Reviewerly/exHarmony huggingface dataset:
Unleashing the Untapped Choir: AI Opens Doors to New Music Education Markets

Traditionally limited by geography, finances, and physical boundaries, music education often excludes many aspiring musicians. This untapped potential, a vast "choir" yearning to create and learn, remains largely inaccessible. Artificial intelligence (AI) emerges as a game-changer, disrupting this static landscape and opening doors to new music education markets. This paper explores how AI-powered platforms and tools democratize music education, reaching previously underserved communities, non-traditional learners, and individuals with disabilities. We analyze the market segments AI unlocks, examining the diversity of learning styles and cultural contexts it caters to. Additionally, we delve into the economic impact of AI music education, assessing its potential for creators, educators, and technology providers. Moving beyond theoretical possibilities, we explore specific AI solutions like personalized learning algorithms, accessible composition tools, and virtual teaching assistants. We examine their effectiveness, functionalities, and ethical considerations, ensuring responsible use and inclusivity in AI-powered music education. Through concrete case studies, we showcase successful initiatives leveraging AI to reach diverse audiences and illustrate the real-world impact of these innovations. This paper paints a vibrant picture of a future where AI empowers musical expression for all. We call upon stakeholders to embrace this transformative technology and orchestrate a symphony of inclusivity, unlocking the boundless creativity of the "untapped choir" and enriching the global musical landscape.
Privacy between Regulation and Technology: GDPR and the Blockchain

Compliance with the GDPR while using blockchain technology for data processing results in compliance issues, due to the fact that the blockchain and the GDPR employ different methods to ensure privacy-by-design and privacy-by-default. The blockchain is built on disintermediation and relative decentralization, whereas the GDPR aims for re-intermediation and relative centralization of the data protection process. This paper provides an overview of and suggestions on how to secure compliance with the GDPR while processing data using the blockchain. A focus is placed on the data protection impact assessment on the blockchain network, issues in identifying and determining the role(s) of sole and joint data controllers and data processors, obstacles to exercising the right to rectification and right to be forgotten when the data is recorded on the blockchain, GDPR data transfer requirements as applied to the blockchain, and the protection of privacy in the process of creating blockchain-based smart contracts.
The Nexus Between Domestic Investment and Economic Growth in G7 Countries. Does Internet Matter?

We examine the effect of the Internet on the relationship between domestic investment and economic growth. Data for G7 countries over the period 1991–2018 are used for panel data analysis. Empirical analysis prove that domestic investment affect positively on economic growth, however the Internet doesn’t have any effect on economic growth. Also, the effect of domestic investment on economic growth proves to be not affected by the Internet.
Deep Learning-Based Model for Text Font Detection and Font Detection in Contents

Despite the diverse forms and designs in which fonts are widely utilized in our daily lives, including videos, print materials, products, websites, and mobile sites, numerous copyright issues continue to arise. Despite efforts to address these concerns and take measures for improvement, a variety of issues persist. This research serves as a preliminary study to enhance this situation. The goal was to implement a detection model for fonts used in texts within challenging media such as images and videos, aiming to mitigate font copyright issues that arise across various mediums. As a preliminary step, a model for recognizing fonts used within videos, image, pdf was developed. The model consists of two primary components: a text detection and background removal model within images, and a font recognition model used within the text. The text detection model was refined through various approaches such as image processing and deep learning to identify the optimal model. For the font recognition model, comparisons were made between CNN and ResNet models to select the most suitable one. As a result, an integrated 2-stage model was constructed. Validation was performed using arbitrary video data, revealing a top-1 rate is 76% and a top-5 rate is 94%.
Penerapan PJBL dalam Pengajaran Sakubun untuk pemelajar tingkat menengah

This article reports the findings of observations regarding the implementation of project-based learning (PjBL) in Intermediate Japanese Writing Courses (Chukyu Sakubun) at one university in Indonesia. The research instruments were observation and online questionnaire for 47 students who attended Chukyu Sakubun course. The project-based learning which is implemented in this course takes the form of a group writing activity with the output of the Tadoku reading books. The activity is divided into three stages: preparation stage, implementation stage, and evaluation stage. 15 tadoku books were produced with various genres. The results of the questionnaire explained that the majority of participants were actively involved in the process. Meanwhile, observation results show that during the project work process, students are able to communicate and collaborate with group members, as well as think critically and creatively in producing products.
Credit Card Fraud Detection and Identification using Machine Learning Techniques

