Reviewerly/exHarmony huggingface dataset:
The personality of the future teacher in the context of the challenges of the innovation space

The paper systematizes approaches to the study of the higher school teacher's work, presents the results of the analysis of the psychological component of his professional self-effectiveness in the space of modern innovations. The content of the article reveals the features of the teacher as a subject of professional activity in the educational environment of the University. The article describes the psychological determinants of success of teacher in terms of resources and risks of modern innovation space, presented mechanisms of development and manifestations of occupational success factors, conditions, strategies and professional development of the teacher of the future, reflecting the current format of supporting the development of the subject training. The article describes the phenomenological and instrumental components of success, factors and conditions for the formation of successful professional behavior, as well as forms and ways of representing success in interaction with students. The article shows the psychological function of competence and reference in the professional activity of a higher school teacher, the role of sociogenic needs of a teacher in self-development, personalization, self-actualization and self-realization in interaction with students. Features of interpretation of the personality image of the future teacher from the point of view of modern students are presented.
Modeling Online Classroom Interaction to Support Student Language Learning

For many years, teacher-student interaction has been indicated to considerably affect student learning. In recent years, research concentration has moved from attempting to evaluate the potent ways of classroom interaction for student learning and how it can be developed. Due to the intricacy of the classroom interaction procedure, the answer to this question is profoundly contingent upon some reasons, such as the learning condition. The aim of this project is to study the classroom interaction process in an online learning context in which English is learned as a foreign language. This is review research employing content analysis technique we intend to examine the published research articles to find out the teachers’ strategies/tools and the contribution of the strategies to students’ language learning in online classroom interaction. This can build up an understanding of the most effective techniques to develop the students’ involvement with the learning process. The result of the analysis is that most teachers use synchronous and asynchronous online classroom interaction and blended learning in which teacher-students, student-student, student-teacher, and student-learning sources are met through discussion. This strategy is mostly preferred by teachers to give students a chance to get involved in communication, collaboration, interaction, and participation. This strategy is believed to be able to engage students in developing language learning opportunities; therefore, this can be a good model for better classroom practices.
Game technologies as a tool of motivation and improvement the quality of university students’ training

The article discusses and describes in detail the specifics of equipping the system of higher professional education with tools of modern pedagogical technologies – game technologies that have a significant impact on the quality of professional training of future specialists and their motivation to obtain general and special competencies. The most relevant game technologies are considered separately. and their effectiveness (or inefficiency) is confirmed by means of a survey of leading teachers of the Crimea who actively use game technologies in their teaching practice. In addition. a significant segment of the study is devoted to reviewing and comparing the experience of predecessors in this matter. The role of various pedagogical approaches in the system of higher professional education is identified and updated. and their separate elements. especially significant for research. are highlighted. The author compares the opinions of various scientists (both practitioners and theoretical researchers) who address the study of the system of modern pedagogical technologies in general and game technologies in particular. on the basis of which a comprehensive conclusion is made. which is reflected in the final part of the article. A special place in the structure of the study is occupied by the selected key method – a questionnaire based on data provided by more than 100 teachers of higher and secondary professional education. This method is one of the most effective and promising methods of scientific-analytical and reflexive research. which is especially important in the framework of pedagogical science.
Factors Affecting Reading Comprehension among Malaysian ESL Elementary Learners

This study explored Level 2 primary school English as a Second Language (ESL) learners' reading comprehension skill of a sub-urban school in Mukah, Sarawak.The objectives of this study were to identify the factors that contributed to learners' comprehension skill, and to compare these factors in term of gender.The respondents comprised of 50 males and 50 females aged between 10 to 12 years old from major ethnics in Mukah namely, Melanau, Iban, Malay and Chinese.A set of questionnaire that consisted of 16 questions were administered via Google Form.The findings suggested that motivation and interest, prior knowledge and vocabulary knowledge influenced learners' reading comprehension skill.In addition, there was no significant relationship between genders and these factors.The findings from this research were important to determine reading strategies based on the factors listed to help the poor readers.
iNethi: locked down but not locked out

