Reviewerly/exHarmony huggingface dataset:
Optimizing the Operation of a Wireless Sensor Network Using a Swarm Intelligence Algorithm

This paper presents the results obtained after testing and comparing the whale algorithm (WOA) and the artificial bee colony (ABC) algorithm in a practical problem. The task of the prepared solution was to support the operation of wireless sensor networks. As part of the experiments, several sets of solutions were generated for studying the number of active sensors and energy consumption. One of the sets of computational simulation results is presented. The research shows that the use of selected swarm intelligence algorithms can increase the effectiveness of wireless sensor networks.
Understanding knowledge leadership in improving team outcomes in the health sector: a Covid-19 study

Purpose This study seeks to examine the role of knowledge-oriented leadership in the health sector to achieve team outcomes in the Covid-19 context. Drawing from the leader–member exchange (LMX), social cognitive and social identity theory, the present study develops a model linking knowledge-oriented leadership and team performance through the underlying psychological mechanisms of team efficacy, team cohesion, team commitment and team collaboration. Design/methodology/approach Utilizing quantitative data methodology, data were obtained from the pharmaceutical employees (health sector) of Pakistan during the pandemic. The partial least squares structural equation modeling was used to test the hypotheses. Findings The findings support the hypothesis that knowledge-oriented leadership significantly influences team outcomes. The study also verified that team collaboration effectively mediates the relationship between knowledge-oriented leadership and team performance. Originality/value The study is unique in the sense that it explores the newly established leader behavior (knowledge-oriented leadership) in understanding team outcomes in the health sector. The study concludes by making significant implications to overcome the challenges raised by Covid-19 pandemic.
Generalized Net Models of Traffic Quality Evaluation of a Service Stage

In the present paper, Generalized Net (GN) models of a service phase and a service stage in an overall telecommunication system are proposed. The proposed models are aimed at traffic quality evaluation. The values of some traffic quality indicators are obtained in the form of characteristics of tokens of the GNs. The proposed GN models are an important step towards the monitoring, prediction and management of the Quality of Service (QoS) in overall telecommunication systems. The GN approach described in the paper can be applied to all service systems and, in particular, to the next generation smart service networks.
InterCriteria Analysis of the Supervisory Statistic Data for Selected 8 EU Countries During the Period 2020–2021

In this paper, we will analyze the correlation between some indicators for financial condition of the significant banks in eight European countries. Banking system in European Union is under supervision of European Central Bank (ECB) and national supervisory authorities. ECB supervised directly significant banks in EU. For analysis purposes, we will use multicriteria decision-making method - InterCriteria Analysis. The presented multicriteria decision making method is based on two fundamental concepts: Intuitionistic Fuzzy Sets (IFSs) and Index Matrices (IMs).
On the Solutions of Some Equations with Intuitionistic Fuzzy Index Matrices

The solutions of three equations with intuitionistic fuzzy index matrices are described. The conditions for their existing are given.
Intuitionistic Fuzzy Evaluation of User Requests Frequency

The aim of the current investigation is to present intuitionistic fuzzy evaluation of the usability of facts in a database. The access to the facts by the users is evaluated. Depending on the time, the most used facts are determined according to the frequency of the users access to them. In each step of the fact selection its intuitionistic fuzzy evaluation is aggregated. An example using relational database with user calls is presented. The proposed investigation can be executed in Big Data systems having the support for the relational operations. Nowadays, many operations in the field of relational databases and NoSQL databases are implemented in Big Data systems.
Generalized Net Model of Overall Network Efficiency Evaluation

In recent years, Generalized Nets (GNs) have been successfully used in the modeling of telecommunication systems. GN models of traffic quality evaluation of a service phase and a service stage in an overall telecommunication system have been developed. In the present paper, a GN model of overall network efficiency evaluation is described. The model is based on a conceptual model of the Quality of Service (QoS) contributions in an overall telecommunication system including users. It can be used for monitoring, prediction and management of the QoS in overall telecommunication systems. The GN approach described in the paper can be applied to all service systems and, in particular, to the next generation smart service networks.
On Intuitionistic Fuzzy Extended Modal Topological Structures

The aim of the current investigation is to present intuitionistic fuzzy evaluation of the usability of facts in a database. The access to the facts by the users is evaluated. Depending on the time, the most used facts are determined according to the frequency of the users access to them. In each step of the fact selection its intuitionistic fuzzy evaluation is aggregated. An example using relational database with user calls is presented. The proposed investigation can be executed in Big Data systems having the support for the relational operations. Nowadays, many operations in the field of relational databases and NoSQL databases are implemented in Big Data systems.
Parameterized Interval-Valued Aggregation Functions in Classification of Data with Large Number of Missing Values

