Reviewerly/exHarmony huggingface dataset:
Benefits of online learning according to recent studies

Abstrak: Pembelajaran online merupakan sebuah inovasi dalam mode pembelajaran yang dibawa oleh keniscayaan teknologi informasi dan komunikasi. Studi ini menekankan pada manfaat pembelajaran online untuk otonomi siswa, motivasi, dan kemampuan kolaborasi sambil memberikan pembelajaran yang fleksibel bagi para pelajar. Penelitian ini dilakukan sebagai penelitian kepustakaan, di mana penelitian sebelumnya tentang manfaat pembelajaran online digunakan sebagai sumber data. Terungkap bahwa pembelajaran online dikatakan secara positif mempengaruhi otonomi, motivasi, dan keterampilan kolaboratif siswa, serta memungkinkan siswa belajar secara fleksibel sesuai dengan ketersediaan tempat dan waktu mereka. Ketersediaan sumber-sumber online, sarana, dan koneksi internet memungkinkan siswa mengatur pembelajarannya sendiri. Implikasi penelitian ini bagi guru, pendidik, dan siswa adalah bahwa semua harus merangkul pembelajaran daring dan berbagai aplikasinya untuk meningkatkan proses dan hasil belajar. Dengan demikian, penelitian ini berkontribusi menambah rujukan mengenai pembelajaran daring terutama kebermanfaatannya bagi siswa.Abstract: Online learning is an innovation in learning mode brought by the inevitability of information and communication technology. The present study highlights the benefits of online learning for students. The study was conducted as library research, where previous studies on the benefits of online learning were used as the data source. The study revealed that online learning is argued to have positively affected students' autonomy, motivation, and collaboration skills while providing flexible learning for the learners. The abundance of online sources, tools, and networking enables learners to navigate their learning. These results imply that teachers, educators, and students should embrace online learning and its supporting applications to improve learning processes and outcomes. The present study contributes to the limited literature on a general overview of online learning benefits seen from the learners' side.
Guiding an Instantiation Prover with Graph Neural Networks

In this work we extend an instantiation-based theorem prover iProver with machine learning (ML) guidance based on graph neural networks. For this we implement an interactive mode in iProver, which allows communication with an external agent via network sockets. The external (ML-based) agent guides the proof search by scoring generated clauses in the given clause loop. Our evaluation on a large set of Mizar problems shows that the ML guidance outperforms iProver’s standard human-programmed priority queues, solving more than twice as many problems in the same time. To our knowledge, this is the first time the performance of a state-of-the-art instantiation-based system is doubled by ML guidance.
Exploring Partial Models with SCL

The family of SCL (Clause Learning from Simple Models) calculi learns clauses with respect to a partial model assumption, similar to CDCL (Conflict Driven Clause Learning). The partial model always consists of ground first-order literals and is built by decisions and propagations. In contrast to propositional logic where propagation chains are always finite, in first-order logic they can become infinite. Therefore, the SCL family does not require exhaustive propagation and the size of the partial model is always finitely bounded. Any partial model not leading to a conflict constitutes a model for the respective finitely bounded ground clause set. We show that all potential partial models can be explored as part of the SCL calculus for first-order logic without equality and that any overall model is an extension of a partial model considered. Furthermore, SCL turns into a semi-decision procedure for first-order logic by extending the finite bound for any partial model not leading to a conflict.
Monitoring structural scale composite specimens in a post‐buckling regime: The integrated finite element stereo digital image correlation approach with geometrically non‐linear regularization

Abstract Even though the simulations used to describe the failure of laminates are becoming more and more predictive, complex testing under multiaxial loadings is still required to validate the design of structural parts in a wide range of industrial domains. It is thus essential to assess the actual boundary conditions to allow for an objective comparison between testing and calculations, in particular since the structural tests are complex and often leads to buckling. Therefore, accurate estimation of force and moment fluxes applied to the specimen is critical. In this context, stereo digital image correlation (SDIC) has proven to be an important measurement tool and provides very well‐resolved surface displacement fields, but the exploitation of such measurements to calculate fluxes remains problematic when testing composites. The first objective of this study is both to reduce the uncertainty associated with fluxes determination on a complex test and to simplify the extraction process with respect to existing procedures. The second objective is to make this methodology robust to geometrically non‐linear deformations. In this paper, we propose a new methodology that extracts minimal boundary conditions in the form of 3D mechanically admissible displacements fields. The approach developed uses a finite element SDIC (FE‐SDIC) method regularized by means of mechanical behaviour admissibility equations. Results show that the new methodology outputs much more accurate fluxes than classical data generated from multiple differentiations of the displacement fields. Excellent noise robustness is obtained and quantified. Numerical predictions have been satisfactorily compared with experimental data from one structural‐scale composite specimen under complex testing.
Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review

