Reviewerly/exHarmony huggingface dataset:
Towards Building Ontology-Based Applications for Integrating Heterogeneous Aircraft Maintenance Records

Over the past decade, advancement in computing and information technology has led to digitization of aircraft operations and maintenance standards and processes. Aircraft maintenance records are increasingly being stored in maintenance repair and overhaul (MRO) information systems. However, harmonisation and interoperability between heterogenous MRO systems has emerged as a key challenge in integrating maintenance records over the lifespan of an aircraft. This adds to the time and cost to maintain aircraft continuous airworthiness, configuration management and valuation. The role of ontologies for knowledge management provides an effective means of using formal semantics for data integration and improved knowledge representation of a domain. In this paper, we examine the application of ontologies for integrating heterogenous aircraft maintenance records. We investigate the key challenges of developing ontology-based applications in practice and reflected on the research efforts needed to counter the challenges. We described 3 research directions and proposed an integrated approach — Agile Development for Ontology-Based Applications (ADOBA) by taking a fine-grained look at the gap between ontological and software engineering methodologies. The proposed approach is part of a research work aimed at creating and validating an ontology model for aircraft through-life support and developing prototypical demonstration as part of the Cranfield Digital Aviation initiative.
Interactive Application Design for Heritage Site Navigation: A Practice Study of an AR Navigation Application for Han Yu-Related Sites in Chaozhou

In the context of the rapid development of digital technology and the current status of cultural heritage conservation and cultural tourism promotion initiatives in China, this design project was aimed toward exploring and experimenting with the role of digital technologies in the revitalization and innovative use of cultural heritage sites, as well as the new interaction between visitors and cultural heritage sites in physical contexts through a practice study design and prototype development of an AR mobile navigation application- Han Yu in Chaozhou (韩愈在潮州)- for Han Yu-related heritage sites in Chaozhou. Starting from an analysis of existing digital heritage practices, a review of literary and historical materials, and a field investigation at each site, an interactive AR navigation system that makes use of AR, QR-Code, and multimodal content, was designed and tested under actual field conditions by representative target users. The practice process and results provide theoretical and technical practical support and references for the digital revitalization of cultural heritage sites in the new media context.
Detection of Credit Card Fraud using a Hybrid Ensemble Model

The rising number of credit card frauds presents a significant challenge for the banking industry.Many businesses and financial institutions suffer huge losses because card users are reluctant to use their cards.A primary goal of fraud detection is to identify prior transaction patterns to detect future fraud.In this paper, a hybrid ensemble model is proposed to combine bagging and boosting techniques to distinguish between fraudulent and legitimate transactions.During the experimentation two datasets are used; the European credit card dataset and the credit card stimulation dataset which are highly imbalanced.The oversampling method is used to balance both datasets.To overcome the problem of unbalanced data oversampling method is used.The model is trained to predict output results by combining random forest with Adaboost.The proposed model provides 98.27 % area under curve score on the European credit cards dataset and the stimulation credit card dataset gives 99.3 % area under curve score.
REWARDS AND PUNISHMENTS GIVEN BY THE TEACHER TO MOTIVATE STUDENTS IN LEARNING ENGLISH AT UPT SMP NEGERI 2 BARANTI

This study intended to investigate (1) The kinds of Reward used by teacher to motivate the students in learning English at UPT SMP Negeri 2 Baranti, (2) The kinds of punishment used by teacher to motivate the students in learning English at UPT SMP Negeri 2 Baranti, (3) The obstacles faced by teacher in motivating students using reward and punishment. This study used a descriptive qualitative method. One of English teacher who teaches in the 8th grade was a participant in this study. The data obtained through an interview consisting of 35 questions, 17 questions about reward and 18 questions about punishment. The Data analysis was carried out by reducing the data, then presenting the data, and after that drawing conclusions and verification. The results have shown that the kinds of reward used by teacher are praise, respect, and sign of appreciation, while the kinds of punishment used by teacher are punishment by action, punishment by word, and corporal punishment. In addition, the obstacles faced by teacher in applying reward was sometimes teacher made mistakes in giving rewards to students causing students protest, while the obstacles that faced by teachers in giving punishment were sometimes difficult determining who was the student who committed the offense, students did not do the punishment well, and students did not pay attention what the teacher said when only given reprimand and if giving punishment too harsh is not allowed
INTERNALIZATION OF CHARACTER EDUCATION THROUGH THE APPLICATION OF WEB-BASED GENRE APPLICATIONS