In this contribution parameterized interval-valued aggregation functions are applied in an algorithm based on k nearest neighbours algorithm which uses interval modelling to improve the quality of binary classification in the case of large number of missing values in datasets. Missing values are common in real-life problems and as a result performance of classifiers may be lowered significantly. One of the approach to cope with this problem is to use diverse methods of filling missing values. In this contribution we proposed to apply a kind of multiple imputation method involved in interval modelling approach. The considered interval-valued aggregation functions belong to diverse families of aggregations considered with respect to monotonicity conditions based on classical partial order for intervals and also other comparability relations for intervals. Moreover, these interval-valued aggregation functions are defined with diverse parameters which makes possibility to fit a given aggregation to the data and considered algorithm.
Hybrid Sensor System for Robot Control with Nonlinear Autoregressive Network with Exogenous Inputs

In this paper, nonlinear autoregressive exogenous input (NARX) neural network has been used for modeling a sensor based hybrid system for possible control of robotic devices. The signals for the model are obtained from the MyoWare surface muscle sensors and the so proposed hybrid sensor system represents a sensor-based control for various task execution and direct human-robot interaction.
A Generalized Net Model of Acute Respiratory Distress Syndrome

A generalized net model of acute respiratory distress syndrome is described. This net model could be used for investigation of blood parameters after HCL administration, after inhalation of liposomes for improvement of lung functions as well as for training medical students to take decisions in case of ARDS pathology after HCl - inhalation or other pathological models.
Dogwhistles and Audience Design: A New Definition

In recent years, scholars have vividly debated over the definition and features of dogwhistles. As Jennifer Saul has widely argued in her works, political dogwhistles are powerful tools of manipulation. However, the current debate still lacks a convincing definition of dogwhistles, which sometimes are treated like spy codes while, at other times, they are labelled as instances of hate speech, as in Santana (2019). Instead, I propose a definition of dogwhistles that is based on the analysis of the audience design of utterances. I claim that dogwhistles are speech acts designed to secretly change the conversational role of a subset of the audience. Furthermore, they qualify as forms of disguisement - and not concealment, as claimed by the received view - that violate two important conversational responsibilities of the speaker (Clark and Carlson 1992).
A Generalized Net Model of Some Nephrological Diseases

This paper describes a generalized net model of the process of diagnostic work-up of the basic nephrological diseases and conditions: AKI, oliguria, proteinuria, hematuria, and its‘implementation in everyday clinical practice for education, differential diagnosis and multidisciplinary collaboration.
Generalized Net Model of Rehabilitation Algorithm for Patients with Shoulder Impingement Syndrome

Shoulder pain related to the impingement syndrome is a common disorder and despite the growing evidence of the importance of rehabilitation, in particular active exercise physiotherapy, little data is available to guide the treatment process. The purpose of the present paper is to present an example of Generalized Nets application in orthopedics and traumatology rehabilitation and the model describes a possible algorithm protocol for rehabilitation treatment of the patients with shoulder impingement syndrome. The proposed model can be implemented in the decision making support systems, telerehabilitation platforms, optimization of the physiotherapy protocols for shoulder impingement syndrome and better rehabilitation strategies.
Code smells in pull requests: An exploratory study

Abstract The quality of a pull request is the primary factor integrators consider for its acceptance or rejection. Code smells indicate sub‐optimal design or implementation choices in the source code that often lead to a fault‐prone outcome, threatening the quality of pull requests. This study explores code smells in 21k pull requests from 25 popular Java projects. We find that both accepted (37%) and rejected (44%) pull requests have code smells, affected mainly by god classes and long methods . Besides, we observe that smelly pull requests are more complex and challenging to understand as they have significantly large sizes, long latency times, more discussion and review comments, and are submitted by contributors with less experience. Our results show that features used in previous studies for pull request acceptance prediction could be potentially employed to predict smell in incoming pull requests. We propose a dynamic approach to predict the presence of such code smells in the newly added pull requests. We evaluate our approach on a dataset of 25 Java projects extracted from GitHub. We further conduct a benchmark study to compare the performance of eight machine learning classifiers. Results of the benchmark study show that XGBoost is the best‐performing classifier for smell prediction.
Problems of formation of pedagogical consciousness in the framework of designing a coherent environment of the future

