Reviewerly/exHarmony huggingface dataset:
The Development of Digital Disaster Mitigation Module: An Earthquake Case Study in Wakatobi Regency in Geography Lesson of IPS Twelfth Grade at SMAN 2 Tomia

The research aimed to develop and to produce a product namely the digital disaster mitigation module, an earthquake case study in Wakatobi Regency. This development research used ADDIE model. Data collection techniques used was validation sheet, observation, tests, literature studies and students’ response questionnaires. The data analysis technique used was a descriptive percentage technique. The analysis result showed that the average value of product validation by media expert was 87%, material expert was 81% and geography teacher was 95%. The significant enhancement occurred in students’ learning outcomes both in knowledge aspect and preparedness aspect. The knowledge aspect gained an average score of 59 in pretestand it increased in posttest which gained 76. Moreover, preparedness aspect gained an average score of 62 in pretestand it increased in the posttest which gained an average value of 76. The result of the study concluded that the digital disaster mitigation module of earthquake case study in Wakatobi Regency that was developed in this study can be classified to be valid and feasible to be used in learning activities. Keywords: digital disaster mitigation module, earthquake, knowledge and preparedness of disaster
Performance of Deep Learning Methods for Different Relay Selection Protocols

Abstract Deep Learning (DL) is an emerging topic that has found its place in various applications due to its many advantages, such as providing good performance and reducing complexity. In this paper, we employed different DL methods for channel estimation in cooperative communication by using Maximum Likelihood Estimation (MLE) to combine different copies of transmitted signal over a Binary Phase-Shift Keying (BPSK) modulation in a Rayleigh fading communication channel. We have presented three different relay selection protocols. The first one is SNR-based; the SNRs of the transmission lines between nodes are calculated, and a relay with maximum SNR is selected among the relays. The second relay selection protocol is threshold-based; average SNRs have been computed for all transmission lines, and the links exceeding the average SNR attend in the collaboration. The last protocol is that all relays in the network participate in cooperation, which achieves better performance metrics in terms of Bit Error Rate (BER) and outage analysis. From our simulation results, DL algorithms achieved good performance for channel estimation in the different relay selection protocols based on the Decode and Forward (DF) relaying system.
Interactive virtualisation in Java Script of electromagnetism when changing the dynamic, static parameters of ferromagnetics

ferromagnetics the dynamic, static parameters of ferromagnetics
Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?

Transformers have sprung up in the field of computer vision. In this work, we explore whether the core self-attention module in Transformer is the key to achieving excellent performance in image recognition. To this end, we build an attention-free network called sMLPNet based on the existing MLP-based vision models. Specifically, we replace the MLP module in the token-mixing step with a novel sparse MLP (sMLP) module. For 2D image tokens, sMLP applies 1D MLP along the axial directions and the parameters are shared among rows or columns. By sparse connection and weight sharing, sMLP module significantly reduces the number of model parameters and computational complexity, avoiding the common over-fitting problem that plagues the performance of MLP-like models. When only trained on the ImageNet-1K dataset, the proposed sMLPNet achieves 81.9% top-1 accuracy with only 24M parameters, which is much better than most CNNs and vision Transformers under the same model size constraint. When scaling up to 66M parameters, sMLPNet achieves 83.4% top-1 accuracy, which is on par with the state-of-the-art Swin Transformer. The success of sMLPNet suggests that the self-attention mechanism is not necessarily a silver bullet in computer vision. The code and models are publicly available at https://github.com/microsoft/SPACH
Teach- Nology: A tool for improving phonemic awareness of non- graded learners with special education needs (LSENs)

This study aimed to determine the effects Teach-Nology as a tool for improving phonemic awareness of non- graded Learners with Special Education Needs (LSENs). Parameters were based on pretest and posttest. The researchers used the experimental descriptive design thus, applying different statistical treatment such as mean, standard deviation and dependent t- test. Overall mean results for pretest and posttest for the phonemic awareness were 4.20 and 7.00 having Fairly Satisfactory and Very Satisfactory verbal interpretation. The overall mean result in the posttest was greater compared to pretest, this implied that there was an increase in the alphabet recognition of the subjects before and after the exposure to Teach-Nology. Also, the dependent t-test results revealed that the level of performance in alphabet recognition of respondents after the exposure to Teach-Nology differ significantly since the p-value is less than 0.05. With such results, the null hypothesis is rejected with verbal interpretation of significant. In conclusion, Teach-Nology helps in improving the level of performance in phonemic awareness of students, specifically the non-graded learners with special educational needs of Guronasyon Foundation Inc. National High School. However, continuous effort and emphasis on enhancing the level of performance in alphabet recognition should be given to improve their skill.
Processes Of Educational Inclusion In Times Of Crisis

