Reviewerly/exHarmony huggingface dataset:
Integrating Blockchain for Renewable Energy in Digital Business Era

In the current era of digitalization, intelligent systems resulting from the rapid development of technologies such as Information Technology (IT) and Information Communication Technology (ICT) play an essential role in carrying out strong collaboration on a system and have been used in many industries in the energy production sector, agriculture, building and other sectors. This rapid technological development has aroused the author’s body to conduct research in the realm of Renewable Energy, which is currently still a hot topic of controversy being discussed. New and renewable energies are considered a realistic alternative to fossil fuels, which have been integral to modern industry but are considered to cause environmental or economic problems, not to mention their limited reserves. Therefore, as renewable energy will gradually replace existing energy sources but will take more time to become fully available, it is essential to find a method of managing it fairly and transparently. From the background of these existing problems, the researcher will contribute a novelty of an efficient Blockchain-based consensus algorithm for the system. The finding of this research is the design of a Blockchain-based model to validate its parallel computing capabilities through simulation.
The Role of Blockchain Technology Applications in Building IoT-Based Smart Cities

The tremendous development of urbanization has affected the living and working conditions of individuals in various fields, such as schools, welfare, and transportation. All things considered, it also has a bad impact on people’s lives as they experience problems like contamination, congestion, ecological asset limit. Therefore, the possibility of creating a brilliant city to improve administration provided by the fields of transportation, welfare, energy and teaching. However, to offer such type of assistance, cities use IoT-based applications to gather information and reach conclusions from that information to adequately monitor assets. In order to accurately explain what occurred while the research was done, this study used a descriptive methodology, which is a research approach used to describe problems that are now occurring or continuing. With the innovation of Blockchain technology resulting in novelty, because of its prominent advantages such as directness and decentralization, can be used to overcome this difficulty. This paper presents a special audit to leverage Blockchain applications as an answer to address the problem of smart city development. This paper also presents the difficulties currently faced by Blockchain technology innovation.
The University's Integrated ICT Infrastructure: UTAUT2 Implementation

Post-pandemic and globalization have accelerated the implementation of Industry 4.0, aided by rapid technological and information advancements. Industry 4.0 has significant consequences for all institutions in developing ICT infrastructure, one of which is educational institutions. Despite the enthusiasm in all educational institutions for using ICT to improve teaching and learning, particularly in universities, integrating technology is becoming problematic. Integrating ICT infrastructure in universities is a multidimensional process involving technology and curriculum, pedagogy, institutional readiness, lecturer competence, and long-term financial planning. Because of this, it is essential to explain the adoption of technology within the scope of this study to make the most of investments made in the integrated ICT infrastructure of the university using the UTAUT2 method.
Artificial Intelligence in Tourism Environments : A Systematic Literature Review

Artificial Intelligence refers to recognizing and adapting to technological breakthroughs. At the same time, artificial intelligence is the key to achieving a creative economy in tourism which is attractive in most modern organizational structures. Artificial intelligence has inadvertently knocked on the door of commercial organizations as a necessary activity. As previous researchers have rightly stated, reviewing is learning from experience. The research team has examined the notion of artifi-cial intelligence in this paper (using a systematic literature review and bibliometric analysis). There are 1,010 articles retrieved from the Scopus database for the 2020–2021 academic year using artificial intelligence keywords and filters (author subject, year, citation, and country). The findings of the Publisher’s analysis highlight key issues for current and future artificial intelligence researchers. Publisher 1: Foundations and Trends in Machine Learning; Publisher 2: IEEE Transactions on Communication and Cognitive Networks; Publisher 3: IEEE Transactions on Smart Vehicles; Publisher 4: International Journal of Information Management; and Publisher 5: Science Robotics. This study uses Tableau Software for data analysis. Then, Supervisor Information researchers get a lot from AI. Deep learning is a particular AI tool that has shown how valuable it can be. From this survey, several valuable subjects for future specialists have emerged.
Intra-Graph-Based Few-Shot Link Prediction in Knowledge Graph

Knowledge graphs (KG) that express the structure of relations between entities have become an increasingly attractive area in research into cognition and intelligence at the human level. One of the most known challenges in KG, which is the fact that a large number of training examples are required for every relation to predict a new triple, has sparked increasing interest in the few-shot link prediction method. Existing work on few-shot link prediction does not fully exploit entity information such as symmetry. In this work, we made a prediction of new triples about relations by observing only a few associative triples. In order to make new predictions of triples, we concentrated on the transfer of the most important common information and the reconciliation of the entity between the set and query set support by using Intra-Graph. Our experiment are lead on two datasets and demonstrated that our results achieved the state-of-the-art in few-shot link prediction.
A Data Fusion Approach for Searching Fault Chains in Power Grid over Datasets from Multiple Systems

