Reviewerly/exHarmony huggingface dataset:
Expanding Intersubjectivity: A Metaverse VR Practice

The common subjects with intersubjectivity face the objective environment together. With the proposal of the metaverse, the subject-environment boundary in the virtual world is broken. Virtual avatars make the human subject no longer limited to the human itself. Therefore, the interaction relationship between subjects has been redefined. This paper studies the expansion of intersubjectivity in the virtual world under the concept of the metaverse and designs a virtual reality (VR) framework based on cloud server technology to test and verify the study. The conclusions show that when the human-environment boundary is redefined by virtual avatars, experimenters have a completely different interaction experience.
Factors affecting human understanding of augmented reality visualization of changes detected by an autonomous mobile robot

In manned-unmanned teaming scenarios, autonomous unmanned robotic platforms with advanced sensing and compute capabilities will have the ability to perform online change detection. This change detection will consist of metric comparisons of sensor-based spatial information with information collected previously, for the purpose of identifying changes in the environment that could indicate anything from adversarial activity to changes caused by natural phenomena that could affect the mission. This previously collected information will be sourced from a variety of sources, such as satellite, IoT devices, other manned-unmanned teams, or the same robotic platform on a prior mission. While these robotic platforms will be superior to their human operators at detecting changes, the human teammates will for the foreseeable future exceed the abilities of autonomy at interpreting any changes, particularly for relevance to the mission and situational context. For this reason, the ability of a robot to intelligently and properly convey such information to maximize human understanding is essential. In this work, we build upon previous work which presented a mixed reality interface for conveying change detection information from an autonomous robot to a human. We discuss factors affecting human understanding of augmented reality visualization of detected changes, based upon multiple user studies where a user interacts with this system. We believe our findings will be informative to the creation of AR-based communication strategies for manned-unmanned teams performing multi-domain operations.
Different Levels of Need to Belong and Information Sensitivity: The Effects of Perceived Warmth and Competence on Continuance Intention to Friendship Virtual Conversation Agents

Although there have been many virtual conversation agents (VCAs) in social media and e-commerce platforms, research on VCAs such as Replika, neglected the effects of human-like traits on users’ continuance intention. This paper identifies the friendship VCAs based on previous studies and investigates two new dimensions of friendship VCAs, perceived warmth and perceived competence. Across three experimental studies, the results indicate that perceived warmth and competence can increase the continuance intention to friendship VCAs. Additionally, consumers’ need to belong (high vs. low) and information sensitivity (high vs. low) related to chat contents moderate the main effects significantly, and perceived value plays a mediating role in our conceptual model. This paper contributes to the literature on the relationship between friendship VCAs and consumers by presenting the influence of perceived warmth and competence and establishing the underlying process. Analogously, the findings can be beneficial for marketers and firms in designing and developing the coding program of friendship VCAs to promote consumers’ continuance intention and communication value by combining perceived warmth with perceived competence.
Analysis of the Operating Modes of the Hybrid Underwater Robotic System

In the modern world, performing oceanological research and underwater technological operations is impossible without using autonomous and remotely operated underwater vehicles. However, each of these types of vehicles has disadvantages limiting their application and allowing only narrow-profile tasks to be performed. To solve this problem, it is proposed to use a hybrid underwater robotic complex consisting of an autonomous and remote-controlled vehicles connected with a cable. The functionality of the proposed hybrid complex is determined. The results of the analysis of the mathematical models of hybrid complex operation in the modes of joint movement and towing are presented. Due to the restrictions imposed on the remotely controlled underwater vehicle and the cable between the vehicles, the speed of the hybrid underwater robotic complex is about 1 m/s in the joint motion mode and about 2 m/s in the towing mode. It is recommended to tow a remote-controlled underwater vehicle on a short cable length when the autonomous uninhabited underwater vehicle is submerged. In addition, it is necessary to adjust the cable length between the devices.
A Study on Input Variables of Distribution Line Load Forecasting Model for Distribution Planning