This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software naturalness, have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection. Notable examples of such applications include the GitHub Copilot powered by OpenAI's Codex and DeepMind AlphaCode. This paper presents an overview of the major LLMs and their applications in downstream tasks related to AI-assisted programming. Furthermore, it explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications, with a discussion on extending AI-assisted programming capabilities to Apple's Xcode for mobile software development. This paper also presents the challenges of and opportunities for incorporating NLP techniques with software naturalness, empowering developers with advanced coding assistance and streamlining the software development process.
On efficient quantum block encoding of pseudo-differential operators

Block encoding lies at the core of many existing quantum algorithms. Meanwhile, efficient and explicit block encodings of dense operators are commonly acknowledged as a challenging problem. This paper presents a comprehensive study of the block encoding of a rich family of dense operators: the pseudo-differential operators (PDOs). First, a block encoding scheme for generic PDOs is developed. Then we propose a more efficient scheme for PDOs with a separable structure. Finally, we demonstrate an explicit and efficient block encoding algorithm for PDOs with a dimension-wise fully separable structure. Complexity analysis is provided for all block encoding algorithms presented. The application of theoretical results is illustrated with worked examples, including the representation of variable coefficient elliptic operators and the computation of the inverse of elliptic operators without invoking quantum linear system algorithms (QLSAs).
DISTANCE LEARNING AS A CAUSE OF PROFESSIONAL BURNOUT OF PHYSICAL EDUCATION TEACHERS

Introduction. Modern challenges concern all pedagogical staff due to the new realities of organizing the educational process, but physical culture teachers deserve special attention. Their work, despite the general problems of mastering a new type of remote communication, consists in the need to create new forms of presenting knowledge, abilities and skills, to find opportunities to concentrate the attention of schoolchildren for the effectiveness of education during distance learning. It is also associated with additional responsibility for their health and satisfactory physical condition and physical development corresponding to age characteristics. It is the totality of the mentioned problems during distance learning that can cause physical culture teachers psychological, emotional, and physical stress, which requires studying the possibilities of their avoidance and finding solutions to prevent further professional burnout. The aim is to justify the problem of emergence and determine the level of professional burnout of qualified physical education teachers during the distance educational process in the conditions of crisis phenomena. Material and methods: analysis and generalization of data from scientific and methodological literary sources and the Internet, questionnaires using a modified questionnaire, methods of mathematical statistics. Results. 12 PE teachers with significant professional experience and a high category from diffrents institutions participated in the survey. The level of professional burnout of qualified physical education teachers was determined with questions of a modified questionnaire, interpreted according to the direction of work during distance learning, taking into account the specifics of practical work. Conclusion. It is possible to state that there is a problem of professional burnout in the pedagogical area. It was found that it is burdened by the challenges of the distance educational process for PE teachers due to the need for practical teaching of the subject in the conditions of crisis phenomena. The summary of the obtained results: 50% of the interviewed PE teachers are beyond the low level of occurrence of the "professional burnout" syndrome (5 people - medium level + 1 person - high level). We see the issue of getting out of such a state as difficult and in need of further qualitative study, which is extremely difficult without the help of specialists of the relevant profile.
Developing STAD Model in Virtual Class in English Tadris Islamic State Institute of Pontianak

Speaking in English is a scary thing for students. This is influenced by psychological effects, namely fear of making mistakes in pronunciation and grammar or using the right words. An unsupportive environment also causes students to be afraid to speak. It is therefore necessary to create a pleasant classroom atmosphere for the learning objectives to succeed. This research uses research and development methods adapted from the development of 4D models that consists of 4 stages, namely define, design, develop, and disseminate.The STAD type learning model used in this study was applied in a virtual classroom using an online learning system to bring the atmosphere of a real classroom into the virtual classroom. In virtual classrooms, students cannot meet face to face with other students so that it will reduce the interaction process and cause individualismThe development of a Cooperative Learning learning model type Student Team Achievement Division (STAD) can help lecturers in teaching English speaking pleasantly in virtual classes. The results of expert team tests and product trials show that the learning model is feasible to develop.
Implementation of n-gram Methodology to Analyze Sentiment Reviews for Indonesian Chips Purchases in Shopee E-Marketplace

