Reviewerly/exHarmony huggingface dataset:
User-centered &amp; Robust NLP OSS: Lessons Learned from Developing &amp; Maintaining RSMTool

For the last 5 years, we have developed and maintained RSMTool -an open-source tool for evaluating NLP systems that automatically score written and spoken responses.RSMTool is designed to be cross-disciplinary, borrowing heavily from NLP, machine learning, and educational measurement.Its crossdisciplinary nature has required us to learn a user-centered development approach in terms of both design and implementation.We share some of these lessons in this paper.
Edukasi Alat Penjernih Air Sederhana Sebagai Upaya Pemenuhan Kebutuhan Air Bersih

Water is source of life on earth. The largest water body is in the ocean at 97 percent and 3 percent remaining is fresh water which is used to support daily needs, so clean water is a basic human need. The limited amount of water and increasing people number caused a clean water crisis. Apart from the amount, the quality of fresh water is getting worse. The struggle to use clean water for various uses and clean water wasteful behavior had resulted in the loss of adequate access of clean water for some people. One of the ways to improve clean water quality and quantity is conducting a water purification process. Water purification can be done using simple tools with filtration techniques. Through the Community Partnership Program’s theme "Education of Simple Water Purification as an Effort to Fulfill Clean Water Needs" able to provide knowledge and understanding to teachers and students of MTs Nurul Qur'an Jakarta as activity partners.
Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders

In this paper, a deep stacked auto-encoder (SAE) scheme followed by a hierarchical Sparse Modeling for Representative Selection (SMRS) algorithm is proposed to summarize dance video sequences, recorded using the VICON Motion capturing system. SAE’s main task is to reduce the redundant information embedding in the raw data and, thus, to improve summarization performance. This becomes apparent when two dancers are performing simultaneously and severe errors are encountered in the humans’ point joints, due to dancers’ occlusions in the 3D space. Four summarization algorithms are applied to extract the key frames; density based, Kennard Stone, conventional SMRS and its hierarchical scheme called H-SMRS. Experimental results have been carried out on real-life dance sequences of Greek traditional dances while the results have been compared against ground truth data selected by dance experts. The results indicate that H-SMRS being applied after the SAE information reduction module extracts key frames which are deviated in time less than 0.3 s to the ones selected by the experts and with a standard deviation of 0.18 s. Thus, the proposed scheme can effectively represent the content of the dance sequence.
METHODS OF GENERATING TASKS FOR KNOWLEDGE ASSESSMENT

The article considers different methods of generation of test assignments . Their characteristics and their shortcomings were provided. Based on the shortcomings of other methods, the optimal method of the tests’ generation is chosen in the form of an online exam system with a test generator function. The article describes various requirements for the test assignments including possible types of tests, questions and tasks. The high relevance of the online exam systems is identified, as well as an analysis of its advantages and disadvantages. A comparison of characteristics of the existing systems of online examination is carried out, the basic parameters of these systems and differences between them are defined. Based on this comparison, the essential components of the system and the stages of its creation were identified. The article provides description of each component/module, the functions it performs, as well as the relationship between the modules. Also described are technologies, with which the system will be developed, and reasons for their selection. This system is developed with the server programming language PHP and MySQL database. The structure of the database is given in the article. A description of the most important database tables and their parameters is given. The article identifies different users roles within the system, their capabilities and functions in each module. It also presents their relationships in the form of UML diagrams and describes access levels by roles. The system will assist teachers with the process of generating assignments for various tests or exams. It will also facilitate educational monitoring of each student, identify their learning strengths and weaknesses, and assess their level of knowledge, thus saving teachers’ and students’ time. The teacher will only need to specify the necessary parameters for the test generation application, and the system itself will generate the appropriate assignments with the relevant tasks. The system will also allow students to take a test or exam from any device anywhere. The possible further improvements of the system are described, as well as potential issues that would have to be considered and addressed in the future.
Prediction of Cocaine Inpatient Treatment Success Using Machine Learning on High-Dimensional Heterogeneous Data