The pandemic marked society and, therefore, educational institutions, since preventive measures revealed large gaps and differences, and led to rethink the role of the school today, to rethink its constitution and redirect its actions towards a renewed entity, with new positions, new approaches and commitments, in order to respond to current needs and, even more so, to serve students with disabilities. This paper represents a research advance in which an analysis is made of the perspective, policies and strategies that the University of Guadalajara implements to guarantee inclusive education, and some of the effects generated in higher education students as a result of the COVID-19 pandemic; in particular, emphasis is placed on the effects that the situation has had on students with disabilities. For this research, some interviews were conducted with blind students from the same institution, in order to learn about their experiences during the health emergency.
THE USE OF QUIZZIZ TO FACILITATE STUDENTS’ VOCABULARY LEARNING

Quizziz is an educational application download that contains an online quiz and can be used in multiplayer. This application can be accessed via the web or can also be downloaded from the Play Store or Appstore on a smartphone. Its use is also very easy and practical. So it is possible to use it as a test, homework, or small quiz in the teaching and learning process. This research discusses about the implementation of Quizziz as a teaching media to facilitate students’ learning vocabulary for Grade X at MA NU 1 Limpung in the academic years of 2022/2023. The study aims to determine : ( 1) This is to determine of using Quizziz Application on Teaching-Learning Vocabulary in students who have difficulty in learning.( 2 ) This is to determine the effect of using Quizziz to motivate students in the teaching-learning vocabulary. ( 3 ) To discover differences between students' after using Quizziz in learning vocabulary.The researchers utilized descriptive qualitative research methodologies in this study. The data obtained shows that Quizziz can motivate students’ in the teaching learning vocabulary process.
Reconstruction of Sparse Polynomials via Quasi-Orthogonal Matching Pursuit Method

In this paper, we propose a Quasi-Orthogonal Matching Pursuit (QOMP) algorithm for constructing a sparse approximation of functions in terms of expansion by orthonormal polynomials.For the two kinds of sampled data, data with noises and without noises, we apply the mutual coherence of measurement matrix to establish the convergence of the QOMP algorithm which can reconstruct s-sparse Legendre polynomials, Chebyshev polynomials and trigonometric polynomials in s step iterations.The results are also extended to general bounded orthogonal system including tensor product of these three univariate orthogonal polynomials.Finally, numerical experiments will be presented to verify the effectiveness of the QOMP method.
On Advances of Lattice-Based Cryptographic Schemes and Their Implementations

Lattice-based cryptography is centered around the hardness of problems on lattices. A lattice is a grid of points that stretches to infinity. With the development of quantum computers, existing cryptographic schemes are at risk because the underlying mathematical problems can, in theory, be easily solved by quantum computers. Since lattice-based mathematical problems are hard to be solved even by quantum computers, lattice-based cryptography is a promising foundation for future cryptographic schemes. In this paper, we focus on lattice-based public-key encryption schemes. This survey presents the current status of the lattice-based public-key encryption schemes and discusses the existing implementations. Our main focus is the learning with errors problem (LWE problem) and its implementations. In this paper, the plain lattice implementations and variants with special algebraic structures such as ring-based variants are discussed. Additionally, we describe a class of lattice-based functions called lattice trapdoors and their applications.
Interpretability and accessibility of machine learning in selected food processing, agriculture and health applications

Artificial Intelligence (AI) and its data-centric branch of machine learning (ML) have greatly evolved over the last few decades. However, as AI is used increasingly in real world use cases, the importance of the interpretability of and accessibility to AI systems have become major research areas. The lack of interpretability of ML based systems is a major hindrance to widespread adoption of these powerful algorithms. This is due to many reasons including ethical and regulatory concerns, which have resulted in poorer adoption of ML in some areas. The recent past has seen a surge in research on interpretable ML. Generally, designing a ML system requires good domain understanding combined with expert knowledge. New techniques are emerging to improve ML accessibility through automated model design. This paper provides a review of the work done to improve interpretability and accessibility of machine learning in the context of global problems while also being relevant to developing countries. We review work under multiple levels of interpretability including scientific and mathematical interpretation, statistical interpretation and partial semantic interpretation. This review includes applications in three areas, namely food processing, agriculture and health.
Challenges of Bitcoin Blockchain Technology in Real-World Apps

