Reviewerly/exHarmony huggingface dataset:
INVESTIGATING PARAGRAPH OF INDONESIAN EFL STUDENTSâ€™ ESSAY: EFFECTIVE OR INEFFECTIVE

The term â€˜effective paragraphâ€™ refers to the presence of its four building elements i.e. unity, coherence, topic sentence, and sufficient development. Related to that, this study investigates studentsâ€™ essays whether they consist of effective paragraphs or dominated by the ineffective ones and whether those building elements consistently present in every paragraph. To answer those two questions, this study employs the content analysis method in which it is a particular method to analyze the texts. As the samples are the studentsâ€™ essays of English major. The result shows that 36 of  76 (47 %) are effective paragraphs and the rest 41 (53 % ) are ineffective which means that more than a half of paragraphs are ineffective. Then it is also revealed there are 62 paragraphs  have topic sentence (33%), 49 have proper sufficient developments (26%), 41 of them have unity (23 %), and  30 paragraph are written with coherence (16 %). From the result it can be inferred that most of studentsâ€™ paragraph are ineffective.
Reader-centered Approach: Investigating Literary Reading Performance Profile of Students from Non-Literature Study Programme

Reading literary works can be frustrating, especially for non-literature students due to a certain extent.Linguistic features, styles, symbolism, and implicit expression are some predominant factors which cause ambiguity and lead to confusion to the readers.Hence, the readers tend to be more passive as they do not know what to do with the text.This fact is then the primary justification for the lecturers to minimize the demand for literary reading subjects, focusing solely on comprehension rather than appreciation.Thus, the idea of employing a reader-centered approach is aimed at helping the students of the non-literature study program to appreciate the literary works by upholding their participation as the readers of the meaning-making process.The result shows that the students of the non-literature study program can actively appreciate the literary works better.In addition, the students' performance of reading literary works shows that they can understand the literary works through the interaction between the readers and the texts.
Symmetry-Aware Semantic Segmentation of a 3D Human Body Model

<p indent="0mm">An automatic segmentation method for a 3D human model is proposed for the symmetry-aware semantic segmentation. Firstly, a symmetry-aware kinematic skeleton is extracted from the input human body model via template skeleton embedding. Secondly, the number of the segments is determined by the kinematic skeleton; some key points corresponding to the segments are extracted meanwhile. Thirdly, while taking the key points as the boundary constraints, the harmonic fields are constructed to obtain a set of isolines as the potential cutting boundaries. Finally, the key points are taken as the clustering centers to conduct a spectral clustering operation, which guides the selections of the cutting boundaries. In this way, the semantic information in the kinematic skeleton can be directly transferred to the segments, and the number of segments can be controlled by the structure of the kinematic skeleton. Results on the SCAPE dataset, the MPI-FAUST dataset and the Princeton Segmentation Benchmark show that the proposed method can automatically achieve symmetry-aware semantic segmentation, and is robust to the shape and the pose of the human body model.
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis

The recently proposed training-free NAS methods abandon the training phase and design various zero-cost proxies as scores to identify excellent architectures, arousing extreme computational efficiency for neural architecture search. In this paper, we raise an interesting problem: can we properly measure the operation importance in DARTS through a training-free way, with avoiding the parameter-intensive bias? We investigate this question through the lens of edge connectivity, and provide an affirmative answer by defining a connectivity concept, ZERo-cost Operation Sensitivity (ZEROS), to score the importance of candidate operations in DARTS at initialization. By devising an iterative and data-agnostic manner in utilizing ZEROS for NAS, our novel trial leads to a framework called training free differentiable architecture search (FreeDARTS). Based on the theory of Neural Tangent Kernel (NTK), we show the proposed connectivity score provably negatively correlated with the generalization bound of DARTS supernet after convergence under gradient descent training. In addition, we theoretically explain how ZEROS implicitly avoids parameter-intensive bias in selecting architectures, and empirically show the searched architectures by FreeDARTS are of comparable size. Extensive experiments have been conducted on a series of search spaces, and results have demonstrated that FreeDARTS is a reliable and efficient baseline for neural architecture search.
Implementation of an Associative Memory Using a Restricted Hopfield Network

