Reviewerly/exHarmony huggingface dataset:
Faktor Pendukung dan Penghambat Niat Penggunaan Blockchain oleh Auditor di Kantor Akuntan Publik

This study aims to examine the factors that influence the intention to use Blockchain by auditors working at audit firm in Bali. The approach taken is based on a combination of the UTAUT and SQBT models. Performance Expectancy, Effort Expectancy, Socio-Cultural Factors and Facilitating Conditions are determined as enabler factors that support the intention to use Blockchain. The inhibiting factors were adapted from SQBT, namely Uncertainty, Loss Aversion, Sunk Costs and Regret Avoidance. The sampling method uses purposive sampling technique. The research data were obtained through a survey method and analysed using multiple linear regression. The results showed that effort expectancy and socio-cultural factors have positive influence on the intention to use Blockchain by auditors working at audit firm in Bali. Meanwhile, only sunk costs were found to have negative influence on the intention of using Blockchain by auditors.&#x0D; Keywords: Blockchain; Enabler; Inhibitor; Auditor.
OPTIMUM PATH FINDING FROM MULTI - PATHS BASED ON FUZZY LOGIC SYSTEM

This paper introduced the fuzzy logic technique to find the optimum path from multipaths.The fuzzy logic system used six input as: distance (front, left obstacle, right obstacle, angle goal, turn and distance goal) that have been membership function for each input, as well as output direction, the success of the fuzzy logic algorithm and ensure the optimum path obtained from six paths, these paths takes at different rules of fuzzy logic system .Fuzzy logic technique is not an optimization technique as the particle swarm technique and the genetic algorithm are considered optimization, but in this research get several paths after changing the rules and then the optimal path is obtained from these paths in terms of short length and minimum time of implementation.This technique give the best result as well as the sixth red path is best path which is characterized by its short length and lowest processing time, This technique applied of mobile robots and use in engineering, medical, military and industrial application.
Teacher Education and Information Literacy

Children of all ages can discover infinite numbers of facts by simply pressing buttons on a computer. With this total access there are no limits on the amount of misinformation, bias and untruth, which may also find their way into a student's knowledge base via the electronic media. How are children to learn the skill of discernment, the ability to discriminate the true from the untrue? The obvious answer to this question is, "The teacher should teach the student to use electronic media properly ." True, but who teaches the teacher? This paper attempts to provide an answer.
Towards a Fuzzy Context Logic

A key step towards trustworthy, reliable and explainable, AI is bridging the gap between the quantitative domain of sensor-actuator systems and the qualitative domain of intelligent systems reasoning. Fuzzy logic is a well-known formalism suitable for aiming at this gap, featuring a quantitative mechanism that at the same time adheres to logical principles. Context logic is a two-layered logical language originally aimed at pervasive computing systems for reasoning about and within context, i.e., changing logical environments. Both logical languages are linguistically motivated. This chapter uncovers the close connection between the two logical languages presenting two new results. First, a proof is presented that context logic with a lattice semantics can be understood as an extension of fuzzy logic. Second, a fuzzification for context logic is proposed. The resulting language, which can be understood as a two-layered fuzzy logic or as a fuzzified context logic, expands both fields in a novel manner.
Malware Knowledge Graph Generation

Cyber threat and attack intelligence information are available in non-standard format from heterogeneous sources. Comprehending them and utilizing them for threat intelligence extraction requires engaging security experts. Knowledge graphs enable converting this unstructured information from heterogeneous sources into a structured representation of data and factual knowledge for several downstream tasks such as predicting missing information and future threat trends. Existing large-scale knowledge graphs mainly focus on general classes of entities and relationships between them. Open-source knowledge graphs for the security domain do not exist. To fill this gap, we've built \textsf{TINKER} - a knowledge graph for threat intelligence (\textbf{T}hreat \textbf{IN}telligence \textbf{K}nowl\textbf{E}dge g\textbf{R}aph). \textsf{TINKER} is generated using RDF triples describing entities and relations from tokenized unstructured natural language text from 83 threat reports published between 2006-2021. We built \textsf{TINKER} using classes and properties defined by open-source malware ontology and using hand-annotated RDF triples. We also discuss ongoing research and challenges faced while creating \textsf{TINKER}.
Best Computer For Minecraft

Minecraft games are loved for a dozen reasons. The first on the list is that the games can increase ones creativity or better still, put ones ...
Modernization of Ticketing System Using Re-Engineering Approach

