Reviewerly/exHarmony huggingface dataset:
Academic Mobility as an Instrument of International Communicative Cooperation

The present research featured the effect of academic mobility on international communicative interaction. The objective was to identify the role of international academic mobility in the PR activities of universities. The study relied on the method of content analysis of domestic and foreign scientific publications. The authors used the method of problem interviews to develop an online questionnaire for students involved in international student exchange programs. The authors described the term of PR-technology, as well as the most popular PR activities and types of international academic mobility in higher education institutions. Academic mobility proved to be one of the most effective PR-technologies of higher education institutions. The article introduces a classification of international academic mobility as a process that provides international communicative interaction. International academic mobility proved to have a significant impact on the image of universities and their students in the international arena, as well as on the status of the country in geopolitical, economic, and civil areas.
Education reform in Kazakhstan: Ways of teacher assessment literacy development

Introduction. The reform of many national education systems is associated with a growing focus on assessment as the dominant aspect of the modern educational process. One of the directions of the reform of the Kazakh school system is focused on the transition to criteria-based assessment of the educational achievements of schoolchildren. In this regard, for Kazakhstan, as well as for many countries of the world, the problem of developing evaluative literacy of both practising teachers and today’s students studying in pedagogical educational programmes has become especially acute. Aim. The aim of the current research is to reveal the essential characteristics of assessment literacy as an important quality of a modern teacher, to actualise the problem of its development in the situation of reforming the school education system, to outline possible directions for solving this problem. Methodology and research methods. The present research is based on the complex use of theoretical and empirical methods. The theoretical research was carried out using a comparative aspect analysis of scientific and methodological sources and normative documents on the problem of the formation of teacher assessment literacy. Empirical data were obtained when conducting a survey of school teachers, systematising and summarising its results. Results and scientific novelty. In the course of theoretical analysis, the authors identified the factors, which actualise the problem of teacher assessment literacy development; its main characteristics are summarised, which made it possible for the authors to formulate the definition of this concept. The results of the survey of school teachers demonstrated that shortterm professional development courses for teachers provide a knowledge component of their assessment literacy, but do not solve the problem of developing practical skills, which are in demand in the course of assessment activities. The development of teacher assessment literacy to the behavioural level is a multi-stage and complex process that involves not only theoretical training, but also the independent design of one’s own assessment activity, taking into account the subject characteristics and other contexts accompanying educational practice. Particular attention should be paid to the formation and development of assessment literacy of future teachers during their studies at the university. An integrated approach to solving this problem should provide: the formation of deep knowledge in the chosen subject area, participation in various assessment practices within the framework of methodological courses and pedagogical practices, interaction with practising teachers. The practical significance of the study lies in the recommendations that determine possible directions for solving the problem of developing school teacher assessment literacy in the context of reforming the educational assessment system.
Self-Supervised Losses for One-Class Textual Anomaly Detection

Current deep learning methods for anomaly detection in text rely on supervisory signals in inliers that may be unobtainable or bespoke architectures that are difficult to tune. We study a simpler alternative: fine-tuning Transformers on the inlier data with self-supervised objectives and using the losses as an anomaly score. Overall, the self-supervision approach outperforms other methods under various anomaly detection scenarios, improving the AUROC score on semantic anomalies by 11.6% and on syntactic anomalies by 22.8% on average. Additionally, the optimal objective and resultant learnt representation depend on the type of downstream anomaly. The separability of anomalies and inliers signals that a representation is more effective for detecting semantic anomalies, whilst the presence of narrow feature directions signals a representation that is effective for detecting syntactic anomalies.
Application of Optimized Convolution Neural Network Model in Mural Segmentation

