Reviewerly/exHarmony huggingface dataset:
Students’ Perceptions in Using YouTube to Improve Their Speaking Skill: A Cross-National Study of Malaysia and Indonesia

Moving into the post-pandemic phase, there is currently a rise in the incorporation of digital tools in teaching and learning particularly in regards to ESL.This most likely is due to the fact that overnight, the global crisis had caused both educators and students to rely heavily on technology and digital tools for teaching and learning.This cross-sectional study explores students' perceptions in using YouTube to improve their speaking skill in Malaysia and Indonesia.Quantitative research instrument was utilized in the form of 5-point Likert scale survey questionnaire that was adapted from past research.The questionnaire was distributed to 50 Malaysians and 50 Indonesians at secondary levels to get the answer to three research questions; 1) What are the English language learners' perceptions on YouTube use in English language learning?2) What are the English language learners' perceptions on the role of YouTube in English speaking skills improvement?And 3) To what extent do the perceptions of Malaysian and Indonesian students vary on the use of YouTube in improving speaking skills?The data was analysed using Statistical Package for the Social Science (SPSS) version 27 as well as descriptive analysis of the mean and standard deviation.Semi-structured interviews were also conducted to obtain in-depth feedback from ten students among the respondents.The findings suggest that although the students' perceptions of using YouTube videos in English language learning are highly positive, they are generally unsure if YouTube is able to improve speaking skills.Apart from that, the findings also indicates that the roles of YouTube in improving speaking skills for both nationalities were almost identical at that stage because p > 0.05 which is 0.712.It can be concluded that there is no difference of perceptions on the use of YouTube to improve speaking skills among Malaysian and Indonesian secondary school students.
Towards a Definitive Compressibility Measure for Repetitive Sequences

Unlike in statistical compression, where Shannon's entropy is a definitive lower bound, no such clear measure exists for the compressibility of repetitive sequences. Since statistical entropy does not capture repetitiveness, ad-hoc measures like the size $z$ of the Lempel--Ziv parse are frequently used to estimate it. The size $b \le z$ of the smallest bidirectional macro scheme captures better what can be achieved via copy-paste processes, though it is NP-complete to compute and it is not monotonic upon symbol appends. Recently, a more principled measure, the size $\gamma$ of the smallest string \emph{attractor}, was introduced. The measure $\gamma \le b$ lower bounds all the previous relevant ones, yet length-$n$ strings can be represented and efficiently indexed within space $O(\gamma\log\frac{n}{\gamma})$, which also upper bounds most measures. While $\gamma$ is certainly a better measure of repetitiveness than $b$, it is also NP-complete to compute and not monotonic, and it is unknown if one can always represent a string in $o(\gamma\log n)$ space. In this paper, we study an even smaller measure, $\delta \le \gamma$, which can be computed in linear time, is monotonic, and allows encoding every string in $O(\delta\log\frac{n}{\delta})$ space because $z = O(\delta\log\frac{n}{\delta})$. We show that $\delta$ better captures the compressibility of repetitive strings. Concretely, we show that (1) $\delta$ can be strictly smaller than $\gamma$, by up to a logarithmic factor; (2) there are string families needing $\Omega(\delta\log\frac{n}{\delta})$ space to be encoded, so this space is optimal for every $n$ and $\delta$; (3) one can build run-length context-free grammars of size $O(\delta\log\frac{n}{\delta})$, whereas the smallest (non-run-length) grammar can be up to $\Theta(\log n/\log\log n)$ times larger; and (4) within $O(\delta\log\frac{n}{\delta})$ space we can not only...
Evaluation of future technology teachers’ readiness for anticipatory vocational training of schoolchildren

