Reviewerly/exHarmony huggingface dataset:
From face-to-face tuition to online classes: ‘Re-styling’ a course of English for academic purposes

Abstract With the outbreak of the COVID-19 pandemic and the ensuing total lockdown in 2020, the EAP course for STEM doctoral students at Parma University (Italy) was suddenly forced to online delivery, like many courses in academic institutions worldwide. Confined at home and with no previous experience in remote teaching, the teacher had to redesign the course and rethink strategies and techniques in a matter of days and with little material at hand. The aim was to maintain the interactivity of the face-to-face course and consolidate the group dynamics of a course that had yet to start. As online teaching took centre stage, the teacher and students alike were confronted with didactic issues stemming from the restyling of a traditionally highly interactive course based on face-to-face tuition, and technical problems, which added to the emotional and psychological factors related to an unknown, unexpected situation. In addition to soft skills, students from different academic backgrounds needed to develop productive rather than receptive language skills, so activities focused mainly on collaborative tasks to develop writing and speaking modes but did not concentrate on academic language only. This paper shares insights into the experience of being ‘thrown in at the deep end’, and attempts to highlight the elements which contributed to its overall positive outcome, the strong social connotation it came to bear, the development of class dynamics, and the learning points that emerged. It also hopes to provide some practical suggestions which can add to the creative solutions found by (language) teachers globally.
Design Method of Meta Operators Based on Equivalence Class Distribution

Abstract Currently, there are many physical systems for quantum computing and to adapt to the physical characteristics of different systems, engineers have designed different basic gate groups for gate circuit calculation models. The quantum algorithm realizes the evolution of the quantum state by applying unitary operators to the quantum states, and the results are obtained by measuring the outputs. These operators will be done in the corresponding physical system by compiling and decomposing into hardware supported basic gates. Universal fundamental gates can construct all quantum algorithms, but their computational efficiency is limited. Drawing on the idea of meta operators in classical machine learning, by constructing a deep learning framework for deep learning models, the common computational logic of operators in different deep learning frameworks is abstracted as ‘meta operators’ [1, 2]. This article aims to study and propose the common computational logic of quantum computing with geometric equivalence class distribution methods, using the volume size of the equivalence class space as a performance indicator for meta operators and identifying the optimal meta operators to optimize the depth of the circuits in the experiments. Which proves the effectiveness of the design method proposed and provides solutions for quantum circuit optimization and dedicated gate design.
Interdisciplinary connections of Mathematics and Literature in the preparation for External Independent Assessment of Humanities students

Abstract This article examines the issue of finding new methodological approaches to the development of problems that can contribute to the preparation of ‘Humanities’ students to pass the state final certification in the format of External independent assessment (EIA). The study analyzes the existing development of methods of qualitative mathematical training of students of humanities classes who study Mathematics according to the basic level program. In particular, the authors of the article were interested in the use of interdisciplinary connections between Mathematics and humanitarian disciplines. The authors of the article identified cross-curricular problems as a means of forming students’ motivation to study Mathematics, as well as a toolkit for measuring their level of readiness to solve typical problems of EIA. The researchers set a goal to develop cross-curricular problems following the EIA program based on the plot of Bulgakov’s novel ‘The Master and Margarita’. An experiment was conducted to confirm the effectiveness of the use of the system of interdisciplinary problems in the preparation for the EIA of humanitarian students. The obtained results confirmed that the implementation of problems in which the intersubject connections of Mathematics and Literature are realized has a more significant effect on both increasing the motivation of students to study Mathematics and their mathematical preparation.
Substantiation of the sustainable education terms as one of the modern views on STEM education taking to account the European experience

Abstract This paper examines the development of concepts related to education for sustainable development, such as sustainability education, sustainability and education, sustainable education, sustainable development education, sustainable didactics, and sustainable learning. The paper argues that these concepts are insufficient for designing educational processes that fully consider the factors of sustainable development. The paper proposes and justifies the need for new concepts: sustainable pedagogy, sustainable educational process, and didactics for a sustainable educational process. These concepts are based on the components of sustainable development: competence, health, and motivation and socialization. The paper discusses how to use these factors to assess the sustainability of educational approaches. It also provides a practical example of how to apply the “sustainability” methodology in the educational process using Google Lens in biology lessons.
The use of immersive technologies in teaching mathematics to vocational students

