Reviewerly/exHarmony huggingface dataset:
RESEARCH ON THE INNOVATION PROBLEMS AND COUNTERMEASURES OF MUSIC EDUCATION UNDER THE INTERNET ENVIRONMENT

With the rapid development of the current social economy, people's education idea has changed greatly, the wide application of the Internet provides great convenience for many industries, in the Internet environment in primary and secondary school music education should speed up the innovation of diversified perspective, active development and use of the Internet, create music education mode to adapt to the development of primary and middle school students. And primary and secondary school music education goal is difficult to achieve the root cause is the current primary and secondary school music education superficial, under the Internet environment hardware facilities and teachers, students, education content, and education way failed to rectification and innovation, lead to many problems in education innovation, make the student's interest in learning is difficult to improve, affect the realization of the goal of music education. Therefore, in the Internet environment, it is very important to actively find out the innovative problems of music education and find out the corresponding solutions. In this regard, this paper aims to analyze the problems of music education innovation in the Internet environment and puts forward some corresponding solutions from the aspects of teachers, students, educational content, and teaching methods, in order to provide a useful reference for realizing the goal of music education in primary and secondary schools.
CORRECTING INERT KNOWLEDGE PROBLEMS IN SECOND/FOREIGN LANGUAGE LEARNING

In language learning, a major challenge that hinders learners’ ability to reach their learning goals and use the target language in real-world communication is the presence of the inert knowledge problem. This paper, therefore, explores the nature of the inert knowledge problem and proposes strategies to address and correct it in the context of language learning.
Experiential Learning Projects as Assessment in Initial Teacher Education

In a rapidly changing global environment, Initial Teacher Educators (ITE) have a responsibility to role-model contemporary teaching approaches, which develop graduates who think creatively and flexibly in educational workplaces.An important aspect of this work is supporting pre-service teachers (PSTs) to understand how to design assessments which facilitate a deep understanding of student learning.This learning can be achieved through the implementation of assessments which model contemporary practices and enrich student learning in ITE courses.This paper discusses new ways to consider the purpose of assessment by focusing on Experiential Learning (EL) as a form of assessment in ITE.This semi-systematic literature review encapsulates the intersecting areas of assessment, EL and ITE.Our resulting recommendation is the use of EL Projects to engage PSTs with meaningful and authentic assessments.This would encourage the development of skilled, knowledgeable and innovative thinkers who respond confidently to unanticipated and impactful educational changes.
THE IMPACTS OF MODELED-READING STRATEGY ON STUDENTS’ REASONING SKILL ABILITY

In spite of being included into receptive skill, reading is no longer considered as a passive activity of learning. It is known that readers should be actively engaged during the reading process in attempt to achieve comprehension. This study sought the impacts of the application of modeled reading in increasing students’ reasoning skill, both verbally and logically. Conducted by following quasi experimental characteristics, this study employed one-group pretest post-test design. There were 23 college students involved as the samples who were given a pretest, teaching treatment, and a post-test. The instruments used were two types of test: verbal reasoning test and logical reasoning test. Each set consists of 10 questions. During the data collection or test, the students were given 25 minutes to answer each set, for both the pretest and the posttest. Later, the data were analyzed using normality test and t-test to see if there is any significance increase after the treatment. The results unveil that both skills enhance after the treatment with modeled reading. First, students’ ability in verbal reasoning ability increases from 34.7 to 77 and logical reasoning ability increases from 65 to 83.4 in average. In conclusion, applying modeled reading can expand students’ cognitive ability in sharpening their verbal and logical reasoning which are beneficial during their learning, especially in reading comprehension.
Study of Anomaly Detection in IoT Sensors

