Reviewerly/exHarmony huggingface dataset:
Entrepreneurship and Innovation in Africa

Entrepreneurship and innovation are pivotal for Africa’s economic progress. However, the continent faces a dearth of investment, with the existing venture capital (VC) majorly channeled towards fintech, sidelining other sectors. Despite the growing VC interest, African entrepreneurs grapple with barriers such as high startup costs, restrictive capital requirements, limited access to financing, regulatory issues, and concerns about bribery of government officials and low trust in governance. Institutional hindrances, the prevalence of informal businesses, management quality issues, and concentrated firm ownership further exacerbate the innovation deficit. Conversely, Africa shows promise with expanding VC investments, budding fintech sectors that can bolster financial inclusion, such as mobile-money platforms, and a surge in female entrepreneurship. Moreover, the continent is becoming a center for financial technology innovations and boasts a rising presence of startup accelerators and incubators, which offer pivotal support to early-stage ventures. This chapter offers a comprehensive look at these facets, intertwining entrepreneurship and innovation in Africa, addressing challenges, and highlighting opportunities and policy-related questions for future exploration.
Sistem Pendukung Keputusan Pemilihan Supplier Bahan Baku Daging Menggunakan Metode Weighted Aggregated Sum Product Assesment (Waspas) (Studi Kasus : Kawasan Kuliner Sekar Mataram)

Individuals or businesses that supply or sell raw materials to other parties, whether individuals or businesses, so that they can be transformed into finished goods or services are referred to as suppliers. Suppliers must also make raw materials available to those who require them. Furthermore, the supplier must ensure that the raw materials are delivered to the buyer in good condition. The Sekar Mataram Culinary Area is located in Bantul City, Yogyakarta, specifically in Bangunjiwo Village. It is one of the business units owned by Village Owned Enterprises in Mbangun Kamulyan Bangunjiwo Village, which is growing and has received many offers from suppliers to supply raw meat materials to the Sekar Mataram Culinary Area.&#x0D; So far, the Sekar Mataram Culinary Area management has chosen raw meat suppliers through manual selection. Using the Weighted Aggregated Sum Product Assessment (WASPS) method, a Decision Support System will be built to determine the best raw material supplier and meet the restaurant's needs. This study requires criteria and alternatives to obtain a solution for making decisions on raw material supplier selection. This Decision Support System is web-based, making it more effective and efficient.
Human Pose Estimation Based on Improved HRNet Model

There exist some issues such as occlusions, variable human body poses, complex backgrounds in the human pose images, so there are still challenges in the task of human body pose estimation. By adding a new attention mechanism module and reweighting the last feature maps by the original HRNet, We propose an improved HRNet model. The ability of the model is enhanced to learn spatial and semantic information. The experiments on the COCO dataset and MPII dataset show that our model could detect some key points that are missed or detected incorrectly by the original network, and the accuracy is also increased.
Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing

Previous research has shown that the design of Meaning Representation (MR) greatly influences model performance of a neural semantic parser.Therefore, designing a good MR is a long-term goal for semantic parsing.However, it is still an art as there is no quantitative indicator that can tell us which MR among a set of candidates may have the best final model performance.In practice, in order to select an MR, researchers often have to go through the whole training-testing process for all MR candidates, and the process often costs a lot.In this paper, we propose a data-aware metric called ISS (denoting incremental structural stability) of MRs, and demonstrate that ISS is highly correlated with model performance.The finding shows that ISS can be used as an indicator for designing MRs to avoid the costly training-testing process.
CSECU-DSG @ Causal News Corpus 2022: Fusion of RoBERTa Transformers Variants for Causal Event Classification

Identifying cause-effect relationships in sentences is one of the formidable tasks to tackle the challenges of inference and understanding of natural language.However, the diversity of word semantics and sentence structure makes it challenging to determine the causal relationship effectively.To address these challenges, CASE-2022 shared task 3 introduced a task focusing on event causality identification with causal news corpus.This paper presents our participation in this task, especially in subtask 1 which is the causal event classification task.To tackle the task challenge, we propose a unified neural model through exploiting two fine-tuned transformer models including RoBERTa and Twitter-RoBERTa.We perform score fusion through combining the prediction scores of each component model using weighted arithmetic mean to generate the probability score for class label identification.The experimental results showed that our proposed method achieved the top performance (ranked 1st) among the participants' systems.
Pengaruh body shaming terhadap body image remaja perempuan

Body shaming is increasingly happening among teenagers. Body shaming is the behavior of negative comments to others or oneself starting from body shape, skin color and condition. Body shaming can lead to body image disturbance, namely negative body image. Researchers suspect that body shaming is one of the causes of a negative body image. Using the quantitative approach, data collected through a body shaming and body image scale with the accidental sampling technique, totalling 355 respondents with the provision that the respondents are teenage girls who have or are currently experiencing body shaming. Data analysis used simple linear regression analysis. The study results showed a sig value of 0.00 &lt;0.05 and an R-value of 0.080, therefore, it was proven that there was an effect of body shaming on adolescent girls' body image, and 100% of respondents had a negative body image. Keywords : Body shaming, body image, teen girl
Differentiating mental models of self and others: A hierarchical framework for knowledge assessment.