Abstract This study examines the use of immersive technologies in teaching mathematics to vocational students. The purpose of the research is to develop theoretical foundations and practical applications for the use of immersive technologies in mathematics education, and to measure the impact of these technologies on student motivation and learning outcomes. The study considers the scientific and theoretical underpinnings of immersive technologies in mathematics teaching, as well as methods for using the AR-Book application. A theoretical model, criteria, and methodological support for evaluating the use of immersive technologies are developed. A pedagogical experiment is conducted to evaluate the impact of these technologies on vocational students. Based on the results of the experiment and training materials, the study presents generalizations on the use of immersive technologies in mathematics education.
Interactive technology use during the study of the Universe

Abstract The development of students’ key competences and their scientific worldview is one of the significant tasks of secondary education. Understanding of the Universe, its objects, structure and research methods is an important part of natural and mathematic worldview. Problem-based learning is a relevant form for develop critical thinking during the study of these topics in the course of astronomy. In the conditions of distant learning this can be realized with the help of interactive online platforms whereas practical tasks can be realized through the use of simulators, mobile and computer applications. This will contribute the development of students’ key competences and practical skills. This research offers to emphasize the Universe study and interactive technology use in order to develop research, information and digital competences as well as competences in natural sciences and technologies.
Analysis of the state of the art of modern e-learning in higher education in Germany

Abstract E-learning as a part of blended learning and a means for implementing distance learning, as well as an independent phenomenon of the modern information society, has been introduced and studied by researchers for several decades. In various countries, the relevance of e-learning and the use of e-learning tools in higher education have increased during the coronavirus pandemic. Different countries have their own approach and experience in using e-learning tools. Therefore, it is advisable to study it both for each country separately and in comparison. In the course of the research, the analysis of scientific publications on the topic of e-learning in higher education in Germany over the last 10 years has been made, which has shown certain changes in the interest in this issue of German scientists. The survey of students and teachers of the University of Konstanz (Germany) has been conducted regarding the use of e-learning tools and their attitude to e-learning. Despite the fact that the results of the analysis of the scientific works of German scientists show a drop in interest in e-learning problems in Germany and a slight rise during the coronavirus pandemic, the results of the survey indicate an unequivocal interest in e-learning tools of teachers. In general, it is possible to state the positive attitude on the part of teachers and uncertainty on the part of students.
The Wheel of Mathematics Learning Methods

Abstract The article is devoted to the creation of a wheel of mathematics learning methods. The study analyzed how Bloom’s taxonomy is used by the educational community. Special attention is paid to the study of the issue of bringing to a single system a set of disparate goals and tasks of learning in new models of education, which provide for the openness of learning to new technologies. Analysis of scientific works and resources helped to determine the structuring of the Wheel of Mathematics Learning Methods (WMLM). The authors of the article identified the areas of didactics that should fill the wheel: Competencies, Motivation, Bloom’s Taxonomy, Activities, and Technologies. A structured set of hints for the teacher is presented in the form of 5 sectors of the wheel: Apply, Analyze, Evaluate, Create, Remember Understand. Each sector is a set of constituent components Methods, Forms, Tools, Activities and Active Verbs. The analysis of the results of the survey of 58 respondents proved that Bloom’s taxonomy is important both in the selection of teaching methods and means and in the determination of effective forms of organization of educational activities. It was concluded that the idea of developing a methodology wheel should be based on the structuring of a set of tips for the teacher at all stages of activity from planning to implementation of mathematics education.
Methodology of project-based learning for training junior students in applied mathematics: general scheme of the educational process

Abstract An original methodology of the project-based learning for junior students of the specialty applied mathematics is proposed in the paper. A complete step-by-step diagram (as a BPMN diagram) of the process of the project-based learning as a business process with a description of the specific actions of all its participants is given. The paper specified and clearly describes all the main aspects of the work on the project, starting from the criteria that the project problem must satisfy, and ending with the form of project defense. The roles of both students within the project team and all teachers are described in detail. Special attention is paid to all documents accompanying the work on the project, which should be submitted by the project team. The results of the article may be useful to those teams of teachers who are just starting to work on the implementation of project-based learning into the educational process for the specialty of applied mathematics or related ones.
Enhancing All-in-Focus Imaging with Opto-Electronic Computational Camera using DE-ED Paradigm

