Reviewerly/exHarmony huggingface dataset:
NFT HUB—The Decentralized Non-Fungible Token Market Place

In today’s digital environment, content ownership is a huge issue. Monetization for material such as artwork, photographs, novels, gifs, and memes is not possible on any social networking platform. This project enables digital creators and owners to produce and sell customized crypto assets that serve as proof of ownership and aid in detecting and preventing digital content counterfeiting and copying. It is also used to raise the ownership standards for digital content. A non-fungible token can be made out of any digital file. NFTs are files that are tracked using the same blockchain technology that is used to support cryptocurrencies such as Bitcoin and Ethereum. This system allows buyers and sellers to keep track of who owns which files. A Non-Fungible Token (NFT) defines a non-transferable data unit that may be bought and traded on a digital ledger described as a Blockchain. Web content such as photography, videos, and audio files can be linked using a variety of NFT data units. NFTs vary from exchangeable cryptocurrencies like bitcoin in that each token is uniquely identifiable. We can identify the owners of digital content and provide remuneration to the creators of digital content using the NFT marketplace. Digital assets, such as cryptocurrencies and tokens, are supported by blockchain technology. Tokens are typically created utilizing smart contracts on top of the blockchain network. Blockchain is a new technique that can solve a variety of problems, such as ten birds with one bullet. As a result, there is no need to discover answers to each and every difficulty with Blockchain. In the next 5–10 years, Blockchain technology will be embraced by 20 different sorts of companies. Blockchain is a decentralized system that solves the issues that centralized systems have. Blockchain combines peer-to-peer networks with cryptographic techniques to create a safe and secure platform for application development.
A Systematic Approach on Blockchain Security Methodologies in Iot

With the advent of smart homes, “shrewd urban communities, and savvy everything, the Internet of Things (IoT) has arisen as an area of amazing effect, potential, and development, with Cisco Inc. foreseeing to have 50 billion associated gadgets by 2020. Be that as it may, the majority of these IoT gadgets are not difficult to hack and think twice about. We audit and order famous security issues as to the IoT layered design, notwithstanding conventions utilized for systems administration, correspondence, and the executives. We frame security prerequisites for IoT alongside the current assaults, dangers, and cutting edge arrangements. Besides, we classify and plan IoT security issues against existing arrangements tracked down in the writing. All the more significantly, we examine how block chain, which is the fundamental innovation for bitcoin, can be a key empowering influence to tackle numerous IoT security issues. The paper additionally recognizes open exploration issues and difficulties for IoT security by processing numerous secure keys through a single special key; a secure hash function is needed in association with a key derivation function to enhance the encrypted mechanism and maximize cryptography efficiency. What's more, we give access control groupings. At last, we feature difficulties and future examination headings in creating generic architecture for security components for IoT frameworks.
Multiplexer-Majority Chains: Managing Correlation and Cost in Stochastic Number Generation

High-cost stochastic number generators (SNGs) are the main source of stochastic numbers (SNs) in stochastic computing. Interacting SNs must usually be uncorrelated for satisfactory results, but deliberate correlation can sometimes dramatically reduce area and/or improve accuracy. However, very little is known about the correlation behavior of SNGs. In this work, a core SNG component, its probability conversion circuit (PCC), is analyzed to reveal important tradeoffs between area, correlation, and accuracy. We show that PCCs of the weighted binary generator (WBG) type cannot consistently generate correlated bitstreams, which leads to inaccurate outputs for some designs. In contrast, comparator-based PCCs (CMPs) can generate highly correlated bitstreams but are about twice as large as WBGs. To overcome these area-correlation limitations, a novel class of PCCs called multiplexer majority chains (MMCs) is introduced. Some MMCs are area efficient like WBGs but can generate highly correlated SNs like CMPs and can reduce the area of a filtering circuit by 30% while sacrificing only 7% accuracy. The large influence of PCC design on circuit area and accuracy is explored and suggestions are made for selecting the best PCC based on a target system's correlation requirements.
TRANSFORMATION OF THE INFORMATION AND EDUCATIONAL SPACE IN THE CONTEXT OF MODERN SOCIO-CULTURAL PROCESSES