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods.
Automatic Detection of Object-Based Video Forgery Using Various Groups of Pictures (GOP)

In recent years, there has been a lot of interest in detecting object-based video forgeries. There has been a lack of satisfactory performance with object-based forgery detectors until recently since a majority of them are still based on handcrafted features. There has been a great deal of interest in passive video forensics in recent years. Forgery of video encoded with advanced codec frameworks remains one of the biggest challenges in object-based forgery research. An object-based forgery detection approach is presented in this paper. To evaluate the proposed method, a derived test dataset of variable video lengths and frame sizes is also used in addition to the SYSU-OBJFORG dataset. This process's efficacy is verified by comparing its results with other methods. When tested on datasets with degraded-quality videos, the proposed framework performed better in real-life scenarios.
Test and Evaluation of Anti-tampering for Protecting Technology of Weapon Systems

Recently, advanced weapon systems such as 3,000-ton submarines and 4.5-generation fighter jets are being localized, and exports of various weapon systems are increasing significantly. Korea’s defense science and technology level was ranked 9th in the world, and arms system exports were also ranked 9th in the world in 2020. Meanwhile, cyberattacks seeking advanced weapon system technology are also increasing, increasing the need for technology protection of export weapon systems as well as technology protection of defense companies. The Defense Industry Technology Protection Act enacted in 2015 defines defense technology as defense technology that needs protection among defense science and technology and enforces the establishment of defense technology protection systems for defense companies. However, there is no system to protect the technology of the weapons system, which is increasing in exports, and there is no test evaluation field until the pre-test evaluation stage of weapons system research and development. In this paper, as a way to protect the defense technology of weapons systems, we studied the weapon system anti-tamping test evaluation process applicable to the defense weapons system research and development process. The proposed method investigated the test evaluation trend of cryptographic modules similar to the anti-tampering test evaluation, and proposed a process including the organization and role necessary for the test evaluation of anti-tampering technology after research and development by referring to security requirements and test requirements.
A COMPREHENSIVE REVIEW OF IT AUDIT METHODOLOGIES IN THE AGE OF QUANTUM COMPUTING

The emergence of quantum computing presents a profound challenge and opportunity for information technology (IT) audit methodologies and IT security. Quantum computing’s potential to break classical encryption methods and its promise of exponential computational power necessitate a proactive response. This comprehensive review explores the fundamentals of quantum computing, the vulnerabilities of classical encryption, the transition to quantum-safe encryption, and the role of IT auditors in navigating this quantum landscape. Additionally, we address emerging quantum technologies, ethical considerations, and the interplay between quantum computing and IT security. To thrive in the quantum era, organisations are advised to plan for quantum-safe transitions, invest in quantum-resistant cryptography, monitor evolving regulations, develop quantum-ready workforces, integrate Quantum Key Distribution (QKD), and adopt a long-term security strategy. Adapting IT audit methodologies to the quantum era requires a multidisciplinary approach, and the recommendations provided here aim to guide organisations in securing their digital assets in this transformative quantum-empowered future.
Analisis Penggunaan Lighting Dalam Fotografi Konseptual Human Interest

Lighting is the main element in the world of photography, without light photography will never exist because light can create character in a photo. With light, you can determine whether a photo is true or not. Light itself is the most important element in photography as is the meaning of photography itself, namely painting techniques using the medium of light, that's why we must understand the meaning and procedures for using lighting to make or produce good photos. good. In every genre of photography we will definitely deal with lighting, be it sunlight or artificial light, of course an understanding of lighting is very much needed to produce photos that have aesthetic value. This journal will discuss or analyze the use of lighting or lighting in photos - photos related to the concept of human interest photos, this journal can also be your guide to create works that have meaning in a work of photography and value beauty in the future.
Shape and style Enhancement for Domain Generalization