It is a challenge to search fault chains for facilities in power grid to provide rapid warning for the smart power grid. In this paper, a data fusion approach for searching fault chains (DF-FCS) is proposed. We take into account the factors for the association between facilities in the fault chains in power grid, such as the operation states, fault risks, spatial distance and fault time when the cascading faults occur. Based on the datasets from WAMS, SCADA and GIS systems, the metrics and the process for determining the initial and successive faults of facilities in fault chains are presented. Experiments over the real datasets from local network of China Southern Power Grid showed the effectiveness and accuracy of DF-FCS.
Improved Hybrid Consensus Mechanism Based on Virtual Power Plant Dispatching

With the continuous emergence of massive distributed resources, blockchain technology provides a new solution to the trust problem in the energy field. However, some existing researches only use the blockchain to record data and currency transactions, resulting in limited blockchain application scenarios. This paper analyzes the blockchain consensus mechanism in detail, and designs a hybrid consensus mechanism that integrates multiple algorithms. On this basis, a hybrid consensus mechanism based on convex optimization problem is proposed, which deeply integrates the consensus mechanism with the scheduling problem. Experiments show that this consensus mechanism has better performance than a single type of consensus mechanism, and can fully utilize computing resources.
Analysis of Safety Index in Substation Considering the Damage of External Ground Network

In order to analyze the influence of the damaged structure of the grounding grid outside the enclosure of the substation on the safety parameters of the station, to improve the safety and stability of the operation of the substation, and to reduce the outage maintenance time in case of failure in the station, the simulation of the main grounding grid and the external grounding grid of the specific substation is established in CDEGS The test model simulates the change of power frequency grounding resistance and safety parameters such as contact voltage and step-by-step voltage after three different forms of off-site grounding grid are damaged. It is concluded that when the external grounding grid of the specific substation is damaged, the safety parameters in the station will no longer meet the requirements of relevant regulations. The research results of this paper can be concluded as follows: The damage of the grounding grid laid outside the enclosure of the substation provides the reference research steps.
A RISC-V System-on-Chip Based on Dual-core Isolation for Smart Grid Security

With the development of digital power grids, the large number but low security level of smart power terminals makes the grid easier to face safe risks. In such a context, based on the protection needs of grid terminals, a secure System-on-Chip(SoC) architecture based on the RISC-V processor is proposed, which features dual-core isolation according to the hardware isolation principle and a secure boot based on the chain of trust. A secure serial port is also provided for digital authentication. The implementation of the architecture on FPGA shows that the influence of secure elements on the transmission is minimal.
Methodology for Online Accuracy Evaluation of Metering Current Transformer in Distribution System

The online accuracy evaluation of metering current transformer in distribution system is of great significance for the fair trade of electric power. In this paper, an evaluation system based on edge-cloud architecture is designed. The edge monitoring module has the characteristics of miniaturization and pluggability, and a cloud platform based on electric energy information acquisition system is specially designed to perform real-time evaluation.
Photovoltaic Power Prediction Based on Gray-scale Satellite Cloud Image and Optimized LSTM

Satellite cloud images can reflect the regional cloud cover information well and have been gradually applied in the field of photovoltaic power prediction. In order to make better use of the cloud image information, on the one hand, the features of the cloud image are extracted, and on the other hand, the cloud image is used for weather classification. Firstly, this paper proposes a weather classification method based on gray interval estimation, and uses gray-level co-occurrence matrix (GLCM) to extract cloud image features. Then the LSTM model with Attention mechanism was established, and the hyperparameters were optimized. Finally, in different weather types, combined with cloud map characteristics and power and meteorological data input into the prediction model for error evaluation. The simulation results show that the prediction accuracy of PV power can be further improved by the optimization of cloud features and models, and the prediction error can be increased by 13.75%, 18.66%, and 5.09% on cloudy, Sunny, and rainy days, respectively.
An Enhanced Adversarial Attacks Method on Power System Based on Model Extraction Algorithm