A community-driven network seeks to overcome the digital divide in South African education by delivering e-learning to bandwidth-constrained learners.
Movie Aspects Identification Model for Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis techniques have been applied in several application domains. From the last two decades, these techniques have been developed mostly for product and service application domains. However, very few aspect-based sentiment techniques have been proposed for the movie application domain. Moreover, these techniques only mine specific aspects (Script, Director, and Actor) of a movie application domain, nevertheless, the movie application domain is more complex than the product and service application domain. Since, it contains NER (Named Entity Recognition) problem and it cannot be ignored, since there is an opinion often associated with it. Consequently, in this paper MAIM (Movie Aspect Identification Model) is proposed that can extract not only movie specific aspects, also identifies NEs (Named Entities) such as Person Name and Movie Title. The three main contributions are 1) the identification of infrequent aspects, 2) the identification of NE (named entity) in movie application domain, 3) identifying N-gram opinion words as an entity. MAIM incorporates the BiLSTM-CRF hybrid technique and is implemented on the movie application domain having precision 89.9%, recall 88.9% and f1-measure 89.4%. The experimental results show that MAIM performs better than baseline models CRF and LSTM-CRF.
A Review of Designs and Applications of Echo State Networks

Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence tasks and have achieved state-of-the-art in wide range of applications, such as industrial, medical, economic and linguistic. Echo State Network (ESN) is simple type of RNNs and has emerged in the last decade as an alternative to gradient descent training based RNNs. ESN, with a strong theoretical ground, is practical, conceptually simple, easy to implement. It avoids non-converging and computationally expensive in the gradient descent methods. Since ESN was put forward in 2002, abundant existing works have promoted the progress of ESN, and the recently introduced Deep ESN model opened the way to uniting the merits of deep learning and ESNs. Besides, the combinations of ESNs with other machine learning models have also overperformed baselines in some applications. However, the apparent simplicity of ESNs can sometimes be deceptive and successfully applying ESNs needs some experience. Thus, in this paper, we categorize the ESN-based methods to basic ESNs, DeepESNs and combinations, then analyze them from the perspective of theoretical studies, network designs and specific applications. Finally, we discuss the challenges and opportunities of ESNs by summarizing the open questions and proposing possible future works.
EARTHQUAKE MAY 20, 2014, ML=2.8, I0=4–5 IN THE CENTRAL PART OF THE REPUBLIC OF BASHKORTOSTAN

The article provides macroseismic data on the earthquake 20.05.2014 18h26mUTC ML=2.8, that had a local but relatively strong macroseismic effect (I0=4–5) in the Chishminsky district of the Republic of Bashkortostan. After the event, macroseismic data were collected in 27 settlements in the epicentral zone. This data allowed to determine the macroseismic epicenter, which is close to the instrumental one. The events in the same area during the historical period were considered in detail and modern seismic records were revised. The data allowed to identify a series of similar seismic events with less magnitudes in 2014. The classification of these activities into two groups has been justified there. There are tectonic and natural events induced by Alkinskoye oil deposit exploration.
Accessibility Service Utilization Rates in Android Applications Shared on Twitter

The number of malware detected has been increasing annually, and 4.12% of malware reported in 2018 attacked Android phones. Therefore, preventing attacks by Android malware is critically important. Several previous studies have investigated the percentage of apps that utilize accessibility services and are distributed from Google Play, that have been reportedly used by Android malware. However, the Social Networking Services (SNSs) that are used to spread malware have distributed apps not only from Google Play but also from other sources. Therefore, apps distributed from within and outside of Google Play must be investigated to capture malware trends. In this study, we collected apps shared on Twitter in 2018, which is a representative SNS, and created a Twitter shared apps dataset. The dataset consists of 32,068 apps downloaded from the websites of URLs collected on Twitter. We clarified the proportion of apps that contained malware and proportion of apps utilizing accessibility services. We found that both, the percentage of malware and percentage of total apps using accessibility services have been increasing. Notably, the percentages of malware and un-suspicious apps using accessibility services were quite similar. Therefore, this problem cannot be solved by automatically blocking all apps that use accessibility services. Hence, specific countermeasures against malware using accessibility services will be increasingly important for online security in the future.
Hindsight and Sequential Rationality of Correlated Play

