Reviewerly/exHarmony huggingface dataset:
Integrated Academic Service Digitalization at Universities Utilizing Blockchain Technology

Facing the challenges of digitization, universities are required to transition from traditional academic processes to digital academic processes. One common issue that frequently arises is the university’s inability to manage data, ensure security, and validate every academic process digitally. This incapacity stems from the limitations of the technology used and the lack of integration among the implemented systems. Therefore, the adoption of blockchain technology is crucial to enhance the efficiency, security, and integrity of the systems used by universities. The objective of this research is to implement blockchain technology in the development of a digital university to maintain resilience to change and transparency in academic processes. The research method involves the analysis of academic service needs, the design of an integration system using blockchain technology, and implementation in a university environment. The use of blockchain concepts is directed at improving security, transparency, and efficiency in academic data management. The applied blockchain technology involves the automation of administrative processes, both academic and financial, recording and reporting teaching and learning processes, providing digital facilities, as well as analyzing challenges and obstacles that need to be overcome in implementing blockchain technology in universities. The results of implementing blockchain technology can enhance the potential quality of educational services, improve data security and validation, and stimulate innovation in the education sector. Therefore, blockchain technology can make a positive contribution to transforming the digitized landscape of higher education.
Topic Modeling of Natural Disaster in Indonesia Using NMF

Natural disasters pose significant challenges in Indonesia, a geographically vulnerable nation prone to earthquakes, floods, and volcanic eruptions. This research applies the Non-Negative Matrix Factorization (NMF) technique for topic modeling to comprehensively analyze text datasets related to natural disasters in Indonesia. The study explores the impact of varying the number of topics (5, 10, and 15) on the effectiveness of NMF in representing the underlying structure of the text data. The findings reveal that the choice of the number of topics significantly influences the granularity and richness of text data representation. A lower reconstruction error is achieved with 15 topics, indicating its effectiveness in capturing the intricate relationships between natural disasters and various domains, including education, agriculture, sports, and regional events. The 5 major themes identified encompass “Floods and Landslides,” “Earthquakes,” “Normal Weather,” and “Other,” providing valuable insights into disaster management and mitigation efforts. Additionally, the analysis with 10 topics expands the scope to encompass legal issues, environmental concerns, and political aspects, reflecting the complex interplay between disasters and broader societal issues. This research underscores the importance of adopting nuanced topic modeling approaches to understand the multifaceted nature of natural disasters and their implications for various sectors. Moreover, it highlights the significance of the reconstruction error score as a crucial metric for evaluating NMF model performance in text data analysis. The study contributes to enhancing disaster preparedness and addressing environmental challenges in Indonesia, offering valuable lessons for disaster management and text analysis methodologies, as well as providing information for more targeted mitigation planners.
Brainstorm optimization for multi-document summarization

Document summarization is one of the solutions to mine the appropriate information from a huge number of documents. In this study, brainstorm optimization (BSO) based multi-document summarizer (MDSBSO) is proposed to solve the problem of multi-document summarization. The proposed MDSBSO is compared with two other multi-document summarization algorithms including particle swarm optimization (PSO) and bacterial foraging optimization (BFO). To evaluate the performance of proposed multi-document summarizer, two well-known benchmark document understanding conference (DUC) datasets are used. Performances of the compared algorithms are evaluated using ROUGE evaluation metrics. The experimental analysis clearly exposes that the proposed MDSBSO summarization algorithm produces significant enhancement when compared with the other summarization algorithms.
A study on teaching and learning beginner’s level of Korean using a digital learning program based on game elements: Focusing on the analysis of Kahoot’s gamification elements

This study investigates the gamification elements of Kahoot, a game-based digital learning program, and proposes a teaching and learning method that can be used for beginner’s level of Korean language education. Gamification is the application of game structures and principles to non-game fields to engage and focus users. By applying gamification to education, it is possible to utilize the extrinsic motivation of games to trigger the intrinsic motivation of games and maximize the effectiveness of education. An analysis of Kahoot, a digital learning program, based on the gamification elements identified in the literature review, shows that Kahoot not only faithfully reflects each element of gamification, but also has a variety of devices that can engage learners in sensory, challenging, and illusory immersion. Based on the needs of digitally savvy learners and the classroom that is equipped with digital equipment, Kahoot is appropriate to be utilized in the practice phase of beginner’s level of Korean. Chapter 4 presents a case study on the application of gamification to Korean language classes. The failure to conduct learner surveys or experimental studies based on the gamification concept and teaching and learning methods proposed in this study can be pointed out as a limitation of this study. In the future, more research should be conducted on how to use Kahoot and other digital learning programs for teaching and learning Korean based on their effectiveness.
A Study on Short-Term Korean Language Education Strategies Using Local Culture