The importance of the power distribution planning is increasing due to expansion of distributed generation in distribution system. The distribution planning is based on accurate load forecasting. Recently, machine learning-based load forecasting models have been developed and applied in distribution planning. However, since the performance of the machine learning-based load forecasting model depends on the combination of input variables, it is important to select input variables. This paper proposed the process of selecting input variables for an ensemble based load forecasting model. The input variable of the distribution line load forecasting model was selected through correlation analysis of predictable input variables, then the improvement of forecasting performance with selected input variable was analyzed by comparing the forecasting and the actual value. In addition, the importance of the selected input variables and the validity of the proposed process were analyzed through XAI analysis.
Efficient Compression Algorithms for Bandwidth- and Memory-Constrained Cyber-Physical Systems

The network that connects a vast number of devices together wirelessly is known as a wireless sensor network. The information acquisition takes place through sensor nodes. The information is processed and communicated among the different nodes of the network. A next engineered version of this network refers to the cyber-physical systems (CPSs), in which the machines are provided with more control and integration. Currently, multiple issues and challenges are being addressed as the cyber-physical systems are in the development phase. In this chapter, we will be looking at the bandwidth constraints and storage issues of the information that is being generated through these machines. On one side, the individual machines connected through the CPS are memory constrained i.e., the on-chip memory is small. On the other side, the quality and the size of the desired image is increasing exponentially. To add to the challenge, the data is being stored or communicated wirelessly among different machines, which requires large bandwidth. Bandwidth of the channels is also limited, which results in delayed reception of the information. In delay intolerant applications, this can result in serious problems. In this chapter, the algorithms for image compression are discussed that result in low-memory and low-bandwidth requirements.
Behavioral design practical application: product policy, public opinion

Abstract Behavioral product design is critical to integrated product policy and opens up new opportunities for manufacturers, consumers and society in whole. Behavioral design theory since the 90s of the XIX century evolved as a synthesis of physiological theory of reflexes and reinforcement, psychoanalytic theory of the crowd and operational conditioning, theories of public opinion management and organizational change, institutional concepts of consumer behavior, computerization and the development of data networks. In foreign practice, behavioral design is used in building human interaction with digital devices, architecture, reducing the crime rate, protecting against errors in organizing processes in healthcare, creating energy-saving and resource-saving products, creating eco-friendly products, increasing the safety of using products, solving problems associated with coronavirus, reduction of screen time, impact on public opinion, creation of virtual products for healthcare, education, welfare. In Russian practice, the potential of behavioral design is not realized. The research methodology is based on the combination of classical and multidisciplinary scientific approaches, including methods of behavioral economics, institutionalism and consumer marketing.
Modern Educational Technologies in Professional Training of Student in Technical Institutes of Higher Education

A new stage in the development of presentday society is evidenced in which modern technologies are an inseparable part in social experience hence the need of their application in instruction and training. New forms of providing education involve interactivity and collaboration in the process of learning. This work aims to demonstrate the authors’ attitude toward contemporary educational technologies and their application in the professional training of students following engineering degree courses. Here we make a review of some modern educational technologies employed in professional training, which are separated into three groups: situational or problem-oriented situational, discussion-based and experimental-empirical methods. Here the application of one concrete method from each of the three groups is also demonstrated in the context of active training of students following degree courses in technical institutes of higher learning.
Desensitized Extended Kalman Filter with Stochastic Approach to Sensitivity Reduction and Adaptive Weights

The desensitized Kalman filter can robustly estimate the state of a system with uncertain parameters without knowledge about uncertainty type. In this paper, the desensitized Kalman filter for nonlinear systems is derived using Taylor series expansion and a stochastic approach to reduce estimation error sensitivity to uncertain parameters. Adaptively normalized weights tune the trade-off between the minimum uncertainty sensitivity and minimum mean square error. Among the main benefits of the algorithm are intuitive tuning concerning uncertainty and a form resembling the classical Riccati equation. The comparison to other robust state-of-the-art algorithms is discussed based on a numerical example.
Computer-based Sentiment Analysis to Solve Real-world Societal Problems and Its Challenges

Due to evolution in every sector around us, a large amount of information is generated at every moment. In recent years, many microblogging platforms have become the primary place for the public to express their mood. So the sentiment analysis data collected from websites, reviews, and feedback can be used in many areas such as in marketing or product analysis or competitive research for future development. So, sentiment analysis is one of the booming domains, a study of analyzing people's reviews, suggestions, feedback from the text format and checking if the data shows positive, negative, or neutral emotion. Applications of sentiment analysis are endless and are applicable in every social domain. We can automatically assess the tone of internet interactions using natural language processing and machine learning methods. Sentiment analysis can be performed with the help of various techniques, depending on how much data needs to be examined and how precise the model needs to be. This manuscript is a comprehensive article that includes all the essential topics related to sentiment analysis, such as its applications, challenges faced and various algorithms.
Deep Learning Approaches for Document Classification: An Insight