Developing an accurate model of another agent's knowledge is central to communication and cooperation between agents. In this article, we propose a hierarchical framework of knowledge assessment that explains how people construct mental models of their own knowledge and the knowledge of others. Our framework posits that people integrate information about their own and others' knowledge via Bayesian inference. To evaluate this claim, we conduct an experiment in which participants repeatedly assess their own performance (a metacognitive task) and the performance of another person (a type of theory of mind task) on the same image classification tasks. We contrast the hierarchical framework with simpler alternatives that assume different degrees of differentiation between mental models of self and others. Our model accurately captures participants' assessment of their own performance and the performance of others in the task: Initially, people rely on their own self-assessment process to reason about the other person's performance, leading to similar self- and other-performance predictions. As more information about the other person's ability becomes available, the mental model for the other person becomes increasingly distinct from the mental model of self. Simulation studies also confirm that our framework explains a wide range of findings about human knowledge assessment of themselves and others. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method

Unmanned Aerial Vehicles (UAVs) proliferate quickly and play a significant part in crucial tasks, so it is important to protect the security and integrity of UAV communication channels. Intrusion Detection Systems (IDSs) are required to protect the UAV communication infrastructure from unauthorized access and harmful actions. In this paper, we examine a new approach for enhancing intrusion detection in UAV communication channels by utilizing the Long Short-Term Memory network (LSTM) combined with the Synthetic Minority Oversampling Technique (SMOTE) algorithm, and this integration is the binary classification method (LSTM-SMOTE). We successfully achieved 99.83% detection accuracy by using the proposed approach and the Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset 2017 (CICIDS2017) dataset. We demonstrated the efficiency of LSTM-SMOTE in defending UAV communication channels against possible attacks and bolstering the overall security posture through the use of a real-world scenario.
A Comprehensive Approach to Evaluating Software Code Quality Through a Flexible Quality Model

The rapid growth of the software engineering sector has led to a detrimental effect on the quality of software being developed. Code quality is crucial in determining the overall quality of software however, it is often observed that quality management programs primarily focus on internal processes within organizations, while the importance of code quality lacks proper attention despite the existence of quality standards for software products and processes. Due to its dynamic nature, the concept of quality poses a challenge in terms of precise definition, however, this paper addresses this issue by providing a comprehensive definition for code quality that considers all its dimensions, thus laying the foundation for conducting research related to quality. Code quality encompasses factors such as readability, scalability, performance, and adherence to industry standards. High-quality code is easy to understand, modify, and test, making it more reliable and less prone to bugs. By considering the multitude of challenges that currently exist and acknowledging the criticality of code quality, this study proposes an approach for assessing code quality, and a comprehensive quality model that considers the most critical code quality attributes and their relevant metrics along with corresponding threshold values specifically use in the contemporary software industry.
Sentiment Reason Mining Framework for Analyzing Twitter Discourse on Critical Issues in US Healthcare Industry

This research study employs machine learning and textual analysis techniques to examine the US healthcare system through the analysis of Twitter data. By leveraging domain-specific keywords and hashtags, a customized data collection algorithm is utilized to gather a substantial dataset of tweets related to #medicaid and Medicaid. The collected tweets undergo a comprehensive analysis using sentiment analysis, sentiment spike detection, keyword extraction, k-means clustering, topic modeling, and textual association. The study aims to extract insights and identify critical issues hindering access to quality healthcare. The findings have implications for marketing strategies, enabling companies to better align their offerings with customer needs. Additionally, policymakers and healthcare organizations can benefit from the insights gathered, gaining valuable knowledge about the public’s concerns, preferences, and satisfaction with US healthcare services and systems. By employing machine learning and textual analysis techniques, this research contributes to a deeper understanding of public sentiment and provides data-driven insights to address challenges in the healthcare domain.
Using blockchain to improve data management in the vINCI Platform

