Reviewerly/exHarmony huggingface dataset:
USE OF EYE TRACKING TECHNOLOGY TO DETECT MILD COGNITIVE IMPAIRMENT

Abstract Early detection of dementia is key to helping individuals and their families cope. Use of eye tracking technology to measure eye movements could provide an objective and sensitive measure of cognitive impairment (Hodgson et al, 2019; Wang et al, 2020). In this pilot study, we predicted that eye tracking metrics differ between people with diagnosed MCI and those of healthy controls. Eleven veterans (≥ 55 years) being seen at the San Francisco VA Health Care System who either had a confirmed diagnosis of MCI or had subjective memory complaints and scored lower than a 26 on the MoCA participated. Their results were compared to that of a previously collected control sample (n = 41) (Liston et al, 2017). Participants completed a five-minute visual tracking test with 48 trials based on a classic step-ramp visual tracking paradigm that was administered on a PC computer with a camera and eye tracking capability. The visual tracking test yields 10 z-scored eye tracking metrics that are summarized in a single scalar summary score. Receiver operating characteristics (ROC) were computed and compared to the control sample. The summary score of the MCI group (median: -2.2) differed significantly from the healthy control sample (median: 0.0), which yielded a significant sensitivity of the test to presence or absence of MCI (ROC area = 0.94, p &amp;lt; .001). Although we view the results as preliminary due to the small sample size, results suggest that use of eye tracking technology may be a viable option for MCI detection.
VIRTUAL REALITY TO SUPPORT THE NEEDS OF OLDER ADULTS WITH AND WITHOUT COGNITIVE IMPAIRMENTS

Abstract Functional, cognitive, and mobility limitations can interfere with older adults’ ability to engage in socially and cognitively enriching activities, which can lead to further decline. Emerging technology solutions, including virtual reality (VR), have the potential to promote engagement in these activities in a way that can account for these limitations, allowing older adults to engage in productive and meaningful activities within their own home. However, an iterative, user-centered design process is necessary to ensure that these solutions reach their potential. This talk provides an overview of a number of recent studies examining the potential of VR to support older adults’ engagement in VR interventions aimed at improving mood and social connectivity. The following questions will be asked and answered during this talk, based on recently collected empirical data; What are the potential usability challenges associated with VR interventions?; What are older adults’ preferences for different VR experiences?; Can VR provide older adults with meaningful opportunities to engage in social interactions when they are distally located, but co-located in the same virtual space?; To what degree do older adults feel present in the virtual environment?; To what degree are older adults’ attitudes toward VR malleable?; If, and under what conditions, is cybersickness a potential barrier? This talk will conclude with a discussion of what is known and what is unknown regarding the use of VR to provide meaningful socially and cognitively engaging experiences for older adults with and without mild cognitive impairment.
DEVELOPMENT OF INNOVATIVE MANAGEMENT COMPETENCIES OF STUDENTS IN THE CONDITIONS OF DIGITIZATION OF EDUCATION

This article shows efficacy of competences-oriented education model, representing effectiveness of pedagogy technology and digital tools blending in transforming education while using tutors support. In correspondence to contemporary requirements and education system special features, conÂ­nection between digital education tools and tutor technology contributing to students key competences development is determined. The paper conÂ­tributes to the field by summarizing some theoretical issues and offering implementation suggestions for effectively integrating digital technologies into teaching and learning. The use of digital technologies in educational activities opens up new opportunities, adequate methods for dissemination and management of digital information, development of necessary compeÂ­tencies on the basis of digital literacy, ensuring equitable access for all who wish to obtain necessary knowledge and decision-making skills, ensuring demand for school graduates in the world labor market in situation of digital economy. Innovative technologies become a problem adding difficulties to students burden. Tutor becomes an intermediary between students and digital technologies to promote students active learning practices there are not so many students who can appreciate digital learning practices and get the most of it. The role of tutoring in digital literacy promotion is hard to overestimate. Tutor support is a pedagogical activity in individualization of education aimed at identifying and developing students educational motives and interests. To form students key competencies in education system, it is necessary to supplement traditional education with tools of digital eduÂ­cational technologies, new trends in pedagogies such as tutoring, which is becoming an integral part of modern learning process.
Research on a Quantum Machine Learning Approach to Mismatched Filter Design

In signal processing of radar systems, mismatched filter is required for suppression of sidelobes. In this work, we propose a quantum machine learning approach to generalized mismatched filter design, which is based on variational quantum algorithm architecture and is applicable to noisy intermediate-scale quantum (NISQ) devices. The algorithm requires sufficiently less storage resources than its classical counterparts, and can be combined with other efficient quantum algorithms to provide building blocks for quantum signal processing workflows. The capabilities of the algorithm is analyzed with random waveforms and its bottleneck is also discussed in detail, with potential enhancement strategies put forward correspondingly,
Students Perspective Toward E-Learning During Covid-19 At SMA Muhammadiyah Maumere In 2022/2023 School Year

