Reviewerly/exHarmony huggingface dataset:
The effect of kindergarten principals’ guidance for curriculum and early childhood teachers’ teaching creativity on early childhood teachers’ play support competency

The purpose of this study is to analyze how kindergarten principals’ guidance for curriculum and early childhood teachers’ teaching creativity affect early childhood teachers’ play support competency. To this end, a survey was conducted on 212 early childhood teachers working in kindergartens and daycare centers located in S, I-city, and G province. To validate the reliability of the measurement tools used in this study, Cronbach's α was calculated, and pearson's correlation analysis and multiple regression analysis were conducted. The results of this study are as follows. First, kindergarten principals’ guidance for curriculum and early childhood teachers’ teaching creativity all had a statistically significant positive correlation with early childhood teachers’ play support competency. Second, teachers’ teaching creativity on teachers’ play support competency showed that teachers’ teaching creativity had a significant positive influence on teachers’ play support competency, while the relative influence of the principal's guidance on educational and childcare curriculum recognized by early childhood teachers was not statistically significant.
Analisis Manajemen Keuangan di UPT Sekolah Dasar Negeri 234 Gresik

This research discusses financial management at UPT SDN 234 Gresik, to extract funds, manage School Operational Assistance (BOS) funds, make reports and financial accountability, and conduct supervision. This study used a qualitative descriptive method. Primary data was obtained through interviews with school treasurers, and additional data from literature studies. Thus, the focus of this research is on the financial management aspect of the school. The research results show that this educational institution implements financial principles involving fairness, efficiency, transparency, and public accountability in managing its financial resources. The school's funding cycle includes steps such as sourcing funds, planning, supervision, and financial reporting. The use of the ARKAS application helps improve transparency and efficiency, although there are still challenges related to reliance on a stable internet connection. This research provides valuable insights for stakeholders in the world of education, efforts to improve the quality of education in the school environment are important.
Memahami Teori Pembelajaran Kognitif dan Konstruktivisme serta Penerapannya

Cognitive theories of learning have had a significant impact on modern education. It focuses on individual cognitive development, emphasizing creativity, deep understanding and meaningful learning. In its implementation, several learning models such as constructivism, discovery learning and meaningful learning become relevant. The advantages of cognitive theory include encouraging students' creativity, developing individualized knowledge, flexibility in learning, and improving memory. However, there are also drawbacks, such as lack of comprehensiveness at all levels of education and limitations in addressing individual student differences. This article summarizes the main concepts of cognitive theory in learning, and identifies the advantages and disadvantages associated with its implementation. A better understanding of this theory can help educators create a more effective learning environment that suits the diverse needs of students.
A Novel Hyperparameter Optimized Quantized MobileNet Architecture for Improving Diagnostic Accuracy in the Medical Image Classification

In recent years, there is a remarkable use of smartphones in the field of computer vision. Deep convolutional neural networks being the backbone of computer vision has forced the DCNN to mitigate from heavyweight network to lightweight network. Smartphones are resource constrained devices with limited storage and processing capabilities. DCNN, a heavyweight network, cannot be implemented directly on such devices. We require a lightweight version for smartphones that meets the desirable properties such as small model size and acceptable accuracy. DCNN can be compressed using the neural network quantization process. But the quantization process introduces quantization error that degrades the accuracy. In this paper we propose a novel hyperparameter optimization of quantized deep convolutional architecture that mitigates quantization loss while maintaining the competing accuracy. The performance of HPOQ-MobileNet is tested and verified for image classification tasks on Diabetic Retinopathy dataset. The accuracy of the baseline model is 83.81% and that of proposed HPOQ-MobileNetv2 architecture is 82.9%. The inference accuracy of fine-tuned Mobilenetv1 is 81.8% and that of proposed HPOQ-MobileNetv1 model is 80.3%. The proposed architecture shows optimized accuracy and effective resource utilization by reducing the memory footprint by 4×, and quantization loss approximately by 1%. Quantization simulation shows that the model can be ported to resource constrained devices.
USE OF KAHOOT! AND JAMBOARD FOR THE REALIZATION AND ORGANIZATION OF CREATIVE SCHOOL ACTIVITIES DURING THE COVID-19 PANDEMIC

