Reviewerly/exHarmony huggingface dataset:
Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent, Information Retrieval

In modern industrial systems, collected textual data accumulates over time, offering an important source of information for enhancing present and future industrial practices. Although many AI-based solutions have been developed in the literature for a domain-specific information retrieval (IR) from this data, the explainability of these systems was rarely investigated in such domain-specific environments. In addition to considering the domain requirements within an explainable intelligent IR, transferring the explainable IR algorithm to other domains remains an open-ended challenge. This is due to the high costs, which are associated with intensive customization and required knowledge modelling, when developing new explainable solutions for each industrial domain. In this article, we present a transferable framework for generating domain-specific explanations for intelligent IR systems. The aim of our work is to provide a comprehensive approach for constructing explainable IR and recommendation algorithms, which are capable of adopting to domain requirements and are usable in multiple domains at the same time. Our method utilizes knowledge graphs (KG) for modeling the domain knowledge. The KG provides a solid foundation for developing intelligent IR solutions. Utilizing the same KG, we develop graph-based components for generating textual and visual explanations of the retrieved information, taking into account the domain requirements and supporting the transferability to other domain-specific environments, through the structured approach. The use of the KG resulted in minimum-to-zero adjustments when creating explanations for multiple intelligent IR algorithms in multiple domains. We test our method within two different use cases, a semiconductor manufacturing centered use case and a job-to-applicant matching one. Our quantitative results show a high capability of our approach to generate high-level explanations for the end users. In addition, the developed explanation components were highly adaptable to both industrial domains without sacrificing the overall accuracy of the intelligent IR algorithm. Furthermore, a qualitative user-study was conducted. We recorded a high level of acceptance from the users, who reported an enhanced overall experience with the explainable IR system.
Quality Classification of Palm Oil Products Using Naïve Bayes Method

Indonesia is a country that can produce the highest palm oil in the world. The islands of Sumatra and Kalimantan are the islands that have the largest plantations, especially oil palm in Indonesia. Riau Province which is located on the island of Sumatra can produce the highest oil palm on the island of Sumatra. Quality is an important component in business continuity in the palm oil industry. The quality of the crude palm oil products of a company in the Kerumutan sub-district, Pelalawan district, Riau, depends on the content of the final processing product. The content consists of impurities in crude palm oil (CPO), CPO moisture content, free fatty acid CPO levels, determination of bleachability index CPO, CPO carotene, dirt kernel, moisture kernel, and broken kernel. The final quality of palm oil products is determined from the combined results of CPO quality and kernel quality. Good quality raw materials will affect the selling price of these raw materials to produce a good quality final product. Determine the quality of palm oil products poses a problem in terms of time because they must be checked one by one through processing in the laboratory. Application builder who can determine the quality classification of palm oil products is the purpose of this research. This application is expected to help labor officers in the process of classifying the quality of crude palm oil products more quickly, precisely, and accurately. This study uses the Naïve Bayes algorithm because it requires smaller amounts of training data in the data classification process. The accuracy level of the Naïve Bayes method in determining the quality of palm oil products is 82.05%.
UPBRINGING IN TEACHING REVISITED

The relevance of one of the patterns of upbringing is considered, namely, that upbringing takes place in the learning process. The article analyses the correspondence of the Model education program proposed in 2020 to the real state of affairs in the education process.
Smart Wheelchair with Voice Control for Physically Challenged People

A wheel chair is a mechanically operated device that allows the user to move about independently. This minimizes the user's personal effort and force required to move the wheelchair wheels. Furthermore, it allows visually or physically handicapped people to go from one location to another. Voice commands and button controls can be used to operate wheelchairs. In recent years, there has been a lot of interest in smart wheelchairs. These gadgets are very handy while traveling from one location to another. The devices can also be utilized in nursing homes where the elderly have difficulties moving about. For individuals who have lost their mobility, the gadgets are a godsend. Different types of smart wheelchairs have been created in the past, but new generations of wheelchairs are being developed and utilized that incorporate the use of artificial intelligence and therefore leave the user with a little to tamper with. The project also intends to develop a comparable wheel chair that has some intelligence and so assists the user in his or her mobility.
A Time Series Forecasting with different Visualization Modes of COVID-19 Cases throughout the World

In real world applications, one of the prosperous field of science is time series forecasting due to its recognition though having some challenges in the development of methods. In medical field, time series forecasting models have been successfully used in various applications to predict progress of the disease, measure the risk dependent on time and the mortality rate. However due to the availability of many techniques which excel in each of a particular scenario, choosing an appropriate model has become challenging. When a huge dataset is considered it is obvious that machine learning is the best way to perform predictive analysis or pattern recognition tasks on the data. Before machine learning can be used, the time series forecasting problems should be reframed into supervised learning problems. The purpose of machine learning in this field is also to tackle the different challenges like data pre-processing, data modelling, training and any other refinement required with respect to the actual data. This paper deals with the predictive analysis and various visualization applications for time series forecasting of COVID- 19 patients throughout the world.
Credit Card Fraud Detection System using Machine Learning Algorithms and Fuzzy Membership