Domain generalization requires that models trained only with source domain data also perform well on unknown target domain data, which requires that existing models trained with source domain have good generalization performance. Compared with semi-supervised learning and unsupervised learning, domain generalization is more challenging and has more practical significance. The research proves that data enhancement method can effectively use source domain data to improve the generalization ability of the model. Therefore, in order to overcome the invisibility problem of target domain data, we generate new samples from the two aspects of geometric shape and style to expand the training samples. For geometric enhancement, samples of the same type in different domains are selected as geometric distortion templates. We gradually expand the distribution deviation between the generated samples and the source samples in the feature space, and use mutual information minimization to ensure the novelty of the generated samples' styles. Finally, we use geometric distortion templates and style enhancement templates to generate new training samples and improve the diversity of training samples. Enhance the generalization ability of the model. Experiments show that our method achieves good performance in multi-source domain generalization.
Swarm GAN: Stabilizing Training of Generative Adversarial Networks via Swarm Intelligence

Generative adversarial networks (GANs) have seen significant research interest over the past decade, yet core issues of training instability and mode collapse persist. This work proposes SwarmGAN, a novel GAN framework incorporating swarm intelligence to address these limitations. Specifically, swarm intelligence exhibits properties well-suited to enhance GAN training: emergent complex behaviors arising from simple individual agents, decentralized adaptability to instantaneous data and hyperparameters, and robustness through simple iterative interactions. SwarmGAN incorporates a particle swarm optimization algorithm to guide generator and discriminator updates. Convolutional neural network architectures and gradient penalties further ensure baseline generation quality and diversity. Extensive experiments over diverse image datasets demonstrate the effectiveness of SwarmGAN. Quantitative evaluations using Fréchet Inception Distance, Inception Score, Peak Signal-to-Noise Ratio, and Structural Similarity Index Score validate performance improvements across stability, sample quality, and convergence speed. The proposed integration of swarm intelligence into adversarial networks shows promising capability to address long-standing GAN challenges.
Research on Graph Signal Sampling and Reconstruction Method Based on Causal Emergence

Graph signal sampling and reconstruction techniques effectively reduce data dimensions while preserving the information and structure contained in networks. The distortion-free reduction of data dimensions not only aids in reducing the computational complexity of data but also facilitates the extraction of network features and attributes. This reduction in data dimensions provides convenience for the better analysis and processing of complex network data, ultimately enhancing the efficiency of complex network analysis tasks. Currently, the field of graph signal processing primarily consists of two fundamental frameworks: graph signal processing based on the graph Laplacian matrix and graph signal processing based on the graph's adjacency matrix . Although these two frameworks make full use of the topological structure of graph models, they do not consider the relationships between node information within networks, nor do they integrate individual node information with network structural information. Furthermore, they fail to approach the problem of sampling and reconstruction from the perspective of overall network information. This paper takes the network's intrinsic topological structure and the causal relationships between nodes as its starting point. The goal is to reduce network size and increase the effective information of individual nodes to make the entire network contain richer, valuable information. Combining the graph signal research method based on the graph Laplacian matrix, this paper introduces a graph signal sampling and reconstruction algorithm based on causal emergence. The algorithm initially employs a causal emergence algorithm based on spectral methods to obtain the sampled network and subsequently utilizes classical representation learning algorithms to acquire node representations of the sampled network. This approach enables us to obtain graph signal representations that are richer than those of the original network. Subsequently, we apply a reconstruction framework to reconstruct the obtained graph signals. Finally, the experimental results conducted on three real networks demonstrate that the proposed algorithm outperforms existing theories of graph signal sampling and reconstruction. This research contributes valuable insights and methods to the further development of the field of graph signal processing.
A Study on DVD Jackets in Movies

In recent years, the movie industry has become more diversified, with DVD rentals and Internet streaming becoming popular means of watching movies and target audiences expanding to include women and the youth. Consequently , obtaining viewers’ impressions is crucial to increase DVD sales. An assessment framework sets and makes clear the relationship among the color of the DVD cover, viewers’ impressions. This study focuses on viewers’ impressions of a film and its DVD cover to analyze the relationship between their reactions and the color of the DVD cover, thereby revealing a notable relationship between the two. The results of this research can be used in movie production.
Harvesting the Rain: The Adoption of Environmental Technologies in the Sahel

