Reviewerly/exHarmony huggingface dataset:
Hera: Evidence for Multiple Mineralization Events and Remobilization in a Sediment-Hosted Au-Pb-Zn-Ag Deposit, Central New South Wales, Australia

The Hera Au-Ag-Pb-Zn deposit of central New South Wales, Australia with a total undepleted resource of 3.6 Mt @ 3.3 g/t Au, 25 g/t Ag, 2.6% Pb and 3.8% Zn occurs on the SE margin of the Cobar Basin. It is hosted by the shallow marine Mouramba Group and overlying turbiditic Amphitheatre Group. The siltstones comprise various mixtures of quartz, plagioclase, muscovite-phengite, biotite and clinochlore, along with accessory titanite and ilmenite. The deposit comprises a number of discrete lodes which are steeply west-dipping and strike NNW. Each lode has different abundances of the main ore minerals sphalerite, galena, chalcopyrite, pyrrhotite and electrum-gold. The North Pod and Far West lenses have the most diverse mineralogy in additionally containing arsenopyrite, native silver, gudmundite, Ag-tetrahedrite, acanthite, dyscrasite, native antimony, nisbite and breithauptite. Electrum (continuous spectrum from Ag-rich to Au-rich) is associated with sulfides in the main ore lenses while native gold occurs in the host rocks along cleavages/lineations and away from the main ore. The sulfur isotope data from across the deposit indicates a magmatic source. Most of the deposit has experienced greenschist facies metamorphism with pervasive green chlorite alteration, though the North Pod differs in being distinctly Ag- and Sb-rich and has reached at least amphibolite facies metamorphism with a garnet-wollastonite-vesuvianite-tremolite assemblage. Tremolite is relatively abundant throughout most of the deposit suggesting widespread low-T skarn alteration. Cross-cutting pegmatites comprise quartz, plagioclase (labradorite-andesine) and microcline. Hydrothermal remobilization is relatively extensive and best explains the unusual Ag-Sb-As assemblages of the North Pod and Far West lodes.
Aplikasi Kesehatan Ibu Hamil Berbasis Android

In Indonesia, the mortality rate for pregnant women is still high. One of the contributing factors is the lack of knowledge and awareness of communication between pregnant women and midwives in the process of antenatal care, resulting in a delay in recognizing the danger signs of pregnancy. The purpose of this study is to provide a pregnant mother's health application that can be run on an android system. Android as a smartphone operating system, supports the development of new application ideas and innovations to add system functionality. The stages of making the application include (1) analyzing user needs by observing the Independent Practice Midwife in the Polokarto Sukoharo area, Central Java. Next, (2) design a system consisting of two users, including midwives and pregnant women. Finally, (3) the implementation of making a system design in the form of an android application. This Maternal Health Application has a feature that pregnant women can communicate with midwives directly to submit complaints, examination results, information about pregnancy and the calculation of Estimated Birth Days (HPL). Thus, it is hoped that this application can be used as a digital health service that facilitates access to information and public health services, especially those related to pregnancy.
IMPLEMENTATION OF GENETIC ALGORITHM IN THE CURRENT SCHEDULING SYSTEM

Scheduling courses is a routine job in academic activities at a college. In its implementation, the scheduling process is not easy to do because many factors need to be considered, several factors that are considered, such as the willingness of lecturers to teach, the availability of classrooms. Besides that, it is also necessary to pay attention to the number of classes in each subject. Course scheduling is a combination of courses, days, time, lecture space, and consideration of lecturers' willingness to teach. To solve the course scheduling problem, a system that can handle the scheduling process is needed. The method that can be used to solve this problem is to use the Genetic Algorithm approach. The genetic algorithm is a scheduling algorithm that can combine lecture time and space automatically by applying a natural or gene selection system. Based on the research that has been done, the genetic algorithm can solve scheduling problems quickly, which only takes 15 seconds for 78 classes and uses as many as 16 chromosomes. Also, the fitness value of all chromosomes is 0, this means that the scheduling results obtained are optimal.
A Model of Speaking Teaching Materials for Primary English Teachers’ Candidates Based On Contextual Approach