Abstract A novel approach called DE-ED (Diffractive Encoding-Electronic Decoding) for rapid all-in-focus capturing is presented in this paper. The paradigm combines the strengths of diffractive networks (DON) for encoding and electronic-based networks (ENN) for decoding, resulting in a robust computational imaging framework driven by advanced deep learning techniques. By leveraging the collaboration between diffractive layers and electronic layers, our model achieves remarkable enhancements in the ability to generalize and fit data. This means that the model can effectively capture and process complex visual information, resulting in superior image quality and enhanced performance.
Modeling training content for software engineers in parallel computing

Abstract This study proposes a robust framework for the training of software engineers specializing in parallel computing. We first curated essential content for parallel computing education based on international standards and evolving recommendations from Computing Curricula. We then systematically structured the content and designed a well-defined learning pathway for aspiring software engineers. Concurrently, we conducted a comprehensive assessment of the current state of training for parallel computing in Ukrainian higher education institutions. We analyzed bachelor’s programs in Information Technologies and scrutinized individual course syllabi to identify valuable insights. By merging our findings with the review of educational programs, we formulated a comprehensive model for training in parallel computing. We also examined the pivotal role of the course ”Parallel and Distributed Computing” in the developed curriculum and identified essential tools and methodologies for developing parallel and distributed programs. Our research contributes to the advancement of parallel computing education and provides a valuable reference point for curriculum designers and educators.
Face and body-shape integration model for cloth-changing person re-identification

Among the existing deep learning-based person re-identification (ReID) methods, human parsing based on semantic segmentation is the most promising solution for ReID because such models can learn to identify fine-grained details of different body parts or apparel of a target semantically. However, intra-class variations such as illumination changes, multi-pose angles, and cloth-changing (CC) across different non-overlapping camera viewpoints present a crucial challenge for this approach. Among these challenges, a person CC is the most distinctive problem for ReID models, which often fail to associate the target in new cloth against the learned feature semantics of the previous cloth worn in a different timeline. In this paper, we propose a face and body-shape integration (FBI) network as a tactical solution to address the long-term person CC-ReID problem. The FBI comprises hierarchically stacked parsing and edge prediction (PEP) CNN blocks that generate fine-grained human-parsing output at the initial stage. We then aligned the PEP to our proposed model agnostic plug-in feature overlay module (FOM) to mask cloth-relevant body attributes except the facial features pooled from the input sample. Thus, our human parsing PEP and FOM modules are attuned to discriminatively learn cloth-irrelevant features of the target pedestrian(s) to optimize the effectiveness of person ReID in solitary or minimally crowded areas. In our extensive person CC-ReID experiments, our FBI model achieves 83.4/61.8 in R1 and 91.7/65.8 in mAP evaluation results on the PRCC and LTCC datasets, respectively; thereby significantly out-competing several previous state-of-the-art ReID methods, and validating the effectiveness of the FBI.
The British Geological Survey Rock Classification Scheme, its representation as linked data, and a comparison with some other lithology vocabularies

Controlled vocabularies are critical to constructing FAIR (findable, accessible, interoperable, re-useable) data. One of the most widely required, yet complex, vocabularies in earth science is for rock and sediment type, or ‘lithology’. Since 1999 the British Geological Survey has used its own Rock Classification Scheme in many of its workflows and products including the national digital geological map. This scheme pre-dates others that have been published, and is deeply embedded in BGS’ processes. By publishing this classification scheme now as a Simple Knowledge Organisation System (SKOS) machine-readable informal ontology, we make it available for ourselves and third parties to use in modern semantic applications, and we open the future possibility of using the tools SKOS provides to align our scheme with other published schemes. These include the IUGS-CGI Simple Lithology Scheme, the European Commission INSPIRE Lithology Code List, the Queensland Geological Survey Lithotype Scheme, the USGS Lithologic Classification of Geologic Map Units, and Mindat.org. The BGS lithology classification was initially based on four narrative reports that can be downloaded from the BGS website, although it has been added to subsequently. The classification is almost entirely mono-hierarchical in nature and includes 3454 currently valid concepts in a classification 11 levels deep. It includes igneous rocks and sediments, metamorphic rocks, sediments and sedimentary rocks, and superficial deposits including anthropogenic deposits. The SKOS informal ontology built on it is stored in a triplestore and the triples are updated nightly by extracting from a relational database where the ontology is maintained. Bulk downloads and version history are available on github. The RCS concepts themselves are used in other BGS linked data, namely the Lexicon of Named Rock Units and the linked data representation of the 1:625 000 scale geological map of the UK. Comparing the RCS with the other published lithology schemes, all are broadly similar but show characteristics that reveal the interests and requirements of the groups that developed them, in terms of their level of detail both overall and in constituent parts. It should be possible to align the RCS with the other classifications, and future work will focus on automated mechanisms to do this, and possibly on constructing a formal ontology for the RCS.
Discriminative projection fuzzy K-Means with adaptive neighbors