Driven by recent successes in two-player, zero-sum game solving and playing, artificial intelligence work on games has increasingly focused on algorithms that produce equilibrium-based strategies. However, this approach has been less effective at producing competent players in general-sum games or those with more than two players than in two-player, zero-sum games. An appealing alternative is to consider adaptive algorithms that ensure strong performance in hindsight relative to what could have been achieved with modified behavior. This approach also leads to a game-theoretic analysis, but in the correlated play that arises from joint learning dynamics rather than factored agent behavior at equilibrium. We develop and advocate for this hindsight rationality framing of learning in general sequential decision-making settings. To this end, we re-examine mediated equilibrium and deviation types in extensive-form games, thereby gaining a more complete understanding and resolving past misconceptions. We present a set of examples illustrating the distinct strengths and weaknesses of each type of equilibrium in the literature, and prove that no tractable concept subsumes all others. This line of inquiry culminates in the definition of the deviation and equilibrium classes that correspond to algorithms in the counterfactual regret minimization (CFR) family, relating them to all others in the literature. Examining CFR in greater detail further leads to a new recursive definition of rationality in correlated play that extends sequential rationality in a way that naturally applies to hindsight evaluation.
A Forensic Analysis of Home Automation Devices (FAHAD) Model: Kasa Smart Light Bulb and Eufy Floodlight Camera as Case Studies

The adoption of Internet of Things (IoT) devices is rapidly increasing with the advancement of network technology, these devices carry sensitive data that require adherence to minimum security practices. The adoption of smart devices to migrate homeowners from traditional homes to smart homes has been noticeable. These smart devices share value with and are of potential interest to digital forensic investigators, as well. Therefore, in this paper, we conduct comprehensive security and forensic analysis to contribute to both fields—targeting a security enhancement of the selected IoT devices and assisting the current IoT forensics approaches. Our work follows several techniques such as forensic analysis of identifiable information, including connected devices and sensor data. Furthermore, we perform security assessment exploring insecure communication protocols, plain text credentials, and sensitive information. This will include reverse engineering some binary files and manual analysis techniques. The analysis includes a data-set of home automation devices provided by the VTO labs: (1) the eufy floodlight camera, and (2) the Kasa smart light bulb. The main goal of the technical experiment in this research is to support the proposed model.
Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation

Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated in some structured prediction tasks, such as semantic segmentation, which requires pixel-level annotations. This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes, and the underlying relations between a pair of images are characterized by an efficient co-attention mechanism. Moreover, in order to prevent the model from paying excessive attention to common semantics only, we further propose a graph dropout layer, encouraging the model to learn more accurate and complete object responses. The whole network is end-to-end trainable by iterative message passing, which propagates interaction cues over the images to progressively improve the performance. We conduct experiments on the popular PASCAL VOC 2012 and COCO benchmarks, and our model yields state-of-the-art performance. Our code is available at: https://github.com/Lixy1997/Group-WSSS.
Unreported MALL Studies

Over the past 26 years, nearly 3,000 Mobile Assisted Language Learning (MALL) studies have appeared. Despite this outpouring of MALL research, only a fraction is referenced in MALL publications (in primary works no less than overviews). The major reason why so many MALL studies are unreferenced is the great diversity of research area topics covered: mobile device ownership, software reviews, technical design, pedagogical frameworks, learning theory, institutional infrastructure, teacher training, learning outcomes. MALL studies appear in educational technology-related and mobile technology publications as well as journals devoted to the teaching of specific languages, primarily English. However, by far the majority occur in publications that have nothing in particular to do with language learning or teaching. But does the great number of unreported MALL studies have any practical effect upon published overviews? Based on a reanalysis of one of the most comprehensive MALL meta-analyses published to date, the answer is definitely yes. Previously unreported studies have a significant impact upon the accuracy of meta-analytic observations and conclusions. If future MALL studies are to be adequately informed about what is already known, it is essential that their authors learn where and how to find essential information about existing MALL research.
Digital Sources in Historiansʼ Research Practices: a Theoretical and Methodological Aspect

