Reviewerly/exHarmony huggingface dataset:
PEDAGOGICAL POSSIBILITIES OF INFORMATION TECHNOLOGIES IN TRAINING STUDENTS

Currently, the Republic of Kazakhstan is developing an education system focused on entering the global informational and educational space. This process makes adjustments to the content of education, teaching methods and technologies. The processes of informatization of society contribute to the intellectualization of all types of human activity, the creation of a qualitatively new information environment of society, in which the development of human creativity takes place. The article discusses the issues of mastering information technology as a condition for developing the professional competence of a future primary school teacher, organizing information and computer training of future primary school teachers and its impact on the development of information and computer culture of elementary school students. The authors consider information and computer culture in the structure of professional competence of a future primary school teacher. The purpose of this article is to identify the possibilities of using information technologies that contribute to the development of the intellectual potential of a teacher.
WEB-TECHNOLOGIES AND MULTIMEDIA SYSTEMS IN THE TRAINING OF PROFESSIONALS IN THE EDUCATION SYSTEM

Contemporary technologies of training specialists in different professions envisage mastering the skills of a 'quality user' of computer technologies. At the same time, the issues of training primary school teachers, specialists in inclusive and remedial education to use more complex multimedia systems with the need to understand programming processes have often been excluded from basic courses as an 'unnecessary' component.But considering the current trends towards distance education, the “rejuvenation” of 'advanced users' and the need to maintain a educator's reputation, the trend towards introducing such systemic courses in the training process for specialists in primary, inclusive and correctional education has become a kind of concept for professional competence. It was based on the needs of today's teachers to transfer knowledge through multimedia systems (creating interactive databases, web pages, blogs or websites, preparing and conducting WebQuests, using computer games from producers or their own development through the platforms Wordwall, Etreniki, Flippity and Scratch programmes, etc.) that became the deciding factor for introducing specific topics on their use into training courses and expanding basic programmes of computer competency. Also, the training process began to include interaction with the training audience through social media. This multi-component work to develop a «modern/advanced» teacher provides the basis not only for improving the quality of the educational process, but also for its individualization according to the needs of each participant and his/her special educational needs, allowing to change the complexity of tasks, the saturation of tasks with audiovisual information.
Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities

We provide new adaptive first-order methods for constrained convex optimization. Our main algorithms AdaACSA and AdaAGD+ are accelerated methods, which are universal in the sense that they achieve nearly-optimal convergence rates for both smooth and non-smooth functions, even when they only have access to stochastic gradients. In addition, they do not require any prior knowledge on how the objective function is parametrized, since they automatically adjust their per-coordinate learning rate. These can be seen as truly accelerated Adagrad methods for constrained optimization. We complement them with a simpler algorithm AdaGrad+ which enjoys the same features, and achieves the standard non-accelerated convergence rate. We also present a set of new results involving adaptive methods for unconstrained optimization and variational inequalities arising from monotone operators.
Nunggak semi Al Suwardi: organology and perspective of harmonic planet music creation

This article examines the artistry of a composer named Aloysius Suwardi. This composer, who grew up in the Javanese gamelan music tradition, has produced musical compositions and created musical instruments as a means/medium to express it on the journey of his creativity. His works have been staged in various forums or world music festivals. The musical instruments he has created fill the laboratory spaces to create his musical compositions. Two musical ensembles that have been created are Gamelan Genta and Gamelan Planet Harmonic. These two musical instruments complement the many personal musical instruments he has created, such as the vibrander, tering, gerendang, and other musical instruments. Regarding the two ensembles, this article examines the aspects of the creation process. For example, how did Al Suwardi choose the materials used, how to make them, to how he determined the barrel system for these tools. In terms of compositing, this article examines how Al Suwardi started his work, his models of creation, and how he composes his works until they are declared final. The gragas and dridis creeds and Nunggak Semi's concept are the main topics in this composition creation model. Both creeds and concepts have ushered in the landscape of making Al Suwardi's musical compositions expand, from the gamelan tradition to more contemporary models of music creation
Pragmatic Code Autocomplete