Chips are a well-known product among Small and Medium Enterprises (SMEs). In order to enhance the quality of chips as an SME product, sentiment analysis is a crucial step. In this research, sentiment analysis of chip purchases on the Shopee E-marketplace was conducted using the Natural Language Processing (NLP) method, utilizing the N-Gram Model and Term Frequent-Inverse Document Frequency (TF-IDF) as feature extraction techniques, and the Support Vector Machine (SVM) algorithm for sentiment classification. The objective of this research is to identify the most suitable feature extraction model and optimal SVM kernel type from the options of Linear, Polynomial degree, Gaussian RBF, and Sigmoid kernels. Results from the experiments indicate that the TF-IDF and unigram feature extraction techniques offer the best performance for SVM classification when utilizing the Linear kernel. By labeling the dataset, it was observed that using a lexicon-based approach for sentiment classification resulted in 84.31% of the total reviews being positive. The words "price", "cheap" and "quality" in unigram have the highest weights above 0.040. In the unigram model, linear kernel accuracy and precision performance values are 88.4% and 87.3%. At the same time, the recall performance values is 88.4%. The results of the F1-Score assessment matrix from Unigram were 86.9%, Bigram was 78.5% and Trigram was 77.4%. Ultimately, the unigram model combined with a linear kernel in the SVM algorithm demonstrates strong potential for application in the development of various systems focused on detecting user reviews in the Indonesian language on the Shopee E-Marketplace.
Contextualizing Problems to Student Interests at Scale in Intelligent Tutoring System Using Large Language Models

Contextualizing problems to align with student interests can significantly improve learning outcomes. However, this task often presents scalability challenges due to resource and time constraints. Recent advancements in Large Language Models (LLMs) like GPT-4 offer potential solutions to these issues. This study explores the ability of GPT-4 in the contextualization of problems within CTAT, an intelligent tutoring system, aiming to increase student engagement and enhance learning outcomes. Through iterative prompt engineering, we achieved meaningful contextualization that preserved the difficulty and original intent of the problem, thereby not altering values or overcomplicating the questions. While our research highlights the potential of LLMs in educational settings, we acknowledge current limitations, particularly with geometry problems, and emphasize the need for ongoing evaluation and research. Future work includes systematic studies to measure the impact of this tool on students' learning outcomes and enhancements to handle a broader range of problems.
Boosted Random Forests for Predicting Treatment Failure of Chemotherapy Regimens

Cancer patients may undergo lengthy and painful chemotherapy treatments, comprising several successive regimens or plans. Treatment inefficacy and other adverse events can lead to discontinuation (or failure) of these plans, or prematurely changing them, which results in a significant amount of physical, financial, and emotional toxicity to the patients and their families. In this work, we build treatment failure models based on the Real World Evidence (RWE) gathered from patients' profiles available in our oncology EMR/EHR system. We also describe our feature engineering pipeline, experimental methods, and valuable insights obtained about treatment failures from trained models. We report our findings on five primary cancer types with the most frequent treatment failures (or discontinuations) to build unique and novel feature vectors from the clinical notes, diagnoses, and medications that are available in our oncology EMR. After following a novel three axes - performance, complexity, and explainability - design exploration framework, boosted random forests are selected because they provide a baseline accuracy of 80% and an F1 score of 75%, with reduced model complexity, thus making them more interpretable to and usable by oncologists.
DIGITAL COMPETENCE OF FUTURE TEACHERS OF VOCATIONAL TRAINING.

This article examines the international trends and strategic documents on the development of digital competence used in the system of domestic and foreign education of modern teachers. The Digital Competence Framework for Citizens (DigComp) and the Digital Competence Framework for Educators (DigCompEdu), developed by the European Joint Research Center, have been identified and their main components identified. The interpretation of digital competence, its essence and structure are presented. The subject of the study is the digital competence of the teacher as one of the priority areas of state policy in modern education, the object of study - the training of future teachers of vocational training, aimed at forming digital competence in them. Theoretical research methods - analysis, comparison, classification - allowed to study the psychological and pedagogical and special literature to determine the conceptual apparatus, experience, identify structural and functional components of digital competence of the individual, substantiate the main provisions of the problem. The content of the article is revealed through the description of the terms «digital competence», «digital literacy», «digital education», «vocational education», «vocational training», «teacher of vocational training». The novelty of the study is to define the digital competence of the modern teacher of vocational training as the ability of the individual to promote digital technologies to empower students, improve teaching and learning; assisting colleagues in learning, mastering the skills of using and adapting new digital resources and learning tools. Analysis of the legal framework for the digitalization of all sectors of society, including education, shows the significant attention of the state to the development of digital competence of modern teachers. There considered the specifics of the activities of educational entities, which are implemented in the learning process with the help of digital resources in the process of training future teachers of vocational training. Particular attention was paid to international experience, where the formation and development of digital competence of future teachers is a strategic task of education systems, and the work of international experts and organizations on the requirements for digital competence and development of conceptual approaches, standards and frameworks.
STEM EDUCATORS PREPARATION: INTERNATIONAL CONTEXT

