Reviewerly/exHarmony huggingface dataset:
Enhancement of Images Under Low Light Conditions Using Artificial Intelligence

Images taken in low light conditions do not contain all the information well-lit images contain. Various features including the colours of objects, details and the quality are lost. Extracting these features from images is very important for any kind of application of it. This study proposes a model to enhance the features of the image taken under low light conditions, by delivering a solution which improves the quality of the image through Artificial Intelligence. Through the proposed method, the clarity of the image is improved, making it closer to a well-lit image equivalent. Both Image Processing and Deep Learning based techniques are explored, including Convolutional Neural Network (CNN) based generative models. The Generative models considered are Autoencoders (AE) and Generative Adversarial Networks (GANs). The study has been carried out by using several datasets combined together, which include image pairs of well-lit and low light images. A comparison between the two CNN-based generative models is carried out. Through the study, it is quantitatively found, by the Structural Similarity Index and supported by the Peak Signal to Noise Ratio, that the proposed CNN based Autoencoder model overrides the proposed CNN-based GAN model. This is further supported by qualitative observations of the image results. Both models, however, greatly enhance the low light images, bringing to light features that were not visible beforehand, and also provide results with good colour accuracy. Through this research study, the methods and solutions to enhance low light images have been addressed, as well as providing a comparison between two suitable models, Autoencoders and GANs. The proposed solution is able to address many of the limitations existing in the extent literature. KEYWORDS: Autoencoder, Comparative Analysis, Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Image Enhancement
The Impact of Virtual Reality Technology on Student Engagement and Learning Outcomes in Higher Education

Virtual Reality technology has gained prominence in higher education as a potential tool to enhance student engagement and learning experiences. This study examines the impact of Virtual Reality technology on student engagement and learning outcomes in higher education settings. Through a qualitative approach, a systematic review of recent studies between 2013 and 2023 were assessed to uncover how the integration of Virtual Reality technology in various courses affected students' motivation, comprehension, and retention of complex concepts. Preliminary findings suggest that students exposed to Virtual Reality-enhanced learning experiences exhibit higher levels of engagement, improved understanding of abstract subjects, and increased retention rates. This research contributes to our understanding of the potential benefits and challenges of integrating Virtual Reality technology into higher education and provides insights into effective pedagogical strategies for its implementation.
Augmented Reality

Augmented Reality (AR) is the integration of digital information, such as images, videos, or 3D models, into the real-world environment, providing an enhanced perception of reality (Yuen, 2011). By overlaying digital information onto the real world, AR enhances the learning experience and provides students with interactive and immersive educational content (Bower et al., 2014). This article explores the concept of AR and its significance in motivating learners, simplifying complex concepts, and creating a dynamic and inclusive learning environment. Additionally, theories that support the integration of AR in educational technology are discussed, followed by myths related to AR and a glimpse into the future of AR in EdTech.
software quality measurement: A Comprehensive review

The measurement of software quality is an essential component in contemporary software development since it guarantees the dependability and effectiveness of software products. This article offers a comprehensive examination of the importance, aspects, and methodologies employed in the assessment of software quality. The paper commences by providing a comprehensive introduction to software quality and its different dimensions. It subsequently explores a range of measurement methods, which span both process and product metrics, as well as objective and subjective metrics. The literature review part presents a comprehensive categorization of various techniques for measuring software quality, offering a perceptive critique of the existing models. This study provides a comprehensive analysis of the difficulties and advantages associated with the implementation of software quality measurement. It offers unique perspectives on the typical obstacles faced during contemporary software quality evaluation. Moreover, the article showcases real-life applications and case studies that exemplify the practical utilisation of various measuring approaches. Through the examination of these applications, readers can gain a pragmatic comprehension of the implementation of software quality measurement in various contexts. The report finishes by elucidating emerging trends and proposing potential avenues for future research in the domain of software quality monitoring. The primary goal of this study is to examine innovations and identify prospective areas for further inquiry within the field of software quality assessment. The study places particular emphasis on future trends and innovations that have the potential to dramatically impact the domain of software quality assessment.
Enhancing Prediction Accuracy through Normalization Techniques: Practical Insights from MLP Neural Network

