Reviewerly/exHarmony huggingface dataset:
Sistemnyi vzgliad na upravlenie shkoloi i model' otsenki upravlencheskikh podkhodov menedzherov obrazovaniia

The chapter presents school management as a management system, including types of management and management objects. The authors propose a model for evaluating the management approaches implemented by the school's management. A systematic view of school management allows you to identify and implement different areas of management – regular, strategic, project, financial and personnel in their systemic connection, without missing a single management object – people, processes, results, resources, information.
Enhancing Multi-Modal Perception and Interaction: An Augmented Reality Visualization System for Complex Decision Making

Visualization systems play a crucial role in industry, education, and research domains by offering valuable insights and enhancing decision making. These systems enable the representation of complex workflows and data in a visually intuitive manner, facilitating better understanding, analysis, and communication of information. This paper explores the potential of augmented reality (AR) visualization systems that enhance multi-modal perception and interaction for complex decision making. The proposed system combines the physicality and intuitiveness of the real world with the immersive and interactive capabilities of AR systems. By integrating physical objects and virtual elements, users can engage in natural and intuitive interactions, leveraging multiple sensory modalities. Specifically, the system incorporates vision, touch, eye-tracking, and sound as multi-modal interaction methods to further improve the user experience. This multi-modal nature enables users to perceive and interact in a more holistic and immersive manner. The software and hardware engineering of the proposed system are elaborated in detail, and the system’s architecture and preliminary function testing results are also included in the manuscript. The findings aim to aid visualization system designers, researchers, and practitioners in exploring and harnessing the capabilities of this integrated approach, ultimately leading to more engaging and immersive user experiences in various application domains.
Multitrack Educational Programs as a Method of Educational Process Personalization at Universities

This study analyzes the approach towards educational process organization at a university. We study existing practices of educational program personalization (and the relevant existing limitations) in the Russian system of university-level education. This study reviewed the general trends in university program personalization and performed an in-depth analysis of actual cases of personalized study plan implementation at Russian universities (we compared the declared objectives of such programs with their real-life implementation results). In this article, the authors also highlight the limitations affecting the maximum attainable level of personalization of study programs (such as the minimum number of students enrolled in a course in a given semester, problems of resource reallocation between different chairs at the university and administration costs of digitalization of a personalized study process). Based on our research, a model of study process organization (based on multitrack educational programs) has been developed and implemented. The article describes an algorithm for implementation of multitrack educational programs, highlights the limitations that this model of educational process complies with and formulates the advantages of this approach towards organization of the university-level study process compared to models employing individual educational trajectories.
AI advances in wheelchair navigation and control: A comprehensive review

This paper presents a systematic review of the literature on integrating artificial intelligence (AI) to improve wheelchair navigation and control for people with mobility impairments. The review covers a range of AI-based approaches including computer vision, machine learning, and path planning algorithms. The paper highlights the potential benefits of integrating AI into wheelchair technology, including increased safety, autonomy, and personalized control. The review discusses the limitations and challenges of current wheelchair navigation and control systems, and how AI can address these limitations. The paper identifies common themes and trends in the literature and summarizes the strengths and weaknesses of existing AI-based wheelchair navigation and control systems. Finally, the paper concludes by discussing the potential future directions for research and development of AI-based wheelchair navigation and control systems. This review paper provides a valuable resource for researchers and engineers interested in developing and improving AI-based wheelchair technology.
Crowdsourcing image segmentation for deep learning: integrated platform for citizen science, paid microtask, and gamification

Segmentation is crucial in medical imaging. Deep learning based on convolutional neural networks showed promising results. However, the absence of large-scale datasets and a high degree of inter- and intra-observer variations pose a bottleneck. Crowdsourcing might be an alternative, as many non-experts provide references. We aim to compare different types of crowdsourcing for medical image segmentation.We develop a crowdsourcing platform that integrates citizen science (incentive: participating in the research), paid microtask (incentive: financial reward), and gamification (incentive: entertainment). For evaluation, we choose the use case of sclera segmentation in fundus images as a proof-of-concept and analyze the accuracy of crowdsourced masks and the generalization of learning models trained with crowdsourced masks.The developed platform is suited for the different types of crowdsourcing and offers an easy and intuitive way to implement crowdsourcing studies. Regarding the proof-of-concept study, citizen science, paid microtask, and gamification yield a median F-score of 82.2, 69.4, and 69.3 % compared to expert-labeled ground truth, respectively. Generating consensus masks improves the gamification masks (78.3 %). Despite the small training data (50 images), deep learning reaches median F-scores of 80.0, 73.5, and 76.5 % for citizen science, paid microtask, and gamification, respectively, indicating sufficient generalizability.As the platform has proven useful, we aim to make it available as open-source software for other researchers.
Source‐Replacement Model for Phase‐Matching Quantum Key Distribution