The article reveals the main characteristics of the transformation of the information and educational space in the context of modern socio-cultural processes. The author proves the complexity of the structure of the modern information and educational space, which is characterized by generalized knowledge, a variety of ways of obtaining information and a high level of communication technologies. It is determined that the information and educational space is a form of socio-cultural processes, so it is constantly being modified. Modernization of the social communications infrastructure, expansion of Internet services, including those related to education, have led to the transformation of the information and educational space. The author distinguishes such components as socio-cultural objects that store and broadcast educational information, provide communication processes in educational activities; subjects of m,educational activities between which there is interaction, transfer of social experience and cultural heritage; resources that ensure the speed and efficiency of information transfer, the quality of educational activities; tools that promote the m,implementation of the principles of educational activities and the introduction of innovative technologies. structural m,and functional components. The spatial and semantic, content and methodological, communication and organizational components of the information and educational space are defined. It is proved that the information and educational space acts as a didactic cluster for the use of various educational materials, a platform of information and resource support with automated data and knowledge banks maintained in computer networks using information and communication technologies; a dynamic system of socio-pedagogical and subject-information relations. It is proved that the information and educational space should be an organizational structure in which managed educational activities are carried out. Within the information and educational space, there is a spatial transformation of educational activities with the formation of an individualized specialized online educational space.
Denoising Image of Mammography Using Wavelet Methods

Breast cancer is a tumor formed by breast cells that grow and develop uncontrollably. Detection of breast cancer can be done with the results of mammography images. In mammography images, there is often noise and a gray level. This study aimed to improve the quality of mammographic images by reducing noise and gray levels from the image to examine cancer areas. This research was conducted by improving the quality of mammographic images using the Haar, Daubechies, Biorthogonal, and Symlet wavelet methods. The tissue classification used is circumscribed, asymmetry, calcification, and normal. Testing the results of improving the quality of mammography images needs to be done using the calculation of the value of MSE and PSNR. In the denoising results of a circumscribed image using the haar wavelet method, the MSE results are 0.0002, and the PSNR results are 37.8425 dB in the salt&pepper noise image, while in the quantum noise image the MSE results are 0.0008 and PSNR results are 31.0751 dB. The results of this study show that the Haar wavelet method can be used for noise reduction in salt and pepper images. In contrast, the Haar, Daubechies, Biorthogonal, and Symlet wavelet methods can be used on images with quantum noise.
Decision Support System of Kalimantan Medicinal Plants for Hypertension Disease

Kalimantan's biodiversity has the potential for indigenous knowledge using various medicinal plants. This study develops decision-making of Kalimantan medicinal plants for hypertension disease. This decision support system (DSS) uses Rank Sum-VIKOR. The Rank Sum method is used for the weighted criteria, while the VIKOR method is used to determine the order of recommendations for medicinal plants for hypertension. The results of DSS are alternative recommendations for medicinal plants. The top alternative rankings are six Kalimantan medicinal plants for hypertension: Bungur (Lagerstroemi a speciosa), Soursop (Annona muricata L.), Pegagan (Centella Asiatica), Starfruit (Averrhoa blimbi), Kelubut (Passiflora foetida L.), and Takokak (Solanum torvum). The alternative recommendations are equipped with processing methods and how to use these plants to treat hypertension disease.
Toxic Comment Identification and Classification using BERT and SVM

Bullying cases like toxic comments on many social media platforms cause a negative impact that occurs in every age circles. From those cases, we would like to make a system that can identify and classify toxic words from a comment before it is sent and seen by others. By utilizing a Machine Learning application, hopefully, the produced system can be useful in reducing bullying cases that are many in social media. Lot of experiments have been done to find the settlement for this problem, but various algorithms and models are used. In this research, we will be doing a comparison of two models, the BERT (Bidirectional Encoder Representations from Transformers) model which is usually used to solve NLP (Natural Language Processing) tasks, and SVM (Support Vector Machine) model which is great at classifying. Both models will be compared to find out which model is better in identifying and classifying toxic comments. The result that is gotten shows that BERT model is said to be superior compared to SVM model, with an accuracy of 98.3% including other metric evaluation scores that show a significant result compared to the result achieved by SVM model.
Implementing Design Thinking in The Development of Digital Signage User Interface for Public Health Education

Covid-19 may spread through public facilities-such as digital signage. Gaze-based touchless technology allows digital signage to be accessed without touching the screen. Previous works, however, merely focused on seamless interaction without rigorous consideration of user interface development. Here we report an implementation of Design Thinking-a user-centered design method-in development of digital signage user interface. We conducted a survey of over 200 participants for categorizing important topics related with Covid-19. Next, we defined and developed personas and user interface. The interface was deployed in a touchless digital signage equipped with a Tobii ET5 eye tracker. Finally, we evaluated the proposed user interface design in a real-time experiment. We asked the participants to complete two tasks through different modes of button-fast (300 pixels/s) and slow (150 pixels/s) moving button. Our experimental results show that the participants achieved 96.88% of average Task Completion Rate for both fast- and slow-moving button. Our study implies that the user-centered design provides a reliable scientific method to develop a more user-friendly interface. Even in a novel technology such as touchless digital signage, appropriate user interface design may provide enjoyable interaction between users and the digital signage.
Hierarchical Neural Network Implementation: Emotion Recognition for Food Security Comments on Twitter

