Reviewerly/exHarmony huggingface dataset:
Internet marketing in the field of higher education

Competition in the education sector is growing every year. Students have the opportunity to embark in both domestic and foreign universities. In prior years, universities did not put a large budget in the promotion, as the demand exceeded the supply. Kazakhstan’s accession to the international market of educational services has increased the need to use effective promotion tools. Marketing is an important element in attracting students. With the development of technology, the role of the Internet increases, which becomes an integral part of marketing. Internet marketing in the field of higher education has great prospects. The key features of this direction are low cost and high efficiency. At the same time, the promotion takes place not only on the regional but also on the global market. Today, promotion and advertising using the Internet is replacing traditional marketing. The article analyzes various Internet marketing tools that apply to the field of higher education. The creation and development of corporate websites of the university is an important stage of promotion. The corporate site must have certain characteristics to attract a potential student. A person spends most of the day on social networks, which are also an Internet marketing tool. Maintaining the image of the university requires the creation of official pages in social networks. These platforms create conditions for cooperation with students, applicants and graduates. Competent use of internet marketing will allow developing the sphere of higher education.
TEACHING METHODS AS A FACTOR OF STUDENTS' LEARNING MOTIVATION

Motivation is an important factor for successfull learning. It encourages the learner to learn, create and has a proactive attitude towards learning. Many teaching methods, forms, strategies, and contents influence the creation, development, and direction of student motivation. The study is based on a mixed-type research design focusing on both quantitative and qualitative research approaches according to the main objectives: to investigate the studies conducted on the use of motivational techniques for effective learning and to investigate the most applied teaching methods by primary school teachers in the Republic of North Macedonia. Due to the theoretical analysis of relevant researches active teaching along with its activities, curriculum integration, interactivity, teamwork, research, individualization, according to the needs and goals, proves to be the best model that fulfills the basic principles of the educational process. The research was conducted through systematic observation of 75 lessons of the Macedonian language in a primary school in the Republic of North Macedonia. The following teaching components were recorded in the Protocol for systematic observation: students' preparation for lessons; the degree of students' cognitive and emotional readiness; teaching methods and strategies. The results showed that the direct instruction model (frontal teaching form) was dominant at every phase of the lessons. There are insufficiently applied methods and strategies that enable research, participatory, and experiential learning. Active teaching methods where cooperative learning is expressed are missing or under-represented. This study may help teachers to make an analysis of their didactic-methodological competencies for teaching that will increase students' motivation to learn.
Weighted Feature Based Imperialist Competitive Algorithm With Ensemble Learning For Imbalanced Data Classification

In recent trends, discovering classification knowledge from imbalanced data received has grabbed much interest by many researchers. Data set imbalancing might occur if any one class comprises considerably smaller number of examples than remaining classes. Various application greatly necessitates minority class which is regarded as quite interesting aspect. The imbalanced classes’ distribution set up a challenge for standard learning algorithms like k-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Neural Network (NN), subsequently biasing is done towards majority classes. An Improved Coefficient vector based Grey Wolf Optimization (ICGWO) Algorithm with ensemble classifier is deployed in previous approaches for classification. Nonetheless, adequate outcomes in terms of accuracy and execution time cannot be achieved. A Weighted Feature based Imperialist Competitive Algorithm (WFICA) with Ensemble Learning (EL) for imbalanced data classification is chiefly suggested for mitigating this issue. Primarily, normalization scheme is exploited for data transformation from different scales to an identical scale through Z-score normalization technique. Synthetic Minority Oversampling TEchnique (SMOTE) with Locally Linear Embedding (LLE) algorithm is deployed for oversampling process. Weighted Feature based Imperialist Competitive Algorithm (WFICA) is utilized for Optimal features selection which is done for classification accuracy enhancement. Ensemble Learning (EL) incorporated with Improved Bidirectional Long Short Term Memory (IBi-LSTM), Enhanced Weighted Support Vector Machine (EWSVM) and k-Nearest Neighbour (k-NN) classifiers is employed on selected features basis for performing classification. The suggested methodology is validated through experimental result and improved performance is attained when contrasted with prevailing system pertaining to accuracy, precision, recall and f-measure.
Image interpretation by iterative bottom-up top-down processing