Abstract Quantum key distribution has emerged as a promising solution for constructing secure communication networks, with its information‐theoretic security rooted in quantum mechanics. One of the recent quantum key distribution protocols, the phase‐matching protocol, can have a quadratic key‐rate improvement. Its security was initially established using an abstract method known as symmetry‐protected privacy. In this study, this security is reevaluated under the more intuitive source‐replacement model, arriving at the same conclusions as the original proof. This model provides a fresh perspective on the protocol's security. As an application of this approach, a beam‐splitting attack is introduced. Leveraging the source‐replacement model, a lower bound on the phase error rate under this attack is derived, further underscoring the robustness of our security analysis method.
Typology of Industrial Revolutions and Their Classifications Through the Prism of Innovations in the Field of Technical and Forensic Support

The rapid development of modern society has triggered profound economic changes in all aspects of human life and state activity. These transformations give rise to a great deal of scientific research, in particular, on the study of the consequences of industrial revolutions. These revolutions changed social paradigms and caused critical shifts in the global economic landscape. Three important conceptual trends have emerged in the scientific study of industrial revolutions: evolutionary, cyclical, and systemic. These paradigms reflect the comprehensive nature of the industrial revolution with its profound transformations in the social, economic, technological, and cultural spheres. Throughout human history, five different industrial revolutions have marked epochal shifts in the trajectory of human civilization. The advent of the fifth industrial revolution (5IR) also brings about significant adjustments in law enforcement in general and in the technical and forensic support of crime detection and investigation, in particular. Big data analytics is a powerful tool that facilitates comprehensive analysis of complex data sets. Three-dimensional scanning technologies increase accuracy and objectivity in crime scene analysis and expertise. Blockchain technology ensures the immutability of evidence. In this context, the transforming potential of 5IR lies in its ability to revolutionize the tactics and techniques of crime detection and investigation. However, the abovementioned technological advances are accompanied by multidimensional ethical and legal challenges.
Readiness for Educational Activities in Faculty Members

The article discusses the readiness of university academic staff to implement educational activities, its essence, and development. Educational activities are an important part of pedagogical training. Faculty members are to be proficient in the forms and methods of educational work with students. In this article, the structure of readiness for educational activities was described as an interaction of its structural elements. The methodology included activity-based and competence-based approaches, as well as literary review and the author’s own practical experience in educational work at university. The pedagogical guidelines were identified as follows: the goal of educational activities at university, the pedagogical tasks to achieve the goal, and the professional skills of faculty members to solve pedagogical problems. The professional skills were grouped by the method and form of their implementation as organizational, developmental, gnostic, perceptual, and reflective skills. The readiness for educational activities in higher education is the readiness to apply this set of skills during the academic process and to develop them. Its structure is an integrity of practice-oriented, motivational, axiological, and reflective components. These components allow faculty members to adjust the academic process to the current education standards. The readiness to implement educational activities depends on what the university administration and the department have to offer the faculty staff in order to ensure the positive dynamics of the educational process. The research made it possible to construct a tool that could catalyze the development of organizational, developmental, gnostic, perceptual, and reflective skills in faculty members on a scientific basis.
OHYEAH AT VLSP2022-EVJVQA CHALLENGE: A JOINTLY LANGUAGE-IMAGE MODEL FOR MULTILINGUAL VISUAL QUESTION ANSWERING

Multilingual Visual Question Answering (mVQA) is an extremely challenging task which needs to answer a question given in different languages and take the context in an image. This problem can only be addressed by the combination of Natural Language Processing and Computer Vision. In this paper, we propose applying a jointly developed model to the task of multilingual visual question answering. Specifically, we conduct experiments on a multimodal sequence-to-sequence transformer model derived from the T5 encoder-decoder architecture. Text tokens and Vision Transformer (ViT) dense image embeddings are inputs to an encoder then we used a decoder to automatically anticipate discrete text tokens. We achieved the F1-score of 0.4349 on the private test set and ranked 2nd in the EVJVQA task at the VLSP shared task 2022. For reproducing the result, the code can be found at https://github.com/DinhLuan14/VLSP2022-VQA-OhYeah
AN IMPROVED INDEXING METHOD FOR QUERYING BIG XML FILES