Healthy youth is the progress of a nation. Marriage under the age of 18 is a problem that must be faced by some young people in the world (especially in developing countries like Indonesia). The high risk of sex outside of marriage has attracted the attention of the government. One of the government efforts related to adolescent health is to provide sex education to adolescents so that they understand the risk of infectious diseases caused by free sex and drug abuse. In addition, the rapid development and advancement of information technology have changed all attitudes, styles, and ways of thinking of modern humans. Even today, the culture of dating, free sex, and the use of illegal drugs has become commonplace and even commonplace among young people. The aim of this study is to provide personality education through the website-based Gendre application to enable young people to understand the negative effects of premarital sex or trying to use illegal drugs. The author hopes that the younger generation can avoid a lifestyle that has a negative impact on their own future and the future of the Indonesian nation. From this research, the Genre website is produced and the testing on this system uses the User Acceptance Testing method, which is a questionnaire with 4 stages, namely user reactions to the system, and reactions. users to the system interface, user reactions in operating the system, and user reactions to system functionality. and the search results were calculated using a Likert scale, and the number of responders was 10 people. From the test on this research, it is concluded that the test results using a Likert scale are 88% of respondents like the results of this website.
THE EVOLUTION OF LATE PALEOZOIC TECTONIC - DEPOSITIONAL ENVIRONMENT IN SIKESHU SAG, JUNGGAR BASIN, NW CHINA

The Late Paleozoic was a critical period for the closure of the ancient Tianshan Ocean. During this period, the Sikeshu Sag on the southern margin of the Junggar Basin experienced a complex process of ocean-continent transition. The determination of the tectonic and sedimentary environment in the Late Paleozoic is helpful to research. the evolution process of the Central Asian tectonic domain. Therefore, based on previous geological research, through detailed field geological survey and profile survey, this paper uses zircon SHRIMP U-Pb isotopic dating to constrain the age of volcanic rocks exposed in the piedmont. According to its lithologic type and geochemical characteristics, the Paleozoic sedimentary environment of Sikeshu Sag has been systematically studied and analyzed. The trace elements of the Devonian volcanic rocks show that they are enriched in LREE, LILE and depleted in Nb and Ta. It is speculated that they were formed in the continental margin arc environment where the oceanic crust is subducted. The positive anomalies of rare earth elements Ba and Eu in Carboniferous volcanic rocks and Th / Zr (> 0.01) and Nb / Zr (< 0.2) indicate that volcanic activities are frequent in the island arc environment. The negative anomalies of transition metal trace elements Cr, and the positive anomalies of Co, indicating that the shallow part of the crust was invaded or sprayed by deep magma. The trace elements characteristics of Permian volcanic rocks (Rb, Nb, Y, Ce, Ga) indicate the depositional environment at the convergent edge of the plate. The volcanic-sedimentary rock assemblages on the field profile includes, basalt, granite, volcanic breccia and tuff with OIB-type geochemical characteristics, indicating that the volcanic activity is gradually weakening and the range of volcanic activities is gradually decreasing, which also reflects the Sikeshu sag. The transition of the Late Paleozoic sedimentary environment from the Devonian remnant ocean-island arc are environment to the Permian active continental margins, also indicates the closure of the ancient Tianshan Ocean to a certain extent, and recorded key information in the evolution of the Central Asian tectonic domain and the formation of the Tianshan orogenic belt.
DECISION SUPPORT SYSTEM OF REWARDING ON LECTURER PERFORMANCE USING FUZZY TSUKAMOTO METHOD CASE STUDY AT MATARAM UNIVERSITY OF TECHNOLOGY

To prepare quality and character human resources, Mataram Technological University strives to provide the best in carrying out the tridharma activities of higher education, one of which is by giving rewards in the hope that morale and loyalty can continue to be improved. However, the gift-giving system that the Mataram Technological University has implemented has not been able to bring about change because the gift-giving system is incorrect. This is because the applied reward-giving assessment system only refers to the assessment without paying attention to other criteria in the tridharma of higher education. Such as the implementation of learning, Research, and community service. Therefore, to overcome this problem, a decision support information system for awarding lecturer performance is needed, which is built using the fuzzy Tsukamoto method by considering several criteria such as Presence, Research Results, and Community Service Results. Lecturer Performance Index in carrying out the learning process. With this decision support system, the implementation of the Tridharma carried out by lecturers can continue to monitor the system and improve the quality and accreditation of study programs and universities.
ICT Literacies in the Context of Some Anniversaries

