Reviewerly/exHarmony huggingface dataset:
The use of innovative technologies in the self-education of students in the study of the Russian language in higher educational institutions

Innovation in the field of education - everything related to the implementation of advanced pedagogical practices. The educational process, which occupies a leading place in modern science, is aimed at providing students with knowledge, skills, and the formation of personality and civic qualities. Changes are determined by time, education, education, changes in attitude to development. This article discusses about the use of innovative technologies in the self-education of students in the study of the Russian language in higher educational institutions.
Innovative improvement of the system of organization and management of MTT activities based on public private partnership

The MTT's content is regarded as a system made up of all of the methods that educators prefer in their activities and are grounded in science. These methods are the most effective ones based on the achievements of science and technology in improving the organization and management of pedagogical processes. Pedagogical processes are improved in this by both the system itself and the content of the activities that educators engage in with other educators. This article offers input and feedback on creative ways to organize and administer MTT operations using a public-private partnership model.
Content Analysis of Hate Speech in the 2019 General Elections in Three Nigerian Newspapers

All over the world, hate speech represents a form of threat to damage the lives of individuals and increase the sense of fear in entire communities. The recent trend in journalism malpractice in the country is the dissemination of hate speech and vulgar language. In this perspective, the study examined the manifestation of hate speech in the 2019 general elections in the Daily Trust, The Nation and The Guardian newspapers. The variables of frequency, location, direction as well as journalistic genre were used to examine the manifestation of hate speech in the 2019 general elections by the selected newspapers. Stratified random sampling by days of the week was used to select the editions in the three newspapers. Critical Race Theory and Critical Discourse Analysis were used as theoretical framework. Based on the findings, the study discovered that the manifestation of hate speech was more frequent on Daily Trust, follow by The Guardian in the 2019 general elections. The findings reveals that the manifestation of hate speech by the selected newspapers in 2019 general elections was significantly high in the inside pages than front and back pages. The paper also found that the manifestation of hate speech by the selected newspapers was in neutral direction in the 2019 general elections. The study concludes that hate speech is a globally-endorsed paradigm and the press, as an important institution in the democratic process plays a key role during elections. As the Fourth Estate of the Realm, the press provides the platform for narratives and discourses in the service of elections, political negotiations and other features of the contestations among politicians and other civil organizations involved in election administration. Keywords: Direction: Frequency: Hate Speech: Journalistic Genre: Prominence.
A Study on Developing the Teacher Education Program to Support Young Children's Creative Play using Creativity Techniques

The study was aimed to develop the teacher education program to support young children’s creative play using creativity techniques and examine the effectiveness. The program was developed to help teachers to promote knowledge, skills, and attitudes related to creative play support. To verify the effectiveness of the program, 70 child-care teachers were included for the study and 35 teachers of the experimental group participated in the program for 9 weeks. The results of the study were as follows. First, it was found to be effective in factors of flexibility, originality, imagination, and openness, which are sub-factors of teacher creativity. Second, the program has been shown to be effective in teachers' creative teaching efficacy in the experimental group. Third, the program has been shown to be effective in the play teaching efficacy. The experimental group's post-scores showed significant improvement over pre-scores in the belief in play teaching efficacy and expectations for play teaching efficacy, which are sub-factors of the play teaching efficacy. Therefore, teacher education for promoting creativity is needed for supporting children’s creative play.
Effects of Early Childhood Teachers’ Goal Orientation on Teaching Creativity Through Intrinsic Motivation and Informal Learning Experience

This study examined the effects of early childhood teachers’ goal orientation on teaching creativity through intrinsic motivation and informal learning experience to investigate the roles of goal-related, motivational, and experiential factors that explain their teaching creativity. Participants were 257 early childhood teachers in early childhood education centers located in Incheon and Gyeonggi-do. This study measured the teachers’ goal orientation (mastery goal orientation, performance goal orientation), intrinsic motivation, informal learning experience, and teaching creativity. Collected data were analyzed using the structural equation modeling. Results showed that early childhood teachers’ mastery goal orientation, intrinsic motivation, and informal learning experience significantly influenced teaching creativity. However, the effects of performance goal orientation on teaching creativity were not significant. Mastery goal orientation significantly influenced intrinsic motivation and informal learning experience, and performance goal orientation significantly influenced informal learning experience. The effects of intrinsic motivation on informal learning experience were significant. Regarding indirect effects, mastery goal orientation significantly influenced teaching creativity through intrinsic motivation and informal learning experience. Also, mastery goal orientation significantly influenced teaching creativity sequentially mediated by intrinsic motivation and informal learning. The results provide academic implications for examining the goal-related, motivational, and experiential factors that affect early childhood teachers’ teaching creativity. They have practical implications for suggesting specific strategies for the teaching creativity of early childhood teachers.
PSYCHODIDACTIC APPROACH TO THE CREATION OF TEXTBOOK TEXTS FOR THE PROMOTION OF INTELLECTUAL DEVELOPMENT OF STUDENTS (ON THE EXAMPLE OF SCHOOL MATHEMATICS)