Blockchain is the newest innovation in the fields of internet of things, social media, and cloud computing. Blockchain has quickly become a force to be reckoned with thanks to its reputable uses and rapid development. However, the application of blockchain is not just restricted to bitcoin or other cryptocurrencies. This paper clarifies blockchain requirements outside of bitcoin. In addition, this study offers insights into the mechanics, problems, and evolution of blockchain technology based on a literature review. The difficulties or difficulties in applying blockchain technology to real-world applications were identified in this study. The main issue that needs to be solved is how to securely utilize blockchain technology on small-scale applications.For the secure deployment of the blockchain on small-scale applications or projects, the proposed consensus mechanism might be helpful.
Blockchain Technology based Smart Contract Model in Indonesia

The primary task of integrated product supply chain management is distribution. The processing, storage, and distribution of high-quality pharmaceutical products makes the drug distribution process crucial in the pharmaceutical sector. The distribution of fake medications by associated parties, such as unauthorized or unregistered distributors or data collecting for unregistered medications distributed by distributors, is a concern in the pharmaceutical industry. Drug production permits that are misused up until they are distributed or circulated do not adhere to the Food and Drug Supervisory Agencies standard requirements. In order to facilitate the distribution process by recording data distribution, providing data security, and giving traceability of transactions between linked parties, these issues must be rapidly resolved with technology help. By utilizing blockchain technology, this study suggests a beneficial medicine delivery approach. A user-centered design and a qualitative methodology are used in the model creation. This study's output is a blockchain-based drug delivery mechanism that has been verified. Transparency, security, traceability, decentralization, automation, immutability, and reliability are all features of the model. By ensuring that the pharmaceuticals supplied are of high quality, this strategy can assist the government in promoting community safety, health, and trust in the drugs that are in use.
Pattern Classification Using Quantized Neural Networks for FPGA-Based Low-Power IoT Devices

With the recent growth of the Internet of Things (IoT) and the demand for faster computation, quantized neural networks (QNNs) or QNN-enabled IoT can offer better performance than conventional convolution neural networks (CNNs). With the aim of reducing memory access costs and increasing the computation efficiency, QNN-enabled devices are expected to transform numerous industrial applications with lower processing latency and power consumption. Another form of QNN is the binarized neural network (BNN), which has 2 bits of quantized levels. In this paper, CNN-, QNN-, and BNN-based pattern recognition techniques are implemented and analyzed on an FPGA. The FPGA hardware acts as an IoT device due to connectivity with the cloud, and QNN and BNN are considered to offer better performance in terms of low power and low resource use on hardware platforms. The CNN and QNN implementation and their comparative analysis are analyzed based on their accuracy, weight bit error, RoC curve, and execution speed. The paper also discusses various approaches that can be deployed for optimizing various CNN and QNN models with additionally available tools. The work is performed on the Xilinx Zynq 7020 series Pynq Z2 board, which serves as our FPGA-based low-power IoT device. The MNIST and CIFAR-10 databases are considered for simulation and experimentation. The work shows that the accuracy is 95.5% and 79.22% for the MNIST and CIFAR-10 databases, respectively, for full precision (32-bit), and the execution time is 5.8 ms and 18 ms for the MNIST and CIFAR-10 databases, respectively, for full precision (32-bit).
Utilization of NLP-Technology in Current Applications for Education and Research by Indonesian Student, Teacher, and Lecturer

This study aims to reveal the use of software that works with Natural Language Processing (NLP) technology by students, teachers, and lecturers in Indonesia. A total of 166 respondents from 51 institutions in 12 provinces in Indonesia have participated in data collection through questionnaires and interviews. Questionnaire data were presented in percentage form, while interview data were analyzed using the purposive sampling technique. The analysis results show that most students are unfamiliar with NLP but have used the technology in various applications. Based on the data analysis, students and lecturers have commonly used Google translate and Grammarly. However, PoP and elicit applications useful for searching metadata for scientific papers are not widely known. VOS-Viewer and Nvivo as qualitative data processing applications have not been widely used. Thus, it is crucial to conduct training on using the applications mentioned.
Implementation of the School Literacy Movement in Fostering Reading Interest in Elementary School Students

