Reviewerly/exHarmony huggingface dataset:
Personal and Professional Position of the Student Group Curator

The article discusses the concept of the personal and professional position of curators working in the system of secondary vocational education. The mentoring work of a curator requires from the teacher not only psychological and pedagogical knowledge, but also high intellectual, moral-volitional and moral qualities. The authors conducted a research among the curators of the College of the Buryat State University named after D. Banzarov to identify their personal and professional position, to determine its productivity. Analysis of the results showed that the pedagogical position of the curator depends primarily on internal causes rather than on external circumstances. The purpose of this article is to identify the personal and professional position of the curators of the BSU College, determination of its productivity. The main methods of this research are the analysis of scientific literature on the topic, regulatory documentation related to the activities of the curator; empirical methods: questioning and interviewing curators of the Buryat State University college, processing the results. In conclusion, it is concluded that a modern curator must have a number of personal qualities that contribute to an effective educational process and the formation of a student’s personality. As the research showed, the development of a productive personal and professional position of curators can contribute to the improvement of educational activities in the system of secondary vocational education.
Formation of the System of Secondary Vocational Education in Russia: Problems and Development Prospects

The training of personnel as a resource for transformations is associated with the effectiveness of state management decisions in the education sector; the use of both direct, legislative tools, and indirect ones that affect the development of human capital, such as motivation, individualization, increasing the stock of knowledge and professional experience during training. The purpose of the article is to substantiate the role of the state in the development of the system of secondary vocational education; with the identification of factors influencing the stages of its transformation. Methodology. The analysis of literature on the topic of research in the unity of logical and historical methods of cognition is used. Results. It is shown that in all historical periods the state regulation of the education system is significant. At present, the social mission of education has been strengthened; modern positive educational strategies have been developed to fill the labor market. Conclusion. It is determined that the formation of the education system, as a historically established system of personnel training, is associated with the unity and integrity of the general educational space of Russia, the uniqueness of the national educational system. The advantage of the Russian experience is the preservation of traditions and continuity of pedagogical experience, the continuous organization of natural science education, the methodological content of subjects. Initiatives for the further development of the system of secondary vocational education are connected with the political and economic foundations of the transformation. The need to preserve traditional Russian national values while preparing a specialist for a technocratic society seems to be the subject of further discussions.
DECODING COMPLEXITIES IN DESIGNING HEALTH COMMUNICATION: LESSONS FROM FIELD CASE STUDIES

Health communication influences the health choices of populations, informs decision makers, and is recognized by the WHO as a “best buy.” The current age is often referred to as a digital age of healthcare and a post-COVID world has accentuated the benefits of digital health because of increased stakeholder awareness and involvement and limited physical access to health setups. This paper aims to understand and evaluate the complexities that arise in the field when designing and delivering mobile-based healthcare interventions by discussing case studies of three programs currently running in India: 1) TAMA/Maitra – an interactive voice response system for people living with HIV/AIDS; 2) mMitra – an automated voice call service for pre- and post-natal women; and 3) Saathealth – a mobile application for sending nutrition messages to families with young children. The paper critically evaluates these programs based on learnings from the field, examines issues that affect program efficacy such as sociodemographic and psychosocial factors, and focuses on the change in perspectives toward health communication in the context of COVID-19. The analysis suggests the importance of self-regulation and self-responsibility as significant contributors to health promotion strategies, indicating possible directions for building a social communication model based on health behavior frameworks.
Evaluating the Efficiency of Vietnamese SMS Spam Detection Techniques

Abstract— This paper is aimed at evaluating the efficiency of Vietnamese SMS spam detection methods on different variants of Vietnamese datasets by utilizing both traditional machine learning models and deep learning models. The researchers experimented with five algorithms, which were Support Vector Machine (SVM), Naive Bayes (NB), Random Forests (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), on three different Vietnamese datasets. The findings reveal that the LSTM and CNN, supported by a transformer learning model - PhoBert, were more efficient than the traditional machine learning models. The LSTM model showed the highest accuracy of 97,77% when operating on the full-accent Vietnamese dataset. Similarly, the CNN model and PhoBert model showed the highest accuracy of 95,56% when dealing with non-diacritic Vietnamese dataset.
Traditions in the System of art Education in Iraq