To address the problems of blurred target boundaries and inefficient image segmentation in ancient mural image segmentation, a multi-classification image segmentation model MC-DM (Multi-class DeeplabV3+ MobileNetV2) that fuses lightweight convolutional neural networks is proposed. The model combines the Deeplabv3+ structure and MobileNetV2 network and adopts the unique spatial pyramid structure of DeeplabV3+ to process convolutional features for multi-scale fusion, which reduces the loss of detail in the mural segmentation images. Firstly, the features calculated at any resolution in MobileNetV2 network are extracted by hole convolution, the input step is expressed as the ratio between the input image resolution and the final resolution, and the density of encoder features is controlled according to the budget of computing resources. Then, the spatial pyramid pool structure is used to fuse the previously calculated features at multiple scales to enrich the semantic information of the feature image. Finally, the same convolution network is used to reduce the number of channels and filter the density feature map. The filtered features are fused with the features after multi-scale fusion again to obtain the final output. In total, 1000 scanned images of murals were adopted as datasets for testing under the JetBrains PyCharm Community Edition 2019 environment. The obtained experimental results indicate that MC-DM improves the training accuracy by 1 percentage point compared with the conventional SegNet-based image segmentation model, and by 2 percentage points compared with the PspNet network-based image segmentation model. The PSNR (peak signal-to-noise ratio) of the MC-DM model is improved by 3–8 dB on average compared with the experimental model. This confirms the effectiveness of the model in mural segmentation and provides a novel method for ancient mural image segmentation.
The Impact of Big Data Technology on the Scale Management of China’s Animation Film Industry

With the development of science and technology, social networking and digital media technology are also constantly developing. Handheld mobile smart terminals have been continuously promoted and popularized. The promotion and popularization of network and digital media technology bring convenience to people’s life and work. People can use their leisure time to shop online and watch related videos. The advent of the Internet era has promoted the development of China’s industry. The era of big data is coming. Big data analysis brings enormous value to all occupations. At the same time, it has brought new developments to Chinese animation films, realizing the marketing and development design of animated films. At the same time, big data has also brought opportunities for the development of the Chinese animation film industry. The application of big data brings a foundation for China’s animation film industry. From investment and financing, topic development and design, marketing promotion, and distribution and development of derivative products, big data brings huge value to the entire industry chain. However, big data is not omnipotent, and there are certain problems in the use of big data in Chinese animation films. At present, the application of big data in China’s animation film industry also has limitations and practical difficulties. Based on this, this paper focuses on discussing and analyzing the historical status of China’s animation industry. Moreover, it analyzes the future development prospects of the animation industry and other related contents. It is hoped that the brief analysis in this article provides some reference and inspiration for relevant practitioners.
Generating Captions of Imagined content from Human Brain Activities Applying An Image Captioning Model

The study of human mental content is a longstanding topic, but its vague nature has made examination difficult. In this paper, we propose a method for cognitive estimation via image captioning models, which estimates conceived content from brain activity. Through the image captioning model, we estimate mental content by examining the relationship between data obtained by functional magnetic resonance imaging (fMRI) and semantic information of conceived content. The decoding model based on the regression scheme learned employing stimulus-evoked brain activity in the visual cortex area is able to estimate the content accurately. Finally, our method enables the generation of accurate captions from fMRI activity obtained while the subject is imagining the image. The results of experiments show that our method can generate accurate captions for the conceived content.
Bilateral Inversion Principles

This paper formulates a bilateral account of harmony that is an alternative to one proposed by Francez.It builds on an account of harmony for unilateral logic proposed by Kürbis and the observation that reading the rules for the connectives of bilateral logic bottom up gives the grounds and consequences of formulas with the opposite speech act.I formulate a process I call 'inversion' which allows the determination of assertive elimination rules from assertive introduction rules, and rejective elimination rules from rejective introduction rules, and conversely.It corresponds to Francez's notion of vertical harmony.I also formulate a process I call 'conversion', which allows the determination of rejective introduction rules from assertive elimination rules and conversely, and the determination of assertive introduction rules from rejective elimination rules and conversely.It corresponds to Francez's notion of horizontal harmony.The account has a number of features that distinguishes it from Francez's.
Research on the Throwing Technique of High-Pole Hydrangea Champion Team in the 12th Students’ Games of Guangxi

By using the methods of literature, mathematical analysis, and comparative analysis, this study expounds the origin and development history of the culture of Hydrangea and the movement of throwing Hydrangea, studies the representative technical theory of high-pole throwing Hydrangea, and makes a comparative analysis with the angle, speed, and other technical movements of throwing Hydrangea in teaching and training, as well as the trajectory data of the arc top height, circle range, and landing point of the parabola of Hydrangea. This study discusses the technical problems existing in the high shot Hydrangea competition of the two students Games in Guangxi. The research shows that projection technology is the foundation and core element of the survival and development of high-pole throwing Hydrangea; the existing projection technology has a certain positive effect on teaching, but it has shortcomings for high-level training and competition. The research shows that throwing technology is the foundation and core element of the survival and development of high-pole throwing Hydrangea; the existing throwing technology has a certain positive effect on teaching, but it has shortcomings for high-level training and competition. The best shooting angle is 640 ≤ α ≤ 720, and the speed is 13.04 m/s ≤ v0 ≤ 13.70 m/s, which is a range of numerical matching between the two; the core problem of shooting technology is to shorten the shooting cycle and running distance through the cooperation of the best shooting angle and speed, so as to lay the foundation for improving the hit rate per unit time. Suggestions. Build national sports culture, improve the technical level, and promote the development of national sports to a higher level.
The Professional Development of English Teachers in Training Institutions from the Perspective of “Double Reduction Policy”—A Case Study on S Institution