Increasingly large document collections require progressed information processing techniques for searching, retrieving, and organizing textual content. Document Classification is the pastime of labeling Natural Language texts with applicable Categories from a Predefined Text. To Classify the document, we need to acquire applicable information from the entire Document. In massive technological-based organizations, the necessities to handle and organize technical documents created by software architects and peers in assisting applicable policy-making have elevated drastically in current years, it came about a better call for a greater accurate, to the point, scalable, and automatic document classification. This review paper gives an insight into the different document classification methods, their phases, and, diverse deep learning strategies. It additionally pursuits at evaluating and contrasting numerous implemented strategies on the foundation in their overall performance and accuracy.
A Leadership Model for Supporting the Mastery of Core Competencies for College English Learners in Application-Oriented Universities in Shanghai, China

On basis of Synergistic Leadership (Irby et al., 2002), Situational Leadership (Hersey &amp;amp; Blanchard, 2008), the support systems in Framework for 21st Century Learning (P21, 2019), and related research, this research employed questionnaire surveys and interviews in four application-oriented universities (AOUs) in Shanghai, characterized by disciplines of humanities, arts, technology, and health. Findings from questionnaire surveys and interviews showed that: 1) 45 leadership factors in the context of AOUs in Shanghai may be synthesized from dimensions of stakeholders&amp;rsquo; perceptions, leadership behaviors, and external forces; and 2) leadership styles may start at any point of directing to coaching styles, and gradually transform to supporting, and specific leadership behaviors have been provided to CE instructors or instructional leaders in determining or adapting situational leadership styles to learners&amp;rsquo; situation in AOUs in Shanghai. Based on the findings, the model Leadership Atomium was developed for instructional leaders and instructors to support learners&amp;rsquo; mastering CE core competencies for their better preparations for future life and work.
Improving neural network methods for recognizing lung lesions with coronavirus infection

In this work mathematical multilayer images segmentation model-based lung lesions recognition methods for multilayer images segmentation were developed. We analysed COVID-19 diagnosis methods, developed lung damage percentage determining software and gave dataset preparing stages description.
Business Resiliency for Enterprise Blockchain Payment Systems

This document explains about technical solution that can be used during disaster situation for financial workloads that use Blockchain for transactions. Blockchain is a highly secure, trusted technology with decentralized, distributed transaction handling capability. Disaster causes outages to entire Blockchain nodes and enterprise workloads. Disaster recovery at the backup geo location helps to continue Blockchain nodes and enterprise workloads. After unplanned disasters, it is not possible to have completely synchronized consistent data copy on the target site, when distance is longer between geo locations. This solution uses predictive analysis on different rolling disaster failure events using Machine learning or Deep learning techniques and does a planned recovery with VM Recovery Manager for DR product (Geographically Dispersed Resiliency) before unplanned outages, so that target site will have complete data copy and brings up Blockchain nodes and enterprise workloads. Planned recovery helps to drain all production site data to the target site gracefully, hence target site will have consistent synchronized copy. Solution provides technical aspects with IBM power hardware and Predictive analysis for Blockchain to continue enterprise workloads without data loss during disaster.
Method for Protection of Deep Learning Models using Digital Watermarking

In this paper, a new method for protection of copyright on pretrained deep neural networks is proposed. The main idea is to embed a digital watermark into a pretrained model by fine-tuning it on a unique trigger set formed by synthesizing pseudo-holographic images (pseudo-holograms) and embedding them into raster images of the original dataset. A pseudo-hologram is a special type of a two-dimensional signal, which encodes a binary sequence. On the example of binary classification, we propose to produce various pseudo-holograms based on the same sequence for one class and on another sequence for the second class. Schemes for preparing the trigger set, model watermarking, and the further copyright verification are proposed. The experimental study is aimed at comparing various embedding strategies in the task of trigger set construction. Experiments were also performed for various model architectures to demonstrate high method efficiency.
SOLBP: Second-Order Loopy Belief Propagation for Inference in Uncertain Bayesian Networks