The article presents the results of a study of the system of Iraqi art education by including objectively existing and expedient traditions in it in various historical periods of its development. Attention is drawn to the creative searches of artists in the many art schools that appeared in Iraq at the beginning of the 20th century, whose professional activities reflected the implementation of the ideas of modernity or/and its reconciliation with the cultural heritage of the country. As an example in the field of sculpture, the D. Selim Freedom Monument is given, the embodiment of which was based on the idea of combining the ancient history of Iraq with the modern themes, techniques and methods used in art at that time. The current system of art education in Iraq is presented, the starting point of which is the Academy of Fine Arts, which today acts as the leading educational institution of artistic orientation among those located in cities such as Babylon, Basra, Devaniya, Mosul and other art institutions. We consider art education itself as an educational process that helps young people with different views and beliefs learn to understand the language of art, use existing traditions in their artistic work. In conclusion, it is concluded that the system of art education in Iraq continues to work and develop, taking into account long-standing and newly acquired traditions in various art schools, reflecting the views, beliefs of artists in their work, formed under the influence of history, culture, politics, etc. of their country.
Agile governance theory: a multi-scenario empirical assessment

Context: Agile Governance Theory (AGT) has emerged as a potential model for organizational chains of responsibility across business units and teams. Objective: This study aims to assess how AGT is reflected in practice. Method: AGT was operationalized down into 16 testable hypotheses. All hypotheses were tested by arranging eight theoretical scenarios with 118 practitioners from 86 organizations and 19 countries who completed an in-depth explanatory scenario-based survey. The feedback results were analyzed using Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA). Results: The analyses supported key theory components and hypotheses, such as mediation between agile capabilities and business operations through governance capabilities. Conclusion: This study supports the theory and suggests that AGT can assist teams in gaining a better understanding of their organization governance in an agile context. A better understanding can help remove delays and misunderstandings that can come about with unclear decision-making channels, which can jeopardize the fulfillment of the overall strategy.
Cloud-Based TPA Auditing With Risk Prevention

The chapter focuses on cloud security audit mechanisms and models. Here the third-party auditor (TPA) will be provided with the authority access scheme where the security of the auditing system will be enabled. The TPA will check out the auditing verification and shows a message about the data audited. The purpose of this work is to develop an auditing scheme that is secure, efficient to use, and possesses the capabilities such as privacy preserving, public auditing, maintaining the data integrity along with confidentiality. It consists of three entities: data owner, TPA, and cloud server. The data owner performs various operations such as splitting the file to blocks, encrypting them, generating a hash value for each, concatenating it, and generating a signature on it. TPA performs the main role of data integrity check. It performs activities like generating hash value for encrypted blocks received from cloud server, concatenating them, and generates signature on it. Thus, the system frequently checks the security of the server-side resources.
Real-Time Object Detection and Audio Output System for Blind Users

This chapter aims to create a real-time object detection and audio output system for blind users using the YOLOv3 algorithm and a 360-degree camera sensor. The system is designed to detect a wide range of objects, including people, vehicles, and other objects in the environment, and provide audio feedback to the user. The system architecture consists of a 360-degree camera sensor, a processing unit, and an audio output system. The camera sensor captures the environment, which is processed by the processing unit, which uses the YOLOv3 algorithm to detect and classify objects. The audio output system provides audio feedback to the user based on the objects detected by the system. The project has significant importance for blind users as it can help them navigate their environment and recognize objects in real time and can serve as a foundation for future research in the field of object detection systems for blind users.
Automated Invigilation System Using MediaPipe and Haar Cascade Frontal Algorithm

Exams are the methods adopted by educational institutions to identify student’s knowledge. Students adopt various ways to cheat in exams like answer sheet exchanging, copying etc, students cheat their way into getting good grades. Detection of cheating manually may not be efficient to identify and prevent cheating during examinations. So, to avoid this the process of invigilation is made automatic. Automated invigilation offers the best method for keeping an eye on the kids and spotting instances of malpractice right away. The proposed work has three phases. In the first phase, the exam management does processes like publishing time table, allocating exam hall, allocating hall to staff etc. In the second phase the posture detection of the student present in the exam hall is done using Computer Vision and Media Pipe to detect whether the student has involved in the malpractice. In the third phase, the emotion analysis and face recognition of the student is done using the Haar Cascade Frontal Algorithm. The proposed work also helps to eliminate impersonation in the exam hall.
PROBLEMS OF ELECTRIFICATION OF THE PERM REGION AT THE BEGINNING OF THE XX CENTURY (BASED ON THE MATERIALS OF THE NEWSPAPER "PERM ZEMSKAYA NEDELYA")

