Reviewerly/exHarmony huggingface dataset:
Management Model of Innovative University

Purpose: The article is devoted to the transformation of universities. Three key areas were selected as transformation mechanisms: management of the university’s centers of competence, capable of ensuring the development and implementation of innovations in the future; administrative and strategic management of the university, capable of ensuring its sustainable development and the scientific process in the framework of the world agenda; management of educational programs based on close interaction with business. Design/methodology/approach: The methodological base of the study includes the theoretical provisions of strategic management and the theory of automatic control of complex systems. The task of creating a model for managing educational programs is solved on the basis of methods for identifying dynamic objects. Research methods - structural and logical analysis allows us to distinguish two planes of university management, and matrixing makes it possible to form a university management model as the center of the space for creating innovations. Findings/results: The results of the study are: a university management system that provides for the separation of management and administration functions, as well as a model for managing educational programs. The role of board of trustees and supervisory boards in the management of the university and their interaction with the regulator is highlighted. The university educational program management system is considered through the method of mathematical modeling. The model is based on a resource support scheme. The model is a matrix formed by resource supply flows. Originality/value: It is proposed to improve the university management system based on the described administration model - management and the educational program management model will allow for the transition from a research model to an innovative one. Transformation of the university management system will allow rethinking the goals and objectives of university education. To ensure Russia’s technological leadership, universities of the country should become not only the centers of scientific, economic and social innovations, but also the centers for the development of territories.
Gestalt Based Evaluation of Health Information Diagrams

Diagrams for four different health care settings have been proposed: Snapshot Diagram, Diagnosis Diagram, Strength of Evidence Diagram and Patient Pathway Diagram. The availability of large amount of digital health care data and potential to utilize its benefits led to the development of these diagrams. This paper presents an analysis of the diagrams based on the selection of a subset of Gestalt principles deemed relevant for each diagram. Although Gestalt and human-computer interaction principles are advanced to apply to all diagrams or user interfaces, in practice a sub-set of principles must be selected to evaluate a diagram or interface. The selection of a subset of principles to use on a diagram has not been widely studied. This paper presents an approach for identifying a subset of relevant Gestalt principles tailored for each of the four diagrams advanced for health care settings.
Patients’ Compliance Behavior in a Personalized Mobile Patient Education System (PMPES) Setting: Rational, Social, or Personal Choices?

Objective: With the advancement of mobile technologies, patients can access medical and patient educational information anytime and anywhere. Computer-aided patient education has been advocated as a key means of interventions for improving patient knowledge and compliance (i.e., adherence). However, evidence of the efficacy of computer-aided patient education remains relatively limited. For example, little is known about how the latest mobile technologies influence patients’ compliance intention and their actual compliance behavior. The objective of this study is to investigate patients’ compliance intention and behavior using a personalized mobile patient education system (PMPES) as a novel technological intervention for patients based on rational choice theory (RCT) and theory of planned behavior (TPB). Materials and Methods: We conducted a field survey with 125 actual patients in U.S. who obtained their patient education through PMPES while seeking medical treatment advice from their doctors. We employed partial least squares (PLS) regression path modeling to test our model. Results: We found that based on the RCT theory, the benefits of compliance and cost/threat of noncompliance positively influenced intention toward treatment compliance; in contrast, costs of compliance negatively influenced intention toward treatment compliance. However, the benefits of noncompliance had no effect on intention toward treatment compliance. The results also indicated that intention toward treatment compliance, response efficacy, and self-efficacy related to TPB would jointly influence the degree of actual compliance behaviors. Social influence factors including subjective norms and descriptive norms had no influence on patients’ actual treatment compliance behavior. Conclusion: Overall, the research model explains 69.2% of the variance in patients’ actual compliance behavior. We find our model robust in using RCT as a key theoretical lens for the assessment of patients’ compliance intention to follow medical recommendations enabled by the PMPES and delivered to mobile devices. The factors associated with RCT and TPB jointly influence patients’ actual compliance behavior. Future mobile patient education programs should consider patients’ age groups, mixed-gender groups, different medical settings, and cross-cultural contexts.
Signal Processing for Contactless Monitoring