The purpose of this study is to design a short-term Korean language curriculum that teaches the Korean language necessary for short-term foreigners to experience Korean culture using local culture. This is to design a Korean language program suitable for them, reflecting the rapid increase in the number of foreigners visiting Korea after COVID-19 and the demand to enjoy Korea in the short term than the previous demand for studying abroad. To this end, a survey was conducted targeting foreigners residing abroad. The survey investigated the content and time of the short-term Korean language curriculum, opinions on the subject of learning and experience activities, desired expected effects, and the region and reason you want to visit. Based on the results of the survey, a short-term Korean language education program focused on the traditions and living culture of Andong was presented. This education program is meaningful in that it designed Korean language classes that can be used with an emphasis on the cultural experience field and situation, and it is also meaningful in that it attempted to develop a Korean language education program centered on local cultural experiences by designing a short-term Korean language education program using local culture
Emerging case studies on the application of AI and blockchain technology in telehealth systems

The fusion of artificial intelligence (AI) and blockchain technology in telehealth systems provides the necessary underpinnings for the development of robust healthcare systems. Telehealth systems are characterized by the generation of big data (medical data). AI algorithms utilize these data in assisting healthcare workers in providing the necessary insights in making crucial decisions in healthcare systems. However, these data need to be protected from invasion of privacy, outright theft, and unauthorized access among other problems. The blockchain technology comes into the fray to solve the problems mentioned above. In this work, we explore the existing case studies with the aim of identifying the gaps inherent in existing works and provide potential solutions to the gaps observed through a systematic review. We also explore the relevant AI algorithm and architecture for various case studies in telehealth systems. Our findings from the case studies have the potential of transforming the telehealth system into a more efficient and effective one.
Government regulatory policies on telehealth data protection using artificial intelligence and blockchain technology

Patients, healthcare providers, and healthcare systems can benefit from telehealth, a fast-growing field of medicine. Although telehealth services are becoming increasingly popular, patient data security and privacy issues present significant difficulties. Blockchain and artificial intelligence (AI) technology development opens up new possibilities for improving telehealth data privacy and addressing these issues. This chapter investigates how blockchain and AI technology could be used to strengthen regulatory frameworks for telehealth data protection. By utilizing AI algorithms, security vulnerabilities in telehealth systems can be identified and mitigated. For example, abnormal data access patterns or potential phishing attempts can be detected. AI can detect and respond to threats in real-time by continuously monitoring telehealth platforms, which lowers the chance of data breaches and illegal access, and using blockchain technology to establish secure, decentralized data storage and sharing, and telehealth providers can ensure patient confidentiality while improving the efficiency and accessibility of healthcare services. The chapter also reviews current telehealth regulatory frameworks that can foster innovation and ensure patient safety in the rapidly changing telehealth landscape.
Legal frameworks for artificial intelligence and blockchain technology in telehealth systems

In recent years, the rapid advancements in artificial intelligence (AI) and blockchain technology have brought numerous possibilities and transformative applications in the field of telehealth systems. However, the integration of these technologies also raises important legal considerations and regulatory challenges. This chapter investigates and addresses the legal frameworks surrounding the utilization of AI and blockchain in telehealth systems. Key aspects, such as data privacy, security, liability, consent, and intellectual property rights, are examined in the context of these emerging technologies. By analyzing existing regulations and proposing potential solutions, this study aims to contribute to the development of comprehensive legal frameworks that promote the responsible and ethical implementation of AI and blockchain in telehealth systems.
Recent advances in AI and blockchain technology in modern telehealth systems

The modernization of medicine is becoming inevitable worldwide, especially with the outbreak of COVID-19, and that has led to the adoption of viable technologies to deliver effective and efficient telehealth systems. This chapter highlights how telehealth is delivered and the remarkable enhancement of telehealth using artificial intelligence (AI) and blockchain technologies. The chapter explores the relevant AI models such as deep learning, machine learning, Internet of Things and others used to deliver healthcare remotely. In addition, the security aspect for medicine, data ownership and data privacy using blockchain in healthcare delivery is presented. Finally, the chapter highlights the recent advancement in these technologies such as RapidAI, ProvChain, extreme data privacy and application layer security.
Pengembangan E-LKPD Berbasis Problem Based Learning Menggunakan Aplikasi Book Creator di Kelas III Sekolah Dasar