Introduction. The most important factor for students’ intellectual development is the teaching content. A textbook text is a didactic unit of teaching content. Therefore, the question arises about the requirements for modern textbook texts in the framework of the psycho-didactic approach. Materials and Methods. The study was conducted based on an analysis of the psychological features of the intellectual development of students within the psycho-didactic approach, which is based on the psychological and pedagogical rationale of the multifunctionality of modern mathematics teaching materials. Results and Discussion. The concept of development-focused educational text, which should be constructed as a multidimensional semantic space (hypertext), is introduced. Classification of development-focused educational texts is presented (using school mathematics as an example) based on the ontological theory of intelligence, targeting the enrichment of the main components of students’ mental experience – cognitive, conceptual, metacognitive, and intensional experience. Typology of pedagogical developmental texts of different types in teaching mathematics in middle school is given. Conclusion. The use of developmental textbook texts contributes to understanding mathematical material and developing students’ intellectual resources.
An Automated Malaria Cells Detection from Thin Blood Smear Images using Yolov4

Malaria is a severe global health problem, with an estimated 241 million malaria infections and 627,000 malaria deaths globally in 2020. Hundreds of millions of blood films are examined annually for malaria, which includes manually counting parasites and infected red blood cells by a trained microscopist. Segmented red blood cells play an important role in applying deep learning for malaria diagnosis. However, traditional segmentation and separation of single red blood cells is challenging and requires much human intervention. Therefore, instead of segmented red blood cells, the performance of deep learning models can be studied using bounded cell images. Various object detection architectures are studied in detecting red blood cells from thin blood smear images. However, there is a lack of study on the performance of Yolov4 to detect infected cells in thin blood smear images. This study aims to evaluate the performance of Yolov4 in detecting red blood cells infected by four types of malaria species and integrate a separate algorithm to automatically crop the infected cells. Different types of malaria images are used to study if the model can still detect cells infected by various malaria parasites and from multiple stages of infection despite their morphology differences. The MP-IDB malaria datasets were used in the experiments. The performance of the Yolov4 model was evaluated by partitioning the train and test dataset by 90/10 and 80/20. The partitioning was done on datasets with and without augmentations. The results show that upon training Yolov4 model can detect infected cells despite their morphological differences. Model 4 with 80/20 dataset partition and augmentation is chosen as the best model with the best mAP of 93.43%.
Topic Analysis in TOEIC Courses in College by LDA Topic Modeling

The purpose of this study is to investigate the TOEIC research topics in college from 2000 to 2022 in Korea. The total 184 papers were collected and analyzed by LDA (Latent Dirichlet Allocation) topic modeling. The results are as follows: The number of TOEIC papers has increased significantly since 2011. By analyzing LDA topic modeling, 12 topics were identified and categorized into two major topics: teaching and learning methods and learner factors. In teaching and learning method category, there were satisfaction with online lectures (T1), listening strategy (T2), reading and grammar (T3), TOEIC intensive program (T4), vocabulary learning (T6), speaking (T10), and teaching method (T11). In learner factors category, there were factors affecting TOEIC course (T5), affective factors and proficiency (T7), learning style and self-directed learning (T8), self-error analysis and report (T9), and achievement and motivation by proficiency level (T12). This study is meaningful in that it covers the 20 years of TOEIC research by LDA topic modeling and hopes to provide useful and meaningful analysis for researchers.
Applications of generative adversarial networks (GANs) in radiotherapy: narrative review

Background and Objective: Radiation therapy (RT) is the dominant method for clinical cancer treatment, which aims to ensure that planning target volume (PTV) receives a sufficient dose while organs-at-risk (OARs) are exposed to little or no radiation. However, obtaining a clinically acceptable radiotherapy plan often requires a long time, tedious work, and a high level of physician experience. The general steps to perform RT include planning [computed tomography (CT)/magnetic resonance imaging (MRI)/positron emission tomography (PET)] image acquisition, contouring the treatment area (gross tumor volume, OARs, etc.), and developing a treatment plan and treatment implementation. But there are still some challenges that need to be overcome. Fortunately, with the development of the computer science, Generative Adversarial Network (GAN) which is composed of a generator and discriminator with opposing optimized goals has been widely used by an increasing number of applications in various fields, especially in CT, MRI, and other images and plays a great role in RT. This review aims to provide an up-to-date snapshot of GAN applications in radiotherapy. Methods: We searched for studies published from January 2018 to March 2022, with English language restrictions on PubMed and IEEE Xplore databases. Key Content and Findings: (I) GAN is an active field of research to support the automation of the radiotherapy workflow; (II) clinical validation is still needed to guarantee the usability of GANs in radiotherapy. Conclusions: GAN model has already been widely used in RT. Thanks to their ability to automatically learn the anatomical features from different modalities images, improve quality images, generate synthetic images and make less time consumption automatic dose and plan calculation. Even though the GAN model cannot replace the radiotherapy doctors’ work, it still has great potential to enhance the radiologists’ workflow. There are lots of opportunities to improve the diagnostic ability and decrease potential risks during radiotherapy and time cost for plan calculation.
Aspect-based Sentiment Analysis for Bengali Text using Bidirectional Encoder Representations from Transformers (BERT)

Public opinion is important for decision-making on numerous occasions for national growth in democratic countries like Bangladesh, the USA, and India.Sentiment analysis is a technique used to determine the polarity of opinions expressed in a text.The more complex stage of sentiment analysis is known as Aspect-Based Sentiment Analysis (ABSA), where it is possible to ascertain both the actual topics being discussed by the speakers as well as the polarity of each opinion.Nowadays, people leave comments on a variety of websites, including social networking sites, online news sources, and even YouTube video comment sections, on a wide range of topics.ABSA can play a significant role in utilizing these comments for a variety of objectives, including academic, commercial, and socioeconomic development.In English and many other popular European languages, there are many datasets for ABSA, but the Bengali language has very few of them.As a result, ABSA research on Bengali is relatively rare.In this paper, we present a Bengali dataset that has been manually annotated with five aspects and their corresponding sentiment.A baseline evaluation was also carried out using the Bidirectional Encoder Representations from Transformers (BERT) model, with 97% aspect detection accuracy and 77% sentiment classification accuracy.For aspect detection, the F1-score was 0.97 and for sentiment classification, it was 0.77.
CRS-Updatable Asymmetric Quasi-Adaptive NIZK Arguments

A critical aspect for the practical use of non-interactive zero-knowledge (NIZK) arguments in the common reference string (CRS) model is the demand for a trusted setup, i.e., a trusted generation of the CRS. Recently, motivated by its increased use in real-world applications, there has been a growing interest in concepts that allow to reduce the trust in this setup. In particular one demands that the zero-knowledge and ideally also the soundness property hold even when the CRS generation is subverted. One important line of work in this direction is the so-called updatable CRS for NIZK by Groth et al. (CRYPTO’18). The basic idea is that everyone can update a CRS and there is a way to check the correctness of an update. This guarantees that if at least one operation (the generation or one update) have been performed honestly, the zero-knowledge and the soundness properties hold. Later, Lipmaa (SCN’20) adopted this notion of updatable CRS to quasi-adaptive NIZK (QA-NIZK) arguments. In this work, we continue the study of CRS-updatable QA-NIZK and analyse the most efficient asymmetric QA-NIZKs by González et al. (ASIACRYPT’15) in a setting where the CRS is fully subverted and propose an updatable version of it. In contrast to the updatable QA-NIZK by Lipmaa (SCN’20) which represents a symmetric QA-NIZK and requires a new non-standard knowledge assumption for the subversion zero-knowledge property, our technique to construct updatable asymmetric QA-NIZK is under a well-known standard knowledge assumption, i.e., the Bilinear Diffie-Hellman Knowledge of Exponents assumption. Furthermore, we show the knowledge soundness of the (updatable) asymmetric QA-NIZKs, an open problem posed by Lipmaa, which makes them compatible with modular zk-SNARK frameworks such as LegoSNARK by Campanelli et al. (ACM CCS’19).
A PSO Based Multi-view Hierarchical Clustering Method

In the era of big data, multi-view data becomes more and more common. Many clustering algorithms have been developed into multi-view clustering to process multi-view data, except for hierarchical clustering. Although it has advantages over other clustering algorithms, hierarchical clustering lags behind due to the lack of a well-defined objective function. In the past five years, many people have devoted themselves to the study of the objective function for hierarchical clustering, and have proposed some objective functions. Inspired by the existing multi-view clustering algorithms and the emerging objective functions for hierarchical clustering, this paper proposes a multi-view hierarchical clustering method called MVHCPSO. The main idea of MVHCPSO is to use the particle swarm optimization algorithm and tree rearrangement operations to fuse multi-view data. The test results on simulated datasets and real datasets show that MVHCPSO is effective.
Visualization of Convolutional Neural Networks with Attention Mechanism