Monitoring human activities from a distance without actively interacting with the subjects to make a decision is a fascinating research domain given the associated challenges and prospects of building more robust artificial intelligence systems. In recent years, with the advancement of deep learning and high-performance computing systems, contactless human activity monitoring systems are becoming more and more realizable every day. However, when looked at closely, the basic building blocks for any such system is still strongly relying on the fundamentals of various signal processing techniques. The choices of a signal processing method depend on the type of signal, formulation of the problem, and higher-level machine learning components. In this chapter, a comprehensive review of the most popular signal processing methods used for contactless monitoring is provided, highlighting their use across different activity signals and tasks.
Scene-Oriented Aesthetic Image Assessment

In recent years, aesthetic image assessment that can be used to predict image quality automatically, has attracted a lot of interest. In most previous work, universal aesthetic features are designed for all types of scenes, neglecting scenes’ special characteristics. As a result, such universal aesthetic features are not very consistent with aesthetic scores evaluated by humans. In this study, we propose a scene-oriented aesthetic image assessment method in this paper. Firstly, we design special aesthetic features for different types of scenes. The contributions of different individual aesthetic features to the subjective feelings of humans are further considered. To accurately map the aesthetic features into the aesthetic scores evaluated by human, we propose an aesthetic image quality classification algorithm based on feature-weighted sparse representation. Finally, a framework of automatic aesthetic image generation from a high resolution and large view photo is suggested. We conduct extensive experiments on public databases such as AVA, AADB, and CUKHPQ to verify the efficiency of the proposed algorithm. Another experiment on automatic aesthetic photo generation demonstrates its potential application prospects in our daily life.
An Example of Privacy and Data Protection Best Practices for Biometrics Data Processing in Border Control: Lesson Learned from SMILE

Biometric recognition is a highly adopted technology to support different kinds of applications, ranging from security and access control applications to low enforcement applications. However, such systems raise serious privacy and data protection concerns. Misuse of data, compromising the privacy of individuals and/or authorized processing of data may be irreversible and could have severe consequences on the individual’s rights to privacy and data protection. This is partly due to the lack of methods and guidance for the integration of data protection and privacy by design in the system development process. In this paper, we present an example of privacy and data protection best practices to provide more guidance for data controllers and developers on how to comply with the legal obligation for data protection. These privacy and data protection best practices and considerations are based on the lessons learned from the SMart mobILity at the European land borders (SMILE) project.
Acceptability of High School class in VR and its Evaluation

In school, there are many situations where face-to-face classes are not possible, such as student mental status, student physical status, and school locked down by disease outbreak like COVID-19. For such situations, we propose a system of school classes in virtual reality (VR). We record a classroom lesson at school with the students as a 360-degree 3D video and show them as VR. Using this system, students can read letters, numbers and figures written on the blackboard and take notes in a virtual space. In this paper, we report the result of the experiment that students watch classes in VR with the system. As a result, about three-quarters of the students answer that classes in VR feel like a real class. Since this number is better than 2D monitor lessons, we think students will feel more like a real classroom than a regular 2D video with the system.
Real-time Data Framework for Marine Environment of the “Southern Maritime Silk Road”

Abstract From the perspective of marine environmental forecasting and in combination with the marine environmental security service for “Southern maritime silk road”, this paper introduces the basic framework of the data-acquisition system for marine-environment forecasting of the “Southern Maritime Silk Road”, which is constructed by research, business and service fields of marine environmental forecasting technology in China: including data source engineering remotely sensed by marine satellite, data source engineering by oceanographic survey (inspection), data source engineering through observation at fixed marine stations, and its support system - transmission network of the observed global Marine environmental data and the supercomputing center. The reanalysis system of data remotely sensed by marine satellite, data system by oceanographic survey (inspection) and data system through observation at fixed marine stations are mainly thrown light upon. The data-acquisition system framework for the marine environment forecasting of the “South Maritime Silk Road” lays a solid foundation for the development and sustainable production of big data industry products of the downstream Marine environment forecasting and security guarantee service.
MODEL WORKING PROGRAM OF EDUCATION IN THE MAIN EDUCATIONAL PROGRAMS OF EDUCATIONAL ORGANIZATIONS OF HIGHER EDUCATION: POSSIBLE APPROACHES TO DEVELOPMENT