Artificial intelligence algorithms fit connections between features and problems from a data-driven perspective. Artificial intelligence algorithms perform iterative training of data through neural network, which provides a new thinking dimension for researchers. The researches on the power grid field are no longer limited to modeling and analysis through traditional physical mechanism methods. However, there are security risks on the neural network model established by artificial intelligence algorithms. Attackers apply model extraction attacks to structure a substitute model of target power system model, which supports other attack algorithms to attack the power system and ultimately affects the normal operation of power system. This paper proposes an enhancement method for adversarial attacks on power system based on model extraction algorithm and tests the promoting effect of adversarial sample attack in various scenarios.
Online Activity Detection with Predicting and Classifying Transformers

Online activity detection means that the model gives the type of action that is happening in real time which is important for management and security of data center in energy stations. Online activity detection is more practical than offline activity detection for such video surveillance cases. However, online activity detection is more difficult in that only part video stream of an activity is available. So we construct invisible future features and connect them with historical features for the classification of current actions. We propose a novel two-stage Predict-Classify TRansformers (PCTR) for online activity detection with two transformers, where one is for predicting features of near future, and the other is for classifying activity by incorporating observed information and the predicted future information. More specifically, the predicting transformer has an encoder and a decoder, where the encoder models the temporal context of observed information, and the decoder predicts the near future features. Then the encoder of the classifying transformer recognizes ongoing activity by integrating observed features and the predicted features. We conduct extensive experiments on THUMOS’14 datasets to validate the proposed PCTR and get excellent performances.
IMPORTANCE OF BLOCKCHAIN TECHNOLOGY IN INCREASING THE EFFICIENCY OF THE CARGO TRANSPORTATION PROCESS AT "UZBEKISTAN TEMIR YOLLARI" JSC

This article examines the peculiarities of the use of blockchain technologies in the process of cargo transportation on the railway trackport. This technology allows you to manage the database during the shipping process. At the same time, a lot of attention is paid to reducing logistic spending along with data security in blokcheyn technology. This allows the shipping process to be optimized
COMPOSITION AND SOURCES OF BUDGET FINANCING OF EDUCATIONAL INSTITUTIONS

The article discusses the composition and sources of financing of educational organizations from the budget, the process of financial planning and forecasting, the principles of financing higher education, as well as the mechanism for financial support of higher education in terms of state funding and the formation of a socially oriented market economy used to achieve the main goals of its activities.
Language Corpora and Principal Components Analysis

The increase in use of statistical analyses to represent linguistic data constitutes an important turning point in the field of linguistics, allowing researchers to move beyond more traditional intuitive and observational paradigms. Language corpora are particularly difficult to analyze using inferential statistics and their analysis is much better handled through exploratory statistical analyses. This chapter will offer a brief overview of the use of statistics in the field of linguistics, focusing specifically on challenges associated with analyzing language corpora. We will then show how a Principal Components Analysis can be used to analyze a dataset of authentic texts of different genres written by post-secondary students in Quebec. This analysis was transformed into teaching modules designed to help post-secondary students improve their writing skills and to provide post-secondary instructors with an empirically-based framework to teach and evaluate genre-specific writing skills in French.
Pedagogical and Future Implications for the Training of Data Translators

Sharing data stories is an exciting opportunity for integrating data into the workplace. The examples provided in the chapters of this book reveal three imperatives for training data translators: 1) interdisciplinarity, 2) a knowledge exchange framework (KEF), and 3) language calibration. We propose that a focus on developing students' skills in these areas must be coupled with data science training if we are to prepare effective data translators. Instructors must look to adjust not just their methods, but the philosophy behind their instruction to train students for the next generation of data translation jobs.
Path Following Control of Autonomous Tractor Using Non-linear Guidance Law

The agricultural tasks of land preparation, sowing, cultivation and harvesting require a certain level of precision to achieve greater efficiency, productivity and profitability. This precision can be increased if autopilots are used in agricultural machinery such as tractors, sprayers and harvesters. This work is intended to evaluate the use of the Non-Linear Guidance Law (NLGL) guidance algorithm, better known as L1, for the steering control of the Maxxum 110 tractor. Initially is dedicated to the dynamic modeling of the vehicle. Secondly its describes the guidance algorithm and its applicability to follow straight paths with land vehicles. Finally, a space is dedicated to present the simulation results in the tracking of a typical path in land preparation works.
Effect of the use of six thinking hats technique in education on academic achievement: A mixed-meta method research