The subject of the study is the problem of the formation of pedagogical consciousness in the conditions of designing a coherent environment of the future. Based on the analysis of the development of pedagogical reality and activity over the past decades from the standpoint of system-structural, humanistic approaches, the concept of pedagogical education as the goal and result of psychological and pedagogical education, the conditions for ensuring the coherence of the environment is substantiated. A unique model of the formation of pedagogical consciousness within the framework of the design of the environment is presented. It is concluded that changes in the public world consciousness lead to the need for the Russian pedagogical community to rethink pedagogical education and consciousness in the system of functioning of the modern educational space of the surrounding world, the country, as well as to search for those guidelines that will remain core, value- and system-forming. The mechanism of formation of pedagogical consciousness among future teachers from the standpoint of the ideas of humanities, mediocrity, is the comprehension of values through the study and analysis of materials, the creation of dialogical situations, games based on the inconsistency of the pedagogical environment and the surrounding reality, inconsistent development of the environment, opposing views in its evaluation, based on the substitution of values. This process should be carried out purposefully, systematically, consistently. The dependence of students on consciousness, self-understanding, professional activity and its basic value orientations of the value attitude to their future profession, a conscious approach to the implementation of activities, understanding the dependence on the result of pedagogical and psychological-pedagogical activity of the conditions for designing a harmonious environment for the development of the younger generation of the future is revealed.
Modeling complex problems by harnessing the collective intelligence of local experts: New approaches in fuzzy cognitive mapping

Developing system understanding and testing interventions are critical steps to addressing wicked problems. Fuzzy cognitive mapping (FCM) can be a useful participatory modeling tool that enables aggregation of individual perspectives to build system models that represent groups’ collective intelligence (CI). However, current FCM aggregation methodologies for creating CI models have rarely been tested and compared. We conducted 51 FCM interviews with local experts in the Flint, MI food system to map their mental models about how different food system sectors influenced desirable outcomes. Using four differing aggregation techniques, based on experts’ identity diversity and cognitive diversity, we generated four CI models. The models were compared based on their similarity to real-world complex systems using performance metrics like network structure, micro-motifs, cognitive distance, and scenario outcomes. We found that using cognitive diversity to group individuals was better suited for modeling systems with diverse holders of knowledge.
A comparison of sensorimotor assessment accuracy between augmented reality and touchscreen environments

Augmented Reality provides a mobile platform that could be used for a variety of human motor control assessments. Sensorimotor evaluations, directed towards hand-eye coordination, have widespread applications in many domains. This study investigated the effect of modality on a sensorimotor assessment by comparing button press location accuracy when administered through a touchscreen and with augmented reality. Participants performed a multidirectional tapping task under two degrees of Task Difficulty and Modality. Average accuracy decreased as the difficulty of the task increased as expected. Average accuracy also decreased when performing the task using AR as compared to using the touchscreen. There was no significant interaction between Task Difficulty and Modality. While AR can be used to assess hand-eye coordination, care should be taken when comparing to measures collected from different modalities. Further work will evaluate additional metrics to support understanding differences and similarities in these interfaces to support design recommendations.
Australia surprised by moderate quake, but rumbling is not unusual

&lt;em&gt;&lt;em&gt;Like a tree falling in the forest, if there is no one around to feel it, then an earthquake goes unnoticed. That doesn’t mean it didn’t happen.&lt;/em&gt;&lt;/em&gt; Originally published in Temblor Earthquake News, September 25, 2021.
Self-Augmentation Graph Contrastive Learning for Multi-view Attribute Graph Clustering

Multi-view attribute graph clustering is a fundamental task which aims to partition multi-view attributes into multiple clusters in an unsupervised manner. The existing multi-view attribute graph clustering methods lack the utilization of comprehensive structural information within each view and further ignore the unreliable relations between different views, leading to suboptimal clustering results. To this end, we develop a Self-Augmentation Graph Contrastive Learning (SAGCL) for multi-view attribute graph clustering, which integrates the comprehensive structural learning of view-specific and the alignment of multi-level reliable relations between different views into a unified framework. Graph self-augmentation strategy is proposed to adaptively explore the structural information within each view, which can comprehensively capture the critical structure of each view for multi-view attribute graph. Dual-alignment constraint is developed to guide the consistency of inter-view relations in the embedding-level and clustering-level, which can extract the consistent structure between multiple views and obtain cluster-oriented graph embedding with more discriminating. Furthermore, with the help of robust contrastive loss, our proposed network can suppress the existence of noisy information within each view and unreliable relations between different views. Extensive experiments prove that SAGCL outperforms the state-of-the-art methods.
Intelligent Classification of Multimedia Images Based on Class Information Mining