Fraudulent transactions have skyrocketed in tandem with the rise in Credit Card users. Since legitimate and fraudulent transactions look similar, it is nearly impossible to tell one from the other. This paper proposes a fraud detection system that uses Machine Learning (ML) and a fuzzy membership function to identify fraudulent transactions. The ML techniques used were Logistic regression (LR), Linear Discriminant Analysis (LDA), and the boosting algorithm XGBoost to create models for the proposed system. The dataset from Kaggle was used for training and testing these models. Many performance metrics were used to evaluate the proposed system models’ efficiency: confusion matrix, accuracy, precision, f1, recall, and AUC. The results showed the superiority of the XGBoost model over the other models.
Metode Pembobotan Jarak dengan Koefisien Variasi untuk Mengatasi Kelemahan Euclidean Distance pada Algoritma k-Nearest Neighbor

k-Nearest Neighbor (k-NN) is one of the classification algorithms which becomes top 10 in data mining. k-NN is simple and easy to apply. However, the classification results are greatly influenced by the scale of the data input. All of its attributes are considered equally important by Euclidean distance, but inappropriate with the relevance of each attribute. Thus, it makes classification results decreased. Some of the attributes are more or less relevance or, in fact, irrelevant in determining the classification results. To overcome the disadvantage of k-NN, Zolghadri, Parvinnia, and John proposed Weighted Distance Nearest Neighbor (WDNN) having the performance better than k-NN. However, when the result is k &gt;1, the performance decrease. Gou proposed Dual Distance Weighted Voting k-Nearest Neighbor (DWKNN) having the performance better than k-NN. However, DWKNN focused in determining label of classification result by weighted voting. It applied Euclidean distance without attribute weighting. This might cause all attribute considered equally important by Euclidean distance, but inappropriate with the relevance of each attribute, which make classification results decreased. This research proposed Coefficient of Variation Weighting k-Nearest Neighbor (CVWKNN) integrating with MinMax normalization and weighted Euclidean distance. Seven public datasets from UCI Machine Learning Repository were used in this research. The results of Friedman test and Nemenyi post hoc test for accuracy showed CVWKNN had better performance and significantly different compared to k-NN algorithm.
Society Publishing Guidelines Creation, Workflows, and Best Practices

MODERATOR: Michael A Friedman Sr Manager for Publishing Operations American Meteorological Society Boston, Massachusetts SPEAKERS: Michael A Friedman Shelley Stall Sr Director, Data Leadership American Geophysical Union Washington, DC Heather Goodell Vice President Scientific Publishing American Heart Association Dallas, Texas REPORTER: Judy Connors Do It Write Editorial, LLC The consideration, development, and implementation of society publishing policies and guidelines is an area that varies greatly based on the culture of the workplace community and the vision of each individual organization. The past 18 months, however, have taught us the value of being flexible and creative, not only in scholarly publishing, but in life. Many associations have had to alter standard work protocols to accommodate a virtual workplace and, for some, that has meant adjusting the existing workflow. Now, with vaccinations readily available and offices reopening, it is a good time to revisit best practices for implementing an efficient and effective workflow and figuring out what will work and what to avoid. This session examined different approaches for the development of publishing guidelines, workflow, best practices, and the challenges around implementation from three perspectives: a small-to-midsize society (American Meteorological Society), a large umbrella society (American Heart Association), and a large society that has been very involved in promoting common approaches across multiple societies (American Geophysical Union). Moderator Michael A Friedman, Sr Manager for Publishing Operations at the American Meteorological Society (AMS), a small-to-midsize society located in Boston, approaches this topic considering all the constituencies they serve—academia, the public, the private […]
A Comparative Study on Eumdok Gugyeol in the Undisclosed Goryeo Dynasty Edition of『Neungeomgyeong Vol 8·9』