interest and attention in learning. This purpose of this research is to determine the validity and practicality of develop E-LKPD using the Book Creator application based on Problem Based Learning in integrated thematic learning in class III elementary schools. This research is Research and Development (R&amp;D) with ADDIE model (analysis, design, development, implementation, evaluation). The results of this research obtained from the results of validity and practicality tests. Validity test results obtained from material, media and language expert validators. The validity test results obtained results in the very valid category with a validity percentage of 96,15% from material experts, 98,48% from media experts, and 96,66% from language experts. Practicality test results obtained from teacher and student response questionnaires. The results of the practicality test at the trial school obtained results in the very practical category with a practicality percentage of 90,63% from the teacher response questionnaire and 90,16% from the student response questionnaire. The implementation school obtained results in the very practical category with a practicality percentage of 93,75% from the teacher response questionnaire and 92,97% from the student response questionnaire.
IMMERSIVE ENVIRONMENTS IN VISUAL STUDIES

In the modern world, two trends coexist in relation to a visual image – the complication of modern forms of visual exploration of the world due to new technologies and a simplified and trusting attitude towards their use. The first trend, requiring multimodal and multisensory perception, enhances the suggestive properties of images and thereby the role of the “optical unconscious”. All this makes the problem of visual control in the exploration of the world very relevant. The purpose of this work is to consider the peculiarities of visual research, to clarify the interpretation of the concepts of “visual image” and “immersive environments” in them. Methods of comparative and structural-functional analysis are used.
Objectives and analytical framework

The theme of the Economic Report on Africa 2023 (ERA 2023) focuses on the impacts of multiple and recurring global shocks on African economies from 2020 and the extent to which these shocks impede Africa’s prospects of achieving the targets set in the Sustainable Development Goals (SDGs). In the past two years, countries have confronted significant hurdles that have impeded progress toward the 2030 Agenda—an objective already off-track prior to the Covid-19 pandemic. As African economies attempted to recover from the 2020 recession caused by the pandemic, the region confronted another economic growth hurdle, the Russian invasion of Ukraine.
El marketing digital en centros educativos del nivel básico regular en el sector privado, 2019-2023

Nowadays we see how everything revolves around technology and that everything that we want to promote or make known is now done through social networks and via the internet. Most private schools in Peru do not promote themselves online or use digital media to do so; Consequently, it prevents them from reaching more audiences and loses part of their market share. Therefore, this literary review article aimed to determine the importance of the implementation of digital marketing in private educational centers at the regular basic level. The study methodology that was applied was qualitative in approach, since relevant information regarding our variable was reviewed; digital marketing, in different aspects, but always focusing on the importance of its implementation and the benefit it brings. A systematic review was developed that included information between the years 2019-2023. Regarding the search for information, it was based on the databases of Proquest, EBSCO, Scielo and ScienceDirect. The results of the information search have been under inclusion and exclusion criteria that in total made up 25,628 articles related to our variable and 46 scientific articles were selected as a sample. Finally, it was concluded that the implementation of digital marketing is important for the development and growth in private schools and in general for companies in the market.
Review of Feedback Analysis of Business Process Outsourcing

Nowadays, online users place a high value on providing feedback in order to enhance their requirements. To extract the requirements from user reviews, a variety of feedback analysis methods were proposed by numerous researchers. However, there is a typical supposition that physically breaking down huge measure of client surveys is testing task and no benchmark has set in execution of client criticism examination. This paper says regarding various clients 'criticism which they have given through web-based in different business areas. We can carry out the criticism examination in both physically and in robotized way. With our audit on criticism examination, we can say not many strategies is generally appropriate for clients to give online input accurately. We have utilized a wide range of NLP Procedures to break down the web-based clients input in different areas like lodgings, cars, staple, clothing, and so on. We conducted a survey to get feedback from users and looked at the different strategies they used in different areas. That paves the way for us to determine which approach is the most effective.
Deep Learning Based Automatic Answer Scoring Through Bi-Directional LSTM

Due to population expansion and the increasing importance of education, it is becoming increasingly difficult for assessors to evaluate the correctness and relevance of the responses provided by students. The LSTM model was initially used to build the answer-scoring system. The Bi-LSTM model has been designed with callbacks to acquire the student answer scoring system due to the LSTM's limitations for optimal scoring. The proposed system has been implemented using the ASAP Short Answer Scoring dataset. The results show that the system developed using Bi-LSTM displays better performance than LSTM.
Perbandingan Klasifikasi Citra Daun Herbal Menggunakan Metode Logistic Regression dan Decision Tree Classifier Berdasarkan Fitur (Warna, GLCM, Bentuk)