Ticketing system is an enormous sector in Malaysia and other countries. Its range of use is applicable to both products and services. The manual ticketing system is tedious with time consuming when the consumers need to wait in a long queue. Due to this reason, a modernization to a legacy ticketing system is proposed in this paper to overcome such problems and to support the growing business operations and technology with the use of re-engineering approach. Constant demands for mobile applications and relatively easy access to the market have been drawing researchers into mobile application development. With this new replacement, it is trusted to improve the whole business processes and increase its usability, thus becomes the solution amongst indigenes within the country. A new system architecture as well as modernization and functional models were proposed and the usability of the prototype was evaluated using two different testing methods.
A Novel Multi-Agent Deep Reinforcement Learning Approach

Abstract Borrowing the power of deep neural networks, deep reinforcement learning achieved big success in games, and it becomes a popular method to solve the sequential decision-making problems. However, the success is still restricted to single agent training environment. Multi-agent reinforcement learning still is a challenge problem. Although some multi-agent deep reinforcement learning methods have been proposed, they can only perform well when the number of agents is very limited. In this paper, by analyzing the dynamic changing observation space and action space of multi-agent environment, we propose a novel multi-agent deep RL method that compress the joint observation space and action space as the time goes on. The proposed method is potential for a large number of agents cooperative or competitive tasks
Adaptive Hierarchical Hyper-gradient Descent

In this study, we investigate learning rate adaption at different levels based on the hyper-gradient descent framework and propose a method that adaptively learns the optimizer parameters by combining multiple levels of learning rates with hierarchical structures. Meanwhile, we show the relationship between regularizing over-parameterized learning rates and building combinations of adaptive learning rates at different levels. The experiments on several network architectures, including feed-forward networks, LeNet-5 and ResNet-18/34, show that the proposed multi-level adaptive approach can outperform baseline adaptive methods in a variety of circumstances.
Length Learning for Planar Euclidean Curves

In this work, we used deep neural networks (DNNs) to solve a fundamental problem in differential geometry. One can find many closed-form expressions for calculating curvature, length, and other geometric properties in the literature. As we know these concepts, we are highly motivated to reconstruct them by using deep neural networks. In this framework, our goal is to learn geometric properties from examples. The simplest geometric object is a curve. Therefore, this work focuses on learning the length of planar sampled curves created by a sine waves dataset. For this reason, the fundamental length axioms were reconstructed using a supervised learning approach. Following these axioms a simplified DNN model, we call ArcLengthNet, was established. The robustness to additive noise and discretization errors were tested.
DSurvey: A Blockchain-Enhanced Survey Platform for the Data Economy

The data economy is predicted to boom and become a 156B dollars business by 2025. In this demo we introduce the use of distributed ledger technologies (DLT) applied to digital surveys in order to create an ecosystem where data becomes a central piece of a complex economy. Our system allows for interesting key features; ownership, traceability, secure profiles, and anonymity where required. Also, the most important feature, is the incentive mechanism that rewards all participants, both users creating surveys and those answering the surveys. DSurvey (decentralized survey) is a novel application framework that aims at moving away from the large commercial data sink paradigm whose business is restricted to gathering data and reselling it. Our solution makes so that no central data sink exists, and it always belongs to the creator, who are able to know who is using it, and receive royalties.
Pengembangan Media Belajar Audiovisual Untuk Pembelajaran Memperbaiki Motor Listrik di SMK Negeri 1 Sarolangun

Learning process for repairing electricity motor competency at skill competency of Installation Electric Power Technique at SMK Negeri 1 Sarolangun has not been run effectively. In fact, learning material in repairing electricity motor just used modul as teaching materi and media learning. While, to get the competency thad needed, learning process should be supported by leaning tool well. For answer the problem above, teacher as a fasilitator in achievment of students competency is demanded to creative in learning management. One of them is by finding alternative solution that is by develop audiovisual media for learning of repairing electricity motor that can give solution in lerning process. Development of this media aim to product efective media in learning material of repairing electricity motor at SMK Negeri 1 Sarolangun. Development model that chosen is based on Borg and Gall model that adapted lerning design Dick and Carey model. Development process is done by 5 main steps: (1) Doing analysis, (2) Design pre product, (3) Validation and revision, (4) Try out at small scale field and, (5) Try out big group field. Based on the try out of audiovisual media for learning of repairing electricity motor at the SMK, responden give suggestion that the media can be benefit in learning process at school. The reason is that media can give information about the jobs steps clearly at matery of repairing electricity motor so give ease in learning. This ease in proved by the result of try out at big group that give conclution, there is increasingly at the result of study. There by, the media can comprehand learning material fast.
Application of blockchain technology to5G-enabled vehicular networks: survey and futuredirections