The exponential growth of bioinformatics in the healthcare domain has revolutionized our understanding of DNA, proteins, and other biomolecular entities. This remarkable progress has generated an overwhelming volume of data, necessitating big data technologies for efficient storage and indexing. While big data technologies like Hadoop offer substantial support for big XML file storage, the challenges of indexing data sizes and XPath query performance persist. To enhance the efficiency of XPath queries and address the data size problem, a novel approach that is derived from the spatial indexing method of the R-tre family. The proposed method is to modify the structure of leaf nodes in the indexing tree to preserve XML-sibling connections. Then, new algorithms for constructing the new tree structure and processing sibling queries better are introduced. Experimental results demonstrate the superior efficiency of sibling XPath queries with reduced data sizes for indexing, while other XPath queries exhibit notable performance improvements. This research contributes to the development of more effective indexing methods for managing and querying large XML datasets in bioinformatics applications, ultimately advancing biomedical research and healthcare initiatives.
Methods of Encoding Information Based on Three-Dimensional Parallelepiped in Teaching Mathematics

Social informatization requires strong information security measures that would allow companies and individuals to protect their data. Courses in information security may solve this problem. However, methods of coding and encryption can be incorporated in the school curriculum as part of Mathematics and Computer Science. This article introduces an original reliable method of encoding to be integrated with the high school course of Mathematics. The authors used geometric methods of representing points in three-dimensional space bounded by a closed parallelepiped and elementary algebraic operations with matrices. The paper proposes flow and k-gram variants of the matrix method of text encoding and their program implementation in the Python programming language. The encoding methods require no special mathematical training, which makes them teachable to 9–11-graders. By mastering these methods, high-school students can increase their personal information security and develop an algorithmic style of thinking, as well as mathematical skills in general. The article contains some extramural tasks that consolidate practical skills of text coding. The academic and methodological materials make it possible to give the methods of coding and encryption while teaching Mathematics, as well as part of academic disciplines related to information security.
Healthcare 3.0: How to Transform Machine Learning Prototypes into Functional Healthcare Applications for Diagnostic Assistance?

Abstract Background As the number of elderly people is rapidly increasing, we are faced with the challenge that diagnostic services are demanded more frequently. At the same time, the number of medical centers and available experts remains almost constant. Tools for diagnostic assistance are urgently needed to improve the effectiveness of healthcare. In four externally funded projects, we will investigate aspects and strategies of how machine learning prototype systems can be translated into functional healthcare applications. Method In the ongoing project “ExplAInation” funded by the German research foundation (DFG), we are developing an artificial neural network framework to generate visual and textual explanations to improve the comprehensibility and interpretability of deep learning models. This effort includes participatory research with clinical users. In the complementary project “TESIComp” funded by the Federal Ministry of Education and Research (BMBF), we will investigate ethical and social aspects of the emerging field of computational psychiatry. Patients, caregivers, and medical doctors will be interviewed, which will be qualitatively analyzed. Within the German Medical Informatics Initiative, we will contribute to the project “Open Medical Inference” (OMI), which will develop a network of distributed machine learning evaluation services. We also participate in the international “Clinical AI‐based Diagnostics” (CAIDX) consortium, which receives funding from the European Interreg Baltic Sea Region program. Result We developed a deep learning application for the detection of dementia atrophy patterns in brain MRI scans. Derived relevance maps highlight diagnostically important brain areas for further evaluation by the radiologists. Interviews with clinicians will provide information on expectations, key requirements and the functional utility of machine learning‐based assistance. Interviews with patients and caregivers will elucidate future changes and challenges in the doctor’s role and responsibilities. The OMI network will allow hospitals to use distributed machine learning evaluation services remotely, without the need of operating all the tools locally. In CAIDX, we will develop best‐practice guidelines for the overarching process of integrating machine learning prototypes and commercial tools into the hospital. Conclusion In “Healthcare 3.0”, the digital transformation will change current diagnostic procedures and roles. Our activities focus on the involved stakeholders, regulatory aspects and implementation strategies to better steer this process.
Identifying probable dementia in undiagnosed Black and White Americans using machine learning in Veterans Health Administration electronic health records