This study aims to analyze and describe students' perspectives on E-learning and the difficulties faced by students during online learning. Researcher use qualitative methods. Data collection techniques used in this study were observation, interviews by distributing questionnaires. From the results of an analysis of students' perspectives on online learning during the Covid-19 period, it was found that students felt that online learning made them quite bored and reduced interaction between teachers and students. Online learning also makes students less understanding of the material provided, so that it has an impact on the level of student focus. The difficulties faced by students in e-learning-based online learning are network access that is not power full, internet quota and facilities such as mobile phones do not yet exist. Based on the explanation above, the researcher concluded that students' perspectives on e-learning can reduce student learning levels and the ability to understand learning.
UPDATING SOCIOCULTURAL ASPECT OF CONTENT OF HIGHER EDUCATION

Introduction. The article analyzes sociocultural conditionality of education content, considers the necessity of its enrichment with humanistic sociocultural values demanded in professional activity of a graduate in multicultural changing world.&#x0D; The purpose of the article is to conceptualize the world-view essence of the sociocultural discourse and its defining role in updating content of education as the necessary condition for bringing up a competent specialist.&#x0D; Objectives of the article are to analyse the problem of updating education content in the context of modern concepts of humanization; determine the essence and structure of the sociocultural aspect of professional training at a modern university; presentation of the conditions for updating the sociocultural aspect of the content of education in the activities of a university teacher.&#x0D; Methodology. In order to achieve the goal, we studied the concept of humanization of education with methodological instruments of social philosophy, anthropology, cultural studies, considering the increasing role of the value aspect in the content of education.&#x0D; Results. The scientific novelty of the article is in determining vector for updating the content of education and showing relevance of its sociocultural aspect for the first time in the context of interrelationship of global humanism, universal evolutionism and post-nonclassical rationality. The article reveals the worldview essence of the sociocultural aspect of the content of education and proves the importance to update it for both humanities and natural sciences. We also propose the structure of the sociocultural aspect and possible ways to renew it in the context of cultural science. Of practical importance are the conditions outlined in the study for updating the content of education, a special place among which belongs to the teacher’s ability to reflect on its hidden component as a significant channel for transmitting sociocultural humanistic values. Conclusion. Education that meets sociocultural challenges of changing world by updating its content serves as an indicator of the quality of professional training at universities, ensures bringing up socioculturally competent specialists capable of resolving contradictions in the modern world and ready to maintain its sustainable development.
FEATURES OF STUDENTS’ PERSONALITY TRUST IN COMMUNICATIVE NETWORK ACTIVITY

The article is devoted to the actual problem of personality trust of students in the conditions of network activity. The study of students’ personality trust in communicative network activity is of scientific significance due to the widespread inclusion of the Internet in life. Personality trust in the virtual world regulates human behavior. The aim of the study is to identify the features of students’ personality trust in communicative network activity.&#x0D; Methodology and methods of research. The article used research methods: theoretical, empirical, methods of mathematical processing of psychological data: Friedman’s Xr2–criterion, Mann-Whitney U-criterion, Spearman’s rank correlation coefficient. Computer data processing was carried out using the software packages “Excel” and “IBM SPSS Statistics 21”. A set of the following methods was used: “Methodology for studying the trust/distrust of a person to the world, other people, and oneself” (A. B. Kupreichenko) (the authors introduced an additional construct “virtual world”); “Questionnaire for problematic use of social networks” (N.A. Sirota, D.V. Moskovchenko, V.M. Yaltonsky, A.V. Yaltonskaya); “Methodology for assessing involvement in the use of information and communication technologies” (A.N. Tatarko, E.V. Maklasova, Z.H. Lepshokova, V.N. Galyapina, M.V. Efremova, D.I. Dubrov, M.A. Bultseva, E. .V Bushina, A.A Mironova). The empirical base of the study consisted of 200 respondents: boys – 100 people, girls – 100 people. Average age 20,6 years.&#x0D; Results and scientific novelty. For the first time, the personality trust of students in communicative network activity is considered. As a result of the empirical study, conclusions were drawn that self-confidence prevails among students in the virtual world. A comparative analysis based on gender showed that girls trust other people online more than boys. Differences were found on the scales “Trust in other people”, “Compulsive use”, “Cognitive absorption”, “Committing economic actions”, “Smartphone use”. The analysis revealed significant relationships between the components of personality trust and communicative network activity. A high level of self-confidence reduces anxious mental return to virtuality, growing trust in other people contributes to a feeling of comfort and security in communication, and with demonstrated trust in the virtual world, the desire to regularly stay in it increases.&#x0D; Practical significance. The results obtained during the study of the characteristics of students’ personality trust in communicative network activity can be used to organize a digital educational environment and make practical recommendations for conducting training work in order to develop digital competence skills.
Jointly Events Extraction for Database Alarm based on Dynamic Matching Strategy and GCN