This mixed study analyzes the students’ perception about the use of Kahoot! and Jamboard in the teaching–learning process about the English language during the COVID-19 pandemic through data science. Kahoot! is a web tool that facilitates and promotes the participation between the educator and students through the realization of digital games and Jamboard is a virtual wall that allows the active role through the dissemination of information and exchange of ideas. The participants are 30 students of Design and Visual Communication who took the English Language course in the distance modality at the National Autonomous University of Mexico, Mexico, during the 2020 school year. The results of the machine learning technique indicate that the realization of digital games in Kahoot! and the dissemination and exchange of information in Jamboard positively influence the motivation, assimilation of knowledge and participation of the students during the Verbs Unit. Data science identified 6 predictive models on the use of these technological tools in the educational process about the English language through the decision tree technique. Finally, educators have the opportunity to create new virtual spaces and promote the active role of the students by incorporating Kahoot! and Jamboard in the school activities during the COVID-19 pandemic.
Optimizing cloud resource allocation: A long short-term memory and DForest-based load balancing approach

Load balancing is an element that must exist for a cloud server to function properly. Without it, there would be substantial lag and the server won’t be able to operate as intended. In a Cloud computing (CC) establishing, load balancing is the process of dividing workloads and computer resources. The distribution of assets from a data centre involves many different factors, including load balancing of workloads in cloud environment. To make best use each virtual machine’s (VM) capabilities, load balancing needs to be done in a way that ensures that all VMs have balanced loads. Both overloading and underloading are examples of load unbalance, and both of these types of load unbalance could be fixed by using techniques created especially for load balancing. The research that has been done on the subject have not attempted to take into account the factors that affect the problem of load unbalancing. Results indicate that the LSTM and DForest-based load balancing approach significantly improves cloud resource utilization, reduces response times, and minimizes the occurrence of overloading or underloading scenarios. To effectively design those programmes, it is essential to first understand the advantages and disadvantages of current methodologies before developing efficient AI-based load balancing programmes. Compared to existing method the proposed method is high accuracy 0.98, KNN accuracy is 0.97, SVM accuracy is 0.99, and DForest accuracy is 0.987.
Machine Learning Can Outperform Ann Arbor Staging in Predicting Survival in Patient with Diffuse Large B-Cell Lymphoma: Analysis of a Large National Cancer Database

Introduction: Diffuse Large B-cell Lymphoma (DLBCL) is the most common lymphoma in the world with usually an aggressive clinical course. The Ann Arbor staging system and International Prognostic Index (IPI) commonly utilized in clinical practice for risk stratification have known limitations. Machine learning (ML) has emerged as a promising tool for more comprehensive and deeper data analysis. We sought to utilize the ability of ML to predict survival in DLBCL compared to Ann Arbor staging system using a large national database. Methodology: We employed the ML algorithm XGBoost on the National Cancer Institute's Surveillance, Epidemiology and End Result (SEER) database to predict overall survival (OS) and the lymphoma specific survival (LSS). For prediction analysis, we transformed the survival labels into a simple Boolean format: “alive” represented as 0, “dead” as 1, and “dead (attributable to this cancer diagnosis)” also as 1. We utilized one-hot encoding to convert categorical features and variables into binary vectors. The data set was divided into two parts: training (80%) and test (20%). Further, we split the training set into the actual training set and validation set by using stratified 5-fold cross validation. Hyper-parameter optimization was done within the validation set. A broad range of attributes were utilized by the model for its prediction algorithm. To understand how each attribute contributes to predictions, we calculated its importance score in XGBoost. Results: A total of 64,912 patients with DLBCL were found and their data were extracted. The majority were Caucasian (78.9%) with a median age range of 60 to 69. The model was able to predict OS and LSS, with an area under the curve (AUC) of 0.89 and 0.75 (Figure 1), respectively. Factors selected by the model for survival prediction included presence or absence of B-symptoms, treatment status, and disease stage. For OS and LSS, the model found B symptoms to be the highest contributing factor with an importance score of 0.205 and 0.167, respectively. Other important factors incorporated by the model included age and stage IV for OS, and stage IV and clinically asymptomatic status for LSS. The least important factors were location of the primary lymphoma site and year of diagnosis (Table 1). Conclusion: Machine learning tools can help predict survival in patients with DLBCL and able to challenge current staging systems. Our results warrant validation in future prospective studies.
Qualidade de Software para Engenheiros de IA: Um Estudo Inicial da Realidade Brasileira

This article presents an analysis of the Brazilian reality regarding software quality for Artificial Intelligence (AI). The study seeks to investigate the software quality assurance strategies adopted during the lifecycle of AI/ML components, covering the development, integration and maintenance phases. We conducted a survey with 40 participants, and the results indicate that the Brazilian industry faces several quality issues in the development of AI systems, including accuracy, performance, interpretability and scalability. In addition, challenges related to maintainability, documentation, reuse and code quality are identified.
Movie Recommendation System Research Based on Improved DeepFM