Abstract Background The application of machine learning (ML) tools in electronic health records (EHRs) can help reduce the underdiagnosis of dementia, but models that are not designed to reflect minority population may perpetuate that underdiagnosis. Method To address the underdiagnosis of dementia in both Black Americans (BAs) and white Americans (WAs), we sought to develop and validate ML models that assign race‐specific risk scores. These scores were used to identify undiagnosed dementia in BA and WA Veterans in EHRs. More specifically, risk scores were generated separately for BAs (n = 10K) and WAs (n = 10K) in training samples of cases and controls by performing ML, equivalence mapping, topic modeling, and a support vector‐machine (SVM) in structured and unstructured EHR data. Scores were validated via blinded manual chart reviews (n = 1.2K) of controls from a separate sample (n = 20K). AUCs and negative and positive predictive values (NPVs and PPVs) were calculated to evaluate the models. Result Among Veterans with undiagnosed dementia who underwent chart review, those diagnosed by expert clinician reviewers with possible/probable dementia had higher ADRD ML risk scores compared to those diagnosed with no dementia (0.45 [0.38] vs. ‐0.02 [0.51] for BAs, and 0.38 [0.41] vs. ‐0.02 [0.47] for WAs; (see figure 1). Of the 1,200 Veterans who underwent chart review, 15.3% (n = 92) of BAs and 9.5% (n = 57) of WAs were identified with possible/probable dementia. However, adjusting for oversampling of Veterans with higher scores, the adjusted estimated prevalence was 4.1% for BA Veterans and 3.6% for WA Veterans. There was a strong positive relationship between risk scores and the prevalence of undiagnosed dementia (Figure 2). As anticipated, for Veterans with scores below the 90 th percentile, the percentages of undiagnosed dementia were low: 3.9% for BA and 2.9% for WA Veterans. Among Veterans with scores above the 90 th percentile, we found that a higher percentage of BA Veterans had undiagnosed dementia than WA Veterans: 25.6% vs. 15.3%. Conclusion Our findings suggest that race‐specific ML models can assist in the identification of undiagnosed dementia, particularly in BAs. Future studies should investigate implementing EHR‐based risk scores in clinics that serve both BA and WA Veterans.
THE VNPT-IT EMOTION TRANSPLANTATION APPROACH FOR VLSP 2022

Emotional speech synthesis is a challenging task in speech processing. To build an emotional Text-to-speech (TTS) system, one would need to have a quality emotional dataset of the target speaker. However, collecting such data is difficult, sometimes even impossible. This paper presents our approach that addresses the problem of transplanting a source speaker's emotional expression to a target speaker, one of the Vietnamese Language and Speech Processsing (VLSP) 2022 TTS tasks. Our approach includes a complete data pre-processing pipeline and two training algorithms. We first train a source speaker's expressive TTS model, then adapt the voice characteristics for the target speaker. Empirical results have shown the efficacy of our method in generating the expressive speech of a speaker under a limited training data regime.
Bitcoin Cryptocurrency and Electronic Commerce in Saudi Arabia

Bitcoin, a well-known cryptocurrency, has attracted much attention worldwide and is becoming more widely used. This study develops a hypothesis to investigate and test the impact of Bitcoin on E-Commerce use in Saudi Arabia using a survey research approach. Analyzing factors such as Bitcoin awareness and usage among Saudi Arabian consumers can increase online transactions, increase payment efficiency, a larger degree of financial inclusion, and a higher level of trust and security. The online-based survey was used to collect responses, and descriptive-analytical and standard results. Assessment criteria approaches were implemented to interpret the results. Responses were collected from individuals and employees of various companies working in different occupations in Saudi Arabia. In addition, statistical tools SPSS and SmartPLS were used to test the study’s hypotheses. The findings indicated a rapid growth in e-commerce transactions and some knowledge of Bitcoin. Also, it shows a positive correlation between digital currencies (Bitcoin) and e-commerce in Saudi Arabia. The study’s conclusions are expected to be valuable for those involved in Saudi Arabia’s e-commerce sector, helping them to decide how to adopt and use Bitcoin in their digital business strategy. The study also opens the way for future investigations into topics including Saudi Arabia’s regulations for Bitcoin, consumer attitudes toward Bitcoin, and the potential of blockchain technology for enhancing the nation's e-commerce processes.
Enterprise Audits and Blockchain Technology

Blockchain technology has become more common in the audit industry, posing new problems for traditional audit work. Researchers have been studying the effect of customers’ use of blockchain technology on audits, especially on audit risk and audit methods. This paper examines audit cases of Chinese enterprises that use blockchain technology. It analyzes the effect of the use of blockchain technology on the audit and the new audit problems. It also examines an Australian blockchain audit case using a questionnaire. This case study showed that the application of new technologies to the audit industry may lead to increased inherent risks and control risks. The audit methodology is uncertain and there are two possible approaches: (1) a combination of direct, indirect, account-level, and entity-level evidence and (2) an increase in indirect and entity-level evidence. This paper puts forward the reference case of the Australian blockchain audit experience and suggestions for developing blockchain audits in China.