Fuzzy K-Means (FKM) based on fuzzy theory is a classic method to effectively handle overlapping regions between clusters. However, redundant features and noises brought by increasing data dimensions affect the effectiveness of FKM. To cope with this issue, we propose a Discriminative Projection Fuzzy K-Means with adaptive neighbors (DPFKM) model, which embeds a discriminative subspace into FKM to facilitate learning of global structure and the most discriminative information. Firstly, a novel projection space with uncorrelated constraints are adopted to promote statistical independence among the data in the subspace as well as to enhance the ability of FKM to discern and utilize discriminative information. Secondly, the Frobenius norm is introduced as the regularization term to eliminate discrete solutions, while preserving the fuzziness and enhancing the sparsity of FKM. Finally, we propose a novel optimization method to finetune the model, with a particular focus on adaptive adjustment of the regularization parameter based on the proximity relationship between the samples and clusters. Comprehensive experiments are conducted on multiple data sets, and the results can prove the superiority of the proposed model.
A comprehensive comparison of machine learning approaches with hyper-parameter tuning for smartphone sensor-based human activity recognition

Human Activity Recognition (HAR) using Smartphone sensors like accelerometers and gyroscopes has proven to be one of the most preferred ways to collect data in a simple manner without the need for any additional hardware installation. Smartphones come with these embedded sensors which can be used for monitoring acceleration and angular velocity to help in detecting Activities of Daily Living (ADL). HAR plays an important role in various applications like elderly care, ICU patient care, surveillance systems, etc. Various Machine learning approaches have been applied in the HAR domain to classify human activities using supervised as well as unsupervised learning approaches on various benchmark or compiled datasets. This paper presents a comprehensive comparison of various Machine learning approaches with hyper parameter tuning on a benchmark dataset from the UCI repository. Results prove that the performance of Support Vector Machine (SVM) and Ensemble learning approaches like Random Forest, Gradient Boosting are comparatively better in classifying the activities with hyper parameter tuning using GridSearch as opposed to without applying it for selecting best hyper-parameter values. The performance has been compared with the latest work in the field of Machine Learning in HAR on same dataset and results prove that the proposed approaches outperform most of the existing works in this domain.
Multi-view graph convolution network for the recognition of human action with spatial and temporal occlusion problems

Human action recognition holds great significance in the field of social security. However, the challenges posed by occlusion and changes in viewpoint create specific obstacles in achieving accurate recognition. In this study, we propose a multi-view graph convolution fusion method to effectively address this issue. Specifically, considering the limited availability of public human action datasets that include occlusion, we introduce an adaptive multi-view spatial–temporal occlusion generation method. It allows us to generate occlusion skeleton data from multiple viewpoints, ensuring a close resemblance to real-life scenarios with minimal modifications to existing public datasets. Additionally, we present a plug-and-play multi-view information fusion module, briefed as MGL, which aims to solve the occlusion problem. The MGL combines the capabilities of Graph Convolutional network (GCN) and Long Short-Term Memory network (LSTM), in which GCN is employed to reconstruct human skeleton information using multi-view spatial occlusion data, and LSTM captures the long-term dependencies within the temporal sequences of the multi-view data. Moreover, an attention mask mechanism is introduced to highlight key joint features. Experiment results illustrate the excellent performance of our method on the NTU RGB+D 60 and NTU RGB+D 120 datasets.