As the world rapidly changes and develops, students are finding it increasingly difficult to achieve their goals. In this era where traditional education methods are being scrutinized for their impact on academic success, educators are exploring various teaching techniques to improve academic outcomes. The Six Thinking Hats Technique (STHT) is one of the teaching techniques. The study aims to examine the effects of STHT applications on the academic achievement of students through mixed-meta method. Meta-analysis is used for this purpose, which provides the analysis of quantitative data, and meta-thematic analysis, which provides the analysis of qualitative data, within the framework of the mixedmeta method. Quantitative data within the scope of meta-analysis were analyzed with the CMA and MetaWin programs. As a result of the analysis, the effect size was found to be g=1.071, which means a large degree of effect. This finding indicates that STHT applications have a significant positive effect on academic achievement. In the meta-thematic analysis process, the data were analyzed through the Maxqda program, and various codes were created in the context of the positive aspects of STHT. Several codes were identified, including the improvement of expressive skills, the acquisition of speaking skills while following social etiquette, the ability to articulate emotions, thoughts, dreams, impressions, and experiences, the opportunity to utilize debate and other discussion techniques, and the enhancement of communication abilities.
Influence of 4th generation broadband cellular network technology on undergraduate education

The influence of 4th generation broadband cellular network technology on education is explored in this paper. With the widespread use of smartphones and tablets, students are increasingly using mobile devices to access learning resources, communicate with peers, and complete assignments. The advent of 4G technology has had a revolutionary impact on undergraduate education, transforming the way students access information and interact with course materials. With 4G, students can now quickly and easily access online resources, download and stream multimedia content, collaborate with classmates in real-time, and participate in remote learning. This technology has had a profound influence on the way education is managed today, providing faster and more efficient access to educational resources and a personalized learning experience while streamlining administrative processes and assisting teachers in instruction. 4G technology has completely revolutionized the way education is thought of and experienced, creating an open environment, interactivity, and increasingly specialized training for students and professionals alike.
Science Gateways and the Humanities: An Exploratory Study of Their Rare Partnership

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Researchers and educators in humanities such as computational linguists, digital humanists, and those doing historical reconstructions are increasingly heavy users of computational and/or data resources. Many know about activities, working groups, and initiatives around the FAIR (Findable, Accessible, Interoperable, Reusable) principles and are a driving force for improving the sharing of data and software. However, it seems humanities researchers are less aware of the science gateways community and the end-to-end solutions that science gateways could provide, therefore lacking a driving force for adoption of this technology. Some may be creating their own gateways outside the community; others may wish to use computational and data infrastructures but may perceive a lack of support or opportunities. Hypotheses about the reasons that humanities are not well represented as gateways builders and users include lack of funding and support by computer centers. This small-scale exploratory study will clarify some of the challenges and needs faced by computational researchers in the humanities that may explain their relatively low participation in the science gateways community. For this paper, we present the results of 19 interviews with 7 women, 11 men, and 1 non-binary individual from seven states and DC in the US with a range of professional backgrounds and roles.</i>
Hubungan Kesiapan Belajar dengan Hasil Belajar Matematika Kelas IV SDN 013 Sukamaju Kecamatan Singingi Hilir

Readiness to learn will affect the learning outcomes obtained by students. But the fact is that not all grade IV learning outcomes at SDN 013 Sukamaju, Singingi Hilir District, who have good learning readiness, finally get good Mathematics learning outcomes. So that it raises the question of whether there is a significant relationship between learning readiness and learning outcomes for fourth grade Mathematics at SDN 013 Sukamaju, Singingi Hilir District. This study aims to determine whether or not there is a significant relationship between learning readiness and mathematics learning outcomes for grade IV SDN 013 Sukamaju, Singingi Hilir District. This research is a descriptive quantitative research. The data collection technique used was in the form of a questionnaire/questionnaire given by 56 respondents containing statements or questions to be answered by students whose contents were students' readiness to learn Mathematics for grade IV SDN 013 Sukamaju, Singingi Hilir District and documentation of learning outcomes. Determination of the sample in this study using a saturated sample technique because all members of the population are used as samples in this study. Data analysis was carried out using the help of computer calculations with SPSS version 24 for windows. The results showed that there was a significant relationship between learning readiness and mathematics learning outcomes for Grade IV SDN 013 Sukamaju, Singingi Hilir District. In addition, the development of readiness is the most influential indicator of learning outcomes.
High-performance intellectual information technologies for the study of filtration systems in different-sized nanoporous particles media

In the paper, the technologies for the high-performance intellectual nanoporous filtration systems based on the mathematical model of the two-level transport «filtration-consolidation» in the system of nanopores in two intraparticle spaces, which includes two subspaces of different-sized nanoporous particles are developed.