Abstract: The rapid proliferation of Internet of Things (IoT) technology has resulted in an exponential increase in the number of connected devices and sensors. These sensors play a crucial role in collecting and transmitting data, enabling various applications and services in diverse domains. However, the large-scale deployment of IoT sensors also introduces new challenges, particularly in the realm of anomaly detection. This research paper presents a comprehensive study of anomaly detection techniques specifically designed for IoT sensors. We delve into the different types of anomalies that can occur in IoT sensor data, including sudden changes, outliers, and malicious attacks. Moreover, we explore the unique characteristics and requirements of IoT sensor networks, such as resource constraints, heterogeneous data, and dynamic network topologies. To address these challenges, we provide an overview of state-of-the-art anomaly detection methods tailored to IoT sensor networks. These methods encompass both traditional statistical approaches and machine learning algorithms, considering their applicability and effectiveness in the IoT context. We discuss the strengths and limitations of each technique, highlighting their suitability for different anomaly detection scenarios. Furthermore, we analyze and compare the performance of these methods using real-world IoT sensor datasets, evaluating their accuracy, efficiency, and scalability. The findings of our study shed light on the strengths and limitations of existing techniques, enabling researchers and practitioners to make informed decisions when choosing an appropriate anomaly detection method for their IoT sensor networks. By enhancing the reliability and security of IoT sensor networks, the outcomes of this research contribute to the advancement of IoT technology and its widespread adoption in various domains, including smart cities, healthcare, transportation, and industrial automation
Introduction to Digital Forensics

Abstract: Crimes committed using computers are increasing rapidly, posing a challenge to law enforcement. Investigations including cybercrime, terrorism, and civil litigation all make use of digital forensics today. Because of the ever-increasing sophistication of modern technology, forensic investigations of this sort can quickly become difficult and time-consuming. In order to successfully retrieve meaningful digital evidence during such investigations, however, a standard framework for digital forensic professionals to follow must be developed. All appropriate steps that a digital forensic investigation would take should be highlighted by the framework and methodology used to conduct digital forensics. This study provides a system for conducting digital forensic investigations with an emphasis on the forensic aspects of those probes, the tools and procedures employed by examiners, and the significance of hashing in preventing the manipulation of evidence. Digital forensics, as a whole, is still a field that is widely growing along with the continually advancing world of technology. This form of forensics is one that is also growing in importance and necessity due to crimes stemming from digital devices becoming increasingly popular as well. These steady numbers are what have and will continue to drive the field of digital forensics into meeting its full potential on a consistent basis, in both a preventative and recovering manner. However, in order to recognize this potential, it is important to first understand what digital forensics really entails
Breast Cancer Detection Based on Convolutional Neural Networks

Abstract: Breast cancer remains a critical health concern, demanding early detection and accurate classification for effective treatment. In this research, we conduct a comparative study between a custom-designed Convolutional Neural Network (CNN) and the pre-trained DenseNet121 model for breast cancer detection and classification. We begin by curating a comprehensive dataset of breast cancer images and apply appropriate data preprocessing techniques for optimal model input. The dataset is divided into training, validation, and testing sets to evaluate model performance. The CNN model is constructed with multiple convolutional and pooling layers, followed by fully connected layers for classification. Meanwhile, the DenseNet121, a powerful pre-trained model, is fine-tuned for breast cancer detection. Through rigorous evaluation, we assess both models using metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. Our results demonstrate that the DenseNet121 outperforms the custom CNN model, achieving higher accuracy and reliability in identifying and classifying breast cancer. To ensure wider accessibility, we integrate the superior DenseNet121 model into a user-friendly web-based interface using Python Flask. This interface empowers medical professionals and the general public to perform real-time breast cancer predictions with ease. Ethical considerations are paramount, ensuring data privacy, security, and transparency in all model predictions. In conclusion, our comparative study highlights the superiority of the pre-trained DenseNet121 model over a custom CNN for breast cancer detection and classification. By leveraging advanced deep learning techniques and a user-friendly interface, our research contributes to improved breast cancer diagnosis and patient care on a broader scale
PENGEMBANGAN MEDIA PEMBELAJARAN INTRAKTIF PADA MATA PELAJARAN MATEMATIKA MATERI PECAHAN DI KELAS IV

Development of interactive in learning mathematics fraction material in grade IV". Dharmasraya. Thesis, Dharmasraya: Elementary School Teacher Education Study Program. Faculty of Teacher Training and Education. Dharmas University Indonesia. The purpose of this study is to develop interactive learning media using games, especially in learning mathematics, to add to existing learning media in schools and produce interactive learning media development in grade IV elementary schools that are valid, practical, and effective so as to increase students' interest in learning at SDN 10 koto new.The model used in the ADDIE development includes the analysis phase, namely needs analysis, analysis of student characteristics, curriculum analysis. the design stage (design) includes the design of learning media, namely the design of research instruments, design of manufacture. The development stage is the development stage where the validity test is carried out by experts. The implementation stage is the practicality trial stage and the evaluation stage is the effectiveness test carried out at SDN 10 Koto Baru.The results of the validation assessment of interactive learning media for mathematics fraction material in the validity test by three people get an average score of 83% included in the valid category, meaning that interactive learning media in learning mathematics fraction material is in accordance with the content, and media, practicality test with a score an average of 86% is categorized as very practical, meaning that interactive learning media can be used easily in the learning process, while the effectiveness of interactive learning media with an average score of 88% is categorized as very effective, meaning that interactive learning media can increase students' learning interest .
Exploring the fluency and comprehensibility of a role-play-based speaking in a tourism are university