Human language is ambiguous, with intended meanings recovered via pragmatic reasoning in context. Such reliance on context is essential for the efficiency of human communication. Programming languages, in stark contrast, are defined by unambiguous grammars. In this work, we aim to make programming languages more concise by allowing programmers to utilize a controlled level of ambiguity. Specifically, we allow single-character abbreviations for common keywords and identifiers. Our system first proposes a set of strings that can be abbreviated by the user. Using only 100 abbreviations, we observe that a large dataset of Python code can be compressed by 15%, a number that can be improved even further by specializing the abbreviations to a particular code base. We then use a contextualized sequence-to-sequence model to rank potential expansions of inputs that include abbreviations. In an offline reconstruction task our model achieves accuracies ranging from 93% to 99%, depending on the programming language and user settings. The model is small enough to run on a commodity CPU in real-time. We evaluate the usability of our system in a user study, integrating it in Microsoft VSCode, a popular code text editor. We observe that our system performs well and is complementary to traditional autocomplete features.
Lifelong Learning of Topics and Domain-Specific Word Embeddings

Lifelong topic models mainly focus on indomain text streams in which each chunk only contains documents from a single domain.To overcome data diversity of the in-domain corpus, most of the existing methods exploit the information from limited sources in a separate and heuristic manner.In this study, we develop a lifelong collaborative model (LCM) based on non-negative matrix factorization to accurately learn topics and domain-specific word embeddings.LCM particularly investigates:(1) developing a knowledge graph based on the semantic relationships among words in the lifelong learning process, so as to accumulate global context information discovered by topic models and local context information reflected by context word embeddings from previous domains, and (2) developing a subword graph based on byte pair encoding and pairwise word relationships to exploit subword information of words in the current in-domain corpus.To the best of our knowledge, we are the first to collaboratively learn topics and word embeddings via lifelong learning.Experiments on real-world in-domain text streams validate the effectiveness of our method.
IoU Regression with H+L-Sampling for Accurate Detection Confidence

It is a common paradigm in object detection frameworks that the samples in training and testing have consistent distributions for the two main tasks: Classification and bounding box regression. This paradigm is popular in sampling strategy for training an object detector due to its intuition and practicability. For the task of localization quality estimation, there exist two ways of sampling: The same sampling with the main tasks and the uniform sampling by manually augmenting the ground-truth. The first method of sampling is simple but inconsistent for the task of quality estimation. The second method of uniform sampling contains all IoU level distributions but is more complex and difficult for training. In this paper, we propose an H+L-Sampling strategy, selecting the high and low IoU samples simultaneously, to effectively and simply train the branch of quality estimation. This strategy inherits the effectiveness of consistent sampling and reduces the training difficulty of uniform sampling. Finally, we introduce accurate detection confidence, which combines the classification probability and the localization accuracy, as the ranking keyword of NMS. Extensive experiments show the effectiveness of our method in solving the misalignment between classification confidence and localization accuracy and improving the detection performance.
MODELLING OF INTRADAY PHOTOVOLTAIC POWER PRODUCTION

Photovoltaic (PV) productions should occur within a time interval of sunlight. Time mismatches are detected between sunrise and first production hour as well as sunset and last production hour in a transmission system operator, Amprion, Germany. Hence, in this paper, we investigate this effect using an additive function of two seasonalities and a stochastic process. Both seasonalities are based on the mimicked locations, corrected by a weighing scale, depending on the first and last production hours' coordinates. The result shows that the proposed deterministic model could capture the effect of sunrise and sunset. Also, the dynamics of random components are sufficiently explained by an autoregressive process of order two. Finally, the Normal Inverse Gaussian distribution is shown as the best distribution in explaining noise behaviour, particularly heavy tails in the production's residuals, compared to the Gaussian distribution.
Necessary and Sufficient Conditions for Avoiding Reopenings in Best First Suboptimal Search with General Bounding Functions

Recent work introduced XDP and XUP priority functions for best-first bounded-suboptimal search that do not need to perform state re-expansions as long as the search heuristic is consistent. However, that work had several limitations that are rectified here. This paper analyzes the sufficiency and necessity of the conditions used to formulate XDP and XUP. The analysis presents a simpler proof and generalizes the result in three aspects: (1) the priority function no longer has to be differentiable everywhere, (2) the quality of the solution does not have to be bounded by a constant factor, and (3) directed graphs are handled correctly. These results allow the introduction of more priority functions, such as piecewise linear functions, and more variants of bounded-suboptimal search, such as constant suboptimality. Several new priority functions are presented in this paper that, according to empirical results, can significantly outperform existing approaches including XDP.
The Use of Pictures as Media to Improve Students’ Reading Comprehension