The high prevalence of drug addiction is a major health challenge that pressures healthcare systems to respond with cost-effective treatments. To improve the treatment success of drug-dependent patients, it is necessary to identify the main associated risk factors for dropping out of treatment. Previous research shows disparate results due to the wide variety of approaches employed, the different and/or poorly defined metrics used, and the different target populations under study. This article presents the design and selection of a predictive model to estimate success of inpatient cocaine treatment based on a high-dimensional heterogeneous set of characteristics, with the aim of learning new associations between independent characteristics. We evaluated different feature selection techniques and machine learning algorithms to design the best predictive model in terms of accuracy, area under the receiver operating characteristic curve, recall, specificity, F1-score, and Matthews correlation coefficient. Random Forest was the top-performing model with a characteristic set consisting of 11 features selected with a wrapper evaluator and the Best First algorithm, achieving 82% accuracy, 0.81 of area under the receiver operating characteristic curve, 0.96 of recall, 0.47 of specificity, 0.89 of F1-measure and 0.53 of Matthews correlation coefficient. The predictive model's performance was enhanced by combining multiple dimensions with variables referring to previous treatments, mental exploration, cognitive functioning, personality, consumption habits, and pharmacological treatment. We have refined the use of machine learning techniques to predict drug addiction treatment success, which could represent a new step in treatment management especially when included in clinical decision support systems.
(No) competition between deverbal nouns and nominalized infinitives in Spanish

This paper explores the distribution of deverbal nouns and nominalized infinitives that are built on transitive verbs and occur in eventive interpretations. The study is empirically oriented and based on an acceptability judgment experiment in which argument realization and interpretational possibilities are manipulated as the independent variables. The results show that deverbal nouns prefer but are not limited to realizing the lower argument of the base, whereas nominalized infinitives are mostly restricted to realizing the higher argument. Furthermore, deverbal nouns turn out to be insensitive with regard to the distinction between episodic and generic event readings, while nominalized infinitives are shown to be specialized on generic interpretations. Deverbal nouns and nominalized infinitives are, thus, mostly neither paradigmatically interchangeable nor complementarily distributed as nominalized infinitives reach the same degree of acceptability as deverbal nouns only under very narrow conditions. With regard to the ecological validity of the experimental approach, a comparison to corpus data indicates that high frequency clearly correlates with acceptability, but that the same does not hold for low frequency and unacceptability, that is forms that are not (sufficiently) attested in the corpus do not necessarily receive low ratings within the judgment task. The study, thus, also addresses a number of methodological issues in the study of event nominals.
Collaboration between Machine Translation and Human Translation for Higher Quality and More Production in Translation

The present study aims at creating a level of collaboration between MT and HT in order to obtain more production in translation projects and save time, money and effort for translators, businessmen and any other non-specialist MT users. This can be carried out by applying both pre-editing and post-editing on the ST before entering to MT, in a specific way. Hence, the present study is based on approaching the techniques in which MT runs and to what extent we can make it more effective and more productive. MT users should understand that MT includes two types; direct and indirect. Direct type works on the word level while indirect types work on higher levels (i.e. phrase, sentence levels). On the other hand, indirect levels are divided into two approaches; transfer approach which is language -dependent and interlingua approach which is non-linguistic specific. Indirect approaches are capable of translating better than direct approach. A MT user should pay heed to the fact that these approaches run better because they are based on ST analysis and then TT generation. In order to benefit more from MT, facilitate translation process, and make the TT seem as natural as possible, each MT user should pay heed to the fact that the more he simplifies the analysis process, the more he gets more natural translation. A MT user can achieve this simplicity in the analysis process by means of pre-editing ST before submitting it to MT. In fact, pre-editing cannot be performed randomly. Hence, the present study encourages MT users to use the rules of controlling language to simplify and adjust STs according to their business requirements. This, in turn, alleviates the load on post-editing because the ST to be entered to MT becomes easy enough to be understood by MT.
Natural Language Processing Applications in Case-Law Text Publishing

Processing case-law contents for electronic publishing purposes is a time-consuming activity that encompasses several sub-tasks and usually involves adding annotations to the original text. On the other hand, recent trends in Artificial Intelligence and Natural Language Processing enable the automatic and efficient analysis of big textual data. In this paper we present our Machine Learning solution to three specific business problems, regularly met by a real world Italian publisher in their day-to-day work: recognition of legal references in text spans, new content ranking by relevance, and text classification according to a given tree of topics. Different approaches based on BERT language model were experimented with, together with alternatives, typically based on Bag-of-Words. The optimal solution, deployed in a controlled production environment, was in two out of three cases based on fine-tuned BERT (for the extraction of legal references and text classification), while, in the case of relevance ranking, a Random Forest model, with hand-crafted features, was preferred. We will conclude by discussing the concrete impact, as perceived by the publisher, of the developed prototypes.
Survival Skills in Digital Era: An Integration of Digital Literacy into EFL Classroom