The School Literacy Movement is an activity initiated by Directorate General of Education of Indonesia which aims to increase students' interest in reading at school. Application the school literacy movement continues to be carried out with the hope that students have the ability to access, understand, use writing information, and have the ability to speak. This study aims to describe implementation of the school literacy movement in increasing interest read students in elementary school. The method used is literature review with data collection techniques using the PRISMA principle data base Google Scholar 2017-2021 which was later in-depth analysis. The results of the study that in increasing interest in reading elementary school students can be marked by love, interest, and enthusiasm in reading books can be seen from the results of the forms of works made such as poetry, rhymes, stories short, competitive achievements and good literacy skills students have. Supporting factors in the implementation of the literacy movement schools include the existence of financial resources, reading parks, corners class reading, school library, interesting reading material, clean and comfortable environment. While the inhibiting factor the school literacy movement, namely the lack of teacher awareness and motivation from the teacher, reading materials used in activities literacy does not attract the attention of students and infrastructure in unsupportive schools.
Can Disaster Risk Education Reduce the Impact of Disasters in Schools?

One of the largest cities in Indonesia is Semarang City. This city has a very high level of disaster proneness. The following are disasters that often occur in the city of Semarang, including floods, tidal waves, landslides, and droughts. The impact of repeated disasters on urban communities is damage and loss of life. However, currently disaster risk education introduces risk mitigation and disaster preparedness in minimizing the impact of disasters in urban areas. Disaster risk reduction has been included in the school curriculum. The purpose of writing this article is to examine the role of schools in disaster mitigation, disaster curriculum in schools. Empowering school-age children to understand disaster mitigation is the first step in building a disaster-aware community. So that when a disaster occurs, people no longer experience confusion and panic, because they have understood how to reduce disaster risk. School-age children are expected to be able to bring the knowledge they have learned from school and become "agents" who can build a culture of disaster awareness in their environment, so that a resilient society can be realized in dealing with disasters.
Using a Fuzzy Genetic Algorithm for Solving Transportation Logistics Problems

Recently, Science defined transportation as the most potent component of logistics. In addition, it has an interdependent relationship with business logistics. Also, AI is intervened in transportation logistics to solve transportation issues. Also, it is used for optimizing and obtaining possible solutions for critical and complex problems. This paper aims to optimize costs and profit to get satisfaction for individuals and organizations using AI techniques. A proposed methodology consisted of two phases. The first phase discusses data collection, and the second involves applying FGA Artificial Intelligence techniques. A proposed Transportation Logistics model was used to determine boundary profit for each Product, and a Fuzzy Genetic Algorithm FGA for transportation logistics was done to solve transportation issues. According to that, outcomes were detected by optimizing the transportation cost by detecting the parent's and the child's chromosomes, and it took the number of iterations =2000. Also, between 100 loops, the best of 5 loops took 1.53 Millie seconds per loop Using GA. Similarly, GA was used for optimizing the minimum total cost of the Product also by determining parents and child chromosomes, which took the Number of iterations= 2000, and among 100 loops, the best five loops took 1.40 ms per loop. Moreover, determining the profit boundary of each predicted Product using triangular fuzzy logic shows that the minimum profit is considered between (20 million and 23.9 million), while the moderate profit is (24 million), and the maximum profit is more than (24.1 million).
Methodical approach to quantitative assessment of the risks of the implementation of threats unauthorized access to an information resource automated systems of internal affairs bodies

Objective. A characteristic feature of the current stage of development of the sphere of informatization of internal affairs bodies (OVD) is a significant increase in the volume and variety of types of service information of limited distribution, stored, processed and transmitted in automated systems (AS). This gives rise to the emergence of a large number and expansion of the range of threats to information security, primarily threats associated with unauthorized access (UAS) to the information resource of the ATS AS, and necessitates the improvement of existing methods to combat this type of crime in order to ensure the information security of objects of informatization of ATS. To obtain information that allows assessing the degree of threats, it is necessary to conduct a quantitative risk assessment. Method. The method for assessing the risks of implementing threats of unauthorized access to the information resource of the ATS AS and obtaining data in a quantitative representation is based on the use of mathematical modeling methods. The advantage of a quantitative assessment compared to a qualitative assessment is the ability to compare risks with the final result, which can be represented in monetary terms, and further use in assessing the likelihood of information threats and calculating the damage caused. Result. A methodical approach to the quantitative assessment of the risks of the implementation of UA threats to the information resource of the ATS AS is proposed, which makes it possible to assess the level of security of service information. Conclusion. The proposed methodological approach to quantitative assessment of the risks of the implementation of UA threats to the information resource of the ATS AS provides a visual representation in monetary terms of the objects of assessment (damage, costs). These calculations can be used to justify the requirements for the level of security of ATS ASs during their development and operation.