Abstract Many agricultural and environmental technologies require upfront investments. This may deter adoption, particularly in settings characterized by information liquidity and credit constraints. We test for these barriers to the adoption of an agricultural technique that helps address land degradation in Niger. We find little evidence that liquidity or credit constraints deter adoption: instead, providing farmers with training increases the share of adopters by over 90 percentage points. Conditional or unconditional cash transfers have no additional effect. Adoption increases agricultural output and reduces land turnover in the longerterm. In our setting, training provides both specific technical knowledge and addresses behavioral constraints.
Development of a Web-Based Student Portal System for University Students

This project aims to design and develop a web-based Student Portal System (SPS) for the University of Malakand to replace the current traditional system and provide needed services for students use. Right now, the current traditional system handles things like grading, attendance, and assignments, but it relies on manual paper-based processes, leading to many inefficiencies. Regrettably, it lacks a student-centric approach. That’s where our exciting Student Portal System comes in, ready to shake things up and make student life a whole lot easier. The proposed SPS provide a consolidated platform that brings multiple services together, making life easier for students. Within this system, students can effortlessly access a range of services, including attendance management, assignment tracking, result retrieval, and quizzes. The proposed system provides a simple and user-friendly interface. Teachers can log in to upload assignments, quizzes, and attendance records. Clerical staff have the power to upload results, monitor attendance percentages, and even create accounts for new students and teachers. Meanwhile, students can log in to check their attendance, view their results, and submit assignments. The Student Portal System (SPS) is developed by using strong tools like PHP, MySQL Database, HTML5, CSS3, JavaScript, and Bootstrap, to make sure everyone has an easy time using it.
Ongraph Vector Computing for Solving Explicit Arithmetic Word Problems

Automatic solutions to Arithmetic Word Problems (AWPs) continue to attract significant attention due to their potential uses in fact-checking, validation as well as problem-solving across science, industry, and education. For the latter field specifically, there is significant untapped potential from the automation of AWPs for the full integration of intelligent tutors into educational processes, being the focus of this work. To aid the demystification of AWP, a range of algorithms has been proposed in recent years with some of these documenting incremental increases in accuracy. Some, however, are not suitable for broad application as they cannot generate readable solutions required for intelligent tutoring. This work aims to increase the accuracy of explicit AWP solutions, bring transparency to the AWP problem-solving process, and generate intermediate readable solutions by proposing a hybrid algorithm that combines superior features from existing methods with “Ongraph” Vector Computing. The essential creativity lies in that the proposed Ongraph vector computing fuses the bests of graph and vector computing and that it integrates the vector syntax-semantics method. Experimental results obtained from the test on six authoritative datasets confirm that the proposed algorithm improves accuracy for automated solutions of AWPs thus making a significant contribution to the body of work on AWP-based problem-solving.
IndoSBERT: Enhancing Indonesian Sentence Embeddings with Siamese Networks Fine-tuning

Sentence embeddings hold a crucial role in Indonesian NLP research, being applied in various domains such as essay scoring, summarization, text-to-image generation, text classification, and others. By this research, we present IndoSBERT, a modification of BERT that has been fine-tuned using the siamese network scheme inspired by SBERT. This model was fine-tuned with the STS Benchmark Dataset which was translated into Indonesian languange. Our model can provide meaningful semantic sentence embeddings for Indonesian sentences. IndoSBERT achieved higher scores in Semantic Textual Similarity task compared to existing and widely used models, such as IndoBERT by IndoNLU and IndoBERT by IndoLEM.
Mining Topic Structure of AI Algorithmic Literature

Staying abreast of the latest research findings in any field is a formidable challenge, demanding substantial effort even from experienced researchers. This challenge is further compounded by the rapid proliferation of scientific and technical publications, making it increasingly challenging for individuals to keep up with the expanding knowledge in their respective fields. To address this problem, we propose a model for extracting the topic structure in AI algorithmic literature sets. In this paper, we provide a comprehensive definition of topics in the algorithmic literature and use prompts and large language models to extract topics from individual articles. In addition, we collaborated with domain experts to construct algorithmic topic structure networks based on extracted content. These networks reveal the intricate interconnections between topics, enabling researchers to easily understand current algorithmic advancements in the field. After comparing relevant literature reviews, our study confirms the effectiveness of the defined themes for aiding researchers in comprehending the complex algorithmic relationships in articles concerning AI algorithms.