The classification research of multimedia images has always been of great concern, and related technologies are constantly being improved to increase accuracy. Adequate evaluation and mining of sample information is an important direction, but it is always a challenge. In this article, we propose a method for constructing deep learning training datasets, which fully considers the intra class and inter class features of the samples. The intra class dispersion of the sample is evaluated by the distance from the sample features to the prototype, while inter class confusion between classes is evaluated by combining the distance of the prototype in the feature space with intra class dispersion. Based on the intra class and inter class features of samples, determine the proportion of imbalanced construction to achieve the construction of imbalanced datasets. This method has the potential to be applied to different multimedia visual tasks.
Formation of Libraries in Uzbekistan as a Part of the USSR Library System

Being a traditional institution of culture, knowledge preservation and transfer, a relatively stable form of organizing social life, ensuring strength of ties and relationships within a society, in the context of its systemic transformations, developing digitalization of resources and communications, the library has the potential, the implementation of which can determine new models of action. In this regard, it becomes important to develop a model for adapting the activities of libraries in the region to the modern system of communications. For to elaborate a specific strategy, it is important to comprehend the development of library and information systems in the context of the historical and cultural development.The libraries of Uzbekistan served as the research object of the paper. The process of their systematic formation in this region started in 1918. The Uzbek Soviet Socialist Republic, which entered the USSR, was created in October 24, 1924. Since that time, there began active creation of mass, special scientific, university and scientific technological libraries. The policy of mass library centralization carried out in the USSR in the 1970s–1980s contributed to the centralized collection acquisition, improvement of library services for different population segments, and raising the library personnel qualification. The library system was formed in the republic, which was systematically supported by the state.The article object is, basing on previously published and new information obtained from statistical collections and documents of state archives, which were not examined by specialists, to concretize concepts about forming and developing libraries in Uzbekistan as a part of the USSR library system for to determine future regularities and trends in the republican library and information system development.
Scientific and Educational Infosphere of the Ural Region in the Common Digital Space of Scientific Knowledge

The Russian scientific community has more than twenty years of experience in the formation and development of the common scientific and information space (CDSSK). The Ural scientists and experts have also made repeated attempts to adapt and implement this idea in the region. The presented research is aimed at forming the content base of the Ural CDSSK subspace by monitoring, systematizing, and analyzing the scientific infosphere of the region. The search and collection of information about the actors of the scientific information sphere of the Ural Economic Region (research organizations, universities, custodians of scientific knowledge – libraries, archives) and scientific information resources generated by them has been done. Based on the data obtained, the analysis of publishing and issuance activities of organizations and their level of representation on the Internet has been carried out. The article presents the preliminary results of the research of the scientific and educational information sphere of the Urals as a part of the Common Digital Space of Scientific Knowledge.
Development of Training for the Master’s Degree in the Direction “Library and Information Activities”

The purpose of the article is to trace the retrospective of training for the library Master’s degree, to comprehend the accumulated experience in order to find new solutions for its development in the context of the rejection of the Bologna system of education. On the agenda is the restructuring of the entire education system in accordance with the proposals of the Ministry of Science and Higher Education of the Russian Federation. The transformation of the higher library education, including library Master’s degree, is also ahead. It arose and evolved in the 2000s, in line with the introduction of the Bologna system in the higher library education in Russia. The normative framework for regulating training for Bachelor’s and Master’s degrees in the field of library and information activities was consistently developed in the form of Federal State standards of higher professional education of the first, second and third generations, model educational programs for the library Master’s degree. The comparison of the mandatory part of the curricula for the preparation of masters of library and information activities in the leading specialized universities showed the existing significant discrepancies in the composition, number, volume of credits and sequence of disciplines taught. During the existence of the library Master’s degree, the model of its graduate has not finally developed, despite the overabundance of general cultural, general professional, professional competencies that, according to Federal State standards of higher professional education, he must cope with. The situation with the employment of graduates of the library Master’s degree has changed cardinally since March 1, 2023 due to the introduction of the Professional Standard of a specialist in library and information activities. At the present stage, many problems of the library magistracy functioning that arose earlier remain. The article presents them as requiring professional discussion and working out a coordinated approach to their solution.