Data normalization plays a pivotal role in enhancing the effectiveness of machine learning models across a myriad of applications. This article delves into the influence of various data normalization techniques on the performance of a Multilayer Perceptron (MLP) classifier, a prominent architecture in Artificial Neural Networks (ANN). The study explores the effects of three distinct normalization methods: Min-Max, Z-Score, and Decimal Scaling, supported by the Knime environment. The impact of these normalization techniques on model performance is investigated using a dataset sourced from social networking sites, containing user information relevant to a new SUV model's potential purchase. The research employs a visual representation of confusion matrices and ROC Curve charts to assess the outcomes of each normalization technique. Findings reveal that without normalization, the model's performance is suboptimal, with low accuracy and high error rates. In contrast, the application of normalization techniques significantly improves model performance. Notably, Z-Score normalization emerges as the most effective, achieving an impressive 92.5% accuracy with a reduced error rate. This study underscores the critical role of data normalization in machine learning and provides valuable insights into the choice of appropriate techniques for different datasets. Such insights can lead to more robust and reliable results, ultimately enhancing the quality and reliability of machine learning applications.
The Impact of Model Capacity on Performance via Weight Pruning: A Study Using the Food-11 Dataset

Deep learning models, particularly convolutional neural networks, have exhibited outstanding performance across a wide array of complex tasks and applications. Although CNNs have proven invaluable in numerous image-related tasks, they come with high computational demands and memory requirements, limiting their deployment in resource-constrained environments, such as mobile devices and embedded systems. Addressing this challenge, model size reduction, and weight pruning have emerged as essential strategies for optimizing model performance and efficiency. This study investigates the relevance of employing high-capacity models and the impact of weight pruning in the context of a specific dataset, the Food-11 dataset and the DenseNet121-based model. Weight pruning is the focal point of this study, with a specific focus on percentage-based pruning. Results show a trade-off between model accuracy and size as pruning percentages increase. While some accuracy loss is observed, the reduction in model size and evaluation time is substantial, making it a compelling strategy, especially in resourceconstrained scenarios. This study underscores the potential benefits of weight pruning as a means to enhance the efficiency and applicability of deep learning models. It highlights the importance of finding the right balance between model compression and performance, ultimately enabling the deployment of deep learning solutions in real-world applications with limited computational resources.
The Role of Explainable Artificial Intelligence in Diagnosing and Mitigating Potential Data Breach Risks in Healthcare

In this study, the impact of Explainable Artificial Intelligence (XAI) approaches on the source detection of potential data breaches and their role in mitigating risks are investigated. The input features of datasets containing cyber-attacks and potential threats were analyzed and their potential to detect threats was evaluated. It is known which types of cyber-attacks are realized with which input features in these datasets. As a result of the evaluations obtained, it is seen that the most needed input features in mitigating attacks on healthcare services are the attacker's targets, vulnerable network assets information, and violation points of security policies. As a result, it is predicted that XAI applications can play an effective role in reducing potential risks and data leaks in patient care applications by interpreting how dataset characteristics affect decisions in artificial intelligence models.
FACTORS OF ENSURING THE IMPLEMENTATION OF A PROJECT-BASED CREATIVE TRAINING METHODOLOGY IN A CREDIT-MODULAR SYSTEM

Independent work of a student is one of the most important components of the educational process when applying a project-based creative teaching methodology, during which knowledge, skills, and abilities are formed and further, the student's assimilation of cognitive activity techniques, interest in creative work and, ultimately, the ability to solve technical and scientific problems are ensured.
Cognitive Load Theory

Cognitive Load Theory (CLT), formulated by John Sweller, describes how working memory processes information and includes three types: intrinsic, extraneous, and germane. Each type of cognitive load plays a crucial role in educational technology and instructional design, and by minimizing extraneous cognitive load and promoting germane cognitive load, educators can enhance learning effectiveness. CLT has become widely recognized as an influential framework in educational research, guiding instructional practices and fostering continuous improvement in designing effective and engaging learning experiences for students.
Research on Robot Path Planning Based on Simulated Annealing Algorithm

Taking the path planning of inspection robots as the research object, a shortest path planning method based on simulated annealing algorithm was proposed. By analyzing the conditions of the shortest path generation, the mathematical solution model of the problem was established, and the global search strategy was formulated. Finally, the shortest path of the robot was solved through MATLAB software programming. The correctness and effectiveness of the algorithm are verified by a large number of examples. In addition, in view of the low efficiency of the traditional simulated annealing algorithm to solve the large-scale shortest path problem, the output result is unstable, and the path is easy to cross, a new solution is constructed in the form of random coordinate exchange. The results show that the shortest path output of this method not only has less crossover, but also the operation efficiency is obviously improved, and the result is more stable.
Impact of Training on the Performance of Front-line Sales Officers of private sector banks