The purpose of the research is to determine the essence and role of digital sources in the system of information support for historianʼs research practices. Methods: analysis and synthesis; system approach; comparative; genetic; logical-linguistic. Main results, concise conclusions. In the scientific, legal and regulatory literature there is the term "electronic document", the definition of which is tied to computer origin. The article discusses the legitimacy of the use of such a term in the research practices of historians. It is shown that the term "digital source" is adequate, taking into account the definition of the concept "historical source", which is based on the method of encoding information, and not on the means of its storage. The article discusses the positive and negative properties of digital sources; levels of information in them that are important to historians; attributes of digital documents. Analyzed approaches to the definition of "historical source". The author proposed an information model of the historianʼs cognitive activity as a communication process. During the study, the concepts of "digital document" and "electronic document", "digital resource" and "electronic resource", "electronic archive" and "digital archive", "electronic library" and "digital library" are compared. The author substantiated that the correct names of computerized information systems of a humanitarian profile are not the concepts of "electronic archive" and "electronic library", but the concepts of "archive of digital documents" and "library of digital resources". Practical value. Refining the terminology will ensure proper understanding and its correct application in the research practices of historians and other humanities scholars. Originality. The study was carried out on the basis of a systematic critical analysis of literature and ideas, their further synthesis and the selection of the most appropriate theoretical concepts from the standpoint of logic. Scientific novelty. The article proposes a modern information model of historianʼs cognitive activity as a communication process. The author indicated the place of digital sources in the historianʼs intelligence communications. Type of article. Theoretical analytical.
Indigenous Peoples, Data Sovereignty and Self-Determination: Current Realities and Imperatives

This study explores the current state and dynamics of the global Indigenous data sovereignty movement-the movement pressing for Indigenous peoples to have full control over the collection and governance of data relating to their lived realities. The article outlines the movement's place within the broader push for Indigenous self-determination; examines its links to big data, open data, intellectual property rights, and access and benefit-sharing; details a pioneering assertion of data sovereignty by Canada's First Nations; outlines relevant UN and international civil society processes; and examines the nascent movement in Africa. The study identifies a fundamental tension between the objectives of Indigenous data sovereignty and those of the open data movement, which does not directly cater for Indigenous peoples' full control over their data. The study also identifies the need for African Indigenous peoples to become more fully integrated into the global Indigenous data sovereignty movement.
The Development of Scene Semantics: First ERP Indications for the Processing of Semantic Object-Scene Inconsistencies in 24-Month-Olds

Finding a bottle of milk in the bathroom would probably be quite surprising to most of us. Such a surprised reaction is driven by our strong expectations, learned through experience, that a bottle of milk belongs in the kitchen. Our environment is not randomly organized but governed by regularities that allow us to predict what objects can be found in which types of scene. These scene semantics are thought to play an important role in the recognition of objects. But when during development are the semantic predictions so far implemented that such scene-object inconsistencies would lead to semantic processing difficulties? Here we investigated how toddlers perceive their environments, and what expectations govern their attention and perception. To this aim, we used a purely visual paradigm in an ERP experiment and presented 24-month-olds with familiar scenes in which either a semantically consistent or an inconsistent object would appear. The scene-inconsistency effect has been previously studied in adults by means of the N400, a neural marker responding to semantic inconsistencies across many types of stimuli. Our results show that semantic object-scene inconsistencies indeed elicited an enhanced N400 over the left anterior brain region between 750 and 1150 ms post stimulus onset. This modulation of the N400 marker provides first indications that by the age of two toddlers have already established their scene semantics allowing them to detect a purely visual, semantic object-scene inconsistency. Our data suggest the presence of specific semantic knowledge regarding what objects occur in a certain scene category.
The Use of Artificial Intelligence in the Educational Process of Higher Educational Institutions (On the Example of Foreign Languages Teaching)

The analysis of foreign and Russian publications on the emergence and use of artificial intelligence (AI) has shown how modern technological advances are being introduced into the educational process of higher educational institutions, including the for-eign languages teaching. This paper defines artificial intelligence as an educational technology, studies this process in order to predict the future nature of the higher education system in the world, where AI is becoming a part of the structure and not only of education, but our entire society. Some of the ad-vantages and disadvantages that can be encoun-tered both by higher educational institutions and students in the study of foreign languages are iden-tified and analyzed. The specific areas of work with AI, which can increase the efficiency of foreign lan-guages teaching, are described.