Event extraction is one of the important processes in building event knowledge graphs. However, existing models cannot combine syntactic construction of text and structured knowledge of event to execute the identification and classification of trigger word and argument. In order to solve this problem, a jointly events extraction model for database fault alarm based on dynamic matching strategy and Graph Convolution Neural Networks (GCN) is proposed. Specifically, dynamic matching strategy is designed to label the event nodes which include the feature of event construction for the text, thus solving the classification of the ambiguous and unseen trigger words. Then, to get better syntactic features, GCN is used to aggregate the semantic features. The experimental results on Oracle alarm dataset show that Precision, Recall and F1 of our model respectively are 76.3%, 72.2% and 74.2%., which are better than JMEE and GREE.
Cognitive Metascience: A New Approach to the Study of Theories

In light of the recent credibility crisis in psychology, this paper argues for a greater emphasis on theorizing in scientific research. Although reliable experimental evidence, preregistration, methodological rigor, and new computational frameworks for modeling are important, scientific progress also relies on properly functioning theories. However, the current understanding of the role of theorizing in psychology is lacking, which may lead to future crises. Theories should not be viewed as mere speculations or simple inductive generalizations.&#x0D; To address this issue, the author introduces a framework called “cognitive metascience,” which studies the processes and results of evaluating scientific practice. This study should proceed both qualitatively, as in traditional science and technology studies and cognitive science, and quantitatively, by analyzing scientific discourse using language technology.&#x0D; By analyzing theories as cognitive artifacts that support cognitive tasks, this paper aims to shed more light on their nature. This perspective reveals that multiple distinct theories serve entirely different roles, and studying these roles, along with their epistemic vices and virtues, can provide insight into how theorizing should proceed. The author urges a change in research culture to appreciate the variety of distinct theories and to systematically advance scientific progress.
End-to-end Resiliency Analysis Framework for Cloud Storage Services

Cloud storage services are becoming increasingly complex with the stored data volume and operational scale. The complexity is the result of the ever-growing components with various functionalities being involved for service rendition, exposing the service to numerous failures or disruptions. In such scenarios, resiliency becomes one of the most crucial parameters to evaluate how well-equipped the system is to withstand the effect of disruptions and maintain a reliable service. The existing evaluations primarily focus on the resiliency of stored user data, which is insufficient to project the storage service level resiliency. This work proposes an end-to-end resiliency framework for cloud storage services that enables the assessment of the overall resiliency. The framework is based on three properties – service expectations, crucial components for service rendition, and their resiliency to meet the expectations while facing various disruptions. The framework is used to model the resiliency of two distinct and well-known storage services, OpenStack Swift and CephFS, as Stochastic Petri Nets. The models enable the effective quantification of system resiliency through the achieved service reliability.
A Review of Text Summarization Techniques Using NLP

Techniques that employ natural language processing (NLP), often known as text summarizing, automatically construct summaries of extensive texts. Extractive and abstractive summarization are two main categories that may be used to classify these methods. In extractive summarizing, the most significant lines or phrases from a text are isolated and used to generate a summary. On the other hand, in abstractive summarization, a summary is generated that is clear, short, and accurate in its representation of the text's primary concepts. NLP methods like sentence segmentation, part-of-speech tagging, named entity recognition, and semantic analysis are used in generating a summary from a text and locating and extracting relevant information from the text. Text summarizing is a subject that has received a significant amount of study and has applications in various fields, including the summation of news articles, documents, and emails, among other things.
Heterostructure transistor for an energy-efficient low-noise radiothermograph amplifier based on monolithic integrated circuits