The article presents possible approaches to the implementation of the Federal Law of July 1, 2020 No. 304-FZ "On Amending the Federal Law" On Education in the Russian Federation "on the education of students." In particular: to the development of an exemplary working program of education in the main educational programs of educational organizations of higher education, the inclusion of education in the educational process, the development of an exemplary calendar plan of educational work in higher school.
Method for Changing Users' Attitudes Towards Fashion Styling by Showing Evaluations After Coordinate Selection

When users perform a fashion coordination search, they tend to select their “preferred styles,” or styles that match their personal tastes, from among the search results. However, for effective fashion coordination, it is also important for one's fashion style to not only be suited to one's own preferences but also be highly evaluated by other people. In this paper, we propose a method that displays the evaluation values, such as the number of favorites, of fashion coordinates in the search results not when the search results are first displayed, but after the user has selected his/her preference from among the search results. The purpose of this method is to emphasize the difference between how a user perceives his/her own preference and how it is evaluated by other people, and to help users become aware of the differences between their preferred styles and others' preferences and perceptions. In this study, we implemented a system for fashion coordination search wherein the inputs are the desired items and styles from the user. In addition, we designed an experiment wherein we would vary the presentation timing of metadata such as user evaluation and style information in the search results. This experimentation method would clarify the effect of the system on changing users' attitudes toward fashion styling with respect to the timing of the presentation of user evaluation scores.
Comparative judgement: assess student production without absolute judgements

What is it? Comparative Judgement (CJ) has emerged as a technique that typically makes use of holistic judgement to assess difficult-to-specify constructs such as production (speaking and writing) in Modern Foreign Languages (MFL). In traditional approaches, markers assess candidates’ work one-by-one in an absolute manner, assigning scores to different elements (analytic marking). In CJ, however, markers compare two pieces and consider the overall merits of each. They make one binary, holistic judgement as to which is better. This approach exploits humans’ natural ability to compare; we find it easy, for example, to say which of two people is taller, but struggle to give precise estimates of height.
Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function

Automatic Target Recognition (ATR) systems are used as decision support systems to classify the potential targets in military applications. These systems are composed of four phases, which are selection of sensors, preprocessing of radar data, feature extraction and selection, and processing of features to classify potential targets. In this study, the classification phase of an ATR system having heterogeneous sensors is considered. We propose novel multiple criteria classification methods based on the modified Dempster–Shafer theory. Ensemble of classifiers is used as the first step probabilistic classification algorithm. Artificial neural network and support vector machine are employed in the ensemble. Each non-imaginary dataset coming from heterogeneous sensors is classified by both classifiers in the ensemble, and the classification result that has a higher accuracy ratio is chosen for each of the sensors. The proposed data fusion algorithms are used to combine the sensors’ results to reach the final class of the target. We present extensive computational results that show the merits of the proposed algorithms.
Vision-Based Activity Recognition System with a Deep Neural Network for Surveillance

Computer vision has gained tremendous attention recently due to what visual data can provide in terms of meaningful information and predictions. Videos, not like other types of data, can carry lots of information about the captured scene. Information such as objects detection, face recognition and action classification can be beneficial to monitoring systems such as traffic monitoring and security systems. Activities recognition, in particular, is a quite significant part of visual data analysis and can provide pragmatic predictions on people’s behaviour. The absence of a well-labelled video dataset makes it more challenging to develop machine learning algorithms for irregular actions recognition. These prediction models can ease the process of monitoring buildings, roads and other common areas monitored by CCTV systems. This paper proposes a method to utilise deep learning in classifying people’s behavior by identifying normal behaviours and classifying any unusual activities and provide a well-trimmed and labelled dataset for abnormal behaviors in CCTV videos.
SENTIMENT LEXEMES PROCESSING IN SONG DISCOURSE (EXPERIMENT IN LINGUISTIC EXPERTISE)

Aim. The purpose of the research is to prove the hypothesis about the possibility of applying sentiment analysis results to linguistic expertise. Methodology. A corpus of song texts of the “Eurovision Song Contest” was collected, sentiment lexical units were automatically extracted from it and the overall sentiment of the composition was calculated. Sentiment scores were assessed using basic statistical criterion. The collected results were analyzed from the point of view of their applicability for linguistic expertise. Results. The analysis of the extracted lexemes and the analysis of the texts of the compositions showed the advantages of sentiment units extraction before the linguistic expertise of the text. Research implications. The article formulates suggestions for dealing with the difficulties that arise in the course of song texts sentiment analysis.
A Multi-Task Learning Approach to Personalized Progression Modeling