Scene understanding requires the extraction and representation of scene components together with their properties and inter-relations. We describe a model in which meaningful scene structures are extracted from the image by an iterative process, combining bottom-up (BU) and top-down (TD) networks, interacting through a symmetric bi-directional communication between them (counter-streams structure). The model constructs a scene representation by the iterative use of three components. The first model component is a BU stream that extracts selected scene elements, properties and relations. The second component (cognitive augmentation) augments the extracted visual representation based on relevant non-visual stored representations. It also provides input to the third component, the TD stream, in the form of a TD instruction, instructing the model what task to perform next. The TD stream then guides the BU visual stream to perform the selected task in the next cycle. During this process, the visual representations extracted from the image can be combined with relevant non-visual representations, so that the final scene representation is based on both visual information extracted from the scene and relevant stored knowledge of the world. We describe how a sequence of TD-instructions is used to extract from the scene structures of interest, including an algorithm to automatically select the next TD-instruction in the sequence. The extraction process is shown to have favorable properties in terms of combinatorial generalization, generalizing well to novel scene structures and new combinations of objects, properties and relations not seen during training. Finally, we compare the model with relevant aspects of the human vision, and suggest directions for using the BU-TD scheme for integrating visual and cognitive components in the process of scene understanding.
More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation

Feature alignment between domains is one of the mainstream methods for Unsupervised Domain Adaptation (UDA) semantic segmentation. Existing feature alignment methods for semantic segmentation learn domain-invariant features by adversarial training to reduce domain discrepancy, but they have two limits: 1) associations among pixels are not maintained, 2) the classifier trained on the source domain couldn't adapted well to the target. In this paper, we propose a new UDA semantic segmentation approach based on domain closeness assumption to alleviate the above problems. Specifically, a prototype clustering strategy is applied to cluster pixels with the same semantic, which will better maintain associations among target domain pixels during the feature alignment. After clustering, to make the classifier more adaptive, a normalized cut loss based on the affinity graph of the target domain is utilized, which will make the decision boundary target-specific. Sufficient experiments conducted on GTA5 $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes proved the effectiveness of our method, which illustrated that our results achieved the new state-of-the-art.
Efektivitas Media Flashcard Terhadap Keterampilan Menulis Kosakata Bahasa Inggris Siswa Sekolah Dasar (SD)

This study aims to determine the effect of flashcard media on English vocabulary writing skills in elementary schools. The study was conducted with pre-experimental design type one group pretest - posttest design. Participants were third grade elementary school students, numbering 21 people, consisting of 7 boys and 14 girls. Data collection was carried out with English vocabulary writing skills tests. Data processed by Paired Sample t-test. The results of data analysis showed an increase in the average results of writing skills tests on students. The t-value of 7.135 and sig 0.000 (p <0.05) showed a significant increase in writing skills, meaning that the flashcard media effectively improved students' writing skills. The results of this study are that the flashcard media can improve children's English vocabulary writing skills.
Pandemic citizenship amidst stateless algorithmic nations: digital rights and technological sovereignty at stake

COVID-19 has hit citizens dramatically during 2020, not only creating a general risk-driven environment encompassing a wide array of economic vulnerabilities but also exposing them to pervasive digital risks, such as biosurveillance, misinformation, and e-democracy algorithmic threats. Over the course of the pandemic, a debate has emerged about the appropriate techno-political response when governments use disease surveillance technologies to tackle the spread of COVID-19, pointing out the dichotomy between state-Leviathan cybercontrol and civil liberties. In order to shed light on this debate, this article introduces the term ‘pandemic citizenship’ to better understand extreme circumstances in which citizens have been surviving. Particularly, this article attempts to provide an overview by focusing on stateless nations and the need to conduct further research and gather policy evidence to articulate counter political strategies as ‘algorithmic nations’. The COVID-19 pandemic has inevitably raised the need to resiliently and techno-politically respond to threats that hyper-connected and highly virialised societies produce. Amidst the increasingly AI-driven governance systems in several nation-states in Europe, this article spotted the need to devolve data power to citizens through data ecosystems in European stateless algorithmic nations. This article argues that in the absence of a coordinated and inter-dependent strategy to claim digital rights and technological sovereignty by a set of stateless algorithmic nations in Europe, on the one hand, Big Tech data-opolies, and on the other hand, the GDPR led by the European Commission, might bound and expand respectively, stateless nations’ capacity to mitigate the negative side effects of the algorithmic disruption. Individually, we already observed subtle reactions in several nations, including Catalonia and Scotland, that are unlikely to be consistent unless a joint strategy takes place at the European level by stakeholders operating in these nations’ techno-political spheres.
BİR GÜNEY KORE ÜNİVERSİTESİNDE BAND APP'IN KULLANIMI ÜZERİNE BİR DURUM ÇALIŞMASI

