Reviewerly/exHarmony huggingface dataset:
Pengalaman Guru dalam Meningkatkan Keterampilan Mengembangkan Ide Pokok Menggunakan Digitalisasi Gambar Berseri (Digibase)

The subject of this research is 29 fifth-grade elementary school students. The instruments employed consist of 1) a written test to assess cognitive abilities, 2) an affective observation sheet to evaluate attitudes, and 3) an observation sheet for products and performance to assess students' writing skills. The objectives of this research are 1) to improve the skills of developing main ideas and 2) to create creative products in the form of comics, short stories, and simple animated videos. This study is a qualitative research in the form of best practice using Digitalized Sequential Image Media (Digibase) to improve the skills of developing main ideas in fifth grade elementary school students. The results obtained from this research are: 1) the subjects have a high ability to develop main ideas of paragraphs. This can be seen from the assessment of the products and observations that have a range of values above 90. 2) the subjects have a very high cognitive ability in finding the main ideas of paragraphs. This can be seen from the posttest scores, where all subjects scored 100. 3) the subjects have a high affective ability, which can be seen from their affective achievement scores in aspects of mutual respect and responsibility that have a range of values of 79 and 83. From the results above, it can be concluded that the use of Digitalized Sequential Image Media (Digibase) can improve the skills of developing main ideas. Researchers found that in using Digibase media, it should be accompanied by specific teaching methods or models to make it more effective. In this case, the researchers combined it with a differentiated learning methods in the teaching process, with the aim of implementing Kurikulum Merdeka in learning.
Innovation HCI Knowledge

Design thinking has a significant influence on innovation in business, education, health, and other vital fields. This involves human-centered approaches lijke fast prototyping, and abductive reasoning. There are many parallels and contrasts between design visualized and a path to the innovative design of Human-Computer Interaction (HCI). In this chapter, we will discuss the method of Hasse diagrams for structured learning domains visualizing the progress of a learner through this domain and reducing attrition through early risk identification, improving learning performance and achievement levels, enabling more effective use of teaching time, and enhancing performance learning design/instructional design.
HCI: An Intelligent Learning Environment

Society has evolved quickly, and individuals are continually forced to acquire new abilities viatraining. This means that education/training resources are substantially restricted; therefore, methods must be developed to tackle this problem. Intelligent Tutoring Systems (ITS) deployment is being proposed as a solution to address this problem. In addition, ITS makes it possible for users to learn and improve their abilities in a particular area. ITS adopts user actions and requirements in a non-intrusive and transparent manner to achieve this aim. The tastes and habits of the users must be known to deliver a tailored and adaptable solution. Therefore, the capacity to learn behavioural patterns becomes a crucial component for an ITS to succeed. In this article, we offer an ITS student model, which monitors the biometric conduct and style of the user throughout e-learning activities. A classification model supervises the student’s work throughout this session. This chapter also emphasises the principles of intelligent learning differences for each activity. Information extraction techniques can automatically extract knowledge from the text by converting unstructured text into relational structures. To achieve this aim, traditional information extraction systems must rely on significant human involvement.
Enhancing Human Movement Classification in Imbalanced Data with a Hybrid AI Approach

The monitoring and classification of human movements have garnered significant importance across various domains, including hazardous situation detection and elderly care. In recent years, the application of Artificial Intelligence (AI) has emerged as a promising approach to predicting and analyzing human movements in such contexts. However, as the complexity and diversity of target movements increase, obtaining sufficient and high-quality data for AI methods becomes increasingly challenging. A particular obstacle researchers and practitioners encounter is the uneven distribution of movement data, where certain movements are frequently repeated and well-represented in the dataset, while others occur infrequently, leading to an imbalanced dataset. This data imbalance poses a critical issue as it hampers the effectiveness of AI models, particularly for those movements with limited available data. The scarcity of samples for certain classes can lead to biased learning, resulting in reduced accuracy and poor generalization of the model. To address this critical challenge, this research proposes a hybrid AI methodology specifically designed to enhance the classification accuracy of two important human movements: turning and walking. The experimental results demonstrate the efficacy of the proposed method. It significantly improves the overall classification accuracy compared to only one AI technique, especially for those movements characterized by limited data availability. By effectively addressing the data imbalance problem, the hybrid AI methodology ensures that the model's performance is not disproportionately skewed towards the overrepresented classes, yielding a more balanced and reliable human movement monitoring and classification system.
The role of academic advisors of student research teams in shaping student engagement in research and learning ownership in Russian universities