This research aims to measure the performance of role-play-based speaking in a tourism area university. This research is projected to determine the student's fluency and comprehension level on role-play-based speaking in a tourism area university. This research also explores the implementation of role-play-based speaking in a tourism area university. The researchers employed exploratory research of the small sample size; twenty-nine students were selected purposively to participate in this study voluntarily. In this research, the instruments that researchers used were speaking tests and an interview guide. The speaking test is designed to measure English speaking proficiency. The speaking test focuses on students' fluency and comprehensibility levels through role-play-based speaking. The researchers used dialogue to measure the student's views on implementing role-play-based speaking; this data was obtained from the interview. The interview results were analyzed in three steps: data condensation, data display, drawing and verifying conclusion. This research demonstrates the students' level of role-play-based speaking. The result showed that role play can improve students speaking performance in fluency and comprehensibility. This research highlights the positive impact of role-play-based speaking activities in an English Tourism class. The findings suggest that students engaging in role-play exercises demonstrate good fluency and comprehensibility in spoken English. Moreover, lecturers and students benefit from this approach by gaining confidence and enjoyment in speaking. Through this exploratory research, researchers generated preliminary hypotheses, which can be further tested using other research designs like descriptive or experimental research.
Application of Speech Recognition in English PronunciationCorrection

In the context of economic globalization, countries are beginning to conduct ordinary trade transactions, so English, asthe most frequently used language, has been incorporated into the education system of each government and studentsare being taught English systematically, but the fastest way to improve English must be for a student to have a one-onone lesson with a native English speaker. However, with the world’s advanced educational resources, teaching Englishto students on a one-to-one basis is obviously not practical. Therefore, implanting a speech recognition system intoeducational software to help the software track or correct students’ English learning in real time is possible. However,replacing a real English teacher with the current speech recognition accuracy rate is still a challenge. Therefore, thispaper explains in detail how to improve speech recognition accuracy through lip motion tracking and MFCC and to givelearners the corresponding vocabulary ratings and suggestions for improvement through this model. In the long run, theknowledge tracking model is used to help students find the most suitable learning style.
Graded teaching Mode in English Teaching in Higher Vocational Colleges

This paper takes English teaching in higher vocational colleges as the research object and discusses the graded teaching model in-depth. Firstly, this paper summarizes the current situation of English teaching in higher vocational colleges and the theoretical basis of graded teaching. Then, it constructs a graded teaching model from student grading principles, teaching content design, and teaching method selection. Finally, it evaluates the practical teaching effect of this model through case analysis. It is found that the graded teaching model can effectively improve the quality of English teaching in higher vocational colleges and provide useful enlightenment for the reform of English education in higher vocational colleges.
E-Commerce Product Demand Modelling Using Machine Learning Algorithm Case Study of Rice Trading Products in PT XYZ

E-commerce XYZ is an Indonesian commerce company that have 3 types of products in its B2C business line: trading, consignment, and marketplace. From January 2021 until October 2022, the company’s trading rice category product sales generated a negative profit. Even though for the last several years e-commerce has been focused on growth instead of profitability, the current economic environment is forcing e-commerce companies to focus on profitability as well. For trading products, maximum profit can be achieved in two ways: selling products with a very high margin but with less quantities or selling in large quantities but with a sub-optimal margin. Hence, the company needs to find a demand function model that can be used to generate maximum profit. To find the best model, the researcher first created a baseline model by using median for every product group which is already grouped based on their Unit of Measurement. Next, to find the best model, the researcher will create a demand function using 4 other models. It is found that Gradient Boosted is the best algorithm to model the demand function. Although this model successfully models a demand function for a product category in e-commerce, business context still needs to be added before this model can be implemented in real life as well as finding other features that might affect the demand function.
Holistic Security Pattern-Based Model to Protect Network Architecture