This study examines the Eumdok Gugyeol, the phonetical Gugyeol reading, in 『Neungeomgyeong Vol 8·9』Eumdok Gugyeol Data, which have not yet been disclosed among the Buddist scriptures published during the Goryeo dynasty. This data consists of two volumes and one book, with only 8 and 9 volumes remaining. The study looks at the chracteristics of Eumdok Gugyeol in the edition of ‘Girimsa Temple’ published in the Goryeo Dynasty through a comparative analysis with the edition of ‘Nam Kwon-hee(A)’. A brief summary of what has been discussed above is as follows. First, considering the form and bibliographic aspects of this book, it is estimated that the pubication date is from the 12<sup>th</sup> to 13<sup>th</sup> century. Second, there are 55 total types of Gugyeol recorded in this data. Among these, there are a total of 53 Gugyeol types written in Volume 8, and a total of 38 Gugyeol types written in Volume 9. Third, there are 275 combined types of Gugyeol recorded in this data. Among them, there are 212 types of combination in Volume 8 and 161 types in Volume 9. Fourth, by comparing and analyzing 『Neungeomgyung』, the different edition of Eumdok Gugyeol and 『Neungeomgyung Eonhae』, the Korean translation of Eumdok Gugyeol from the end of the Goryeo dynasty to the early Joseon dynasty, the usage was examined focused on the types of combination such as ‘-□-’, ‘-□-’, ‘□(□)□□[딧ᄒᆞ-]’, ‘-□□[-ㅅ다]’, ‘□□[ᄒᆞ사]’, ‘□[셔]’, ‘□□□□□[ᄒᆞ여드샤가]’ and so on, which are considered to be a characteristic of ‘the edition of Girimsa Temple’.
Design of Blowdown Line LNG Filling Station ISO Tank

LNG (Liquid Natural Gas) is a liquefied natural gas, with composition of 87% - 96% methane, 1,8-5,1% ethan, 0,1-5,1% propane and other compounds. The composition of natural gas (LNG formation) varies depending on the source and the process of its formation. Methane gas in LNG has odorless, non-corrosive and non-toxic properties (Air Products, 1999). LNG is basically an alternative method to deliver gas from producer to consumer. When cooled to -162˚C at 1 atm pressure, natural gas becomes liquid and its volume decreases up to 600 times (Handbook of Liquefied Natural Gas, 2014). With such a large volume reduction, liquefied natural gas (LNG) can be transported through the Tanker Ship and the ISO Tank Truck. PT Badak NGL has 3 (Three) T.U.K.S (Terminal For Ownership) As Tanker Ship Facilities and 1 (One) LNG Filling Station as a means of filling LNG to ISO Tank. Development of business process of PT. Badak NGL leads to an increase in LNG filling Station capacity. Along with the plan to increase the filling station capacity, the problem of BOG (Blow of Gas) wastage along with some LNG to ground flare becomes a serious concern. When the number of fillling stations is only 1 (one) station, the BOG wasted condition is not significant. However, with plans to increase the number of filling stations, BOG wasted need to be considered to be fully utilized. Therefore, there is a thought to utilize BOG to become more economic value, through the design of blowdown line on LNG ISO Tank filling station. In the Design of this line Blowdown, Using Pipe Ø2 "(PIPE BE 40S SS A312-TP305 SMLS) interconnecting with Pipe Ø6" (PIPE BE 40S SS A312-TP305 SMLS) BOG Header of new Filling Station under construction (Beginning January 2018 ).
The Astronaut Game – A Vocabulary Game Employing Natural Language Processing Techniques

Serious games are viable alternatives for learning and for acquiring new skills.Serious games introduce users with a learning environment that mimics real life situations, where they can practice and improve their skills.While considering education, we focus on games used for language learning, which cover multiple categories that range from vocabulary acquisition, comprehension, collaboration, to free text writing.The serious game described in this paper -the Astronaut Game -is design for English as Second Level users and consists of two parts.The first part consists of a Platform Jumper for vocabulary acquisition, in which an astronaut runs and jumps to collect rocket pieces that symbolize words; the aim of this sub-game is to teach words from different lexical fields to the players, depending on the level they are playing.The second part, the Rocket Hangman, is centered on comprehension and users must identify the central keywords from the reference text.Our game was tested by a group of 16 individuals and received positive feedback.
Sentimental Analysis based on Text and Emoticons

Long range informal communication has bit by bit become an everyday practice for individuals to post their feelings, perspectives and remarks on any item or individual. Individuals share their sentiments online in an exceptionally casual language. Along these lines, it is exceptionally troublesome errand to break down precise slants connected with that common language. Estimation Analysis is an investigation of individuals' mentality, assessments, and feelings to arrange whether it is certain, negative or impartial. Utilization of emojis via web-based networking media has expanded quickly as of late. Subsequently, we have concentrated more on how emojis assume a significant job in opinion examination. Different elements that influence supposition examination are talked about quickly in this paper. Likewise, different issues like mockery identification, multilingualism, taking care of abbreviations and slang language, lexical variety and dynamic word reference dealing with are talked about. A few examinations were then performed to break down the impact of emojis on tweets utilizing AI strategies. At long last, the general execution investigation of the classifier was figured utilizing the NLTK stemming system.
Supplemental Material for Subtypes of the Missing Not at Random Missing Data Mechanism