The purpose of this article is to determine the role of an academic advisor of student research teams in shaping student engagement in research and learning ownership in universities. The scientific novelty of the study lies in the identification of the roles of an academic advisor of student research teams in the formation of engagement and ownership; characterizing the process of organization of an academic advisor’s work, depending on the level of the motivational, cognitive, operational, reflexive and volitional spheres of students; determining difficulties in students’ taking ownership and recommendations for overcoming them through various methods aimed at promoting students’ motivation and ownership. The study has scientific value, as it allows for a deeper understanding of the role of an academic advisor in shaping student engagement and ownership in universities. Various aspects of scientific advising are discussed, including methods for motivating and supporting students, creating conditions for professional growth and personal development, and organizing student teams at different stages of research activity. The result of the study is a revised list of an academic advisor’s roles in shaping learning ownership and developing motivation. It is concluded that creating favorable conditions for the development of students’ creative potential is required to ensure the efficient operation of student research teams. To achieve this, an effective organization of academic advisors’ work is necessary, as the latter will constantly develop students’ scientific thinking and culture.
Exploring the optimization path of teaching in college piano course based on deep learning

Abstract Piano teaching should not continue to be limited to the mere cultivation of students’ performance skills but should be integrated into curriculum thinking and politics while following the characteristics of the profession. This paper conducts piano teaching experiments through deep learning LSTM algorithm and extracts a series of Civic and Political elements from them as a fitting point of piano teaching and curriculum Civic and Political to discuss, using multiple teaching modes to strengthen the cultural transmission of piano courses to improve students’ ideological and moral level and comprehensive quality. The experimental results show that the accuracy of the deep learning LSTM algorithm based on the integration of the deep learning piano course curriculum Civics is stable up to 98.17%, the average false detection rate is stable at 1.97%, the average positive recall rate is stable at 94.7%, and the negative recall rate is 73.2%, and the LSTM algorithm improves the integration degree by 31% compared with the traditional SVMSGD and RF algorithms. It shows that carrying out experiments of deep learning based on deep learning for teaching Civics in college piano courses effectively strengthens the cultural transmission of piano courses and improves students’ ideological and moral level and comprehensive quality.
Design of a literature-based poetry appreciation system based on perceptron model

Abstract With the continuous promotion and popularity of the digitalization of big data information, it has gradually penetrated various industries and played an increasingly important role in people’s lives. In this paper, the design of a literary poetry appreciation system is studied using a perceptron model, focusing on the construction of a literary poetry appreciation system through a generalized multi-layer perceptron model structure and the evaluation of metrics on the poetry appreciation system dataset using the GBP training algorithm and genetic training method. In the generalized multilevel perceptron model, the data indicators are classified into four categories: poetry music, poetry sound effects, poetry vocals, and other types, and the accuracy rates of the four categories of indicators are analyzed. The analysis shows that the accuracy rate of all four categories of indicators is between 92% and 97%, indicating the feasibility of the poetry appreciation system. The generalized multilevel perceptron model was used to analyze and evaluate the students from the ability perspective. It can be seen that the students’ data showed only a small and insignificant improvement in the dimensions of literacy, comprehension, and analysis, while the scores in the dimensions of reasoning, strategy, and expression improved significantly. It provides a good research perspective for perceptual machine model-assisted teaching and cultivating students’ quality from a competence perspective, which is of historical importance for developing and transmitting Chinese literature.
Optimization of talent training path of broadcasting and hosting art based on matrix model