Ensuring a high level of security and privacy for medical data is the primary task of e-health systems. From the point of view of the EU General Data Protection Regulation, medical data is considered to be sensitive data, as they are one of the main cybercrime targets due to the concentration of confidential personal information such as identity, contact details, billing data, and medical history. The paper presents the multi-module system for collecting and processing medical data, with the blockchain platform used to ensure a higher level of security and confidentiality of these data in the vINCI platform. The developed blockchain module ensures the confidentiality and high security of medical data. Hyperledger Fabric is the basis of the created blockchain platform, which contributes to the management of information access and permissions for collecting health data in the vINCI platform. Also, the integration concepts of the two platforms, implementation considerations, and initial validation results are presented.
Lightweight cryptographic secure random number generator for IoT devices

In recent years IoT devices have started to be used in more and more applications. With their increasingly frequent use, various problems have arisen in ensuring the security of the data they transmit. Many of these can be ensured using various cryptographic primitives whose efficiency can only be ensured by using appropriate random number generators. In this paper, we propose a cryptographically secure random number generator solution, based on an entropy source, that uses data collected from motion sensors and a lightweight cryptographic primitive. The proposed random number generator ensures the property of enhanced forward secrecy and has been evaluated from the point of view of entropy and randomness using methodologies proposed by NIST standards. The performance analysis highlighted the fact that the proposed solution can be implemented on microcontrollers that have limited resources, obtaining higher generation speeds and power consumption than the DRBG_CTR solution, proposed by NIST.
Building a personalised education system in conditions of a rural basic secondary school

The article considers the prerequisites for the construction of personalized educational systems in rural primary schools. Modern conditions force the rural school to reconsider the main tasks of its activities, primarily related to the professional mobility of students, which is directly expressed in a conscious and responsible choice of their future profession. In this regard, the conditions and possibilities of the personalized education system (PSV) in the professional orientation of high school students of rural primary school are analyzed. The necessity of the use of PSV in the formation of professional self-determination of schoolchildren in the conditions of rural basic secondary school is identified and justified. The practical experience of cooperation between the school and the family is summarized, which allows, on the basis of an individually differentiated approach, to build the interaction of PSV participants in the plane of professional self-determination. The experience of pedagogical interaction of schools of the Klin city district of the Moscow region in the construction of PSV is presented. The approbation of methods and means of building a PSV, implemented in the conditions of a rural primary school, was carried out. The specifics of the conditions of the main rural school, favorable to the creation of an environment in which personalized educational systems will be designed and implemented, are summarized. Based on the data obtained, conclusions are drawn about the dependence of the probability of participation of high school students in building their own personalized educational system in a rural primary school on the level of educational potential of their family.
Using Haze Level Estimation in Data Cleaning for Supervised Deep Image Dehazing Models

Recently, supervised deep learning methods have been widely used for image haze removal. These methods rely on training data that are assumed to be appropriate. However, this assumption may not always be true. We observe that some data may contain hazy ground truth (GT) images. This can lead to supervised deep image dehazing (SDID) models learning inappropriate mapping between hazy images and GT images, which negatively affects the dehazing performance. To address this problem, two difficulties must be solved. One is to estimate the haze level in an image, and the other is to develop a haze level indicator to discriminate clear and hazy images. To this end, we proposed a haze level estimation (HLE) scheme based on dark channel prior and a haze level indicator accordingly for training data cleaning, i.e., to exclude image pairs with hazy GT images in the data set. With the data cleaning by the HLE, we introduced an SDID framework to avoid inappropriate learning and thus improve the dehazing performance. To verify the framework, using the RESIDE data set, experiments were conducted with three types of SDID models, i.e., GCAN, REFN and cGAN. The results show that our method can significantly improve the dehazing performance of the three SDID models. Subjectively, the proposed method generally provides better visual quality. Objectively, our method, using fewer training image pairs, was capable of improving PSNR in the GCAN, REFN, and cGAN models by 3.10 dB, 5.74 dB, and 6.44 dB, respectively. Furthermore, our method was evaluated using a real-world data set, KeDeMa. The results indicate that the better visual quality of the dehazed images is generally for models with the proposed data cleaning scheme. The results demonstrate that the proposed method effectively and efficiently enhances the dehazing performance in the given examples. The practical significance of this research is to provide an easy but effective way, that is, the proposed data cleaning scheme, to improve the performance of SDID models.
STUDY OF THE ELECTRONIC INFORMATION SYSTEM (SEI) IN A MULTICAMPI HIGHER EDUCATION INSTITUTION, WITH EMPHASIS ON THE PERIOD OF THE COVID-19 PANDEMIC