In the dynamic landscape of the banking industry, front-line sales officers play a pivotal role in acquiring and retaining customers. This research paper aims to investigate the impact of training on the performance of front-line sales officers in private sector banks. The study explores the methods, content, and duration of training programs and their effects on the employees' performance, customer satisfaction, and overall success of the banks. The research employs a mixed-methods approach, incorporating surveys and interviews with front-line sales officers, managers, and customers, as well as a comprehensive review of relevant literature. The findings offer valuable insights for private sector banks seeking to enhance their sales teams' performance and competitive edge.
Reform Practice of Front-end Development Curriculum System Oriented by Occupational Skills

The field of front-end development has always been one of the highly regarded and in-demand occupations in the field of information technology. In the digital age, almost every business and organisation needs to have great websites and applications, which makes the skills of front-end developers more and more in demand. However, the rapid growth and constant evolution of the front-end development field means that traditional curriculums can lag behind market demand. In order to cultivate front-end developers with practical application skills, the front-end development curriculum system needs to be constantly reformed and upgraded. This paper analyses the necessity of reforming the front-end development curriculum system oriented to vocational skills, and explores the strategy of reforming the front-end development curriculum system oriented to vocational skills.
Validating the Digital Competence (Dig-Comp 2.1) Framework in Higher Education Using Confirmatory Factor Analysis: Non-Western Perspective

This Pilot study aimed at validating the Digital Competence (Dig-Comp 2.1) Framework instrument within the context of a non-Western developing country. With the rapid advancements in technology and the increasing integration of digital tools in various domains, evaluating learners’ digital competencies is crucial to ensure the effective utilisation of digital resources in educational settings. However, existing frameworks often lack validation in developing and non-western country contexts. Therefore, this study addresses this gap by assessing the reliability and validity of the Dig-Comp 2.1 Framework Questionnaire in a higher education context, involving a sample of ninety-nine (99) university students from Uganda. By implementing a quantitative design, we collected data through an online survey administered to the selected student samples. The analyses employed confirmatory factor analysis (CFA) and confirmatory composite analysis (CCA). The findings indicated that the Dig-Comp framework portrayed an excellent fit with pilot data as indicated by various model fit indices through CFA. Moreover, the CCA demonstrated satisfactory reliability and validity of the Dig-Comp 2.1 framework, suggesting that the instrument’s measures are appropriate and reliable for future investigations. The results of this study can contribute to enhancing the understanding of digital competence and its measurement in non-western countries, thereby informing policies and interventions to bridge the digital divide.
Efektivitas Pembelajaran Daring Pada Mata Kuliah Bahasa Inggris Di Masa Covid 19 Dalam Meningkatkan Kemampuan Mahasiswa Jurusan Teknik Mesin STTI Bontang

During the current Covid-19 pandemic, schools are required to carry out distance learning. This learning is carried out using online learning media such as E-learning, Google Classroom, Zoom, and others. The objectives of this study are (1) to add insight into the patterns of distance learning during the Covid-19 pandemic, (2) to find out the problems in distance learning during the Covid-19 pandemic, (3) to know the methods or alternative learning assessments, especially in courses English. This article was prepared using a quantitative method by collecting data through observation and interviews. The effect of doing distance learning raises many obstacles that hinder the process of teaching and learning activities. The results of the study show that not all students are ready to take part in distance learning. Starting from media that is less supportive, there is still minimal internet network and uneven quota assistance from the government. As a result of the ineffectiveness of distance learning during the Covid-19 pandemic, students did not understand the learning material provided by educators.
MENINGKATKAN KEMAMPUAN MENGENAL HURUF MENGGUNAKAN MEDIA KARTU HURUF PADA ANAK KELOMPOK B TK AL IHSAN KECAMATAN SUNGAI TABUK

This research is motivated by facts in the field which show that the ability to recognize letters in Group B of Al Ihsan Kindergarten is still lacking. Learning media available to introduce letters is still lacking, only using blackboards and LKA . The fun learning process to introduce letters is lacking and monotonous. In terms of language, especially the ability to recognize letters in group B children at TK Al Ihsan is still low. The low ability to recognize letters of kindergarten children will of course have a negative impact on those concerned. This impact will be greatly felt entering elementary school. So the researchers tried to apply the use of letter card media to improve the ability to recognize children's letters. The formulation of the problem in this study is how are children's learning activities in recognizing letters using letter card media? and how to improve children's learning outcomes in recognizing letters by using letter card media? The purpose of the research conducted was to find out an overview of the application of letter card media in improving the ability to recognize letters in group B children of Al Ihsan Kindergarten. The type of research used is classroom action research (CAR). The subjects in this study were the children of Al Ihsan Kindergarten Group B1 for the 2020/2021 academic year, which consisted of 10 children with 4 boys and 6 girls. Based on the research results obtained, it shows that the ability to recognize letters in group B1 children has increased. This is evidenced by the results of the achievement of child development which has increased in each cycle, namely (1) in cycle I, the achievement of child development is 70%. In cycle II, the achievement of child development was 90%. Thus, it can be concluded that the use of letter card media can improve the ability to recognize letters in group B1 children
Upaya Mengembangkan Kemampuan Kognitif Anak dalam Mengenal Lambang Bilangan 1-10 Menggunakan Media Loose Part Melalui Kegiatan Bermain Variatif pada Anak Kelompok A TK Teratai Birayang