Currently, there is a growing interest in medical practice in such a method of diagnostic human internal organs as microwave radiothermometry. However, a number of strict requirements are imposed on the parameters of radiometric receivers, which are the main elements of a radiothermograph, including the amplification path. The problem lies in the fact that the amplifying path of the radiothermograph consumes, with transistors available on the market, an unacceptably large amount of energy, which leads to an increase in heat generation and, as a result, to the introduction of additional errors in measurements. Therefore, in order to increase the reliability of the radiothermograph, it is necessary to develop a basic transistor for MIC amplifier with reduced power consumption. Using numerical modeling methods, the parameters of an energy-efficient transistor on the proposed heterostructure were researched. As a result of optimizing the transistor design based on the presented strict requirements for the parameters, a calculated steepness characteristic was obtained, clearly showing the increased amplifying properties of the proposed transistor in the low-current region, which directly leads to the possibility of a significant reduction in current consumption of the entire chip. A significant increase in transconductance of the proposed transistor design indicates the possibility of using this promising element base as part of microwave radiometers, which will allow combining the principles of multi-channel, multi-frequency and miniaturization in one radiometric complex and will lead to an expansion of its functionality and a significant reduction in size. The research was carried out with the financial support of the Russian science Foundation in the framework of agreement No. 19-19-00349-П in the theme: “A method and a multichannel multifrequency microwave radiothermography on the basis of monolithic integrated circuits for finding the 3D distribution and dynamics of brightness temperature in the depths of the human body”.
Lip-Synchronized 3D Facial Animation Using Audio-Driven Graph Convolutional Autoencoder

The majority of state-of-the-art audio-driven facial animation methods implement a differentiable rendering phase within their models, and as such, their output is a 2D raster image. However, existing development pipelines for MR (Mixed Reality) applications utilize platform-specific render engines optimized for specific HMDs (Head-mounted displays), which in turn necessitates the use of a technique that works directly on the facial mesh geometry. This work proposes an innovative lip-synchronized, audio-driven 3D face animation method utilizing a graph convolutional autoencoder that learns detailed facial deformations of a talking subject while generating a compact latent representation of the 3D model. The representation is later conditioned with the processed audio data to achieve synchronized lip and jaw movement while retaining the subject's facial features. The audio processing involves the extraction of semantic features, which strongly correlate with facial deformation and expression. Qualitative and quantitative experiments exhibit the method's potential usage in MR applications, as well as shed light on some of the disadvantages of the current approaches.
UML Profile for Decision-Making Systems Development with the Logic Scoring of Preference Method

The rationality and benefits of software model-driven development (MDD) continue to be the subject of studies and debates, both scholarly and among practitioners. This study argues the usefulness of applying a UML profile as an MDD artefact. The developed UML profile intends to model the Web service selection process by non-functional properties with the Logic Scoring of Preference (LSP) method. The primary profile's stereotypes determine the concepts of Web service quality criterion and LSP aggre-gators. The models created using the proposed profile were validated by the MagicDraw tool, and their effectiveness was confirmed when the actual model-driven development of the software application.
Can neural networks count digit frequency?

In this research, we aim to compare the performance of different classical machine learning models and neural networks in identifying the frequency of occurrence of each digit in a given number. It has various applications in machine learning and computer vision, e.g. for obtaining the frequency of a target object in a visual scene. We considered this problem as a hybrid of classification and regression tasks. We carefully create our own datasets to observe systematic differences between different methods. We evaluate each of the methods using different metrics across multiple datasets.The metrics of performance used were the root mean squared error and mean absolute error for regression evaluation, and accuracy for classification performance evaluation. We observe that decision trees and random forests overfit to the dataset, due to their inherent bias, and are not able to generalize well. We also observe that the neural networks significantly outperform the classical machine learning models in terms of both the regression and classification metrics for both the 6-digit and 10-digit number datasets.
Design of Rider Invasive Weed Optimization Algorithm for Unmanned Aerial Vehicle Path Planning

The unmanned aerial vehicle (UAV) is concerned with extensive attention in either military or civilian domains due to minimum cost, flexibility, smaller size, and so on. Proper and reliable path planning (PP) is fundamental to ensure security and mission success because of the gradually difficult atmosphere. The UAV only depends on its restricted sensors for obtaining partial threat data. PP is an essential technology for the autonomous flight of UAVs. The standard PP techniques take some deficiencies and restrictions in difficult and dynamic environments due to massive action and state spaces. PP for the UAV has recently developed a research hotspot and received varied attention from researchers. This study designs a Rider Invasive Weed Optimization Algorithm for the Unmanned Aerial Vehicle Path Planning (RIWO-UAVPP) technique. The presented RIWO-UAVPP technique focuses on identifying optimal routes for data transmission in the UAV network. To accomplish this, the RIWO-UAVPP technique exploits the characteristics of the RIWO algorithm, which is a hybridized version of the Rider Optimization Algorithm (ROA) and Invasive Weed Optimization (IWO) algorithm. The design of the PP process involves a Fitness Function (FF) with varying UAV parameters. The experimental output examination of the RIWO-UAVPP procedure is investigated under several measures. The experimental outcomes signified the enhanced achievement of the RIWO-UAVPP procedure over other recent procedures.