Abstract This study explores how to optimize the path of talent cultivation in broadcasting and hosting arts. In this paper, the accuracy matrix is established according to the knowledge network graph theory. The accuracy matrix is estimated by the great likelihood function and solved using the Glasso algorithm to construct the accuracy matrix, network model. The matrix model is used to analyze the influencing factors of the career development of broadcast hosts and the relevance of the employment of broadcast host graduates to their majors. In the development path of the broadcasting hosts, the top three influencing factors are academic level, resilience, and expression ability, accounting for 20.69%, 18.77%, and 13.41%, respectively. Regarding professional relevance, only 5.88% of broadcast hosts have a relevance of 0.9 or above in their majors, while only 15.35% of broadcast graduates are engaged in industries with a relevance of 0.9 or above in their majors. The cultivation of talents in broadcasting and hosting art should focus on students’ professional skills and personalities, and always look at the world with a developing vision and seek a way out with an innovative attitude.
Design and implementation of a virtual teacher teaching system algorithm based on facial expression recognition in the era of big data

Abstract With the development of big data, virtual course teaching has developed rapidly. In virtual teaching, the lecturer’s position is pivotal, and the virtual teacher’s lecture mainly conveys information through intuitive facial expressions, so the effectiveness of facial synthesis is crucial. However, traditional facial expression synthesis methods suffer from local distortion and lack of subtle expressions due to driving complex topological models and delays due to large computational effort. To this end, this paper proposes a facial expression extraction method based on feature point texture mapping. First, a video face expression feature point tracking algorithm is designed. Firstly, we design a video face expression feature point tracking algorithm. We use a monocular camera to capture facial expression images, use a texture mapping algorithm to detect faces and extract face feature points, and add a time threshold processing mechanism to the relevant filtering algorithm to detect face frame tracking to realize face feature point tracking in video. Secondly, the parameters are controlled by face expression animation, including head pose and face expression parameters. The head pose is solved by the Laplace algorithm, and the head rotation matrix and translation vector are output to establish the coordinate system. Finally, the stability and real-time performance of the feature point texture mapping algorithm are verified by expression capture test and recognition effect. The results of this paper show that: in the expression capture test, the average data transmission time is about 1ms, and the total expression transmission time of each video image frame is 32ms, which is 1.23ms and 9.8ms shorter than the traditional algorithm, respectively; in the expression recognition effect test, the successful recognition rate is increased by 10.2% on average compared with the traditional algorithm; and in the application to the classroom teaching effect test, 86.6% of the students are satisfied with the virtual teacher. 86.6% of the students highly agreed with the virtual teacher teaching, and 83.3% of the students thought that the model had a positive effect on the learners’ learning, which proved the superiority, real-time, stability and practicality of the algorithm designed in this paper compared with the traditional algorithm. The research work in this paper also provides solutions to the problems of virtual course teaching.
Forecasting Students' Employment Rate Under the OBE Model

With the rapid development of information technology and its penetration into the field of education, it has become a worthwhile research topic to explore how to combine Outcome Based Education(OBE) model with data mining techniques to obtain predictions of secondary school students' employment rates. Therefore, this paper first collected attribution data affecting the employment rate of secondary school students, and then used three models to predict the highly correlated attribution data. Finally, based on the analysis and prediction results, the OBE model was implemented for the corresponding classes. The experimental results found that the combination of machine learning algorithms and educational practices facilitated the development of secondary education and significantly increased the employment rate of secondary school students.
Application of genetic algorithm in model music composition innovation

Abstract The genetic algorithm-assisted musical composition combines evolutionary computational techniques and artistic composition. In this article, the evolution of musical compositions based on traditional genetic algorithm compositions is combined with interactive genetic algorithm techniques, mainly in model music innovation, using a genetic algorithm to generate an initial population with real number encoding of all individuals. The article focuses on studying the fitness function used to automatically evaluate the automatically generated melodies, perform evolutionary operations on the generated melodies, and generate high-quality model music melodies based on the treatment of evolutionary operations. The evaluation time of the generated melodies by the improved model is reduced from 4 minutes to 0.002 seconds, which makes the composer much more efficient in performing model music composition. The evaluation data of 16 pieces before and after the genetic manipulation were also compared, and it was found that the system evaluated the music after the genetic manipulation significantly higher than the music without the genetic manipulation. The different forms of musical compositions created by the composing system developed based on the technical research of computer genetic algorithm-assisted model music innovation are also a useful supplement to composers’ creations and open a new chapter for the field of artificial intelligence.
Effectiveness Of Electronic Pediatric Triangle Assesment Flipchart To Nurse Knowledge