The development of digital technology has made digital natives encounter situations that require survival skills to face intense competition in the digital age. This case study investigates how students integrate their digital literacy in the EFL classroom and what components of digital literacy are frequently integrated by the students. Three students from a senior high school in Surakarta were recruited purposively. A case study design was used in this research study. The results of interviews and observations revealed that in practice, students integrate digital literacy in every activity related to the use of the internet and digital tools. Students tended to integrate digital literacy according to the activity and objective to be achieved, ranging from searching for and understanding information to make new products. Meanwhile, digital literacy frequently integrated by the students was photo-visual literacy, information literacy, reproduction literacy, and real-time thinking literacy.
IMPLEMENTATION OF SCHOOL FROM HOME (SFH) THROUGH ONLINE LEARNING IN THE COVID-19 STATE EMERGENCY IN SMP N 1 SEMIN

The spread of the Corona virus that causes Covid-19 disease led the government issuing a new policy in the field of education. The policy aimed to prevent the Corona virus spread within the educational unit. The policy adopted was the Online School from Home (SFH) implementation. Online learning used smart phones through the Internet network. SFH online has been implemented for the first time in SMP N 1 Semin. Many obstacles occurred during the implementation of SFH, among others: unstable internet connection or network, internet data and limited facilities of android smart phones. The survey results from the students stated that there were things that are not pleasant during SFH that are boring, lack of student motivation, too many tasks, lack of focus in learning, and the learning atmosphere is less fun. Ninety percent of students wanted to go back to school and interacted with schoolmates. The implementation of the SFH continued to be extended until the education environment is safe for teaching and learning. Schools were trying to implement strategies so that SFH runs well. This strategy was a joint discussion between the school and the school committee, adjusting the SFH school schedule, and implemented IT training for teachers.
RESULTS OF PROJECT “5 - 100”: POSITIONS OF LEADING RUSSIAN UNIVERSITIES IN TOP-100 OF THREE LEADING WORLD RANKINGS

The analysis of the positioning of 52 leading Russian universities in three world rankings (QS, THE, ARWU) over a nine-year time interval has been carried out. It is shown that none of these universities, except for Moscow State University, was included in the TOP-100 of the rankings under consideration. Universities that are included in the QS ranking for 8 or 9 consecutive years, as well as universities included in 2020 in the stratified 100-rank intervals of the top parts of this ranking were identified.
Improving the Quality of Future Bilingual Mathematics Teachers Knowledge at University Using Digital Education Recourses

This study is devoted to the study of improving the quality of knowledge of future bilingual teachers of mathematics in a modern university using digital educational resources. One of the key objects for assessing the quality of education is the quality of students' knowledge. In the framework of this study, we considered the quality of knowledge of future bilingual teachers of mathematics as the ability to use the Russian and Tatar language in pedagogical activity at the same level. The purpose of the study is theoretical substantiation, development and testing of a model for improving the quality of knowledge of future bilingual mathematics teachers using digital educational resources. The results of the study can be used both nationally and internationally, since the study is aimed at the scientific and practical solution of the problems of modernizing society based on strengthening the national-cultural identity of a new generation of citizens, which is a national priority today. And on an international scale, the study is aimed at training modern specialists who can easily integrate into the international community, based on the development of intercultural communication skills with representatives of various ethnic groups and cultures. The developed model for improving the quality of knowledge of future bilingual teachers by means of DER in the educational process is universal in nature, which makes it possible to use it in other universities.
Pattern Recognition Based On Intelligent System

Neural Network (NN) is an information processing system that has been developed as generalization models of human cognition of neural biology.Two important learning methods for NN are discussed to be our comparative study; these methods are Back Propagation (BP) and Particle Swarm Optimization (PSO). These two methods are found to learn the NN by modifying the weights of the NN, this is done by calculating the fitness value which is considered as a threshold value.In this work the Pattern Recognition (PR) problem will be manipulated, which is considered one of the important applications of classification filed. The Greek Text Letter Recognition (A..) is the objective of this paper, the upper and lower cases are discussed.
Hybrid professionalism in journalism: Opportunities and risks of hacker sources