A trainable analog restricted Hopfield Network is presented in this paper. It consists of two layers of nodes, visible and hidden nodes, connected by weighted directional paths forming a bipartite graph with no intralayer connection. An energy or Lyapunov function was derived to show that the proposed network will converge to stable states. The proposed network can be trained using either the modified SPSA or BPTT algorithms to ensure that all the weights are symmetric. Simulation results show that the presence of hidden nodes increases the network’s memory capacity. Using EXOR as an example, the network can be trained to be a dynamic classifier. Using A, U, T, S as training characters, the network was trained to be an associative memory. Simulation results show that the network can perform perfect re-creation of noisy images. Its recreation performance has higher noise tolerance than the standard Hopfield Network and the Restricted Boltzmann Machine. Simulation results also illustrate the importance of feedback iteration in implementing associative memory to re-create from noisy images.
THE NEED OF RUSSIAN REGIONS FOR EMPLOYEES WITH EDUCATION IN SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS

O. Koropets1, E. Tukhtarova2, A. Fedorova1 1Ural Federal University (RUSSIAN FEDERATION) 2Institute of Economics of the Ural branch of the Russian Academy of Sciences (RUSSIAN FEDERATION)
STEM education in semi-virtual interactive environment

The Covid-19 pandemic has forced us to change the way we used to handle education in our homes, through sanitary restrictions that seek to prohibit or reduce physical contact, moving from the traditional way to a virtual methodology; reason for which we must look for new solutions to adapt to this new teaching strategy. In addition, the pandemic has accelerated the arrival of the 4.0 revolution or technological revolution, where new challenges arise for society. The above reasons serve to justify and encourage that STEM education cannot stop. However, given the crisis we are experiencing and the solutions provided, the excessive use of virtual environments can affect areas such as psychological, social, etc. Through this project, a learning model implemented through volunteers is proposed, based on an interactive semi-virtual environment, where the participant can interact synchronously with a volunteering model provided by the volunteer tutor, and the learning process is supported through workshops and the use of virtual tools.
TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers

In this paper, we present TransMVSNet, based on our exploration of feature matching in multi-view stereo (MVS). We analogize MVS back to its nature of a feature matching task and therefore propose a powerful Feature Matching Transformer (FMT) to leverage intra- (self-) and inter- (cross-) attention to aggregate long-range context information within and across images. To facilitate a better adaptation of the FMT, we leverage an Adaptive Receptive Field (ARF) module to ensure a smooth transit in scopes of features and bridge different stages with a feature pathway to pass transformed features and gradients across different scales. In addition, we apply pair-wise feature correlation to measure similarity between features, and adopt ambiguity-reducing focal loss to strengthen the supervision. To the best of our knowledge, TransMVSNet is the first attempt to leverage Transformer into the task of MVS. As a result, our method achieves state-of-the-art performance on DTU dataset, Tanks and Temples benchmark, and BlendedMVS dataset. The code of our method will be made available at https://github.com/MegviiRobot/TransMVSNet .
EXPERIENCES OF TEACHER TRAINING STUDENTS WITH SELECTED CYBER THREATS AS AN ASPECT OF DIGITAL COMPETENCE DEVELOPMENT

The paper deals with the experiences of the Czech students of teacher training programs with selected cyber threats that are related to risk phenomena and online communication in cyberspace. The current period is characterized by the movement on the Internet, which can be seen as one of the leading social aspects of human life. In this sense, it is necessary to develop skills and gain formal and informal experience in working with modern technologies. Just as there are certain risks in the natural living space, so it is in virtual space-time. Knowledge and experience with threatening cyber phenomena, such as cyberbullying, cyber grooming, sexting and others, belong among the factors in the development of digital competencies not only for students but also for teachers. Already in undergraduate education, it is necessary to prepare future teachers - professionals for the possibilities of prevention and solution of online risk phenomena, optimal media socialization and responsible use of digital technologies. Undergraduate teacher training is even more important; as current teacher students belong to the cyber generation of young people, who will educate the next generation of children, whose lives will be more affected by the use of modern technologies.The paper aims to present the experience of teaching students with selected cyber threats. Furthermore, it is also indicated whether teaching students perceive the link between their own experience and the development of digital competencies in the field of e-safety behaviour and online communication in cyberspace. An integral part is a link to the study of teaching. A quantitative strategy using a questionnaire technique is used to meet the set objective. The obtained results can be used to discuss whether the goals of the European Framework of Digital Competences of Teachers or the Czech Strategy of Educational Policy of the Czech Republic until 2030+ are met.
THE RELATIONSHIP BETWEEN TECHNICAL THINKING AND DIGITAL LITERACY OF FUTURE TEACHERS OF TECHNICAL SUBJECTS