Blockchain is disrupting several sectors as it continues to grow mainstream. The attraction for Blockchain isincreasing from various application domains looking to takeadvantage of its immutability, security, cost-saving, transparencyand fast processing properties. Blockchain has empowered severalsectors to upgrade their existing systems or operate an entiresystem architecture shift. For instance, Blockchain has enabledIoT systems to improve their quality of services while simultaneously ensuring their security requirements. Particularly, severalworks are applying Blockchain to manage trust in 5G-enabledautonomous vehicular systems to ensure secure vehicle authentication and handover, guarantee message integrity and providean irrefutable vehicle reputation record. Vehicular networksystems require proper data storage management, highly securetransactions, and non-interference networks. The immutability,tamper-proof, and security by design of Blockchain make it asuitable candidate technology for 5G vehicular network systems.We present in this paper a methodical literature analysis of theapplication of Blockchain to 5G vehicular networks, architecture,and technical aspects. We also highlight and discuss some issuesand challenges facing the application of Blockchain technologyto 5G vehicular networks
Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data

With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing. To reduce the storage footprint and facilitate scalable post-hoc analyses of such scientific data sets, various data reduction/summarization methods have been proposed over the years. Different flavors of sampling algorithms exist to sample the high-resolution scientific data, while preserving important data properties required for subsequent analyses. However, most of these sampling algorithms are designed for univariate data and cater to post-hoc analyses of single variables. In this work, we propose a multivariate sampling strategy which preserves the original variable relationships and enables different multivariate analyses directly on the sampled data. Our proposed strategy utilizes principal component analysis to capture the variance of multivariate data and can be built on top of any existing state-of-the-art sampling algorithms for single variables. In addition, we also propose variants of different data partitioning schemes (regular and irregular) to efficiently model the local multivariate relationships. Using two real-world multivariate data sets, we demonstrate the efficacy of our proposed multivariate sampling strategy with respect to its data reduction capabilities as well as the ease of performing efficient post-hoc multivariate analyses.
Algoritma Backpropagation dalam Memprediksi Jumlah Angka Kemiskinan di Provinsi Sumatera Utara

Poverty is one of the phenomenal problems that Indonesia faces every year. Therefore, this study was conducted with the aim to predict the number of poverty figures by district/city in the province of North Sumatra. The algorithm used to conduct this research is the backpropagation algorithm. This algorithm is one algorithm that is often used to make data predictions. The data used is the data of the poor population in North Sumatra in 2013-2017, which was sourced from the Central Statistics Agency of North Sumatra. Based on this data will be formed and determined the network architecture model used with the Backpropagation algorithm, including 3-9-1, 3-16-1, 3-18-1, 3-23-1, and 3-40-1. From these 5 models after training and testing, it was found that the best architectural model was 3-23-1. The accuracy rate of this architectural model is 97% with an MSE test value of 0.00359. The results of this study are in the form of predictions of the number of poverty in North Sumatra for the next 5 years. The results of this study are expected to be a reference for the regional government of North Sumatra to see the level of development of poverty in North Sumatra for the coming year.
The Main Developments in the Education System in Russia in 2018

In 2018, the main developments in the education system can be divided into the two unequal phases: one before the President’s May 2018 Decree and the other after it. Before May 2018, the main attention was focused on teachers’ salaries; the “struggle” to raise them to the average pay in a relevant region was already waged for six years running. Average salaries of higher-education teaching personnel attract less attention though by 2000 their value was to be equal to 200 percent of the average salary in a relevant subject of the Russian Federation where the higher educational institution was situated. From May 2018, the main focus in education was switched over to development of the “Education” national project and the volume of budget funding to be allocated on the specified goals. In addition, in the higher education system serious debates started on the issue of state accreditation of higher educational establishment.
Model Optimization of a Shell and Tube Heat Exchanger by Using HGAPSO