Modern Hierarchical Neural Network (HNN) implementation combines several deep learning algorithms working together, connected in a hierarchy layer. For this HNN architecture to work well, the problem and the data must be in a hierarchical format. Emotion recognition is the best example of a layered problem where each emotion is attached to a sentiment. This research proposes an HNN model to solve the emotion recognition problem with three deep learning, one for the sentiment in the first layer and two models for the emotion prediction in the second layer. There are two combinations to be compared, full-LSTM and full-CNN. Surprisingly, the overall HNN performance for both combinations is similar, and both are below a control model without HNN architecture. However, solving the emotion recognition problems in the food security domain was still possible despite poor performance. The application result creates a rough estimation of what people feel about the current food security trend.
MSTA-SlowFast: A Student Behavior Detector for Classroom Environments

Detecting students' classroom behaviors from instructional videos is important for instructional assessment, analyzing students' learning status, and improving teaching quality. To achieve effective detection of student classroom behavior based on videos, this paper proposes a classroom behavior detection model based on the improved SlowFast. First, a Multi-scale Spatial-Temporal Attention (MSTA) module is added to SlowFast to improve the ability of the model to extract multi-scale spatial and temporal information in the feature maps. Second, Efficient Temporal Attention (ETA) is introduced to make the model more focused on the salient features of the behavior in the temporal domain. Finally, a spatio-temporal-oriented student classroom behavior dataset is constructed. The experimental results show that, compared with SlowFast, our proposed MSTA-SlowFast has a better detection performance with mean average precision (mAP) improvement of 5.63% on the self-made classroom behavior detection dataset.
Teachers’ Perceptions and Challenges in Integrating Technology in English Reading Course: A Systematic Research Review

This study aims to analyze teachers' perceptions and challenges in integrating technology to teach English reading skills. This study uses a systematic analysis method that identifies existing literature on Google Scholar. Data collection was carried out through systematic literature review stages: 1) framing questions for a review, 2) identifying relevant work, 3) assessing the quality of studies, 4) summarizing the evidence, 5) interpreting the findings. The results of this study found that the teacher's positive perception of integrating technology in learning English and integrating technology can help teaching and learning activities and make classes interesting. The challenges faced by teachers in integrating technology include the lack of technology media facilities, slow internet connections, lack of teacher skills in mastering technology, teacher or student motivation to use technology in learning English, and proper material preparation. Based on these results, it can be concluded that teachers have a positive attitude towards integrating ICT in English learning activities, especially for teaching students' English reading skills.
REASONING OF THE EFFICIENCY OF CONSISTENT FILTRATION ON RAPID FILTERS

Consistent filtration of water through rapid filters with different filter loadings is a promising way to reduce operating costs at water treatment plants. Operating costs are related with the need to regenerate the granular loading, i.e. restore its retention capacity. Regeneration is carried out by washing with clean water, which removes retained sediment from the loading pores. Washing takes place with a fairly high intensity, which is the reason for significant volumes of washing water. The amount of washing water can be reduced increasing the period between washings. The frequency of washing is also influenced by the turbidity of the initial water. When increase of rapid filters efficiency is reasoning, it is necessary to take into account oscillations in initial turbidity. Purpose is reasoning of the practicability of using a consistent scheme and research of its effectiveness under conditions of change in the content of suspended solid in the initial water. Theoretical studies were carried out on the basis of the known dependencies of the theory of filtering by D.M. Mintz. These dependencies establish a relationship between the time of the protective action and the time when the head loss reaches the limit values with the filtering parameters: filtration rate, equivalent diameter of the granular loading, thickness of the loading layer, quality of the water entering the treatment. The algorithm and principle of comparison of consistent and traditional schemes are described. Graphical dependences of the influence on the efficiency of consistent filtration of pretreatment degree and turbidity of the initial water were obtained. As an efficiency criterion, the ratio for the compared schemes of the longest periods of filter operation between washings is taken. The effectiveness of increasing filtration rate when the initial water quality is worsening is shown. The conducted studies showed the advantages of consistent using of rapid filters: an increase in the period between washings, more stable operation when the turbidity of the initial water changes. Keywords: consistent filtration, protective action, head losses, filtering granular loading, turbidity.
Establishing community and school libraries: African Library Project experience