&#x0D; Reading is an important skill for developing second language competence. The success of a learning a second language is by taking the power of reading. In the classroom, we will face many techniques applied to the students under the expectation that they are able to or easy to understand the lesson. For a teacher, it is necessary to find new teaching media to overcome the problems and not to forget to motivate the students. In this paper, the writer wants to know the effect of picture as a media to improve students’ reading comprehension. The purpose of this research is to find out whether the use of pictures can improve students’ reading comprehension or not. To make the students easily understand about the reading material, teacher needs to have a teaching media. Then, the writer chooses visual media that is picture to increase students’ reading mastery. Pictures are considered as the best media in teaching reading because using picture as a teaching media are able to give the students a clear illustration regarding the correlated topics in texts, tell the students what is going on, and what are the persons in the text are talking about. Nowadays, the pictures and the technologies are becoming more and more advanced. In this decade, we can already materialize a more advanced picture that added by motion and sounds by using simple applications provided in home personal computer. Moreover, the students now are quite used and interested to apply any high tech media.&#x0D; Based on the result of discussion, it can be concluded that pictures are theoretically can be a good media to help the students enhancing their reading comprehension. Therefore in this view, it is justifiable to hypothetically conclude that using pictures for strengthening students’ visual sense can help them to understand a text.
RELATIONSHIP BETWEEN PSYCHOSOCIAL WORKING CONDITIONS AND THE ENTREPRENEURIAL INTENTIONS OF STUDENTS

The present article describes research findings about students' entrepreneurial intentions, defined as an intention of starting one's own business.The main purpose of the research was to identify the relation between the assessment of entrepreneurial work and entrepreneurial intentions.In addition, the research assessed the psychosocial risks related to working as an entrepreneur and examined key individual and socio-demographic variables related to attitudes regarding entrepreneurial work.The results showed that participants believe that entrepreneurial work has a high level of psychosocial risk.The results also showed that there are some weak yet statistically significant correlations between the index of entrepreneurial psychosocial working conditions and the level of intention of starting one's own business and the probability of becoming an entrepreneur within five years.
Neural Combinatory Constituency Parsing

We propose two fast neural combinatory models for constituency parsing: binary and multibranching.Our models decompose the bottomup parsing process into 1) classification of tags, labels, and binary orientations or chunks and 2) vector composition based on the computed orientations or chunks.These models have theoretical sub-quadratic complexity and empirical linear complexity.The binary model achieves an F1 score of 92.54 on Penn Treebank, speeding at 1327.2 sents/sec.Both the models with XLNet provide near state-of-theart accuracies for English.Syntactic branching tendency and headedness of a language are observed during the training and inference processes for Penn Treebank, Chinese Treebank, and Keyaki Treebank (Japanese).
Use of GIS technology in geographical education

At the present stage, digital information technologies create a new education system focused on the global educational space. In general education schools, in connection with the adoption of the updated program, the section Geoinformatics and cartography provides for the use of developing a map-scheme, modeling and conducting small studies on the topic under study. As a result, digital technology has a place in geographical education. This is due to significant changes in the pedagogical and methodological approach in teaching geography and other disciplines. As a result, the education system has changed, the content of education has been updated, a new approach has appeared, a new attitude to geoinformation technologies in schools. The article discusses the importance of computer technologies in the education system, including the effectiveness and necessity of using geoinformation technologies. The article substantiates the relevance of the use of geoinformation technologies in the teaching of geography.
A Novel Exploitation of Errors in Redundant Residue Number System Architecture

Residue Number System (RNS) is an unweighted number system that symbolizes big integers with smaller numbers.It can perform operations in particular addition and multiplication in parallel.Because of this property, RNS is extensively used in communication, Finite Impulse Response (FIR), cryptography and signal processing devices.The transfer of data in digital channels is very important for some critical applications where accuracy is very important.In this study, we proposed a novel algorithm that is premised on the Hamming Distance (HD) and one of the reverse conversion methods, which is, the Chinese Remainder Theorem (CRT) and) as a joint technique for the detection and correction of multiple bit errors in RNS.The proposed algorithm provides a more efficient technique that improves on the hardware size and increases the processing speed with fewer iterations when compared with other stateof-the-art schemes.The work analyses the area and delay of the hardware architecture and compared with other similar schemes.The results indicated the effectiveness of our proposed scheme in terms of the area and delay specifications.
Conditional GAN for Diatonic Harmonic Sequences Generation in a VR Context