The background of this research is that children's ability to recognize number symbols 1-10 is still low due to the lack of varied teacher and media approaches. If this problem is not solved, it will have a low impact on knowing numbers and children will experience difficulties in their cognitive aspects. The purpose of this study was to find out the teacher's activities, children's activities, and their developmental achievements in the cognitive aspect in recognizing the number symbols 1-10 through various play activities using loose part media as a model in learning.&#x0D; This research was conducted using a qualitative research approach in the type of Classroom Action Research (CAR) with research subjects of class A children at Teratai Birayang Kindergarten, totaling 12 children. The type of research data is qualitative research where the data collection method is obtained from observation sheets of teacher and child activities while quantitative research methods for collecting data are obtained from children's learning outcomes sheets. Data analysis was descriptive qualitative and cross-tabulation with indicators of teacher activity achieving a score of ≥ 27 in the "Very Good" category, children's classical activity with a score of ≥ 76 in the "Very Active" category, and the child's development results at least got more than or equal to three stars.&#x0D; From the results of the study it was concluded that using loose part media through these varied playing activities can develop children's ability to recognize the symbols of numbers 1-10. For school principals it is suggested to be able to provide guidance to all teachers, for teachers it is suggested to be able to practice the combination of models that researchers do at school and for other researchers it is suggested to be able to develop a combination of these models.
Implikasi dan Pranggapan proses Negosiasi di Pasar Setonobetek Kota Kediri serta Penerapanya menurut Teks Negosiasi sebagai Bahan Ajar di SMAN 7 Kota Kediri

As social humans, we cannot do without communication activities. The purpose of this study is to find out how presumptions and verbal cues are used in community negotiations in the Setonobetek market, Kediri City for high school textbooks. The research method used is descriptive and qualitative. data collection and observation, interviews, recording and independent listening skills, engaging in dialogue and note-taking skills, and data analysis methods Myers and Huberman. The results of this study are (1) Speech presuppositions in the Setonobetek market negotiations, Kediri City, namely. Latent presuppositions inform the existence of a person, factual presuppositions inform an event, structural presuppositions of the unmentioned recipient are understandable, and lexical presuppositions indicate sentences. It refers to certain elements after the verb and the question mark. Presets and Non-factual Presets No counterfactuals were found in the negotiations at the Setonobetek market in Kediri, and (2) verbal implications in the negotiations at the Setonobetek market in Kediri. The Setonobetek Market of Kediri City is a traditional meaning that is universal so it is easy to understand. conversational meaning Provide temporary meaning, (3) use the results of presuppositions and analysis of meaning as teaching materials. The text in the form of handouts is based on core competencies and basic competencies in the 2013 syllabus. Conclusion There are Verbal Presumptions and Suggestions in Negotiations in the Setonobetek Market Community, Kediri City as a Textbook High School Student
Analisis Tokoh Utama Dalam Novel Konspirasi Alam Semesta Karya Fiersa Besari: Kajian Psikologi Sastra

This study aims to describe the psychological conflict, psychological aspects, and the factors that influence the psychological aspects of the main character Juang. The results show that the conflict experienced by Juang's character is an external conflict which is divided into conflicts between humans. The conflict is based on the stigma that says that Juang's family is someone traitor to the nation, the internal conflict that Juang experienced was when his friends were busy teasing Nadiya while she was reading a book so she felt disturbed and chose to separate herself from the real world and continue reading her book. The psychological aspect of fighting based on Sigmund Freud's theory works perfectly. The understanding of the id is the most basic personality system, the system that is innate instincts lives with instinctive desires. The fighting ego cannot fulfill the id then the superego works less than perfect and cannot control the id's instinctive desires. Broadly speaking, Juang Juang's psychological turmoil is divided into 2, namely, (1) Factors that influence Juang to be strong in living life as an idealist, including competence motives, love motives, values motives, and attitudes. (2) Factors that influence Juang's feelings include curiosity motives, cultural factors and ecological factors.