Recently, “double reduction policy” has been issued throughout China, which encourages English teachers in training institutions to accommodate their professional development. More researches are demanded to be done so as to cope with the challenge. This research mainly focuses on the professional development of English teachers in training institutions from the perspective of the “double reduction policy” taking S institution in Chengdu City as an example and 30 teachers in S institution as research participants in order to bolster the professional development of English teachers in training institutions. And this investigation can be fulfilled through qualitative and quantitative research methods including the literature analysis, questionnaire investigation and some related interviews so as to solve the following research questions: 1) What is the status quo on the professional development of English teachers in training institutions in the context of double reduction policy? 2) What factors may affect the professional development of English teachers in training institutions in the context of double reduction policy? 3) What valid and feasible strategies can be induced to enhance the professional development of English teachers in training institutions from the perspective of double reduction policy? Based on the results of research questions, this research project will give an advisable direction for those English teachers in training institutions to adjust themselves to the double reduction policy.
ASQA: Factoid Questions Meet Long-Form Answers

An abundance of datasets and availability of reliable evaluation metrics have resulted in strong progress in factoid question answering (QA). This progress, however, does not easily transfer to the task of long-form QA, where the goal is to answer questions that require in-depth explanations. The hurdles include (i) a lack of high-quality data, and (ii) the absence of a well-defined notion of the answer's quality. In this work, we address these problems by (i) releasing a novel dataset and a task that we call ASQA (Answer Summaries for Questions which are Ambiguous); and (ii) proposing a reliable metric for measuring performance on ASQA. Our task focuses on factoid questions that are ambiguous, that is, have different correct answers depending on interpretation. Answers to ambiguous questions should synthesize factual information from multiple sources into a long-form summary that resolves the ambiguity. In contrast to existing long-form QA tasks (such as ELI5), ASQA admits a clear notion of correctness: a user faced with a good summary should be able to answer different interpretations of the original ambiguous question. We use this notion of correctness to define an automated metric of performance for ASQA. Our analysis demonstrates an agreement between this metric and human judgments, and reveals a considerable gap between human performance and strong baselines.
How Different are Pre-trained Transformers for Text Ranking?

In recent years, large pre-trained transformers have led to substantial gains in performance over traditional retrieval models and feedback approaches. However, these results are primarily based on the MS Marco/TREC Deep Learning Track setup, with its very particular setup, and our understanding of why and how these models work better is fragmented at best. We analyze effective BERT-based cross-encoders versus traditional BM25 ranking for the passage retrieval task where the largest gains have been observed, and investigate two main questions. On the one hand, what is similar? To what extent does the neural ranker already encompass the capacity of traditional rankers? Is the gain in performance due to a better ranking of the same documents (prioritizing precision)? On the other hand, what is different? Can it retrieve effectively documents missed by traditional systems (prioritizing recall)? We discover substantial differences in the notion of relevance identifying strengths and weaknesses of BERT that may inspire research for future improvement. Our results contribute to our understanding of (black-box) neural rankers relative to (well-understood) traditional rankers, help understand the particular experimental setting of MS-Marco-based test collections.
The methods of forming soft skills of university students: theoretical and practical aspects