Purpose This paper aims to disseminate the lessons learned from the African Library Project (ALP), which has worked in collaboration with more than 1,500 partners to stock 3,762 community and school libraries in 13 African countries with more than 3,883,082 books. Hopefully, these lessons will help other decision-makers and practitioners in different contexts. Design/methodology/approach The study is based on the authors’ implementation experience and secondary data the project has collected since its commencement. Findings The paper identified the following best practices that other organizations can adopt to enhance or establish additional school and community libraries: the application and book-receiving process, using collaboration, partnerships and volunteerism to mobilize resources are just a few best practices that have been highlighted. Use of student and teacher librarians in the absence of professional librarians, as well as materials and forums for increasing capacity among teachers and community librarians. Practical implications Other stakeholders in developing countries can replicate the ALP best practices to set up and run school and community libraries. Originality/value Although many individuals and organizations are involved in promoting literacy activities, no particular framework guides the process. This paper summarizes ALP best practices that can inform a framework that can guide the practice.
Optimization of transformer parameters at distribution and power levels with hybrid Grey wolf-whale optimization algorithm

Oil-type transformers (OTT) are used more than dry-type transformers, based on cost in the transmission and distribution of electrical energy. Therefore, this usage density increases the importance of cost in OTT. Weight is important in transformer cost. The weight of the transformers depends on the variable parameters of the weights of the core and windings, C (iron cross section conformity factor) and s (current density), respectively. In this study, unlike the previous heuristic optimization studies, an innovative and complementary optimum weight was obtained by using the Gray Wolf - Whale Optimization hybrid algorithm for both distribution type and power transformer type OTT. A weight reduction of 44% and approximately 14% in power transformers was achieved. It was determined that this decrease in weights provided the same reduction in OTT costs. The comparison test of the study was performed both with the values of other algorithms and statistically.
Impact of Bitcoin mining and crypto market determinants on Bitcoin-based energy consumption

Purpose While Blockchain can serve us, Bitcoin threatens our survival. If Bitcoin is assumed to be a country, it will rank 38th globally for energy consumption. With 90.2 metric million tonnes of carbon dioxide, Bitcoin mining and trading has emerged as an environmental threat. The current study investigates how the trading-specific variables, the prices of Crypto Index and Ethereum, affect bitcoin-based energy consumption. Also, the role of mining-specific variables is analyzed. Design/methodology/approach The study uses monthly data from various sources collected from December 2018 to January 2023. The authors used the Autoregressive Distributed Lag (ARDL) Model to determine the short- and long-term relationships between variables. This study uses the Theory of Green Marketing and the Theory of Cross Elasticity of Demand as a theoretical lens. Findings The findings show that escalating crypto market index and Ethereum prices with a one-month lag increases bitcoin-specific electricity consumption and carbon emissions. Green investors may shift to cryptocurrencies based on consensus other than of Proof-of-Work. Ethereum behaves like a substitute for Bitcoin, reflected by the long-term positive relationship between Bitcoin's energy consumption and Ethereum prices. Originality/value The study analyses how the crypto market index and Ethereum price affect bitcoin-based energy use. The relationships identified are substantiated by the literature to provide suggestions to green investors and policymakers to mitigate the harmful impact of Bitcoin's colossal energy consumption on the natural environment.
Three classes of BCH codes and their duals

BCH codes are an important class of cyclic codes, and have wide applications in communication and storage systems. However, it is difficult to determine the parameters of BCH codes and only a few cases are known. In this paper, we mainly study three classes of BCH codes with n=qm−1,q2s−1q+1,qm−1q−1. On one hand, we provide the parameters of C(n,q,δ,1) and its dual code in some cases. On the other hand, we provide the sufficient and necessary conditions to guarantee that C(n,q,δ,2) is a dually-BCH code.
Optimal Round and Sample-Size Complexity for Partitioning in Parallel Sorting

State-of-the-art parallel sorting algorithms for distributed-memory architectures are based on computing a balanced partitioning via sampling and histogramming. By finding samples that partition the sorted keys into evenly-sized chunks, these algorithms minimize the number of communication rounds required. Histogramming (computing positions of samples) guides sampling, enabling a decrease in the overall number of samples collected. We derive lower and upper bounds on the number of sampling/histogramming rounds required to compute a balanced partitioning. We improve on prior results to demonstrate that when using p processors, O(log* p) rounds with O(p/log* p) samples per round suffice. We match that with a lower bound that shows that any algorithm with O(p) samples per round requires at least Ω(log* p) rounds. Additionally, we prove the Ω(p log p) samples lower bound for one round, thus proving that existing one round algorithms: sample sort, AMS sort [2] and HSS [16] have optimal sample size complexity. To derive the lower bound, we propose a hard randomized input distribution and apply classical results from the distribution theory of runs.