The use of AI models for music generation receives an important attention from scientific communities.Different architectures of deep learning neural networks have been applied for this specific task, such as Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), Autoencoders, Variational Autoencoders (VAE) and Transformers.One of the important aspects of the generation process is the possibility to control the output by providing the input parameters, and a conditional generation was widely used in a computer vision domain to meet this need.In a present research we adopt the principles of conditional generation using GAN architecture and convolutions, applying them to a temporal domain, resulting in building a conditional GAN for diatonic harmonic sequences generation.The model is further used as a core feature of the VR module for computer-aided composition from "Graphs in harmony learning" VR project, where the sequence generation is conditioned by the user's input and the result is mapped to 3D representations of the generated chords.Conditional GAN.
Creative self-realization of a future teacher-artist in the process of professional training at the university

The article reveals the conceptual problems of creative self-realization of future teachers-artists. The authors note that the level of readiness of future professionals to work depends on the formation of creative pedagogical potential at the university, which consists of several components, such as motivational-axiological, cognitive and manipulative. The article justifies the need for specialists in the field of artistic and aesthetic education, who are able to work effectively in the new socio-economic conditions. It is possible to increase the effectiveness of the creative self-realization of future teachers-artists in the process of higher education by creating certain pedagogical conditions.
Multi-modal User Intent Classification Under the Scenario of Smart Factory (Student Abstract)

Question-answering systems are becoming increasingly popular in Natural Language Processing, especially when applied in smart factory settings. A common practice in designing those systems is through intent classification. However, in a multiple-stage task commonly seen in those settings, relying solely on intent classification may lead to erroneous answers, as questions rising from different work stages may share the same intent but have different contexts and therefore require different answers. To address this problem, we designed an interactive dialogue system that utilizes contextual information to assist intent classification in a multiple-stage task. Specifically, our system incorporates user’s utterances with real-time video feed to better situate users’ questions and analyze their intent.
Learning Latent Structures for Cross Action Phrase Relations in Wet Lab Protocols

Wet laboratory protocols (WLPs) are critical for conveying reproducible procedures in biological research.They are composed of instructions written in natural language describing the step-wise processing of materials by specific actions.This process flow description for reagents and materials synthesis in WLPs can be captured by material state transfer graphs (MSTGs), which encode global temporal and causal relationships between actions.Here, we propose methods to automatically generate a MSTG for a given protocol by extracting all action relationships across multiple sentences.We also note that previous corpora and methods focused primarily on local intrasentence relationships between actions and entities and did not address two critical issues: (i) resolution of implicit arguments and (ii) establishing long-range dependencies across sentences.We propose a new model that incrementally learns latent structures and is better suited to resolving inter-sentence relations and implicit arguments.This model draws upon a new corpus WLP-MSTG which was created by extending annotations in the WLP corpora for inter-sentence relations and implicit arguments.Our model achieves an F1 score of 54.53% for temporal and causal relations in protocols from our corpus, which is a significant improvement over previous models -Dy-GIE++:28.17%;spERT:27.81%.We make our annotated WLP-MSTG corpus available to the research community.
Raising The Issue Of Subjects' Recomposition In Higher Education System

The key development in organizing the educational process makes it required to address pedagogical science and practice to the problems of planning and implementing pedagogical discourse in the modern educational space.This article actualizes the problem of thekey subjects'recomposition (teacher, student) in the system of modern higher education in general, and in higher education in a foreign language, in particular.The aim of our research is to consider theoretical positions and experience of transforming pedagogical reality (educational environment, activities of subjects).The main research methods we have applied were theoretical analysis of scientific literature, study of documentation, purposeful pedagogical observation, study and generalization of pedagogical experience, retrospective analysis of the own pedagogical experience.The research was based on the Chelyabinsk State University.The results of the study were the following, firstly, on the basis of the system-functional analysis, the main characteristics of the pedagogical discourse were revealed, and secondly, on the basis of the synergetic approach, we have reasoned the conditions for providing the experience of the constructive transformation of the pedagogical reality and productive educational technologies, providing a step-by-step value-semantic organizing the educational process and self-realization of students and teachers.