Introduction. Professional self-identification of schoolchildren on the 5th-9th years of training should be attributed mainly to the result of technology teacher’s career guidance work. However, the due training of technology teachers themselves in this domain is not paid enough attention to in the theory and practice of higher educational establishments. The focus of the present publication is assessment of such readiness in students – future technology teachers – and development of recommendations for higher educational system on the basis of the obtained results. The purpose of this study is the evolution of future technology teachers’ readiness for anticipatory vocational training of schoolchildren. Materials and methods. The questioning involved 120 respondents, future technology teachers (3rd-5th year students trained under the programme of higher pedagogical education in the profile “Technology”) from 5 state universities of Moscow, St. Petersburg, Novosibirsk, Nizhny Novgorod and Kazan (Russian Federation). The original questionnaires were developed with the use of Google Forms tools. The statistical data processing method was Pearson’s chi-square test. The research was based on the application of theoretical methods: analysis (relative to the semantics of the concept “teacher’s readiness for advanced vocational training of a schoolchild”), synthesis (essential categorial characteristics of teacher’s readiness for anticipatory vocational training of schoolchildren), comparison (theory and practice in higher education system), specification (key provisions), modelling. Results. A total of 92% of the 3rd-5th year students are aware of the need for anticipatory vocational training of schoolchildren. At the same time, only 66% of the respondents (from 50% on the 3rd year to 75% on the 4th-5th year) consider themselves ready for such schooling. The analysis of the questioning results of students who answered in a different way made it possible to reveal the causes of educational deficiency. These reasons were interpreted as components of the work offered by us for the university system for integration into the programme of higher pedagogical education in the profile “Technology”. On the 4th-5th year students feel ready for advanced vocational training (p&lt;0.01). Conclusion. Students’ – future technology teachers’ self-assessment of readiness for anticipatory vocational training of schoolchildren made it possible, on the one hand, to affirm the formation of such readiness as a pedagogical problem and, on the other hand, to identify the growing points for elimination of the identified educational deficiency in the higher education system.
Data Clustering using Two-Stage Eagle Strategy Based on Slime Mould Algorithm

Data clustering is considered an important component of data mining which aims to split a given dataset into disjoint groups having the same similarities.The developed techniques for clustering have some challenges to cluster entities in complex search space and most of them aim to maximize the sum of inter-cluster distances and minimize the sum of intracluster distances.This objective function is nonlinear and hard to optimize especially for complex search space.Metaheuristics are becoming a trend for solving this task thanks to their promising results.In this study, the eagle strategy is used to take advantage of the exploration provided by Levy Flight (LF) and the exploitation strength of the Slime Mould Algorithm (SMA) to solve the clustering problem.The SMA algorithm is an efficient technique for solving complex optimization problems which has a high exploitation competence.On the other hand, LF tends to have good exploratory behavior.Our strategy exploits these advantages in a balanced way and through welldesigned rounds to ensure the optimality of the clustering solutions.The proposed method is computationally efficient and inexpensive.It also achieves high accuracy in terms of average, worst, best, and the sum of intracluster distance.The method is also evaluated according to the speed of convergence and using statistical tests, namely Wilcoxon.The obtained results are compared with seven benchmarked metaheuristics, namely Grey Wolf Optimizer (GWO), Slime Mould Algorithm (SMA), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO) and Genetic Algorithm (GA) using eighteen datasets of shapes and UCI repositories.
Image Super-Resolution using Auto-Encoders with Parallel Skip-Connections

Super-resolution images are highly desired when employed for numerous analytical purposes and obviously because of their superior attractive visual effect.To create a High-Resolution (HR) image from one or more Low-Resolution (LR) images, an image super-resolution technique is used.When dealing with natural environments and settings, it is not always easy to access high-resolution images.The main barriers to the same are limitations in acquisition methods.In the domains of forensics investigation, remote sensing, digital monitoring, and medical imaging high-resolution images are always required.Modern methods using deep learning models have improved performance when compared to classic image processing methods.Using a convolution auto-encoder architecture with six parallel skip connections, a novel technique for improving lower solution natural images of size 256 × 256 to high-resolution images is proposed in this paper.The use of parallel skip connections between the encoder and decoder allows the network to reconstruct high-resolution images while still extracting relevant features from low-resolution images.Furthermore, the network is skillfully tuned using the right filters and regularization techniques.To create high-resolution output images, the decoder component makes use of the features' reduced representation in latent space.The model was evaluated using DIV 2K, CARS DATA, Set5, Set14, and the General data set after being trained using various data sets like CARS DATA and DIV 2K.Peak Signal to Noise Ratio, Structural Similarity Index, Mean Squared Error, and model behavior on numerous data sets are used to compare the proposed method to various current methods.Results reveal that the suggested model works better than the existing method and is reliable.
HISTORY OF DEVELOPMENT OF THE SYSTEM AND ACTIVITY-BASED APPROACH TO EDUCATION