The purpose of this research is to develop teaching materials in English speaking skills that are suitable for the needs of primary English teachers’ candidates in the Teacher Training and Education Faculty of Muhammadiyah University of Tangerang. The research is designed with a research and development study or R and D cycle consisting of seven steps: literature study, needs analysis, syllabus analysis, teaching material development, product validation, product testing and final product. Data collection techniques used observation, interview, questionnaire and product tests. The findings of the study reveal that 12 chapters of speaking teaching material was chosen for Primary English teachers’ candidates based on contextual approach, namely: greeting and introducing, counting numbers, talking about family, describing objects, talking about houses, school and class environment, telling time and talking about daily activity, talking about feeling and health issues, shopping time, asking and giving directions, talking about plans and telling past experiences. The results of the effectiveness test show that speaking teaching materials are stated to be good. These findings call for lecturers to apply the teaching material developed in the learning process as a main reference or as a supplementary material so that it can make learning material more interesting, innovative, and effective.&#x0D;
Heterogeneous information network-based interest composition with graph neural network for recommendation

Heterogeneous information networks (HINs) are widely applied to recommendation systems due to their capability of modeling various auxiliary information with meta-paths. However, existing HIN-based recommendation models usually fuse the information from various meta-paths by simple weighted sum or concatenation, which limits performance improvement because it lacks the capability of interest compositions among meta-paths. In this article, we propose an HIN-based Interest Composition model for Recommendation (HicRec). Specifically, user and item representations are learned with a graph neural network on both the graph structure and features in each meta-path, and a parameter sharing mechanism is utilized here to ensure that the user and item representations are in the same latent space. Then, users' interests in each item from each pair of related meta-paths are calculated by a combination of the user and item representations. The composed user interests are obtained by their single interest from both intra- and inter-meta-paths for recommendation. Extensive experiments are conducted on three real-world datasets and the results demonstrate that our proposed HicRec model outperforms the baselines.
Evaluation of Student Academic Performance Using Naïve Bayes Classifier

The best way to achieve the highest quality in the higher education system is to increase the ability of students to improve their performance.Students' ability to find information is called rules for identifying information.The Bayesian Classification represents a supervised learning method as well as a statistical method for classification.Data mining is used to find knowledge that may be useful in applying active learning in the technology perspective.There are two approaches which can be used to discover knowledge by data mining techniques such as prediction, classification, clustering, association rule and statistical methods.Among them, this paper predicts using data mining classification method.The data used in this paper is based on the student records that collected from department at the end of each semester.The main purpose of this paper is to assess students' ability to study in quality education and to assess student performance through the Naïve Bayes classification.These findings will be very helpful to teach data mining subject in the coming year for students.
ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

Aspect based sentient analysis (ABSA) is identified as one of the current research problems in Natural Language Processing (NLP). Traditional ABSA requires manual aspect assignment for aspect extraction and sentiment analysis. In this paper, to automate the process, a domain-independent dynamic ABSA model by the fusion of Efficient Named Entity Recognition (E-NER) guided dependency parsing technique with Neural Networks (NN) is proposed. The extracted aspects and sentiment terms by E-NER are trained to a Convolutional Neural Network (CNN) using Word embedding’s technique. Aspect categorybased polarity prediction is evaluated using NLTK Vader Sentiment package. The proposed model was compared to traditional rule-based approach, and the proposed dynamic model proved to yield better results by 17% when validated in terms of correctly classified instances, accuracy, precision, recall and F-Score using machine learning algorithms.
Evaluation of Service Dimensions of Traditional Banking and Digital Banking: The Case Study of Lloyds Bank, United Kingdom

Banking has evolved over a period of time with the technological advancement.Customer preferences of traditional versus digital banking need to be studied in order to provide better banking experience for the customers.There are various effects of digital banking, which have been identified and discussed by reviewing literature sources.In order to collect the data, customers of Lloyds bank plc, United Kingdom were selected.Two scales are developed to measure the effects such as usefulness, accessibility, easiness to use, efficiency, availability, cost effectiveness, security, responsiveness and credibility of digital banking and traditional banking.Through the study, it was found out that security of the digital banking is the key aspect that customers prefer in the traditional banking over digital banking.Hence, recommendations are made to Lloyds bank to maintain higher level of security in the digital banking to enhancethe customer satisfaction.
ORCA - a Benchmark for Data Web Crawlers

The number of RDF knowledge graphs available on the Web grows constantly. Gathering these graphs at large scale for downstream applications hence requires the use of crawlers. Although Data Web crawlers exist, and general Web crawlers could be adapted to focus on the Data Web, there is currently no benchmark to fairly evaluate their performance. Our work closes this gap by presenting the Orca benchmark. Orca generates a synthetic Data Web, which is decoupled from the original Web and enables a fair and repeatable comparison of Data Web crawlers. Our evaluations show that Orca can be used to reveal the different advantages and disadvantages of existing crawlers. The benchmark is open-source and available at https://w3id.org/dice-research/orca.
AJET in 2021: Change, bibliometrics and future directions