The development of plant science is growing rapidly regarding herbal plants. Herbs have many benefits for life to prevent, cure diseases. To find out the types of herbal plants is done by the classification process. Classification of herbal plants can be done by identifying the shape of the leaf image of herbal plants by extracting color features, GLCM and shape from herbal leaves. The 275 dataset consists of 25 leaf types with 11 total datasets. There are several kinds of classification methods that can be used. In this study, the classification methods used were the Logistic Regression and Decision Tree Classifier methods. Based on the results of trials conducted using the Logistic Regression method, the train classification accuracy value was 72.9% and the classification test accuracy was 60.24%, while the Decision Tree Classifier method had a train classification accuracy value of 100% and the accuracy of the classification test was 78.31. This shows that the performance of the Decision Tree Classifier method is better than the Logistic Regression method
Aplikasi Pendaftaran Seminar dan Sertifikat Online pada STIKOM Uyelindo Kupang

College of Computer Informatics Management (STIKOM) Uyelindo Kupang is an IT-based school that has been established since 2000. STIKOM Uyelindo Kupang often holds many activities, one of which is seminars. Problems that arise when carrying out seminars that use many third-party applications such as Google Forms, Photoshop, WhatsApp, or Google Drive for registration of seminar participants, filling in attendance lists, recording participant attendance data, making certificates, and sending certificates so that they are less effective and efficient. The next problem is manually collecting data on seminar participants in attendance. Then the admin will enter the participant's name into the certificate manually. This will take time and require a large enough storage to store the certificate files that have been created. Another problem is that duplication of seminar certificate data can occur, due to the absence of certificate data validation. Therefore an application is needed that can solve existing problems to facilitate the process of running seminar activities. The purpose of this research is to build an online seminar application that can help with seminar activities that will be held at STIKOM Uyelindo Kupang using the Waterfall Method. The expected end result of this research is to be able to build online seminar and certificate registration applications which can later assist in the process of seminar activities which cover all seminar activities.
Distributed Shor’s Algorithm with Sequential Quantum Teleportation

A distributed Shor’s algorithm is used to factorize a semiprime with two quantum computers using quantum teleportation to transport quantum states of qubits in parallel. We propose another distributed Shor’s algorithm with sequential quantum teleportation to improve the existing one. It explores how to employ distributed quantum computing through a quantum entanglement pair of qubits to transport quantum states of qubits sequentially. We combined two quantum computers to perform the quantum phase estimation (QPE) of Shor’s algorithm. This reduced the required number of qubits for a single quantum computer, decreased quantum circuit depth, mitigated the impact of insufficient decoherence time, and accelerated the process of breaking cryptographic systems such as Rivest-Shamir-Adleman (RSA). The designed quantum circuit was executed on the IBM Quantum Lab platform to verify its correctness. We analyzed and compared the required numbers of qubits to run different versions of Shor’s algorithm for individual quantum computers, as well as the numbers of qubits required to break different RSA cryptographic systems.
Importing Diffusion and Neural Styles for Realistic Face Generation

Face generation has made significant progress with large-scale diffusion models, attracting widespread interest. The success of Generative Adversarial Networks (GANs) in face generation is particularly notable. We developed a new approach of diffusion modeling combined with neural style transfer for realistic face image generation. We applied the concept of "style loss" from neural style transfer to maintain the original style features while transforming random photos into face images. This integrated approach provides more artistic and personalized results while maintaining image fidelity. The result has far-reaching impacts and improves face synthesis techniques in art and entertainment. Further research is needed to realize this potential.
Interactive System Based on Millimeter Wave And Wavelet Transform

In contemporary society, ever-increasing levels of stress have emerged as a pressing concern with the potential to result in both physical and psychological ailments. To tackle this multifaceted challenge, we created an innovative system for stress relief to enhance the well-being of individuals. The system converged technology and well-being and offered a unique fusion of rhythmic music engagement and interactive physical activity. Specifically, the proposed system orchestrated pointer swings in sync with music rhythms, providing users with an immersive and soothing audio-visual experience. Simultaneously, we integrated advanced techniques such as wavelet transform and deep learning (DL) to dynamically optimize rhythm analysis to enhance precision. Furthermore, we adopted the recent advancements in millimeter-wave (mmWave) radar and thermographic camera technologies to recognize subtle gestures and translate them into meaningful control over a rotating sphere. This dynamic interaction fosters an enjoyable and physically engaging experience for users. Successfully coordinating these complex subsystems enables them to communicate with each other and accomplish a variety of controls over the rolling sphere.