This study aims to determine the effectiveness of electronic PAT flipchart to the Nurse’s knowledge. The research is Quasi Experiment without control group. The sample was the nurses in the emergency room and the pediatric inpatient room who met inclusion and exclusion criteria.We collected sample using total sampling technique and collected 46 samples. Our study showed that knowledge score before the intervention was 16.37 and most of them had good knowledge, namely 38 (82.6) while after the intervention was 19.22 and most of them had good knowledge, namely 45 (97.8). We proved significant difference in knowledge scores before and after giving the PAT electronic flipchart (Pvalue=0.000).
Blockchain-Based on Dew Computing for Unreliable Network

Dew computing is cache-based computing used in areas with unreliable Internet connectivity. The Blockchain client does not need to keep the Blockchain data and also has the features of a Blockchain full node. Dewblock was developed based on Dew computing principles and the Dew-Cloud architecture. A node connection protocol, the Pair Connection Protocol, has been proposed that could be used in similar Dew-Cloud applications. Edge intelligence based on Dew computing helps to establish distributed connections when the Internet connection is interrupted/disconnected for a short period of time. When the Internet connection is interrupted, the intermediary Dew device holds the data based on the available cache memory. After the Internet connection is restored using Blockchain-based mining technology, the correct edge device can be connected based on the previous hash value. This article is about Blockchain based on the Dew computing paradigm and provides all the basic information about Dew computing and Blockchain technology.
Empowering the visually impaired: A YOLO-v5 CNN-based android app for currency recognition and object detection

This paper presents an Android mobile Application (app) that assists people with visual impairments in currency recognition and general object detection. Our project's goal is to assist visually impaired individuals by offering them a practical alternative with a cost-effective solution. The YOLO-v5 CNN model-based currency note detection and object recognition app is proposed for this purpose, and we find it is fast and accurate. The app can identify rupee notes with denominations of 10 new, 10 old, 20 new, 20 old, 50 new, 50 old, 100 new, 100 old, 200, 500, 2000, and general objects such as mobile phones, laptops, chair, water bottle, vehicles, persons, book, door, watch etc.
Developing Modern Flash Cards Using Canva with Quick Response Code to Enrich Students’ Vocabulary and Improve Pronunciation

This research aims to develop modern flash cards at seventh-grade students at MTs N 2 Boaang Mongondow that integrated with technology such as quick response to enrich student vocabulary and pronunciation, also for enhancing students’ understanding towards English lessons. This topic is raised because the learning model that implemented nowadays is still focused on the conventional method or teacher-centered learning model where the teachers in front of the class become the center of knowledge. Meanwhile, the junior high school students are the generation who were born and living in technology era. This research is conducted to ascertain the students’ needs in the English lessons, then develop modern flash cards using quick response code based on the seventh grade English lesson. The research flow refers to 4D model that consist of four stages; define, design, develop and disseminate. Data was collected through questionnaires, analyzing student needs and preferences. The result revealed students’ average English proficiency level was very low both in terms of vocabulary and pronunciation. However, this developed modern flash cards product successfully addressed the students’ needs and pronunciation. Leading to increased motivation, promote students enthusiastic and active participation in English language learning. Keywords: Modern Flash Cards, Canva, QR-Code, Learning Media, 4D Model
Breast Cancer Detection Using Machine Learning Classifier

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. The diagnosis is based on the qualification of histopathologist, who will look for abnormal cells. However, if the histopathologist is not well-trained, this may lead to wrong diagnosis. Computer- aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures. Convolutional Neural Networks for Binary class classification and multiclass classification. The Binary class classification is used to classify the cancer cells to malignant and benign. And the Multiclass classification these classes into different subclasses like adenosis, fibroadenoma, phyllodes tumour, tabular adenoma for benign class and ductal carcinoma, lobular carcinoma, mucinous carcinoma, papillary carcinoma for malignant class. The result will show Convolutional Neural Networks outperformed the handcrafted feature based classification with high accuracy in both binary and multiclass classification.