In recent years, the application of deep learning in many fields has gradually increased, and the practice in the field of information recommendation has also achieved excellent consequence. DeepFM model is used to share input data between wide and deep parts, it does not need artificial feature engineering, which can handle the advantages of low and high-order features simultaneously. In this paper, the hidden layer of DeepFM model is improved to solve the problem of gradient vanishing and gradient explosion in deep neural network effectively, and the improved algorithm model is verified by experiments on the MovieLens dataset. The improved model achieved a 1.29% increase in AUC indicator, and reduced the deviation between the predicted value and the true value of the model to a certain extent. Practice has proved that the improved model solves the problem of sparse film user rating data in the movie recommendation scenario, the accuracy of movie recommendation has been improved indirectly.
PENGEMBANGAN MEDIA PEMBELAJARAN BONEKA TABLET UNTUK MENINGKATKAN KEMAMPUAN MEMBACA PADA SISWA KELAS I SEKOLAH DASAR NEGERI 27 GERNIS

This study aims to develop tablet puppet learning media to improve reading skills in first grade elementary school students. The research was conducted using the Research and Development method with the ADDIE development model through the analyze, design, development, implementation and evaluation stages. The research was conducted at 27 Gernis Public Elementary School in grade 1 with 70 participants. The tablet dummy media developed was validated by 2 material experts with an average score of 4.80 in the "valid" category, 2 design experts with an average score of 75 in the "valid" category, and 2 media experts with an average score of 4.86 in the "valid" category. ”. The results of the analysis were carried out by empirical tests including individual tests, small group tests, and large group tests each showing a "very good" attitude in using tablet puppet learning media. The test for the effectiveness of media use was carried out by a gain score test with a value of 0.71 in the "High" category. Based on the results of this analysis, it can be concluded that tablet puppet learning media can be applied to improve reading skills in grade 1 elementary school students.
Understanding and Accelerating Neural Architecture Search With Training-Free and Theory-Grounded Metrics

This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS), with high performance, low cost, and in-depth interpretation. NAS has been explosively studied to automate the discovery of top-performer neural networks, but suffers from heavy resource consumption and often incurs search bias due to truncated training or approximations. Recent NAS works Mellor et al. 2021, Chen et al. 2021, Abdelfattah et al. 2021 start to explore indicators that can predict a network's performance without training. However, they either leveraged limited properties of deep networks, or the benefits of their training-free indicators were not applied to more extensive search methods. By rigorous correlation analysis, we present a unified framework to understand and accelerate NAS, by disentangling “ <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TEG</b> ” characteristics of searched networks – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rainability,</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xpressivity,</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eneralization</i> – all assessed in a training-free manner. The TEG indicators could be scaled up and integrated with various NAS search methods, including both supernet and single-path NAS approaches. Extensive studies validate the effective and efficient guidance from our TEG-NAS framework, leading to both improved search accuracy and over 56% reduction in search time cost. Moreover, we visualize search trajectories on three landscapes of “TEG” characteristics, observing that a good local minimum is easier to find on NAS-Bench-201 given its simple topology, whereas balancing “TEG” characteristics is much harder on the DARTS space due to its complex landscape geometry.
MuscleParseNet: a Novel Framework for Parsing Muscles of Drosophila Larva in Light-sheet Fluorescence Microscopy Images

Accurately parsing (i.e., segmenting and recognizing) muscles of freely-moving animals such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Drosophila</i> larva in light-sheet fluorescence microscopy images is necessary to study the relationship between muscle activity and animal motions. However, this task is challenging due to the large inter-class similarity and intra-class variance of muscles, as well as the in-homogeneous intensity and blurred boundaries of neighboring muscles. Existing semantic and instance segmentation methods cannot effectively overcome these challenges, resulting in poor segmentation and unreliable classification. In this work, we propose a novel framework named MuscleParseNet that explicitly utilizes sequential and spatial contexts to address these challenges. MuscleParseNet contains a deformable muscle candidate detector (D-CMD) to detect candidate muscles, and a sequential and spatial context-based fine muscle parser (SS-FMP) to refine the candidates. D-CMD boosts Mask RCNN with deformable convolutions to capture shape variations for more accurate muscle segmentation. Moreover, SS-FMP re-classifies the detected candidates by establishing a global spatial context to explicitly reflect spatial relative location, then optimizes the classification using the sequential associations of candidates in adjacent frames, which significantly improves muscle recognition accuracy. Experiments on the synchronized muscle-motion dataset of nearly freely-moving larvae show that MuscleParseNet produces promising results, outperforming state-of-the-art semantic and instance segmentation methods.
USER EXPERIENCE ANALYSIS OF THE CANVA APPLICATION USING THE SYSTEM USABILITY SCALE (SUS) METHOD.