This supplemental material is an extension of the "Generating Missing Values from MNAR Subtypes" section of the article.Specifically, the MNAR generation methods of Table 1 of the article are compared side by side in a small-scale simulation study.Readers may assume the notation, terminology, and set-up here are equivalent to the main text of the article in all respects unless explicitly noted otherwise.
European Union Higher Education and Science Programs During the Covid-19 Pandemic

The author looks at the impact of the COVID-19 crises on the European higher education and scientific cooperation programs. During the pandemic the national governments announced strict lockdowns and other protective measures. Free movement across borders was hampered, university campuses mostly closed and many activities put on hold. In these circumstances participants of international exchange programs became particularly vulnerable: some were unable to move to their destination university or institution, others were experiencing difficulties returning to their home countries. Numerous logistical, administrative, health-related and other problems made the situation difficult for the students, professors and researchers, as well and for the staff responsible for the implementation of such programs. In response to this situation, the European Commission and its executive agencies issued guidelines for the participants, extended project deadlines and made other arrangements. At the same time, additional resources were channeled to digital education initiatives and to supporting research aimed at overcoming the pandemic, including diagnostics, treatment and prevention of COVID-19 and other related research fields. In the new program cycle the budget for the EU education and scientific cooperation programs, such as Horizon and Erasmus have been significantly increased and linked to the post-crisis recovery package. A new impulse has been given to the European education area and European research area. The author argues that despite serious problems caused by the pandemic itself and the lockdowns, the crisis became an additional factor for extending higher education and research programs and deepening integration efforts in these areas.
Penggerombolan Sekolah pada Penerimaan Mahasiswa Baru Jalur SNMPTN di IPB Menggunakan Metode Two-Step Cluster

New student admissions are opened in three pathways including SNMPTN, SBMPTN, and Seleksi Mandiri. In order to improve the SNMPTN selection system at IPB, a study was conducted on the quality of SMA/MA which registered to IPB through school clustering. In general, cluster analysis cannot handle large and mixed-type data, so this school clustering used the Two-Step Cluster method with two alternatives, namely without handling outliers and handling 5 percent outliers. Both of these alternatives produced an average Silhouette coefficient value of 0.2 and 0.3 respectively, which was still under the good category. However, clustering without handling outliers resulted in more detailed cluster criteria with 4 optimal clusters. The criteria for these four clusters include, Cluster 1 is a category of Low Commitment, Low Quality, and Low Consistency schools, Cluster 2 and 3 are categories of schools that have special criteria in certain categories, and Cluster 4 is a category of High Commitment, High Quality, and High Consistency.
Features Of Simulation Of Self-Regulated Learning Of Students In The Student Group

One of the modern approaches describing the success of learning in a situation of uncertainty is an ontological model of the stages and ways of the students’ agency formation, and the ways of performing educational actions correspond to the principle of fractality due to the stages of agency formation. A model of self-regulating learning in a study group is proposed, in which the methodological technique of cross-delayed correlation is used to obtain causal relationships between individual stages and methods of performing educational actions. Empirical studies conducted on samples of 59 university students (М=19.52; SD=2.36; all women) and 45 students of secondary vocational education (М=16.81; SD=0.69; 98 per cent young men) showed that there is an influence of the stages of agency formation on the ways of performing educational actions, which in turn affect the stages of agency formation. The condition of self-regulation of learning with the allocation of attractors according to the mathematical theory of dynamical systems has not been confirmed. The absence of attractors in the third-year study group is due to the mixed learning of students both in distance and in traditional full-time forms. The absence of a specific structure in the educational environment created conditions of uncertainty that influenced the formation of self-regulated learning.
Problematização: Experiências de estudantes de Medicina e Enfermagem na prática profissional

Introduction: in view of the need for curricular reforms to meet the requirements of national curriculum guidelines, new teaching proposals are needed. To this end, it proposes the use of active learning methodologies, which stimulate a student-centered teaching and learning process, promoting active, critical and reflective learning. Goals: to describe the experiences of medical and nursing students inserted in professional practice under the logic of problematization. Methods: qualitative research, carried out in a Higher Education Institution in 2018. The research took place through semi-structured interviews with medical and nursing students from the first and second series of the courses. The data were analyzed using the content analysis technique in the thematic modality and discussed in the light of the problematization. Results: the data were organized into three categories: possibilities of learning in the practice setting; the role of the facilitator in problematizing; difficulties to implement the problematization in professional practices. Conclusions: The students highlighted that learning in the practice scenario favors the articulation between theory and practice, through an integrated and reflective movement, in addition, the role of the facilitator and the link with the health unit, influence, in a way considerable impact on student performance.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp