Reviewerly/exHarmony huggingface dataset:
DIDACTIC CONDITIONS OF FORMATION VOCAL SPEECH SKILLS! IN JUNIOR SCHOOLCHILDREN IN MUSIC LESSONS

The article touches on the problem as formation of scholars’ vocal speech experience, that demands efficacious solution in definite didactic condition. Among of such conditions the author emphasizes the major: students’ acquisition of knowledge, skills and experience of vocal culture's foundation; application of intonation phonopathy exercises by V.V. Yerne Ivanov methodics; systematic, gradual and purposeful work information of scholars' vocal speech experience.
RetroMAE: Pre-training Retrieval-oriented Transformers via Masked  Auto-Encoder

Pre-trained models have demonstrated superior power on many important tasks. However, it is still an open problem of designing effective pre-training strategies so as to promote the models' usability on dense retrieval. In this paper, we propose a novel pre-training framework for dense retrieval based on the Masked Auto-Encoder, known as RetroMAE. Our proposed framework is highlighted for the following critical designs: 1) a MAE based pre-training workflow, where the input sentence is polluted on both encoder and decoder side with different masks, and original sentence is reconstructed based on both sentence embedding and masked sentence; 2) asymmetric model architectures, with a large-scale expressive transformer for sentence encoding and a extremely simplified transformer for sentence reconstruction; 3) asymmetric masking ratios, with a moderate masking on the encoder side (15%) and an aggressive masking ratio on the decoder side (50~90%). We pre-train a BERT like encoder on English Wikipedia and BookCorpus, where it notably outperforms the existing pre-trained models on a wide range of dense retrieval benchmarks, like MS MARCO, Open-domain Question Answering, and BEIR.
Multi-agent Reinforcement Learning with Clipping Intrinsic Motivation

Intrinsic motivation is one of the potential candidates to help improve performance of reinforcement learning algorithm in complex environments.The method enhances exploration capability without explicitly told by the creator.This is suitable for the case of multi-agent reinforcement learning where the environment complexity is beyond standard.In this paper, the Random Network Distillation method is applied to implement intrinsic motivation in the multi-agent environment.Two intrinsic motivation architectures are developed and compared with the benchmark in different scenarios.The experiments show an increase in performance of the very complex environments while little to no improvement over the non-complex ones.Although there exists some overhead which results in less sample efficiency, the centralized intrinsic motivation architecture shows a long-term on par or even better optimization performance as it could explore on more states.The performance of the centralized architecture shows a solid improvement in 2s3z environment and achieves almost 70%win rate over the benchmark of 43%.
Latest Research Results and ITU-T Standardization Activities on Soft Errors Caused by Cosmic Rays

Due to advances in digital transformation, it is important to take measures against soft errors caused by cosmic rays to maintain a safe and secure network.This article explains the latest research results on soft errors from NTT laboratories, commercialization of soft-error test technology, and standardization activities in the International Telecommunication Union -Telecommunication Standardization Sector (ITU-T).
Dataset Comparison Tool: Utility and Privacy

Synthetic data has been more and more used in the last few years. While its applications are various, measuring its utility and privacy is seldom an easy task. Since there are different methods of evaluating these issues, which are dependent on data types, use cases and purpose, a generic method for evaluating utility and privacy does not exist at the moment. So, we introduced a compilation of the most recent methods for evaluating privacy and utility into a single executable in order to create a report of the similarities and potential privacy breaches between two datasets, whether it is related to synthetic or not. We catalogued 24 different methods, from qualitative to quantitative, column-wise or table-wise evaluations. We hope this resource can help scientists and industries get a better grasp of the synthetic data they have and produce more easily and a better basis to create a new, more broad method for evaluating dataset similarities.
Vision Transformer: Vit and its Derivatives

Transformer, an attention-based encoder-decoder architecture, has not only revolutionized the field of natural language processing (NLP), but has also done some pioneering work in the field of computer vision (CV). Compared to convolutional neural networks (CNNs), the Vision Transformer (ViT) relies on excellent modeling capabilities to achieve very good performance on several benchmarks such as ImageNet, COCO, and ADE20k. ViT is inspired by the self-attention mechanism in natural language processing, where word embeddings are replaced with patch embeddings. This paper reviews the derivatives of ViT and the cross-applications of ViT with other fields.
Phenomenon of competition in the educational environment

The article presents the results of a study of the phenomenon of competition in scientific work and a conceptual consideration of this phenomenon as a distinct institution showing all elements of such a distinction: the designation of boundaries and forms of scientific competition. The work provides substantiation for the need to differentiate between the competitiveness of scientific research and that of a researcher and, at the same time, for the need to examine these definitions comprehensively from the standpoint of their dialectical unity. The results of the research prove that the presence of competition in the academic environment is a positive phenomenon as it requires ensuring high competitiveness, i.e., high quality of the scientific works of various subjects – researchers both in a single subject area and in interdisciplinary directions.
Technologies of pedagogical excellence under the conditions of digital reality

The object of the study is pedagogical excellence as “the teacher’s ability to engage students in collaborative learning activities to achieve planned developmental, upbringing, and learning outcomes” in the face of new challenges and risks. The study aims to formulate substantial requirements for pedagogical excellence in the conditions of the formation of a new educational environment. In a changing world, pedagogical excellence involves a combination of several techniques and technologies that contribute to the formation of a new image of the creative leader of the pedagogical process, which combines both the classical teacher, a methodologist, a speaker, a manager, and an organizer of time and space of the educational continuum around the student, who is actively involved in the process of receiving and actively acquiring knowledge.
Universal Dependency Treebank for Odia Language

This paper presents the first publicly available treebank of Odia, a morphologically rich low resource Indian language. The treebank contains approx. 1082 tokens (100 sentences) in Odia selected from "Samantar", the largest available parallel corpora collection for Indic languages. All the selected sentences are manually annotated following the ``Universal Dependency (UD)" guidelines. The morphological analysis of the Odia treebank was performed using machine learning techniques. The Odia annotated treebank will enrich the Odia language resource and will help in building language technology tools for cross-lingual learning and typological research. We also build a preliminary Odia parser using a machine learning approach. The accuracy of the parser is 86.6% Tokenization, 64.1% UPOS, 63.78% XPOS, 42.04% UAS and 21.34% LAS. Finally, the paper briefly discusses the linguistic analysis of the Odia UD treebank.
CASE FRAME AS MEAN OF ORGANIZATION OF COGNITIVE COMPOSITION ACTIVITY OF STUDENTS

The prospect of application of organizational case fra m es by development of creative capabilities of students is examined in the article. An author takes the special p la c e f o r model of control after implementation of composition exercises.
The virtuality of students in the rural area

The health emergency due to COVID-19 that affected the world, forced each of the governments to take measures that allowed protecting the health and integrity of its inhabitants , Ecuador, as a result of the declaration of a state of exception in the Educational Units of the country, adopted measures to meet the needs of the students, one of them the modality that from its application presented advantages and disadvantages for the educational community, this method was used. teaching-learning method, where teachers taught their virtual classes, improving their professional practice, with new strategies in the use of technological tools. The objective of the research is to identify how virtual education affected the learning of students in rural areas, through argumentative research, with bibliographic review strategies through documents, scientific journal articles. The study was carried out in the Educational Unit "Guaranda No. 43" of the Chirijos rural parish of the Portoviejo Canton, it was obtained as a result that the educational plan implemented by the Ministry of Education for the health emergency evidenced the educational reality in the country where the Virtual educational practice was negative due to the impact caused and the problems exposed.
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning

Deep transfer learning techniques try to tackle the limitations of deep learning, the dependency on extensive training data and the training costs, by reusing obtained knowledge. However, the current DTL techniques suffer from either catastrophic forgetting dilemma (losing the previously obtained knowledge) or overly biased pre-trained models (harder to adapt to target data) in finetuning pre-trained models or freezing a part of the pre-trained model, respectively. Progressive learning, a sub-category of DTL, reduces the effect of the overly biased model in the case of freezing earlier layers by adding a new layer to the end of a frozen pre-trained model. Even though it has been successful in many cases, it cannot yet handle distant source and target data. We propose a new continual/progressive learning approach for deep transfer learning to tackle these limitations. To avoid both catastrophic forgetting and overly biased-model problems, we expand the pre-trained model by expanding pre-trained layers (adding new nodes to each layer) in the model instead of only adding new layers. Hence the method is named EXPANSE. Our experimental results confirm that we can tackle distant source and target data using this technique. At the same time, the final model is still valid on the source data, achieving a promising deep continual learning approach. Moreover, we offer a new way of training deep learning models inspired by the human education system. We termed this two-step training: learning basics first, then adding complexities and uncertainties. The evaluation implies that the two-step training extracts more meaningful features and a finer basin on the error surface since it can achieve better accuracy in comparison to regular training. EXPANSE (model expansion and two-step training) is a systematic continual learning approach applicable to different problems and DL models.
Neural Network Assisted IDS/IPS: An Overview of Implementations, Benefits, and Drawbacks

Modern IDS use inflexible knowledge bases, rule sets and rely on human interaction for successful threat mitigation.While this approach to network and hardware security has been effective in the past, the explosion of large data breaches in the past few years reveals a lack of effective detection for unknown or undocumented threats.We infer that a change in detection and prevention of cybercrime needs to start at the system level and use more intelligent methods of attack detection and prevention: Neural Networks and Artificial Intelligence assisted IDS.This paper gives a broad overview of the modern state of IDS/IPS systems, discusses the benefits and drawbacks of modern implementation, gives a broad overview of current research into the field of neural networkbased IDS, and discusses benefits and drawbacks of NNIDS systems.Finally, we conclude with a few examples of modern implementations of NNIDS and areas for future study in the field.
Interpretation Quality Score for Measuring the Quality of interpretability methods

Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years. In addition to making accurate decisions, the necessity of understanding how models make their decisions has become apparent in many applications. To that end, many interpretability methods that help explain the decision processes of ML models have been developed. Yet, there currently exists no widely-accepted metric to evaluate the quality of explanations generated by these methods. As a result, there currently is no standard way of measuring to what degree an interpretability method achieves an intended objective. Moreover, there is no accepted standard of performance by which we can compare and rank the current existing interpretability methods. In this paper, we propose a novel metric for quantifying the quality of explanations generated by interpretability methods. We compute the metric on three NLP tasks using six interpretability methods and present our results.
Passenger Surveillance Using Deep Learning in Post-COVID-19 Intelligent Transportation System

Intelligent Transport System should be renovated in many aspects in post-pandemic situation like COVID-19. The passenger-count inside a car will be restricted based on the vehicle capacity and the COVID-19 hot-spot zone. Traffic rules will be impacted to align with a similar contagious outbreak. The on-road 'Yellow-Vulture' cameras need to incorporate such surveillance rules to monitor related anomalies for preventing contamination. To maintain safe-distance, an automatic surveillance system will be preferred by the Government very soon. Moreover, facial mask usage during the journey has become an essential habit to stop the spread of the infection. In this article, we have proposed a deep-Learning based framework that employs an augmented image data set to provide proper surveillance in the transport system to maintain the health protocols. Fast and accurate detection of the number of passengers inside a car and their face masks from the traffic inspection camera feed has been demonstrated. We have exploited the advantages of the popular Transfer Learning approach with novel variations of images while performing the training. To the best of our knowledge, this is the first attempt to watch over in-vehicle social-distancing in post-pandemic circumstances through deep-Learning based image analysis. The superiority of the proposed framework has been established over several state-of-the-art techniques using different numerical metrics and visual comparisons along with a support of statistical hypothesis test. Our technique has achieved 98.5% testing accuracy in various adverse conditions. Zero-shot evaluation has been explored for the Real-Time-Medical-Mask-Detection data set Wang et al. (Real-Time-Medical-Mask-Detection, 2020a https://github.com/TheSSJ2612/Real-Time-Medical-Mask-Detection/, Accessed 14 Nov 2020), where we have attained 96.4% accuracy that manifests the generalization of the network.
FORMATION OF A PROFESSIONAL ORIENTATION OF THE FUTURE TEACHER DURING THE ORGANIZATION OF EDUCATIONAL PROCESS AT PEDAGOGICAL UNIVERSITY

The article deals with significance of pedagogical focus as an important component of future teachers ’ professional and pedagogical training
REGULARITIES OF MENTAL MANAGEMENT SUPERIOR MOVEMENTS OF THE MUSICIAN WHILE READING NOTE FROM THE SHEET

As reading musical notation and its actualization of the stave are directed by man’s psychological process, there arises the necessity to control these movements.The investigation of free movements is based on a person’s image reflection o f music he is performing and this fact makes the orientated reaction its necessary functional component. Thus, the movements resulted from the actions in terms o f their orientation are viewed at deferent levels.
A Novel Approach for Discovering the Patterns by using PDBD Model in Big Data

Big data is a term that refers to information collected from a wide range of sources, including transaction processing systems, sensors, digital photographs, internet click stream logs, movies, and social media.The goal of text mining is to reveal new patterns and relationships that could lead to the discovery of previously unknown sources.It provides ample scope for interpretation of genuine user intent or contextual meanings as the case be.This research focuses on the creation of a novel approach for discovering patterns in big data using the Pattern Discovery in Big Data (PDBD) model in text mining.From textual sources, text-mining techniques extract a range of patterns and important facts.Sequential pattern mining helps to extract informative elements from a set of sequences based on the frequency of occurrences.The Pattern Discovery in Big Data (PDBD) model study has four primary contributions to uncovering patterns uniquely and effectively: Semantic information extraction, pattern improvements, LDA Topic Modeling, and cluster assignments.The proposed work compared the Pattern Taxonomy Model (Inner Pattern Evolving) pattern inner evolving model with other existing similar benchmark models to evaluate the model performance.The empirical results prove that the proposed model outperformed the compared models with significant improvement in accuracy Improvement in this Pattern Discovery in Big Data (PDBD) model yields better results in discovering the patterns.
PREPARING A HISTORY TEACHER FOR CREATIVITY PEDAGOGICAL ACTIVITY

The article deals with the conditions for forming professional qualities of a future teacher.
Mining of Movie Box Office and Movie Review Topics Using Social Network Big Data

In order to solve the problems of high investment and low box office losses in the film industry, this study analyzes the topic of film box office and film and television reviews based on social network big data. Firstly, the factors that affect the box office of the movie are analyzed. Secondly, continuous and discrete feature parts, text parts, and fusion parts are merged. The box office prediction model of mixed features using deep learning is established, and the movie box office is predicted. Finally, compared with other algorithms and models, the box office prediction model of mixed features using deep learning is verified. The results show that compared with other models, the prediction accuracy of the mixed feature movie box office prediction model using depthwise separable convolution (DSC)-Transformer is higher than that of other algorithm models. Its optimal mean square error (MSE) value is 0.6549, and the optimal mean absolute error (MAE) value is 0.1706. The constructed model predicts the box office of nine movies, and the error between the predicted value and the true value is about 10%. Therefore, the established movie box office prediction model has a good effect. This study can predict movies' box office to reduce investment risk, so it is of great significance to movie investors and the social economy.
Preparing data for pathological artificial intelligence with clinical-grade performance

[Purpose] The pathology is decisive for disease diagnosis, but relies heavily on the experienced pathologists. Recently, pathological artificial intelligence (PAI) is thought to improve diagnostic accuracy and efficiency. However, the high performance of PAI based on deep learning in the laboratory generally cannot be reproduced in the clinic. [Methods] Because the data preparation is important for PAI, the paper has reviewed PAI-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. The in-depth analysis of methods for preparing data is performed, including obtaining slides of pathological tissue, cleaning, screening, and then digitizing. Expert review, image annotation, dataset division for model training and validation are also discussed. We further discuss the reasons why the high performance of PAI is not reproducible in the clinical practices and show some effective ways to improve clinical performances of PAI. [Results] The robustness of PAI depend on randomized collection of representative disease slides, including rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and the amount of data. The digital pathology is fundamental of clinical-grade PAI, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. [Conclusion] The representative data, the amount of labeling and consistency from multi-centers is the key to performance reproduction. The digital pathology for clinical diagnosis, data standardization and technique of WSI-based weakly supervised learning hopefully build clinical-grade PAI. Keywords: pathological artificial intelligence; data preparation; clinical-grade; deep learning
KAJIAN LEKSIKON MENTAL SUBJEK TUNGGAL LAKI-LAKI DAN PEREMPUAN MELALUI ASOSIASI KATA

This research aims to analyze the mental lexicon capacity of a male and a female subject using word association test. Distributional and identity methods were used in analyzing the data. This research employed Kent-Rosanoff word association test to measure and analyze the mental lexicon capacity and investigate the word class and conceptual areas. The findings of this research show that the mean of male and female mental lexicon capacity are significantly different, with male producing 11.26 words and female 13.39 words per stimulus. The female subject gave 213 more responses than the male subject. In this research, word class association forms uncovered striking similarities between the two sexes based on the response percentage hierarchy. The order from the highest number of word class association form to the lowest one is fairly the same: N-N ± 40%, A-N ± 20%, N-A ± 15%, N-V ± 8%, A-N ± 4%, V-N ± 3%, and A-V ± 2%. This research found that the male and female subjects' differences in mental lexicon capacity were due to psychological and social factors. Keywords: Mental lexicon capacity, male language, female language, word association
THE SIGNIFICANCE OF MAKHAMBET'S POEMS IN PATRIOTIC EDUCATION OF YOUNG PEOPLE

The article describes how to educate young people in an ambitious spirit by teaching the works of Makhambet Utemisuly. Makhambet is a truly national poet. He constantly sang about the people's thoughts, dreams, interests, desires and goals. Gave energy to the people's dreams and hopes. In his poems, The Poet instills courage, patriotism, and heroism. The purpose of the event is to inform the public about the history of the Kazakh people. The main purpose of the event is to familiarize students with the history of the Kazakh people. He calls people to love our country, own land, and be a patriot.