The Canva Design application has gained popularity among users seeking to design graphics and visual content. However, users face challenges such as difficulty finding features, slow performance, unclear user interface, and functional issues. This research aims to identify and analyze these problems through User Experience and System Usability Scale (SUS) analysis. The findings provide valuable insights to Canva's development team to address weaknesses and improve usability. This research serves as a foundation for enhancing the quality and user-friendliness of the Canva application to meet the evolving needs and expectations of users.
Hyperchaotic Image Encryption System Based on Deep Learning LSTM

This paper introduces an advanced method for enhancing the security of image transmission. It presents a novel color image encryption algorithm that combines hyperchaotic dynamics and deep learning medium and long short-term memory (LSTM) networks. Firstly, the chaotic sequence is generated using the Lorenz hyperchaotic system, then the Lorenz chaotic system is discretized and iteratively processed using the fourth-order Runge-Kutta (RK4) method, and then the deep learning LSTM model is used to transform the chaotic sequence processed by the Lorenz hyperchaotic system into a new sequence for training. Finally, according to the new chaotic signal, the Arnold disruption and Deoxyribo Nucleic Acid (DNA) encoding double disruption diffusion are performed to derive the ultimate encrypted image. Through the analysis of multiple color image simulation experiments, the algorithm presented in this paper can well realize the encryption on color images and can achieve lossless encryption, with strong resistance to differential attack, statistical attack and violent attack. Compared with the literature analysis, the correlation coefficient, information entropy and pixel change rate of this paper are closer to the ideal value, and it has higher security and better encryption effect.
Performance Analysis of Different Machine Learning Classifiers for Prediction of Lung Cancer

Cancer is, beyond doubt, among the most significant causes of death today. Cancer continues to be a major mortality factor despite several decades of clinical research and experiments of new treatments. It can occur in any part of the body, including the lungs. Primary lung cancer symptoms frequently lack specificity and could be linked to smoking. In clinical and medical data analysis, the prediction of lung cancer is a difficult task. A subdivision of artificial intelligence, also called “machine learning,” employs distinguished analytical, stochastic, and optimization techniques for helping machines to be trained from past understandings and analyze extensive and diverse data sets. As a result, machine learning is widely utilized in the treatment and prediction of cancer. Machine learning (ML) classifiers are useful in contributing to the making of decisions and forecasting the severity of cancer by using cosmic amounts of data. Through the mediums of this study, we have proposed some classification algorithms to deter the existence of lung cancer in a person’s body influenced by the symptoms one experiences. Different machine language classifiers are implemented over the Lung cancer dataset. With 93% precision, the accuracy of the SVM classifier has been the highest. A new ensembled model has been introduced with the help of ensemble learning which combines three different models – Logistic Regression (LR), KNN and Random Forest (RF). The accuracy achieved using applied ensemble model is 93.5%.
Pengaruh Penggunaan Media Kartu Kata Bergambar terhadap Kemampuan Membaca Permulaan pada Peserta Didik Kelas B TK Nurfalah Tattakang kabupaten Gowa

This research aims to determine the effect of using picture word card media on the beginning reading abilities of class B students at Nurfalah Tattakang Kindergarten, Gowa Regency. This research is pre-experimental with a research design, namely one group pretest-posttest design. The population in this study were early childhood children aged 5-6 years, with a total of 15 people in Nurfalah Tattakang Kindergarten. The research sample involved Class B of Nurfalah Tattakang Kindergarten, Gowa Regency, which was selected through saturated sampling. This research uses instruments in the form of observation and documentation sheets. The data obtained was then analyzed using descriptive statistics. This research showed increased initial reading ability through picture word cards in class B students at Nurfalah Tattakang Kindergarten, Gowa Regency. Thus, picture word cartoon media can be used to improve students' initial reading skills.
Major Research Achievements

From the perspective of earth system sciences and based on the analysis technology and detection means of modern earth sciences, the Plan was carried out by Chinese scientists through systematic observations, experiments and theoretical analyses, with an emphasis on the spatio-temporal distributionSpatio-temporal distribution range and process of the NCC destructionNCC destruction, the interaction and dynamic mechanism of different earth spheres during craton destructionCraton destruction, the resources and biological effects of craton destruction, and the cognitive level of the formation and evolution of continents. Through ten years of research and focusing on core scientific issues, we have successfully completed the predetermined scientific objectives and achieved the following innovative research results.