The recent advancements in mobile technologies and widespread use of social media have initiated the utilization of different mobile social media apps in educational context. In many cases, the focus of attention has been the adaptation of these apps on teaching/learning processes. Unfortunately, providing communication among educational institutions or supporting administration processes has been underestimated. This paper shows an implementation of a social media app called “Band” from South Korea. The researcher focused on a private institution in which the Band app was utilized for nearly two years to serve administration and communicative purposes among students, professors and managers. This paper demonstrates how to use the Band app, its usage statistics for two years, survey results from students (n=52) and email interviews from professors (n=5). Although both professors and students have some issues (such as the quality of some contents and frequency of posts), the general findings about the adaptation of the Band app were positive enough to support its contribution to the faculty.
Recognition and Processing of NATOM

In this paper we show how to process the NOTAM (Notice to Airmen) data of the field in civil aviation. The main research contents are as follows: 1.Data preprocessing: For the original data of the NOTAM, there is a mixture of Chinese and English, and the structure is poor. The original data is cleaned, the Chinese data and the English data are processed separately, word segmentation is completed, and stopping-words are removed. Using Glove word vector methods to represent the data for using a custom mapping vocabulary. 2.Decoupling features and classifiers: In order to improve the ability of the text classification model to recognize minority samples, the overall model training process is decoupled from the perspective of the algorithm as a whole, divided into two stages of feature learning and classifier learning. The weights of the feature learning stage and the classifier learning stage adopt different strategies to overcome the influence of the head data and tail data of the imbalanced data set on the classification model. Experiments have proved that the use of decoupling features and classifier methods based on the neural network classification model can complete text multi-classification tasks in the field of civil aviation, and at the same time can improve the recognition accuracy of the minority samples in the data set.
Partial Correlations in Compositional Data Analysis

Partial correlations quantify linear association between two variables adjusting for the influence of the remaining variables. They form the backbone for graphical models and are readily obtained from the inverse of the covariance matrix. For compositional data, the covariance structure is specified from log ratios of variables, so unless we try to “open” the data via a normalization, this implies changes in the definition and interpretation of partial correlations. In the present work, we elucidate how results derived by Aitchison (1986) lead to a natural definition of partial correlation that has a number of advantages over current measures of association. For this, we show that the residuals of log-ratios between a variable with a reference, when adjusting for all remaining variables including the reference, are reference-independent. Since the reference itself can be controlled for, correlations between residuals are defined for the variables directly without the necessity to recur to ratios except when specifying which variables are partialled out. Thus, perhaps surprisingly, partial correlations do not have the problems commonly found with measures of pairwise association on compositional data. They are well-defined between two variables, are properly scaled, and allow for negative association. By design, they are subcompositionally incoherent, but they share this property with conventional partial correlations (where results change when adjusting for the influence of fewer variables). We discuss the equivalence with normalization-based approaches whenever the normalizing variables are controlled for. We also discuss the partial variances and correlations we obtain from a previously studied data set of Roman glass cups.
Towards Computational Linguistics in Minangkabau Language: Studies on Sentiment Analysis and Machine Translation

Although some linguists (Rusmali et al., 1985; Crouch, 2009) have fairly attempted to define the morphology and syntax of Minangkabau, information processing in this language is still absent due to the scarcity of the annotated resource. In this work, we release two Minangkabau corpora: sentiment analysis and machine translation that are harvested and constructed from Twitter and Wikipedia. We conduct the first computational linguistics in Minangkabau language employing classic machine learning and sequence-to-sequence models such as LSTM and Transformer. Our first experiments show that the classification performance over Minangkabau text significantly drops when tested with the model trained in Indonesian. Whereas, in the machine translation experiment, a simple word-to-word translation using a bilingual dictionary outperforms LSTM and Transformer model in terms of BLEU score.
Breast Anatomy Enriched Tumor Saliency Estimation