This article is concerned with the analysis of possible aspects of the development of technical thinking and how they are influenced, or supported, or what patterns emerge in this area along with the development of digital literacy. Nowadays, the issue of literacy has become a rather fundamental social phenomenon that evolves along with the development of society. Each of us certainly remembers the original meaning of the word literacy - the ability to read, write and count. These skills have not been commonplace in human history and, until about the 15th century, were primarily the preserve of the social elite. Today, the situation is different in most of the world, and literacy rates have essentially become an indicator of a society's cultural maturity. Thus, when we consider the notion of digital literacy, which has become a phenomenon in today's technologically advanced society, we logically see a link between technical thinking, technical literacy (in this case, rather technological literacy) and digital literacy. This paper looks at how digital literacy relates to technical literacy in relation to technical thinking, and how these literacies condition each other. It is a theoretical study in the form of a reflection based on the results of research conducted in the Czech Republic in recent years in this area and in comparison with foreign research.
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation

Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning paradigm, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, which try to mimic the human learning process either by making the student directly copy behavior from the teacher or selecting reliable learning materials, we propose to conduct further exploration to imitate the human learning process from different aspects, \textit{i.e.}, adaptively updating learning materials, selectively imitating teacher behaviors, and analyzing learning materials structures. The explored three components, collaborate together to constitute a new method for unsupervised domain adaptive person re-identification, which is called Human Learning Imitation framework. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method.
ViraPart: A Text Refinement Framework for Automatic Speech Recognition and Natural Language Processing Tasks in Persian.

The Persian language is an inflectional subject-object-verb language. This fact makes Persian a more uncertain language. However, using techniques such as Zero-Width Non-Joiner (ZWNJ) recognition, punctuation restoration, and Persian Ezafe construction will lead us to a more understandable and precise language. In most of the works in Persian, these techniques are addressed individually. Despite that, we believe that for text refinement in Persian, all of these tasks are necessary. In this work, we proposed a ViraPart framework that uses embedded ParsBERT in its core for text clarifications. First, used the BERT variant for Persian following by a classifier layer for classification procedures. Next, we combined models outputs to output cleartext. In the end, the proposed model for ZWNJ recognition, punctuation restoration, and Persian Ezafe construction performs the averaged F1 macro scores of 96.90%, 92.13%, and 98.50%, respectively. Experimental results show that our proposed approach is very effective in text refinement for the Persian language.
Social sciences to the rise and development of cryptocurrencies: an analysis of the notion

The aim of the article is to conceptualize, that is, to explain, analyze the meaning and indicate the framework for the interpretation of the concept of “cryptocurrency” in social sciences, including political science. As issue an interdisciplinary, polysemic and at the same time novum technological novelty, cryptocurrencies are a challenge for representatives of the world of science. The proposed heuristic model of concept analysis based on the technological, legal and economic aspect indicates that in the broad sense of cryptocurrencies it should be understood as: decentralized, functioning in a network with a peer-to-peer architecture, cryptographically secured, based on trust and consensus, type of virtual currency, that meets some of the functions of money. Explaining the content by one aspect of the functioning of cryptocurrencies is its narrowing down.
Gadgets and new educational technologies: Real assistance or an obstacle?

The rapid spread of modern computer and communication technologies resulted in some global problems in society, including the adoption and rejection of such technologies in education. The COVID pandemic forced a widespread use of modern-style communications between tutors and students but unsolved problems still exist and will exist in the post-COVID era. The main goal of the paper is to find the potential borders of using modern technologies for students learning economics and business management in Russia. The authors conducted the survey in March of 2021 in the Yaroslavl Region, Russia. The main finding of the paper is the necessity of limiting the use of computer technology. The main reason for such limits is a possible decrease in students’ health and the quality of education. All the results gained by the authors are applicable in the practice of organization of educational processes for students involved in economics and business management. Probably, the results may be inapplicable for students of other fields such as medicine, art, engineers, etc.
Hybrid Tabu Search and Genetic Algorithm Imputation Approach for Incomplete Data