With the continuous development of deep learning, convolutional neural networks are gradually playing an increasingly important role in classification models. Adding attention mechanisms based on convolutional neural networks to improve the classification effect of the model has been proven a lot. However, only theoretically understanding how attention mechanisms enhance the model's performance is a lack of intuition. Therefore, to explore the role of the attention mechanism in the model, this article will start from visualization and analyze how the attention mechanism improves the model's performance in combination with theory. The method makes attention more intuitive and more accessible for non-professionals to understand the working principle of attention mechanism, facilitating the promotion of attention mechanism and convolutional neural networks.
ICPCH: A Hybrid Approach for Lossless Dicom Image Compression Using Combined Approach of Linear Predictive Coding and Huffman Coding with Wavelets

Dicom Images play important role in everyday life. Healthcare is the most important urge of human being. Bio- Medical domain has adopted imaging system to identify, analyze and detect diseases. These images equip themselves with intense embedded data and occupy huge memory. So, the world is in need of technique which reduces the memory requirement for storing dicom images. Transmission of this type of huge data consumes more bandwidth and thereby increases communication cost. So, a novel technique has to be invented which reduces the memory requirement for storing dicom images. Dicom image compression is the solution proposed to manage space requirement for dicom images which also does not degrade the quality of the image. Several techniques have been presented for compressing the image,but achieving desired performance is a challenging task. In this paper, Image compression scheme is presented using wavelet transform, Lifting Scheme. Linear Predictive Coding and Huffman Coding. The outcome of proposed approach is compared with various existing techniques. The experimental analysis shows that proposed approach achieves better performance in terms of histogram, PSNR, MSE and SSIM.
SecVT: Securing the Vehicles of Tomorrow Using Blockchain Technology

Internet of Things (IoT) is one of the fields which has flourished to a great extend over the years because of its varied use cases and versatility. One of the fields which has flourished technologically is the automotive industry where the main focus is not just on improvising the vehicles internally but also to develop communication and interaction between vehicles on the road so as to facilitate a network of interconnected vehicles. However, the exchange of large volumes of data often poses a huge threat to security and privacy and necessitates the incorporation and integration of intense cybersecurity measures to ensure that the system and network is safe from attacks. Blockchain however has proven to be one of the useful techniques in terms of protecting data. This chapter mainly deals with the implementation of securing the information regarding next generation intelligent vehicles.
Face Image-Based Gender Classification of Children

In this work, we have addressed the problem of Gender Classification in children which is a challenging area in the face recognition system. We have adopted two alternative approaches by varying feature extraction techniques: conventional and convolutional techniques. In the conventional technique, the local and texture features such as HOG, M-LBP, LGP, and Histogram are taken. Subsequently, feature fusion is also performed. In the convolutional technique, the pre-trained deep neural network models such as AlexNet, GoogLeNet, VGG-19, and ResNet-101 are utilized for feature extraction. For the classification task, standard learning models and their ensemble are tried out. The proposed model has experimented on our longitudinal data of toddlers (four years) to preteen age (fourteen years) consisting of 56000 face images from 450 children. During the experimentation, the performance of gender classification with age information and without age information is evaluated. Finally, group-based gender classification is also estimated for further analysis of the model. From this extensive experiment, we have observed that gender classification of young children concerning age is quite challenging than children with groupage information. The results from deep features achieved 99.1% of F-Measure for group-based gender classification of children.
Telugu OCR Framework Using a HMM and Transfer Learning Approach

Optical character recognition (OCR) for complex scripts such as Telugu has gained much attention over the past decade due to the significant advancements made in this area of research. The Telugu OCR framework in this work proposes a hidden Markov model based approach using transfer learning to estimate the emission probability parameter of the model. This approach incorporates knowledge of the Telugu language into the framework via the hidden Markov model, while the pre-trained convolutional neural network, VGG-16, aids in estimating the emission parameters. The results from this framework show that using a pre-trained CNN for parameter estimation significantly reduces the resources and training time required for developing a Telugu OCR framework.
Real-Time Phishing Detection Using Statistic Database Check, DNS and Who Is Check, Verifying ASCII Content of the URL and Visual Similarity

Phishing happens to be a severe cyber-crime that affects tens of lakhs of people every day. Cyber-attacks are now getting to end consumers, exploiting the weakest security component. So, to rectify these types of issues we need to develop different types of phishing detection techniques. We proposed a variety of phishing strategies for detection, rectification, and prevention, all of which are necessary to identify phishing. We detect phishing attacks using these four methods, Statistic Database check, DNS and who is check, Verifying Ascii content of the URL and Visual similarity and we have derived impressive results out of these methods.