Breast cancer investigation is of great significance, and developing tumor detection methodologies is a critical need. However, it is challenging for breast cancer detection using breast ultrasound (BUS) images due to the complicated breast structure and poor quality of the images. This paper proposes a novel tumor saliency estimation (TSE) model guided by enriched breast anatomy knowledge to localize the tumor. First, the breast anatomy layers are generated by a deep neural network. Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers. Meanwhile, a new background map generation method weighted by the semantic probability and spatial distance is proposed to improve the performance. The experiment demonstrates that the proposed method with the new background map outperforms four state-of-the-art TSE models with an increasing 10% of Fmeasure on the public BUS dataset.
Research of Environmental Law Teaching Based on MOOC under COVID-19

The use of MOOC in environmental law teaching is an inevitable choice for environmental law education in the context of COVID-19. It is also an inevitable requirement for ecological civilization construction to adopt MOOC. The integrated online and offline teaching design, various and abundant teaching methods, a serious and lively teaching organization and management method based on motivation, and diversified teaching assessment and evaluation should be adopted.
Independent work as pedagogical condition for development of intellectual and creative abilities of future music teachers

The article analyses the independent work of future music teachers in higher educational institutions of art as a pedagogical condition for the development of intellectual and creative abilities. The concept of “independent work” is considered in the works of modern and domestic researchers. The role of independent work is defined as an integral system and forming factor of the educational environment of higher art institutions. The tasks are highlighted and the classification of types of independent work of future music teachers is considered. The phenomenon of independence, which is an integral attribute, the inner essence of independent work and acts as a sign of its intellectual component, is analysed. On the other hand, independence is seen as the highest step in the formation of personal competence. The future music teacher makes the significant both intellectual and creative efforts and thus reaches a certain level of intellectual and creative abilities by mastering professional training, carrying out professional and creative selection of the repertoire, methods of its processing, based on their preferences, intentions, and motivations. The author investigates the most important component of education — a self-education, the ability which is exclusively formed through the self-educational activities. The self-educational activity of the future music teacher is considered as an effective means of professional self-improvement and self-development of personality, which also provides the significant professional qualities and abilities such as both intellectual and creative, the development of which determines the level of professional self-development. It is proven that professional self-realization is the highest level of manifestation of professional training of future music teachers, in the process of which are carried out the intensive development of both intellectual and creative abilities.
A Survey on Ensemble Learning under the Era of Deep Learning

Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networks (ensemble deep learning) has shown significant performances in improving the generalization of learning system. However, since modern deep neural networks usually have millions to billions of parameters, the time and space overheads for training multiple base deep learners and testing with the ensemble deep learner are far greater than that of traditional ensemble learning. Though several algorithms of fast ensemble deep learning have been proposed to promote the deployment of ensemble deep learning in some applications, further advances still need to be made for many applications in specific fields, where the developing time and computing resources are usually restricted or the data to be processed is of large dimensionality. An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required expenses so that many more applications in specific fields can benefit from it. For the alleviation of this problem, it is essential to know about how ensemble learning has developed under the era of deep learning. Thus, in this article, we present fundamental discussions focusing on data analyses of published works, methodologies, recent advances and unattainability of traditional ensemble learning and ensemble deep learning. We hope this article will be helpful to realize the intrinsic problems and technical challenges faced by future developments of ensemble learning under the era of deep learning.
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control

Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications. In this paper, the vulnerabilities of such DRL agents to adversarial attacks is studied. In particular, we consider multiple DRL agents that perform both dynamic channel access and power control in wireless interference channels. For these victim DRL agents, we design a jammer, which is also a DRL agent. We propose an adversarial jamming attack scheme that utilizes a listening phase and significantly degrades the users' sum rate. Subsequently, we develop an ensemble policy defense strategy against such a jamming attacker by reloading models (saved during retraining) that have minimum transition correlation.