The article considers one of the newest perspective vectors of STEM trainings development, connected with specialized training of educators of formal and non-formal education (educators). The rapid movement of science and technology around the world determines the emergence of new didactic areas. According to the State Scientific Institution «Institute for Modernization of the Content of Education» of the Ministry of Education and Science of Ukraine, the main activities of domestic STEM centres as of 2020 are robotics (over 50%), environmental studies using digital measuring systems (45%) and 3D - modelling and printing (over 20%). The structure of STEM competences of educators includes pedagogical knowledge, affective-motivational competences and beliefs, innovative approaches to learning that contribute to cognitive and socio-emotional learning of students: this is convenient for developing survey tools in international comparative studies. A more specific approach to the formation of the content of STEM training of educators is proposed by domestic authors: problem solving, cooperation, work with technology, organizational skills. The article reflects the experience of the United States, Australia, Germany, Malaysia, Ireland - the leading countries in the implementation of STEM, and analyses the plans and directions in the preparation of STEM educators for the coming years. It is shown that the training of competent specialists in this pedagogical field is supported at the state level by countries with advanced economies. The issue of gender inequality of those interested in relevant education remains relevant. Malaysia's education development plan is described. An example of quality training of STEM educators in Ireland is given. The most popular types of training of STEM educators in the international market are considered. Analysed of the content of online training courses for teachers of science and mathematics in the United States and for volunteers in the UK. Attention is paid to an alternative way to gain authentic experience of an educator - a summer camp, which is demonstrated by the example of such a centre, which is funded and coordinated by the Ministry of Education of Australia. The main advantages of master's training programs for STEM educators are clarified. The National Centre «Junior Academy of Sciences of Ukraine» provides STEM-oriented training for teachers, as well as involves educators in working on STEM projects through the methodological materials presented at www.stemua.science.
Development of a Virtual Environment-Based Electrooculogram Control System for Safe Electric Wheelchair Mobility for Individuals with Severe Physical Disabilities

Conventional wheelchairs are predominantly manual or joystick-operated electric wheelchairs. However, operating these wheelchairs can be difficult or impossible for individuals with severe physical disabilities. Due to losing control of their physical limbs, they depend on an attendant for assistance. As a remedy, bio-signals may be used as a control mechanism since they are readily available and can be acquired from any body part. This research proposes to use EOG signals to vail a control mechanism and test it in a virtual and actual electric wheelchair. The main contribution of the study is an investigation of the use of EOG to control an electric wheelchair in a virtual environment to determine safe control parameters for wheelchair use in complex environments. A customized data acquisition circuit was developed to acquire single-channel EOG signals using wet electrodes. The acquired signal was filtered and processed using feature extraction and classification techniques in MATLAB software. Two customized control environments were developed in Unity 3D, one with equally partitioned sections and the other with sections decreasing in size as the robot wheelchair approaches the target. Twenty-two test subjects (mean age 24.5, std 1.5) participated in the study, controlling the robot wheelchair in real-time with non or least instances of collision and oversteering. The system achieved an accuracy of 96.5% with a response time of 0.7s, translating to an ITR of 70.6 bits/min. Overall, the participants managed to navigate the virtual environment with a completion time of 101.94s ± 19.71 and 109.07s ± 13.25 for the male and female participants, respectively. In the scene with decreasing section sizes, 72% and 54% instances of collision and oversteering were reported, respectively, highlighting the need to consider the complexity of the control environment and the sufficiency of the participants' control skills to ensure safety in operations. The results confirm the usefulness of EOG as a control interface, with little or no need for recalibration. It provides a promising avenue for individuals with severe physical disabilities to operate wheelchairs independently in complex environments, enhancing their quality of life.