This year marks several anniversaries related to ICT literacy. 20 years of the ICETA conference, 10 years of the project “Aj Ty v IT - You too in IT” focused on the education and work of women in IT, but also 60 years of education in Computer literacy in our country. Literacy is inherently linked to the content intent of the ICETA conference. In the paper, we focus on general level of literacy, as well as ICT literacy in terms of evolutionary and innovative changes. Special attention is paid to the situation in our country. We began with computer literacy education already in the academic year 1962/1963. The experience from the previous period is also presented, which could serve as a “best practice” in ICT literacy implementation now and in the next period. The role and place of ICT literacy in the digital economy, its implmentation in our country and in the surrounding countries is also presented and discussed. The EU Digital Economy and Society Index - the DESI - which summarises the relevant indicators of Europe's digital performance is used. A connection of ICT literacy with innovation is discussed. We compare the situation in our country with the surrounding countries through the European Innovation Scoreboard 2021 We focus on new technologies, first of all to Artificial Intelligence (AI) and related knowledge and skills. The most attention to Artificial Intelligence (AI) is paid, because, paradoxically, we not only have a long tradition with AI, which is almost identical to the tradition of computer literacy education, but also good and bad experiences. In conclusion, we draw attention to the lessons learned from past experiences.
Visual media in library services

The core terms for the visual media sphere are defined; visual media purpose and role in library services are examined. The examples and approximate sequence of library’s mediavisual product development are discussed.
A Membership-Based Resampling and Cleaning Algorithm for Multi-Class Imbalanced Overlapping Data

Real-world datasets frequently have an imbalanced class distribution, which significantly degrades classification performance. However, some studies have suggested that the adverse effects of class imbalance occur only when datasets have other intrinsic characteristics (such as class overlap, noise, and data scarcity). Noise and class overlap have the greatest effect. To deal with other intrinsic characteristics that affect classification performance in a multi-class environment, such as class overlap, noise, and data scarcity, we propose a method that can directly handle multi-class overlapping data, called the membership-based multi-class resampling and cleaning (MC-MBRC) algorithm. The proposed method divides samples into safe, overlapping, and noisy areas based on their membership degrees, and then according to the influence of the samples in each area on classification performance, it performs various operations such as noise removal, interpolation oversampling, and energy-based cleaning of the overlapping region. An extensive comparison using various datasets shows that, compared with state-of-the-art methods, the proposed method makes significant statistical improvements in different classification performance metrics and is robust to data containing class overlap and label noise. Furthermore, for datasets that do not have complex intrinsic features, MC-MBRC will not significantly degrade classification performance.
Semantic Image Synthesis via Hierarchical Structure Features

Semantic image synthesis, which converts semantic masks into photo-realistic images, is essentially a special form of a label-to-image task. In this area, previous work has made great progress, but we found that their models usually lose certain semantic information during the generation process, and the metrics of each generated result have a certain degree of fluctuation. So how to generate stable and high-quality images is still a challenge for this task. In this paper, we propose a Hierarchical Feature Block (HF-Block) from the perspective of improving the stability of generation. It generates different hierarchical features through a Hierarchical Feature Encoder (HF-Encoder) and merges them into the generator. We conducted extensive experiments on several very challenging datasets: ADE20K, Deepfashion, and Deepfashion2 datasets. Compared with the state-of-the-art methods, ours can provide more stable and high-quality images.
Asked and Answered: On Qualities and Quantities of Answers in Online Q&amp;A Sites

This paper builds upon several recent research efforts that have explored the nature and qualities of questions asked on these social Q&amp;A sites by offering a focused examination of answers posted to three of the most popular Q&amp;A sites. Specifically, this paper examines sets of answers responding to specific types of questions and explores the degree to which question types are predictive of answer quantity and answer quality. Blending qualitative and quantitative methods, the paper builds upon rich coding of a representative sets of real questions — drawn from Answerbag, (Ask) MetaFilter, and Yahoo! Answers — in order to better understand whether the explicit and implicit theories and predictions drawn from coding of these questions were borne out in the corresponding answer sets found on these sites. Quantitative findings include data underscoring the general overall success of social Q&amp;A sites in producing answers that can satisfy the needs of those who pose questions. Additionally, this paper presents a predictive model that can anticipate the archival value of answers based on the category and qualities of questions asked. Qualitative findings include an analysis of the variation in responses to questions that are primarily seeking objective, grounded information relative to those seeking subjective opinions.
Performance Analysis of ACO and FA Algorithms on Parameter Variation Scenarios in Determining Alternative Routes for Cars as a Solution to Traffic Jams