The demand to develop an efficient heat exchanger have been prompted in order to save the energy and materials as well as economic incentives within the industry. To establish a reliable heat exchanger that be able to operate under its normal operating conditions, a model should reflect the true behaviour of the process itself. Modelling of heat exchanger helps to understand process that occurs during the operation. Hence, the primary objective of the project is developing a structural model using an ARX (Auto-Regressive with eXogenous input) equation. The data from experiment was used to determine the parameter of ARX equation. Using HGAPSO (Hybrid Genetic Algorithm and Partical Swarm Optimization) algorithm, ARX parameters are optimized to obtain the transfer function that represents the plant for modelling. Validation test of autocorrelation and cross-correlation were used to validate between normalised data input and error. Based on the result on the six order model, parameter a0, a1, a2, a3, a4, and a5 values are -0.00083, -0.00202, -0.00253, -0.00095, -0.00213 and -0.00275 while parameter b0, b1, b2, b3, b4 and b5 values are -0.7642, -0.5759, 0.3118, 0.0715, 0.1140 and 0.0683 respectively. From validation test, all graphs are within the 95 percent of confident line.
Semantic Web-Based Blueprint for Digital Healthcare in A Resource Constrained Environment: Towards A Connected Healthcare Ecosystem

This research endeavor is an attempt to illustrate the usefulness of Web 3.0 in enabling healthcare in marginalized communities of Dutywa Health District in the Eastern Cape. The semantic web guides the computer to access authentic data, and this evolves into AI when the information is utilized. The research study adopts a Design Science Methodology to model a Semantic Web-Based Healthcare Framework for rural healthcare and utilizes an Apache Jena Fuseki Server or Protégé to develop and deploy a prototype to the framework for proof of concept. The Semantic Web-Based Healthcare Framework development is benchmarked against BioMedLib Search Engine to prove its efficiency based on the projection of economic factors. The evaluation shows that the proposed Semantic Web-Based Healthcare Framework developed projects significant Return on Investment over time.
Engaging EFL Learners in a Blended Learning Approach for Creative Writing Course

This article aimed at knowing the pedagogical part and the impact of using blended learning for Indonesian EFL learners in higher education level. However, our institution has revolutionary implemented blended learning for the past five years. By using simple random sampling technique, the participants were 25 students of the third semester of English Education Department in one of private universities in Central Java province, Indonesia. It showed that students have significant different way of learning in terms of offline and online process. The preliminary research was done to find the pedagogical parts of having blended learning and the students’ belief as well as their challenges and difficulties on experiencing the model of blended learning which has been implemented in Creative Writing course. Moreover, the result showed that 84% students agree that blended learning is very beneficial to build their skill cognitively, psychologically, and socially in Writing. In addition, they have self-managed language learning experience, as blended learning promotes autonomous as well as discipline, it makes students are free to catch into materials in a certain time without depending themselves to teacher, beside that they also must have punctuality as they must do their assignment in a range of time.
BLENDED LEARNING IN THE TRAINING OF MASTERS OF VOCATIONAL EDUCATION

The article analyzes the scientific and pedagogical research on the results of the application of methods, practices and technologies of blended learning in higher education. The research methodology included a combination of traditional theoretical research with the analysis of the practice of blended learning in higher education institutions of Ukraine on the basis of generalization of research results presented in scientific articles. According to the criteria of openness, citation and practical value, the most important studies of Ukrainian scientists are highlighted to spread the positive experience of effective implementation of blended learning in teaching disciplines for masters of vocational education. In particular, the monograph, dissertation research and scientific articles were analyzed. Based on this analysis, the models proposed by Ukrainian scientists are determined: the organization of blended learning in technical disciplines; methodical system of practical and technical training of future teachers of computer science in the conditions of blended learning; organization of blended learning in the pedagogical practice of future teachers of computer science. The positive effect of the rotational model of blended learning, the model of the "inverted classroom", the feasibility of information and communication technologies, in particular the distance learning system Moodle. Based on the generalization of the results of the analysis, the authors also provide recommendations for the effective implementation of blended learning in the training of masters of vocational education, which include: a clear understanding of the concept of blended learning blending options and models; determining the strategy for the implementation and implementation of blended learning in higher education; definition and substantiation of the list of information and communication technologies to ensure blended learning; ensuring high quality teaching materials and a variety of learning outcomes monitoring tools for distance learning courses; review and update the content and pedagogical design of distance learning courses; notification of students of requirements for training, accounting of results and conditions of drawing up of the final control; constant feedback and communication in the study of the discipline; monitoring the learning process of higher education students and informing students of the results of evaluation of their educational activities. Key words: blended learning, professional education, teaching experience, recommendations, master 's degree.