The objective of this work was to evaluate the importance of using the Electronic Information System (SEI), at ``Universidade Federal de Campina Grande``-UFCG, since the beginning of its implementation at UFCG, with emphasis on the period of the COVID-19 pandemic.It was based on a bibliographical survey, data available in the SEI itself, reports provided by the SEI Support of the UFCG, and the National Electronic Process (PEN).The following were studied: registered units, processes and documents generated at SEI, from 2017 to 2021 during the COVID-19 pandemic.It was found that the SEI/UFCG has 735 units and 30,549 registered users, with 7,072 internal users and 23,477 registered external users.The number of documents generated and received since the implementation of the SEI at UFCG until November 3, 2020, was 1,005,868 digital electronic documents.According to the data analysis, it was estimated that the number of processes registered in 2021 will reach a total of 5,012,552 processes, that is, an increase of 842% in 2021 compared to 2020.It was concluded that SEI /UFCG allowed a great advance in the management of processes and documents in all sectors of the UFCG.The use of SEI at ``Universidade Federal de Campina Grande` `can be considered a decisive tool to mitigate the effects caused by the COVID-19 pandemic, allowing the development of activities remotely in a satisfactory way, thus significantly contributing to curb the spread of the new coronavirus and reduce the number of deaths from COVID-19.
Exploring Research Idea Growth with Litmap

This study presents a method for tracking the growth of research studies in a particular field or topic using a graphical representation of literature review with the help of Libmap. The step-by-step method involves selecting an article or a set of articles that represent the topic of interest and using Libmap to analyze and visualize the relationships between different concepts and ideas. The study starts with the article entitled "The role of mobile learning on the learning environment shifting at high school in Indonesia" written by Sulisworo et al. Libmap serves as a valuable tool for researchers during the process of writing scientific articles, as it allows for visual mapping and tracking of the development of research articles, identification of key articles or authors, exploration of citation patterns and influences, and contextualization of research within the existing literature. The visual representation provided by Libmap can also aid in identifying significant citations, highlighting influential articles, and identifying potential areas for further research. Overall, Libmap can enhance the literature review process, improve the quality of scientific articles, and contribute to the advancement of knowledge in various fields.
Pengembangan Multimedia Interaktif dengan Model Addie Dapat Menumbuhkan Semangat Belajar Peserta Didik Sekolah Dasar

Interactive multimedia is a combination of several types of media that are combined and can be used in propiding explanations related to learning material in the learning process. Many interactive media were created with the goal of creating interactive media so that they can be used in the educational process. In developing interactive multimedia the model used is the ADDIE model (analysis, desaign, development, implementation, and evaluation). In develoving interactive multimedia, the authors chose the ADDIE model because this model is constanty evaluated at each level. When develoving and perfecting a product in interactive multimedia in the form of audio-visual, visual, audio, the ADDIE model can be used. The development of interactive multimedia means that students have high enthusiasm in the learning process.
Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods

Artificial Intelligence is a sub-branch of artificial intelligence used to produce new data or content. These methods can create recent examples in different categorical fields such as natural language processing, image processing, music, and video creation by using models from learning clusters with artificial intelligence (AI) tools in this field. AI tools that can solve real-world problems are also created using different methods apart from generative AI methods. With generative-based artificial intelligence tools, it can facilitate people's work in jobs that require creativity. However, they can offer the opportunity to build advanced models that learn from data with other artificial intelligence methods. In the study, the public dataset has been used. This dataset includes trending artificial intelligence tools, AI methods, and user scores. In this study the working area and user trend of the ai tools in the dataset and the effect of generative AI methods on the development of the tool are discussed. Random Forest and Naive Bayes algorithms from classification methods have been used to measure the impact and estimation. Several AI tools help solve real-life problems. Identifying what type of category is needed for AI tools and method selection are interlinked, and the research provides an overview of this connection.
Development of a postgraduate-management website in a higher-education institution to enhance user experience

The University of Guanajuato keeps a website for postgraduate course administration. However, because the website was created in 2010, it currently has a number of issues that limit the user experience. Among the website's most serious drawbacks, they point out that the website is not responsive, which means that visitors cannot navigate using mobile devices. Furthermore, the administrators believe that the website's design is unattractive and are taking steps to improve its appearance. The goal of this project is to create a website for the University of Guanajuato's postgraduate management that is based on the digital quality evaluation model "e-GovQual," specifically in two dimensions: efficiency and reliability, with the goal of improving the site's usability and, as a result, its performance.