This study proposes several alternative optimal routes on traffic-prone routes using Ant Colony Optimization (ACO) and Firefly Algorithm (FA). Two methods are classified as the metaheuristic method, which means that they can solve problems with complex optimization and will get the solution with the best results. Comparison of alternative routes generated by the two algorithms is measured based on several parameters, namely alpha and beta in determination of the best alternative route. The results obtained are that the alternative route produced by FA is superior to ACO, with an accuracy of 88%. This is also supported by the performance of the FA algorithm which is generally superior, where the resulting alternative route is shorter in distance, time, running time and there is no influence on the alpha parameter value. But in each iteration, the number of alternative routes generated is less. The contribution of this research is to provide information about the best algorithm between ACO and FA in providing the most optimal alternative route based on the fastest travel time. The recommended alternative path is a path that is sufficient for cars to pass, because the selection takes into account the size of the road capacity.
A Software Security Entity Relationships Prediction Framework Based on Knowledge Graph Embedding Using Sentence-Bert

Recently, the need for complex cyber attack knowledge is increasing with the rising risk of software vulnerabilities and weaknesses on the internet. To spread knowledge and strengthen software security defense, researchers record software vulnerabilities, weaknesses, and attack patterns through software databases, including CVE, CWE, and CAPEC, etc. However, software security databases are time delayed and thus miss unobserved facts. Attackers can take advantage of this problem to execute an attack successfully. Therefore, the reasoning task of predicting software security entity relation is critical to supplementing software security data. This paper constructs a software security knowledge graph and proposes a knowledge graph representation learning method combining Sentence-Bert and GAT. The way can implement link prediction and classification tasks for knowledge graph completion. We finally designed a large number of experiments to evaluate the effectiveness of our model in knowledge graph completion and knowledge graph classification. The experimental results demonstrate that the proposed method can effectively improve the effectiveness of prediction.
To the 90th anniversary of the birth of Yuri Konstantinovich Korolev (1929–1992)

Abstract. The article is dedicated to the memory of Yuri Konstantinovich Korolev (1929–1992), corresponding member of the USSR Academy of Arts, people’s artist of the USSR, laureate of the RSFSR state prize named after I.E. Repin and the State prize of the Russian Federation, Secretary of the Board of the Union of Artists of the USSR, General Director of the all-Union Museum Association “The Tretyakov Gallery”.
SQrum: An Improved Method of Scrum

Software systems are having a major impact on many aspects of personal and professional life.Safety-critical applications, such as production line controls, automotive operations, and process industry controls, rely significantly on software systems.In these applications, software failure may result in bodily damage or death.The proper operation of software is essential to the safety and well-being of individuals and businesses.Therefore, software quality assurance is of paramount relevance in the software business today.In recent years, Agile Project Management and particularly Scrum, have gained popularity as a method of dealing with "vuca" business environments, which are characterized by rising Volatility, Uncertainty, Complexity, and Ambiguity.This paper contributes to the software development body of knowledge by proposing a metamodel of Scrum quality assurance, named SQrum ('SQ' of Software Quality and 'rum' of Scrum).Our objective is to make Scrum more efficient and reliable and to assist enterprises in undertaking quality assurance activities while considering agile practices and values.
Classification of Imagined Speech of Vowels from Eeg Signals Using Multi-Headed Cnns Feature Fusion Network

Brain-Computer Interface (BCI) provides a platform to communicate using Electroencephalogram(EEG) signals by converting them into commands that can be used by the output device toperform the desired tasks. This paper focuses on the identification of vowels from EEG signals.First, a dataset of EEG signals has been created for the identification of vowels by collecting datausing a 14-channel EEG device Emotiv- epoc+ from 16 subjects. Then, a deep learning-basedmodel is proposed using a multi-headed Convolutional Neural Network for feature extractionand classification of imagined speech of vowels. Butterworth lowpass and bandpass filter oforder five are implemented for denoising and sub-banding of the EEG signals which are furtherpre-processed using Hilbert Huang Transform. The model has achieved an average accuracy of97.67% with a five-fold cross-validation technique using all six sub-bands of the EEG signals.This is reported to be higher than existing deep learning model studies with an improvementof 10.17% in the classification accuracy. The model has achieved an average precision and recallof 95.54% and 95.11% respectively. The proposed model is statistically tested using theMann-Whitney U test and paired t-test with a p-value less than 0.05.
Image Denoising Method and System Based on Enhanced Deep Dilated Convolutional Neural Network

In order to improve the performance of image denoising, in the case of can not only reduce the computational cost at the same time to ensure the superiority of denoising, we made a change on the basis of the original network, through the way of increasing network breadth rather than depth for more features, and to improve the running speed, by means of expansion convolution to extract more information used for denoising task. A large number of experimental results show that this kind of network can not only reduce the gradient explosion, but also effectively reduce the noise intensity of the image.
On the Calculation of the Minimum Distance of a Linear Code

Some aspects of the algorithms for calculating the minimum distance of linear codes over finite fields are presented.