results of this study cannot be generalized due to the relatively small sample size, they still provide valuable insights into students’ satisfaction levels and their needs regarding writing classes.
Optimization Model for Determining an Observation Order of Scientific Surveillance Equipment

Due to the rapid population decline, the ROK Army has introduced a scientific security system in the GOP area since 2015. But, there has been no evaluation indicators for the efficiency of the operating scientific security system. In this study, we investigated an optimization model for determining an observation order of scientific surveillance equipment. Key idea of our research is that we construct M-TSP model where the environment that originally existed in 3D space which was reduced to 2D space. We hope that this study will be used in the field and improve the security operation level.
Improvement of Prostate Cancer Aggressiveness Prediction Performance Using a Self-Supervised Learning Model Fine-Turned on Similar Medical Images from Multi-Parametric MR Images

In this paper, we propose a prostate cancer aggressiveness prediction model using self-supervised learning based on SimCLR with multi-parametric MR images. Self-supervised learning model is initially trained on the STL10 dataset, and then fine-tuned on the ProstateX dataset, which is similar to the downstream task dataset. To predict prostate cancer aggressiveness, downstream tasks are performed using each sequence of images from the multi-parametric MR dataset. The predicted results are combined using either majority voting or average voting for ensembling. Experimental results demonstrate that the self-supervised learning model fine-turned with similar images improves the performance by an average of 4.56% in accuracy, 20.69% in sensitivity, and 12.02% in negative predictive value. The ensemble method using majority voting with the self-supervised learning model fine-turned on similar images from the multi-parametric MR dataset yields the best performance in terms of accuracy (72.58%), balance accuracy (72.16%), and sensitivity (67.86%).
Evaluating the Usability of an LLM-Aided Hybrid Avatar Agent System

The demand for conversational systems using digital humans has increased across various fields, including education and psychological counseling. Conversational digital humans can be categorized into avatar and agent. This paper introduces the Hybrid Avatar Agent System (HAAS), designed to implement a conversational digital human capable of addressing diverse questions. To facilitate seamless transitions between avatars and agents, we developed a Korean Dialogue Breakdown Detection (DBD) model. While a digital human typically functions as an agent, the DBD model identifies conversational pauses, prompting human operator intervention for user assistance. We trained DBD model using the RoBERTa_base model, achieving an accuracy of 60.77%. In a user study, a comparison was drawn between HAAS and the Agent Only System (AOS). Users noted that HAAS provided more appropriate answers than AOS.
Polyakov's Readings: Keeping Traditions and Attracting Innovations

&lt;p style="text-align: justify;"&gt;On March 23-24, 2023, the jubilee conference "Third Polyakov Readings" was held, which has already become a tradition. This article describes the main events and achievements of the conference, evaluates the dynamics of the conference on the basis of general data on the number of participants and conducted events. An attempt is made to highlight the prospects of further development of the conference.&lt;/p&gt;
Recognition of Hijaiyah Letters with Punctuation Using Augmented Reality

Learning hijaiyah letters is an initial step to reading the Al-Qur'an. Because the Al-Qur'an is written in Arabic using hijaiyah letters with special punctuation. Currently, learning hijaiyah letters still uses simple media in the form of books, posters, display boards, etc. so it is less interesting. The rapid development of technology allows mobile devices to become smartphones that can be used as learning media. Therefore, mobile devices can be used for learning hijaiyah letters to make them more attractive. One technology that can be utilized is Augmented Reality which can combine the virtual world with the real world in the form of 3D through applications accessed on mobile devices. This research developed the introduction of hijaiyah letters equipped with punctuation and pronunciation using marker based augmented reality. The development of mobile application applies the Mobile Application Development Lifecycle (MADLC) method. The development of augmented reality applications utilizing Blender, Vuforia and Unity 3D Game Engine. The results of the Black box testing show that all functional requirements have been met and are running well.
Improving Performance of RNN-Based Models With Genetic Algorithm Optimization For Time Series Data

Stock price data or similar time series data can be used to carry out forecasting processes using past data. The method that can be used is like a neural network, one type of neural network that is used is the Recurrent Neural Network. When using the Recurrent Neural Network (RNN) method, we need to determine the appropriate parameters in order to get the best forecasting results. It takes experience or . In this study, this problem can be solved using optimization algorithms, such as Genetic Algorithms. With genetic algorithms, neural networks can be trained to get the best objective function. So that after implementing the RNN which was optimized using the Genetic Algorithm on stock time series data, when the trial was carried out without optimization the Genetic Algorithm got an RMSE value of 0.108, after being combined using the genetic algorithm it got an RMSE value of 0.106.