The common problem for data collection is happening missing value during the data collection and processing process that the quality of the data testing is decreased. A computational based technique for dealing with missing values, namely Genetic Algorithm Imputation (GAI). The usage was used to estimate the dataset's missing values. GAI generates the optimal set of missing values with the acquisition of information as a function of fitness to measure individual solutions' performance. GAI conducts continuous searching until the missing criteria value is found according to best fitness. So, it is trapped in optimal conditions temporarily. The improvement of GAI with tabu search is known as TS-GAI, that strength is two metaheuristic techniques modified at the mutase stage to distract the local optima's search. In applying missing values, this technique works better when many possible values are used instead of the mixed attribute having missing values. Because the new generation chromosome values generate many opportunities to make up for the missing values. The experimental results show that the TS-GAI shows better performance on 30% MV with a fitness value of 0.212. It converges at 159 iterations. Generally, TS-GAI is a faster iteration than simple GAI and it has a lower RMSE level than other imputation techniques.
Impact of Entrepreneurship Development on Employment Generation in Nigeria

This paper considers the development of entrepreneurship as a means of creating the needed jobs in Nigeria. Descriptive and inferential Statistics (Multiple Regression Analysis) were used in the study, a sample size of 336 respondents was selected using the Cochran formula (at an alpha level of 0.05) from 2,690 registered SMEs in the Federal Capital Territory, Abuja. Stratified random sampling technique was considered while SPSS 25 was used for data analysis. The study concludes that there is a strong positive relationship between entrepreneurship development and employment generation in Nigeria. Findings revealed that the employment generation of the entrepreneur is influenced by its access to finance and access to stable electricity. The study, therefore, recommends that Government should focus on the establishment of an Entrepreneurship Commission whose role will be to oversee all entrepreneurship activities in the country, ensure financial institutions set aside 5% of their profit as low-interest loans to entrepreneurs and enforce its implementation.
Difficulties and Breakthrough Strategies in the Team Construction of Part-time Teachers in Application-oriented Universities

Strengthening the team construction of part-time teachers is required by the transformation and development of the universities, the realization of their goals of being developed into an application-oriented universities and their talent cultivation and long-term development . The existing problem regarding the part-time teachers in current application-oriented universities are the low proportion of part-time teachers, a shortage of high level part-time teachers, the absence of relevant laws and regulations, a lack of systematic policies or financial support for part-time teachers as well as the unsound management mechanism with insufficient scientific and reasonable employment scheme for part-time teachers. To strengthen the team construction of the part-time teachers in application-oriented universities, firstly we should improve the existing laws, regulations and policies and broaden the financial support channels. Secondly, we should develop the recruitment channels for part-time teachers and pay attention to introducing high-tech and highly-skilled talents. Thirdly, we should improve the management over part-time teachers and promote their value. Fourthly, it should adopt a people-oriented management concept and establish an effective evaluation and incentive mechanism for part-time teachers.
A Semantics-based Model for Predicting Children's Vocabulary

Intelligent tutoring systems (ITS) provide educational benefits through one-on-one tutoring by assessing children’s existing knowledge and providing tailored educational content. In the domain of language acquisition, several studies have shown that children often learn new words by forming semantic relationships with words they already know. In this paper, we present a model that uses word semantics (semantics-based model) to make inferences about a child’s vocabulary from partial information about their existing vocabulary knowledge. We show that the proposed semantics-based model outperforms models that do not use word semantics (semantics-free models) on average. A subject-level analysis of results reveals that different models perform well for different children, thus motivating the need to combine predictions. To this end, we use two methods to combine predictions from semantics-based and semantics-free models and show that these methods yield better predictions of a child’s vocabulary knowledge. Our results motivate the use of semantics-based models to assess children’s vocabulary knowledge and build ITS that maximizes children’s semantic understanding of words.
Randomized Stochastic Gradient Descent Ascent

An increasing number of machine learning problems, such as robust or adversarial variants of existing algorithms, require minimizing a loss function that is itself defined as a maximum. Carrying a loop of stochastic gradient ascent (SGA) steps on the (inner) maximization problem, followed by an SGD step on the (outer) minimization, is known as Epoch Stochastic Gradient \textit{Descent Ascent} (ESGDA). While successful in practice, the theoretical analysis of ESGDA remains challenging, with no clear guidance on choices for the inner loop size nor on the interplay between inner/outer step sizes. We propose RSGDA (Randomized SGDA), a variant of ESGDA with stochastic loop size with a simpler theoretical analysis. RSGDA comes with the first (among SGDA algorithms) almost sure convergence rates when used on nonconvex min/strongly-concave max settings. RSGDA can be parameterized using optimal loop sizes that guarantee the best convergence rates known to hold for SGDA. We test RSGDA on toy and larger scale problems, using distributionally robust optimization and single-cell data matching using optimal transport as a testbed.