Based on the materials of the newspaper «Permskaya Zemskaya Nedelya», the problems of electrification of the Perm province at the beginning of the 20th century are considered. Publications that deal with economic, social and cultural aspects, as well as security issues associated with the process of electrification are analyzed. It is concluded that the zemstvo authorities, through their periodical printed edition, promoted among readers the idea of building a power plant in the territory of the Kama region, and also focused on the benefits of electrification both in domestic and industrial terms. Many intentions and plans of the Zemstvo, reflected on the pages of the newspaper, were already implemented in the Soviet period.
Benchmark Datasets

This chapter introduces benchmark datasets in the field of fine-grained image recognition and fine-grained image retrieval by summarizing their own characteristics, presenting their detailed protocols and showing example images of these fine-grained image datasets.
Introduction

The human visual system is inherently capable of fine-grained image reasoning – we are not only able to tell a dog from a bird, but also know the difference between a Siberian Husky and an Alaskan Malamute (see Fig. 1.1). Fine-Grained Image Analysis (FGIA) was introduced to the academic community for the very same purpose, i.e., to teach machines to “see” in a fine-grained manner.
Linguistically-Inspired Neural Coreference Resolution

The field of coreference resolution has witnessed significant advancements since the introduction of deep learning-based models. In this paper, we replicate the state-of-the-art coreference resolution model and perform a thorough error analysis. We identify a potential limitation of the current approach in terms of its treatment of grammatical constructions within sentences. Furthermore, the model struggles to leverage contextual information across sentences, resulting in suboptimal accuracy when resolving mentions that span multiple sentences. Motivated by these observations, we propose an approach that integrates linguistic information throughout the entire architecture. Our innovative contributions include multitask learning with part-of-speech (POS) tagging, supervision of intermediate scores, and self-attention mechanisms that operate across sentences. By incorporating these linguisticinspired modules, we not only achieve a modest improvement in the F1 score on CoNLL 2012 dataset, but we also perform qualitative analysis to ascertain whether our model invisibly surpasses the baseline performance. Our findings demonstrate that our model successfully learns linguistic signals that are absent in the original baseline. We posit that these enhance ments may have gone undetected due to annotation errors, but they nonetheless lead to a more accurate understanding of coreference resolution.
Fine-Grained Image Retrieval

In this chapter, we focus on modern fine-grained image retrieval approaches based on deep learning. Two main areas of fine-grained retrieval, i.e., content-based fine-grained retrievalFine-grained image retrieval and sketch-based fine-grained retrieval, are involved. As a summary, we discuss the common insights between fine-grained recognition and fine-grained retrieval.
Resouces and Future Work

In this chapter, we first introduce two deep learning-based toolboxes, i.e., one for recognition and the other for retrieval. Then, we present the concluding remarks, as well as some potential future directions in the field of fine-grained image analysis.
Path Exploration Using Hect-Mediated Evolutionary Algorithm (HectEA) for PTP Mobile Agent

The algorithmic decision for any point-to-point (PTP) locomotion of an autonomous agent requires both sensor prospect and computation agility. Eventually, the methods in evaluating obstructed and complex dynamic environments depend on the performance of path searching techniques. A non-deterministic environment is a major challenge for a mobile agent to execute proper planning and decision-making in choosing an optimum path from the initial to the goal point. Simultaneous map creation of the traversed path serves to be a significant visual reference for the PTP locomotion of agents like UGV (Unmanned Ground Vehicle) to compute further state transition steps. This paper focuses primarily on handling the aforesaid situation and proposes a noble approach for computing a near-optimum path to be traversed within a finite amount of time. 2D visual scanning of the surrounding has been accomplished by LiDAR (Light Detection And Ranging) sensor for perfect periphery detection of the existing on-road obstacles. Simulation of the proposed algorithm by 2D Hect-mediated evolutionary path searching technique (HectEA) with proper filtration of the explored path was found to be effective in comparison to other existing MRN (Mobile Robot Navigation) strategies. The HectEA was found to be superior in precision and accuracy for executing optimal-time path navigation. Comparative visual and numerical analysis confirms the effectiveness of the proposed algorithm.
Image Captioning on Apparel Using Neural Network

Every individual has a different perspective of understanding the image. If there is no proper description of the image, then it could have a great impact on the people who does online shopping, using social media, websites, etc. To address this issue, image captioning came into picture. The caption to an image does not usually describe the image in great detail but instead works in conjunction with the image, which helps the user to better understand the content of the image. To solve this issue, a website is developed using CNN and Django framework which provides description to an image. The text description is further converted to audio which could help visually impaired person to understand the image. The experiment focuses on the InFashAIv1 dataset, which contains around 16,000 images of African fashion items.
The Liberation of Individuals from the Limitations Imposed by Their Physical Bodies Represents a Paradigm Shift in the Realm of Communication

I was inevitably drawn to the field of illusion research, due to the captivating nature of illusions in my undergraduate period. Throughout my undergraduate and graduate studies, I engaged in research aimed at uncovering a facet of the mechanism of vision. One of the central focuses of my current research is the examination of human perception through the use of virtual reality (VR) technology. This approach is also being employed extensively in my research within the Jizai Body Project, which endeavors to investigate human cognition and action in virtual environments that diverge from reality. Prolonged use of an avatar or interaction with other avatars in a virtual environment may result in changes in the mind and brain of the individual. Upon being invited to participate in the Inami Jizai Body Project, I was already engaged in the study of body editing. The ultimate objective of body editing is to enable individuals to freely navigate between states of physical embodiment, non-physical embodiment, and to alter their physical form at will. The way of communication will change with body editing and the Jizai body as well. Currently, our research endeavors are focused on the study of joint avatars with distinct left and right halves, in which the control of these halves is delegated to separate individuals. It may lead to an ultimate form of communication in the future. Jizai Body technologies present the possibility of creating novel forms of communication by significantly altering human bodies, and provide a novel perspective of the human body and mind.
Barrage Sentiment Analysis Based on Snow NLP—An Example of Liu’s Fitness Video

In the era of big data, netizens can express their views on various video platforms. Judging users’ attitudes to the videos they see through the barrage reviews has become a hot spot in data mining research. This paper takes Liu’s fitness video as the research object, uses Python to crawl the data, pre-process the data, extract effective commentary information, draw the word cloud for data visualization, and use Snow NLP for sentiment analysis, so as to help users obtain valuable information more efficiently and conveniently.
An Improved Image Colorization Algorithm Based on Pix2Pix

Image colorization, a challenging task in computer vision, purposes to generate a color image by using a given re-input. In this paper, we propose PR-U-Net by using U-Net as a Ga generator of Pix2Pix to solve the problems which are low color quality by the current image colorization algorithm. Our work mainly includes the following aspects: firstly, we use residual network (ResNet) as the encoder of origin U-Net; secondly, partial convolution is used taking place of standard convolution to eliminate invalid information caused by zero padding operation; thirdly, instance normalization is used to enhance image coloring to further improve image colorization quality; fourthly, in order to match the encoder, we also design PR-U-Net as a new decoder. The experimental results show that our method has great advantages in coloring effect.
MSAN: Multi-stage Human Pose Estimation Universal Network Based on Attention Mechanism

The task of human pose estimation aims to detect and locate all human key points in an image. Many existing methods based on Convolutional Neural Networks (CNNs) can obtain the appearance features of key points very well and detect the visible key points. However, due to their limitations, it is difficult to model the relationship between key points. In this paper, we design a network that implicitly models joint relationships, called a multi-stage attention network (MSAN), to model the constraint relationship between joints and improve the recognition ability of complex key points such as “knee” and “ankle”. Specifically, the proposed method uses the channel spatial attention network to obtain key features from the backbone network and suppress redundant features. In order to better model the constraint relationship between key points, this paper uses a multi-stage self-attention network to implicitly model the joint constraints and iteratively calculate the correlation between key point features to explicitly learn the constraint relationship between key points. In order to make the model better at recognizing complex key points, we use focus loss to make the model pay more attention to complex key points. We use HRNet and ResNet as the backbone networks, respectively, and prove the effectiveness of the method in this paper on the benchmark datasets MPII and COCO.
Predicting Social Events Using Analyses of Arabic Dailies

The research is devoted to a novel method for a real-time prediction of significant discontinuities in social states using the automatic analyses of Arabic dailies’ overall logical structures. The paper introduces the novel, named the super-frequent N-gram approach and the Regression Mean Rank Dependency characteristic, and presents their arrangement with a new model. It makes it possible to reliably forecast social changes based on the dailies’ semantic content’s high-level repercussions. An evaluation of the approach on four prominent Arabic dailies demonstrates its high ability to reflect changes in the social state and particularly to reveal significant events of the “Arab Spring” even though they took place in countries other than the newspaper’s hosting land. The study shows that the resulting N-gram models, constructed for the different dailies, correspond, in general, to different sizes of N and are capable together of providing a more vital prediction tool. The methodology consistently succeeds in predicting substantial changes in the social state in an online simulation fashion.