Hackers have a double relevance with regard to the transformation of the journalistic field: first, they have established themselves as journalistic actors, even if their work may sometimes seem unfamiliar. Second, hackers have not only become important sources for information but they are also a topic of public interest in a data-driven society increasingly threatened by surveillance capitalism. This paper critically discusses the role of hackers as news sources by analyzing the “stalkerware” investigation carried out by the online news magazine Motherboard. Drawing from field theory and boundary work, the article sheds light on how hackers exert an increasing influence on journalism, its practices, epistemologies, and ethics, resulting in an increasing hybridization of journalism. Journalism has become a dynamic space, in which hackers are not only becoming relevant actors in the journalism field, but they often represent the only sources journalists have to shed light on wrongdoings. Hence, hackers are increasingly defining the conditions under which journalism is carried out, both in terms of its practices as well as in its normative framework.
Classification Breast Cancer Revisited with Machine Learning

The article presents the study of several machine learning algorithms that are used to study breast cancer data with 33 features from 569 samples. The purpose of this research is to investigate the best algorithm for classification of breast cancer. The data may have different scales with different large range one to the other features and hence the data are transformed before the data are classified. The used classification methods in machine learning are logistic regression, k-nearest neighbor, Naive bayes classifier, support vector machine, decision tree and random forest algorithm. The original data and the transformed data are classified with size of data test is 0.3. The SVM and Naive Bayes algorithms have no improvement of accuracy with random forest gives the best accuracy among all. Therefore the size of data test is reduced to 0.25 leading to improve all algorithms in transformed data classifications. However, random forest algorithm still gives the best accuracy.
The Digital Divide and its Impact on Quality of Education at Jordanian Private Universities Case Study: Al-Ahliyya Amman University

The study aimed to identify the Impact of digital divide in its dimensions (Technological dimension, Knowledge dimension, and Legislation &amp; laws dimension) on the quality of education (University presidency's commitment to quality, Academic reputation and published scientific research) at Al-Ahliyya Amman University.To achieve this goal, the researcher used the descriptive and analytical approach, the study tool for collecting information and data was a questionnaire distributed to all employees, whose number is (630).The study questions and hypotheses were analyzed and tested through the Statistical Package for Social Sciences (SPSS) program. This study has found many results, the most important is:The digital divide in its combined dimensions has a statistically significant impact on the quality of education at Al-Ahliyya Amman University. It concluded with many recommendations, the most important is that a strategic plan be drawn up for universities to develop its infrastructure, improve and develop it continuously in order to enable digital access and bridge the digital divide among researchers and academics.
The Pliocene Xoconostle high sulfidation epithermal deposit in the Trans-Mexican Volcanic Belt: Preliminary study

The Xoconostle prospect in northeastern Michoacán state, south-central Mexico, is constituted by high sulfidation epithermal breccias and stockworks with Au and Hg prospective anomalies. The mineralization is hosted by latest Miocene to Pliocene rocks grouped into the El Terrero ignimbrite and the Siete Cruces dome complex and a stock of intermediate composition and undetermined (Pliocene?) age. Two alunite samples from deep hypogene advanced argillic alteration assemblages within the deposit yielded 40Ar/39Ar ages at 5.57 ± 0.44 (Messinian) and 3.67 ± 0.20 Ma (Zanclean). Such ages are in good agreement with those of volcanic rocks at a semi-regional scale, especially those associated with the nearby Amealco caldera. Assuming that the formation of Xoconostle deposit could be genetically related to any of the eruptive units in this caldera, it would be associated with dacitic-andesitic rocks at ~4.7 Ma or with bimodal andesite-basalt volcanism at ~3.7 Ma, with which rhyolites at the southwest rim of the caldera (nearer to the epithermal deposit) are contemporaneous. The obtained ages are also in good agreement with those determined for the youngest stages in the evolution of the Trans-Mexican Volcanic Belt (TMVB). In addition, such ages compare well with those established for the E-W striking Morelia-Acambay normal fault zone (or Acambay graben). The occurrence of E-W structural features in the study area support their correlation with those in the Acambay graben. Although the metallogenesis of the TMVB needs further endeavours that contribute to its understanding, the Xoconostle prospect adds up to other dated magmatic-hydrothermal deposits that may collectively constitute a Pliocene metallogenic province whose inception was geologically circumscribed to this volcanic arc. However, this and its companion papers in this issue confirm the metallogenic potential of the TMVB in most of its stages of evolution, particularly in the late Miocene-Pliocene stage of acid and bimodal volcanism.