The issue of network security holds significant importance in the contemporary interconnected global landscape, as networks encounter a wide range of both internal and external threats that can result in severe ramifications.The task of protecting networks continues to pose challenges, despite the implementation of security measures.These challenges arise from the growing complexity and rapidity of attacks, as well as misconceptions held by users.Current security solutions frequently focus on individual layers of the OSI model in isolation, resulting in potential weaknesses within the broader network.This study introduces a comprehensive conceptual model that integrates security patterns to effectively tackle network security concerns.The proposed model expands upon the OSI model by incorporating a human layer and dividing the network into three distinct layers: organization, host, and media.The systematic analysis of potential threats is facilitated through the utilization of anti-goals and attack surface identification.The retrieval of relevant attack patterns and the derivation of defensive control patterns are facilitated through the integration of the Comprehensive Attack Pattern Enumeration Classification Repository (CAPEC), enabling the performance of risk assessment.The proposed model presents a comprehensive and organized methodology for network security, offering network administrators practical recommendations for integrating security measures throughout all layers and improving overall network safeguarding.
A distant area university investigation on students' activity, learning outcomes, and responses using synchronous online reading

Using synchronous online reading in the first semester of English students at a distant area university, this study proposes to determine the students' activity, learning outcomes, and responses to reading comprehension. Researchers gathered numerical data for this study to give a thorough and objective depiction of student activity, learning outcomes, and student reactions to reading comprehension. In this study, seventeen English-speaking students from a rural institution in the Indonesian region of South Sulawesi took part and were selected purposively. In an organized environment, the researchers used structured observation techniques to document particular actions or events methodically. A reading test and questionnaire were also used in this study. The reading test evaluated participants' characteristics, skills, or levels of knowledge. A questionnaire is a set of organized questions asked participants to gather their responses through the Likert scale. This study found that using synchronous online reading in a university located in a distant area has a positive effect on experiences and technology use, and it can alter how subjects are taught, how learning is carried out, and the difficulties that lecturers must overcome. Therefore, when choosing online learning that is regarded to help the learning process in the classroom, a lecturer might consider the study's findings. Future research may assess synchronous, asynchronous, and even hybrid learning in some distant area universities using large samples and a more diverse participant population
Multipartite Dense Coding in Social Internet of Things

The world today is interconnected. As we live in the contemporary era, we will undoubtedly feel the importance of information interaction in all aspects. This kind of information interaction needs to be carried out or mediated. Quantum communication is a new way of information interaction that has been pushed to the forefront in the past decade. It not only has the low cost of traditional radio communication but also has the characteristics of long relay distance of optical communication system. Compared with these characteristics, the most important thing is that it has super high confidentiality. This is the most critical point in the information age. Dense coding is an important application of quantum information. It reflects the difference between quantum bits and classical bits in transmission, providing a reliable theoretical basis for improving the effectiveness of remote communication in the future. We discuss two kinds of extensions of the original dense coding. They respectively work for high-dimension and multipartite situations. We give the laws of the two attachments. In the high-dimensional system, we use the Bell state as the target state of any quantum state and realize the one-to-one correspondence between the entangled state and the Bell state. In multipartite space, we can find that the transformation results can form a dimensional space, and the quantum states obtained are one-to-one, corresponding to the classical bit states. In the multipartite system, we can get the transmitted information more efficiently and accurately by limiting data processing by partial senders. At the same time, this restriction will not reduce the amount of information transmitted initially. Therefore, this limited transformation can improve quantum computing power without damaging quantum states.
Training neural networks with end-to-end optical backpropagation

Optics is an exciting route for the next generation of computing hardware for machine learning, promising several orders of magnitude enhancement in both computational speed and energy efficiency. However, to reach the full capacity of an optical neural network it is necessary that the computing not only for the inference, but also for the training be implemented optically. The primary algorithm for training a neural network is backpropagation, in which the calculation is performed in the order opposite to the information flow for inference. While straightforward in a digital computer, optical implementation of backpropagation has so far remained elusive, particularly because of the conflicting requirements for the optical element that implements the nonlinear activation function. In this work, we address this challenge for the first time with a surprisingly simple and generic scheme. Saturable absorbers are employed for the role of the activation units, and the required properties are achieved through a pump-probe process, in which the forward propagating signal acts as the pump and backward as the probe. Our approach is adaptable to various analog platforms, materials, and network structures, and it demonstrates the possibility of constructing neural networks entirely reliant on analog optical processes for both training and inference tasks.