Abstract. the article reveals the meaning of the notions “activity” and “system” both in general and pedagogical science context as the integration components of the system and activity-based approach to education. The purpose of the article is to characterize the concept of the system and activity-based approach and describe its development applied to the educational process. Emphasis is placed on the history of the development of the system and activity-based approach.&#x0D; The results of the investigation made it possible to conclude reveal that the idea to combine system-based and activity-based approaches does not belong to one scientist. It appears in a number of works of philosophers and psychologists of the first part of the 20th century. Considering the issues of teaching and raising a child gave a substantial impulse to the development of this idea in the 1960s-1990s. Nowadays the studied methodological approach to educational process complements other ones.
FORMATION OF LEADERSHIP COMPETENCE OF FUTURE PSYCHOLOGISTS BY MEANS OF PSYCHOLOGICAL AND PEDAGOGICAL TRAINING IN THE PROCESS OF PROFESSIONAL TRAINING

Abstract: the article found out that the formation of leadership competence among future psychologists is now becoming acutely relevant. Based on the research of scientists, the essence of the concepts of “leader” and “leadership competence” was considered. The study states that the effective formation of leadership competence takes place through the individual's acquisition of successful leadership experience in simulated training situations. In order to check the effectiveness of the psychological and pedagogical training, aimed at forming the leadership competence of future psychologists, we organized a pedagogical experiment, the ascertainment stage of which took place in the autumn semester of the 2021/2022 academic year of the NTU “KhPI”.
DISTANCE LEARNING EXPERT AND LEADER

Abstract: the paper deals with the theoretical foundations of training experts and leaders of distance learning in order to improve their training program. An expert is a specialist who has an experience in developing distance courses, conducting distance learning classes and has and uses the skills of a content curator. Leaders create the conditions for innovative changes that allow people and organizations to share their vision and move in its direction; contribute to the management and implementation of ideas. The paper describes the basic skills to be developed by an expert and a leader, provides typical mistakes of distance course developers.&#x0D; Training of experts and leaders was based on the distance course "Distance Course Examination" for experienced developers of distance learning courses and administrators of distance learning systems from different organizations. During the training, students analyzed the purpose of learning according to the Bloom's taxonomy and its accessibility, the complexity of the text and its design, tasks and learning activities, test quality and evaluation system as well as other elements responsible for the quality of learning. During the pilot&#x0D; training, the results were obtained in the form of reports of students about the examination of the distance course as well as analysis of this work was done. 47 teachers of educational institutions of Ukraine were trained and only 12 teachers successfully completed their training.&#x0D; Based on the results of training and taking into account the experience of emergency training during the pandemic and military actions in Ukraine, the course program has been refined. It is planned to use a team of experts, "playing" coaches and new methods for conducting distance learning. Particular attention is paid to training experts, leaders and measuring their competencies.
THE DEVELOPMENT OF LEADER INTELLIGENCE

Abstract: one of the characteristic manifestations of the evolution of society is considered which consists in a significant increase in the educational and qualification level of the broad masses of the population and an increase in the level of their culture. The share of intellectual work and its creative nature in various spheres of social production is becoming more widespread. It was found that the noticeable increase in the total number of different groups of the intelligentsia and its qualitative composition and level of culture requires a corresponding increase in the level of their general and professional culture, their intelligence, in order to ensure the effectiveness of the influence of leaders on other people.&#x0D; It has been proven that the leader's intelligence, through his specific influence, actively contributes to the development of the level of society's intelligence, which, due to the requirements for the leader's personality, contributes to the further development of his intelligence, increasing his role in the development of culture and the spiritual world of the population. The essential role in the formation and development of the leader's intelligence as one of the attribute qualities belonging to the education system and pedagogical practice is revealed. To do this, they should pay more attention to the proper performance of the functions of education, personal development and socialization of students, development of their culture and leadership potential.
Transformation Of Didactic Space Of Blended Learning At University

The article substantiates the relevance of the strategic focus on modern education modernization, specifically, its technological transformation through the combination of traditional and electronic media formats of education in the educational process.Blended learning is the key of the innovative development of higher education; it benefits individual educational trajectory and the formation of universal and professional competencies in the context of digitalization.The necessity of designing a didactic space of blended learning at a university is revealed.The methodological basis of the study is a set of systemic, competency-based and technological approaches, whose leading ideas and specifics enabled to present the main provisions of an integrative didactic learning system in the digital educational environment of a university.The article proposes an organizational and didactic classification of blended learning models in higher education based on the generalization and systematization of theoretical and empirical experience.The classification includes organizational, supportive, didactic and self-developing models.The authors substantiate the approach to the typology of blended learning models that can be implemented at different levels.Based on the study, the requirements for the organization of blended learning, creating new opportunities for transforming the didactic space and solving a set of educational tasks in the conditions of the electronic educational environment of the university, are discussed.It is concluded that it is necessary to determine didactic strategies for the transformation of the educational process at the university, which are a complementary basis for building a polymodal educational process regarding the possibilities of augmented reality.
ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation

Weakly Supervised Semantic Segmentation (WSSS) is a challenging task aiming to learn the segmentation labels from class-level labels. In the literature, exploiting the information obtained from Class Activation Maps (CAMs) is widely used for WSSS studies. However, as CAMs are obtained from a classification network, they are interested in the most discriminative parts of the objects, producing non-complete prior information for segmentation tasks. In this study, to obtain more coherent CAMs with segmentation labels, we propose a framework that employs an iterative approach in a modified encoder-decoder-based segmentation model, which simultaneously supports classification and segmentation tasks. As no ground-truth segmentation labels are given, the same model also generates the pseudo-segmentation labels with the help of dense Conditional Random Fields (dCRF). As a result, the proposed framework becomes an iterative self-improved model. The experiments performed with DeepLabv3 and UNet models show a significant gain on the Pascal VOC12 dataset, and the DeepLabv3 application increases the current state-of-the-art metric by %2.5. The implementation associated with the experiments can be found: https://github.com/cenkbircanoglu/isim.
Predicting the Type and Target of Offensive Social Media Posts in Marathi

The presence of offensive language on social media is very common motivating platforms to invest in strategies to make communities safer. This includes developing robust machine learning systems capable of recognizing offensive content online. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English and a few other high resource languages such as French, German, and Spanish. In this paper we address this gap by tackling offensive language identification in Marathi, a low-resource Indo-Aryan language spoken in India. We introduce the Marathi Offensive Language Dataset v.2.0 or MOLD 2.0 and present multiple experiments on this dataset. MOLD 2.0 is a much larger version of MOLD with expanded annotation to the levels B (type) and C (target) of the popular OLID taxonomy. MOLD 2.0 is the first hierarchical offensive language dataset compiled for Marathi, thus opening new avenues for research in low-resource Indo-Aryan languages. Finally, we also introduce SeMOLD, a larger dataset annotated following the semi-supervised methods presented in SOLID.
Quasi-Newton Sequential Monte Carlo

Sequential Monte Carlo samplers represent a compelling approach to posterior inference in Bayesian models, due to being parallelisable and providing an unbiased estimate of the posterior normalising constant. In this work, we significantly accelerate sequential Monte Carlo samplers by adopting the L-BFGS Hessian approximation which represents the state-of-the-art in full-batch optimisation techniques. The L-BFGS Hessian approximation has only linear complexity in the parameter dimension and requires no additional posterior or gradient evaluations. The resulting sequential Monte Carlo algorithm is adaptive, parallelisable and well-suited to high-dimensional and multi-modal settings, which we demonstrate in numerical experiments on challenging posterior distributions.
Dynamic Loss For Robust Learning

Label noise and class imbalance commonly coexist in real-world data. Previous works for robust learning, however, usually address either one type of the data biases and underperform when facing them both. To mitigate this gap, this work presents a novel meta-learning based dynamic loss that automatically adjusts the objective functions with the training process to robustly learn a classifier from long-tailed noisy data. Concretely, our dynamic loss comprises a label corrector and a margin generator, which respectively correct noisy labels and generate additive per-class classification margins by perceiving the underlying data distribution as well as the learning state of the classifier. Equipped with a new hierarchical sampling strategy that enriches a small amount of unbiased metadata with diverse and hard samples, the two components in the dynamic loss are optimized jointly through meta-learning and cultivate the classifier to well adapt to clean and balanced test data. Extensive experiments show our method achieves state-of-the-art accuracy on multiple real-world and synthetic datasets with various types of data biases, including CIFAR-10/100, Animal-10N, ImageNet-LT, and Webvision. Code will soon be publicly available.
Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models

Natural language often contains ambiguities that can lead to misinterpretation and miscommunication. While humans can handle ambiguities effectively by asking clarifying questions and/or relying on contextual cues and common-sense knowledge, resolving ambiguities can be notoriously hard for machines. In this work, we study ambiguities that arise in text-to-image generative models. We curate a benchmark dataset covering different types of ambiguities that occur in these systems. We then propose a framework to mitigate ambiguities in the prompts given to the systems by soliciting clarifications from the user. Through automatic and human evaluations, we show the effectiveness of our framework in generating more faithful images aligned with human intention in the presence of ambiguities.