This is the first issue of AJET for 2021 and the first for the new Lead Editors. We introduce ourselves and celebrate the contributions of the outgoing team. In this editorial, we then present the AJET bibliometric data updated for 2020 and describe the changes to the team of Associate Editors that support the work of this journal. Finally we outline the articles that are included in this issue and present our plans for 2021 and into the future.
Multi-Sensor Fusion for Online Detection and Classification of Table Tennis Strokes

Sports training generally focuses on speed of response and variety of strategies aimed at encouraging sustainable physical activity and improving learning skills for players, thus, enhance their performance and skills through matches.In this paper, we are presenting a methodology for multi-sensor fusion method of IR depth camera and smart band sensors using curve fitting to recognize and group the characteristics of strokes performed by players and adapt the training accordingly.The main aim of the methodology is to classify various techniques played in table tennis and enhance the strokes based on various body joints.Moreover, the main contribution of this paper is to experiment different sensors to get the most optimal tools in classifying players strokes.Also, to test various classification algorithms to get the optimal and best result possible.Overall, based on the experiments we have concluded that sensor fusion between internal sensors and IR depth camera has increased the classification results and robustness of the solution.The system's results indicate an average accuracy of 95% -100%.
The Role of Academic Supervision and Communication on Teacher Performance

Teachers as education personnel have an important role in determining success in achieving educational goals. Schools pay special attention to teacher competence so that teachers are required to continue to develop professionalism and performance. This study aims to obtain information about the role of academic supervision and interpersonal communication. research place at public elementary schools in the administrative city of South Jakarta. This study used a survey method with path analysis techniques and a causal quantitative approach. The sample in this study was 81 teachers who were taken using simple random sampling technique or simple random sampling and the teachers who were the object of the research were determined using the sample technique formula from Slovin. The data analysis technique was performed using descriptive and inferential statistics by first doing the prerequisite analysis, namely population normality and regression analysis. The findings from the study that academic supervision activities and smooth interpersonal communication play a major role in improving teacher performance. Professional teachers will be able to manage learning activities well and will be able to provide motivation to students so that the goals of learning will be achieved and produce students who excel in their respective fields.
Semantic Recommender System Based on Semantic Similarity Using FastText and Word Mover’s Distance

Lecturers and students use research papers as references for their projects, such as research, final projects or theses.Obtaining suitable papers is critical for the success of their projects.Nonetheless, finding the correct papers, especially in determining the correct words to paper searching, is challenging.This paper proposes a method that combines FastText and Word Mover's Distance for a recommender system to solve the challenge.FastText and Word Mover's Distance are utilized to gather word embedding and reach semantic similarity, respectively.Data on papers from the IEEE Xplore Digital Library, ACM Digital Library, Science Direct, SpringerLink, and Wiley Digital Library are collected as case studies in this paper.Experiments for verifying the proposed method referring to seven scenarios in which additional three types of queries.The first query applies the original word from the case studies, whereas the second query operates the original word with one word or two words chosen by a user.The third query uses the original word-combining with chosen words based on the proposed method.After implementing these scenarios, most of the results indicate that results produced by the proposed method gain higher accuracy and F-1 score than user query and original query.The proposed method increases a mere 2.3 percent of accuracy and 1.7 percent of F-1.
The Construction of Blended Teaching Mode of Cooking Technology Course Under the Background of &amp;quot;Gold Course&amp;quot;

Under the trend of "Internet+Education", the online and offline blended teaching mode has become an important direction of teaching reform.This article takes the course "Cooking Technology" as an example to build an online and offline blended teaching mode, and investigates the application of blended teaching in the teaching of applied undergraduate courses.Using empirical analysis and interview methods, the article found that the curriculum teaching of "Cooking Technology" has insufficient combination of theory and practice, single teaching mode, poor teaching effect, and heavy teaching tasks.Base on the principle of student-centered, pedagogical coherence, integration of theory and practice, this article designs the blended teaching activities for the "Cooking Technology" course from the three major links, which is the pre-course phase, in-class stage and afterschool phase.This article puts forward the issues that should be focus on interaction in blended learning and teaching, establishing a reasonable and sound appraisal method, pay attention to the percentage of online instruction used, so as to provide reference and reference for the construction of "golden courses" of culinary majors and other majors.Finally, the study found that the blended teaching can better cultivate students' autonomy in learning and solve the basic problems that most students are not interested in learning.
Towards and Open Source Finite-State Morphological Analyzer for Zacatlán-Ahuacatlán-Tepetzintla Nuhuatl

In this paper, we describe an in-progress, free and open-source Finite-State Transducer morphological analyzer for an understudied Nahuatl variant. We discuss our general approach, some of the technical implementation details, the challenges that accompany building such a system for a low-resource language variant, the current status and performance of the system, and directions for future work.
Data-driven measures to mitigate the impact of COVID-19 in South America: how do regional programmes compare to best practice?