This paper describes the theoretical and practical aspects of the methodology for the formation of students soft skills. Innovation processes are implemented today in almost all spheres of human activity. In particular, this applies to the field of education. As for professional education, the university should maintain interest in modern technologies, socio-economic changes in society, and the development of new trends and directions. Such processes include the formation of supra-professional skills, in other words, soft skills. They are not related to a particular profession, but are important for a career and advancement in it. The process of forming such skills can be implemented in English classes. In this regard, to create conditions conducive to the formation of soft skills, as well as to make the learning process more informative and memorable, the authors of the paper developed a set of tasks for the formation of soft skills competencies. Since the development of these skills is important for preparing for a future profession, the target audience is junior students. The purpose of this paper is a theoretical justification of the methodology for the formation of soft skills competencies. The practical significance of the research lies in the fact that the results can be used in the course of foreign languages teaching methodology.
Probing for Constituency Structure in Neural Language Models

In this paper, we investigate to which extent contextual neural language models (LMs) implicitly learn syntactic structure. More concretely, we focus on constituent structure as represented in the Penn Treebank (PTB). Using standard probing techniques based on diagnostic classifiers, we assess the accuracy of representing constituents of different categories within the neuron activations of a LM such as RoBERTa. In order to make sure that our probe focuses on syntactic knowledge and not on implicit semantic generalizations, we also experiment on a PTB version that is obtained by randomly replacing constituents with each other while keeping syntactic structure, i.e., a semantically ill-formed but syntactically well-formed version of the PTB. We find that 4 pretrained transfomer LMs obtain high performance on our probing tasks even on manipulated data, suggesting that semantic and syntactic knowledge in their representations can be separated and that constituency information is in fact learned by the LM. Moreover, we show that a complete constituency tree can be linearly separated from LM representations.
Virtual Reality in Language Teaching

Virtual reality (VR), which is based on three fundamental principles, namely immersion, interaction and user involvement, is seen as having great a potential in language learning (Merchant et al. 2014; Chen 2016; Lloyd, Rogerson, and Stead 2018). This paper presents the experience of developing VR sequences in language teaching in the ‹Around the world in 5 days› project. The analysis presented here draws on the sociological perspective of ‹Science and Technology Studies› (STS) to take a critical look at human-machine interaction. Each phase of the project, from the development of lesson planning and VR sequences to user testing and classroom use, was documented and scientifically monitored. The article first gives an overview of approaches to VRLEs in terms of ‹immersion› and ‹presence›, presents the main findings made from theoretical conception to technical implementation.
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms

Sampling biases in training data are a major source of algorithmic biases in machine learning systems. Although there are many methods that attempt to mitigate such algorithmic biases during training, the most direct and obvious way is simply collecting more representative training data. In this paper, we consider the task of assembling a training dataset in which minority groups are adequately represented from a given set of data sources. In essence, this is an adaptive sampling problem to determine if a given point lies in the convex hull of the means from a set of unknown distributions. We present adaptive sampling methods to determine, with high confidence, whether it is possible to assemble a representative dataset from the given data sources. We also demonstrate the efficacy of our policies in simulations in the Bernoulli and a multinomial setting.
Machine learning approach towards language identification of Code-Mixed Hindi-English and Urdu-English Social Media Text

Social media becomes an important and convenient tool to access information that is beneficial in education, marketing, finance and communication. The number of social media users grows significantly with each passing day, resulting in a massive volume of data easily available for Natural Language Processing (NLP) researchers. People especially in multilingual societies prefer to write in multiple languages and use code-mixing and code-switching approaches to express their views, thus making the task of NLP more challenging and complex. Therefore, a language identification system for building complex NLP systems using code-mixed data is an absolute necessity. Although language identification for English and other monolingual languages is a solved problem in many NLP applications, but due to noisy nature of code-mixed text, language identification is a complex task and still an unsolved problem. From the recent past machine learning approaches have gathered significant attention in the field of classification problems. In this paper machine learning approaches using Multinomial Naïve Bayes, Decision Tree and Support Vector Machine have been used for word-level identification of languages in English-Hindi and English-Urdu code-mixed social media text. Support Vector Machine with accuracy of 83.58% and 75.79 % for Hindi-English and Urdu-English respectively performs better than other two approaches.
What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity

We present an analysis of eight measures used for quantifying morphological complexity of natural languages. The measures we study are corpus-based measures of morphological complexity with varying requirements for corpus annotation. We present similarities and differences between these measures visually and through correlation analyses, as well as their relation to the relevant typological variables. Our analysis focuses on whether these `measures' are measures of the same underlying variable, or whether they measure more than one dimension of morphological complexity. The principal component analysis indicates that the first principal component explains 92.62 % of the variation in eight measures, indicating a strong linear dependence between the complexity measures studied.