In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i.e., probabilities over probabilities. The delta-method has been applied to extend exact first-order inference methods to propagate both means and variances through sum-product networks derived from Bayesian networks, thereby characterizing epistemic uncertainty, or the uncertainty in the model itself. Alternatively, second-order belief propagation has been demonstrated for polytrees but not for general directed acyclic graph structures. In this work, we extend Loopy Belief Propagation to the setting of second-order Bayesian networks, giving rise to Second-Order Loopy Belief Propagation (SOLBP). For second-order Bayesian networks, SOLBP generates inferences consistent with those generated by sum-product networks, while being more computationally efficient and scalable.
Same Difference: Identity and Diversity in Linked Open Cultural Data

Linked Open Data (LOD) was designed to respect heterogeneity in source datasets. However, the fundamental mechanisms of interlinking require sameness without nuance, so Linked Data is at risk of the problems associated with lack of diversity in big data generally. This article investigates the tension between difference and sameness specifically as it relates to asserting the identity of entities. It links ambiguity in natural language and cultural expression to Derrida's notion of différance and the foundation of Linked Data structure in Peircian semiotics. Representing entities so as to foreground rather than suppress subtle differences or ambiguity is a challenge given the lack of anything in between owl:sameAs and owl:differentFrom that can be supported by formal logic. The reuse of Uniform Resource Identifiers (URIs) recommended as a best practice for data interlinking is troubled by confusion over the relationship between URIs and representation, whereas owl:sameAs suffers from a range of forms of misuse. Despite these challenges to representing nuance and ambiguity, however, there are several ways in which humanities researchers and cultural institutions can pursue better means of representing diversity and difference using LOD, particularly through interdisciplinary and multisectoral collaboration.
Work programs of academic disciplines: purpose and modern features

The article analyses the work programs of academic disciplines in the aspect of the relationship of their purpose and modern structural and content features. The author shows that the implementation of the main purpose of work programs, which is to contribute to the effective organization and high quality of teaching and study of academic disciplines, is seriously hindered by such modern structural and content features of work programs as excessive formalization, bulkiness, overload with abstract institutions, which generate pronounced alienation of the main users — teachers and students from the work programs. The article also outlines the contours of the new look of the work programs of academic disciplines.
Methodology for assessing the potential of the university for the training of qualified personnel

Despite the changes made to the accreditation indicators of universities, the proposed evaluation criteria indirectly reflect the ability of universities to carry out high-quality training of graduates. It is known that the main purpose of educational activity is to transfer to students the established knowledge, skills, skills on the basis of which competencies are formed that are necessary and sufficient for graduates of higher educational institutions in their future professional activities. Thus, for an objective assessment of the ability of institutes to train highly qualified personnel, it is necessary to evaluate the pedagogical qualifications of teachers who conducted classes and the results of training students. The article discusses the methodology of assessing the potential of the university for the training of qualified personnel, which is based on the assessment of the educational and professional activity of students in the educational programs of the university. The indicator of students’ academic activity in the educational program is determined taking into account individual assessments received during the period of study in the studied disciplines, taking into account the qualifications of teachers who conducted classes, as well as assessments obtained from the results of practices and state final certification. The qualifications of teachers are determined by the results of assessments obtained during control visits to their classes by officials and the assessment obtained by the results of the test tasks in the disciplines taught. The student's professional activity indicator takes into account the results of his research work, awards, incentives and penalties received during the study period, as well as scholarships. The potential of the university for the training of qualified personnel is defined as the arithmetic mean of the coefficients of the quality of education of graduates according to the educational programs of the university.
On the problems of physical education in a Technical University

The main problems associated with teaching physics to future bachelors in technical areas of training are considered. These include the insufficient level of basic readiness of freshmen, necessary for the perception and assimilation of natural science and professionally oriented academic disciplines, low motivation of students to study physics, lack of time allotted for classroom activities. In modern conditions, to improve the quality of physical education in a technical university, a reasonable differentiation of the content of teaching physics is proposed, which is most important for the development of general professional and special disciplines in a specific direction of training, the formation of the competencies of graduates provided for by the Federal State Educational Standard. Successful assimilation of the selected educational material is possible as a result of the implementation of the “flipped learning” model in the educational process.