Abstract • This article analyses data-driven measures used in South America to mitigate the impact of COVID-19. Based on a broad review of relevant programmes in the region three selected cases from Argentina (Cuidar App), Brazil (use of personal data by IBGE), and Chile (CoronApp) are evaluated against best regional and international practices. • Our findings suggest that programmes in South America mirror approaches in other global regions and as such face many similar challenges. There is no clearly defined purpose, a lack of transparency, and the need for readjustment soon after initial development. • While the region is heavily affected by COVID-19, the three case-studies analysed demonstrate that policy makers in the region failed to establish trust in the measures. This can be deducted from low penetration rates of the programmes in Argentina and Chile. • Finally, there are serious concerns regarding the long-term impact of these programmes upon human rights (especially privacy) and human dignity.
DIGITAL LITERACY AND CYBERBULLYING BEHAVIOR OF YOUTHS IN INSTAGRAM

This study aims to describe the level of digital literacy and cyberbullying behavior in adolescents on social media Instagram and to analyze the effect of digital literacy on cyberbullying behavior among adolescents on Instagram social media. The theory used is Gilster's theory of digital literacy. This study uses a quantitative approach with a correlational research type. The population in this study is late adolescents who are active students at the Faculty of Economics and Business, Universitas Islam Kebangsaan Indonesia Bireuen who use Instagram as media, totaling 70 people. The sampling technique is the total sample, namely the number of samples is the same as the population. With certain criteria by researchers, namely late adolescents aged 18-21 years, using Instagram, and active students at the Faculty of Economics and Business, Universitas Islam Kebangsaan Indonesia, Bireuen. The results showed that the level of competency in digital literacy at the average of the subject of the research is high totaling 55 persons or 78,57%, the remaining is 15 persons or 21,42%, which pose at medium level. The level of cyberbullying behavior at the average of the research subject is categorized as low totaling 50 persons or 71,42 %, the remaining is 20 persons or 28.57%, which is categorized as medium. This indicates that the subjects of the research are not only competent in using the social media but also they have had critical thinking on the information they obtained from internet or social media. They also can identify the validity of the information, recheck and used digital media to prove the validity of the information.
Physics Laboratory Management: A Novel Use of QR Code Tag on Physics Equipment Storage Cabinet

The application of QR codes in laboratory management is an increase, but the study of its use in the physics laboratory equipment storage is rarely done. There are numerous in various laboratory equipment stored in the physics laboratory. Well-organized storage with QR-code installment in the cabinet should be a big consideration to assist laboratory users at trace and emplace the equipment in an appropriate. This study aims to develop the application of QR codes in designing the layout of laboratory equipment at the cabinet. The development of the storage layout of laboratory equipment refers to the research and development methods. There are two main focuses in this study, specifically are how to design the layout storage of laboratory equipment and how to develop the QR code scanning system in a physics laboratory. The questionnaire was distributed to 23 respondents, the highest result with 100% agreement shown when they assessed the utility of a QR code-based laboratory storage system and the design of a laboratory equipment layout. The laboratory users give a positive response to the laboratory management system that developed based on QR code. They consider the QR code system is an effective way of trace and emplace the laboratory equipment.
An Extensive Analysis of the Vision-based Deep Learning Techniques for Action Recognition

Action recognition involves the idea of localizing and classifying actions in a video over a sequence of frames.It can be thought of as an image classification task extended temporally.The information obtained over the multitude of frames is aggregated to comprehend the action classification output.Applications of action recognition systems range from assistance for healthcare systems to human-machine interaction.Action recognition has proven to be a challenging task as it poses many impediments including high computation cost, capturing extended context, designing complex architectures, and lack of benchmark datasets.Increasing the efficiency of algorithms in human action recognition can significantly improve the probability of implementing it in real-world scenarios.This paper has summarized the evolution of various action localization, classification, and detection algorithms applied to data from vision-based sensors.We have also reviewed the datasets that have been used for the action classification, localization, and detection process.We have further explored the areas of action classification, temporal and spatiotemporal action detection, which use convolution neural networks, recurrent neural networks, or a combination of both.