Modeling disease progression is an active area of research. Many computational methods for progression modeling have been developed but mostly at population levels. In this paper, we formulate a personalized disease progression modeling problem as a multi-task regression problem where the estimation of progression scores at different time points is defined as a learning task. We introduce a Personalized Progression Modeling (PPM) scheme as a novel way to estimate personalized trajectories of disease by jointly discovering clusters of similar patients while estimating disease progression scores. The approach is formulated as an optimization problem that can be solved using existing techniques. We present efficient algorithms for the PPM scheme, together with experimental results on both synthetic and real world healthcare data proving its analytical efficacy over other 4 baseline methods representing the current state of the art. On synthetic data, we showed that our algorithm achieves over 40% accuracy improvement over all the baselines. On the healthcare application PPM has a 4% accuracy improvement on average over the state-of-the-art baseline in predicting the viral infection progression.
Digital Humanities Scholarship: A Model for Reimagining Knowledge Work in the 21st Century

The essay situates the academic subfield of the digital humanities (DH) as a notable example of late 20th century theories of knowledge (e.g. writings of Jean-François Lyotard and Fredric Jameson) that called for or predicted a shift from modern to postmodern cultures of knowledge work, knowledge organization, and knowledge production. The essay reviews DH as a case study of knowledge culture transitioning within the academic industry and suggests insights for how similar transitions may develop in other industries.
Use of Data Science during Worldwide Pandemic : An Efficient and Reliable System to Support Decision Making in Different Sectors

Any calamities or emergency situations have created drastic and everlasting impacts on mankind since history. Such situations need to be managed in an efficient and effective manner. There are countries of the world where the use of evolving technology is not a part of the management system. In this paper using a combination of technologies and tools we will provide a better alternative solution to the intuitive decisions thus making systems more reliable. Data Mining, Data Analytics, Data Visualization and Decision making have key applications demonstrated in this paper. Upon the choice of User Selections, this system will look into the number of deaths and recoveries that have occurred everyday from the start of spread of the COVID-19. Based on this information, the goal will be to analyze and formulate a death and recovery analysis, which will in turn help us in understanding the effect of corona virus on different work sectors, which in our case will be the Business sector of the country. The system will also focus on forecasting the GDP based on the past datasets of the country. Different visualization methods will be shown according to the Automated Machine learning system that will generate statistical depiction from the inflow of dynamic data. These info graphic visuals will provide a better understanding to the user as to how the situation will affect her own sector/domain as well as current stance of the GDP and enhance the overall User Experience (UX) of the user.
LibGuides Gone Viral: A Giant LibGuides Project during Remote Working

Librarian-created course guides were created at The Ohio State University using the LibGuides system, with 460 courses being connected to undergraduate courses in six departments. The process of identifying content, creating guide templates and courses, and working with staff members doing remote work projects is covered. A special subset of the guides involves a more intense examination of courses’ content and the collaboration of the campus’s Mathematics and Statistics Learning Center. Guide use in the first semester and future directions are discussed.
Analysis of Logs in the Environment of Email Services

The work deals with the analysis of logs and audit records from email services. The growing trend of using email services as well as other electronic communication creates the need and emphasis on ensuring the reliability of these services. In the presented proposal, the work uses modern methods of log analysis and then interprets them into a visual form, uses knowledge from non-relational databases, methods of analysis and processing of audit records and uses a number of tools that participate in creating outputs. Among non-relational databases such as ElasticSearch, Apache Kafka, Redis, Splunk and MongoDB, the work selects the most suitable database for its purposes. It also discusses methods of data collection as well as their subsequent analysis and visualization. The work also uses the tools of third parties or protocols covering the collection and creation of outputs from audit logs, such as NXlog or Syslog-NG. Finally, the work focuses on the evaluation of possible safety as well as functional risks.
Lexicons for Broad Coverage Semantics

This chapter describes the use and organization of dictionaries for broad coverage semantics. We discuss theoretical aspects of using dictionaries for background information and some practical matters that have arisen from using current on-line dictionaries, including the results of an experiment in ordering background information according to plausibility (a first step towards sense disambiguation). Finally, we present our vision of the ideal dictionary.